From 0e7e95023f475dae848f1fa9765dacedc0f989a4 Mon Sep 17 00:00:00 2001
From: "Eugeniy E. Mikhailov" <evgmik@gmail.com>
Date: Fri, 20 Jan 2023 09:32:54 -0500
Subject: added spectral_utils

---
 qolab/math/__init__.py       |  8 +++++++
 qolab/math/spectral_utils.py | 50 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 58 insertions(+)
 create mode 100644 qolab/math/__init__.py
 create mode 100644 qolab/math/spectral_utils.py

diff --git a/qolab/math/__init__.py b/qolab/math/__init__.py
new file mode 100644
index 0000000..51d5715
--- /dev/null
+++ b/qolab/math/__init__.py
@@ -0,0 +1,8 @@
+
+from .spectral_utils import spectrum, noise_density_spectrum, noise_spectrum_smooth
+
+__all__ = [
+    "spectrum",
+    "noise_density_spectrum",
+    "noise_spectrum_smooth",
+]
diff --git a/qolab/math/spectral_utils.py b/qolab/math/spectral_utils.py
new file mode 100644
index 0000000..150078c
--- /dev/null
+++ b/qolab/math/spectral_utils.py
@@ -0,0 +1,50 @@
+import numpy as np
+from scipy.fft import fft, rfft, irfft, rfftfreq
+
+def spectrum(t, y):
+    """
+    Spectrum of real value signal with stripped away zero frequency component.
+    Preserves amplitude of a coherent signal (Asin(f*t)) independent of sampling
+    rate and time interval.
+    """
+    N = t.size
+    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
+
+def noise_density_spectrum(t,y):
+    """
+    Calculates 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)
+    return freq, yf
+
+def noise_spectrum_smooth(fr, Ampl, Nbins=100):
+    """
+    Smooth spectrum, especially at high frequency end.
+    Could be thought as logarithmic spacing running average.
+    Since we assume the spectrum of the nose, we do power average (rmsq on amplitudes)
+    Assumes that set of frequencies is positive and equidistant set.
+    Also assumes that frequencies do not contain 0.
+    """
+
+    frEdges = np.logspace(np.log10(fr[0]), np.log10(fr[-1]), Nbins)
+    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) )
+    ind = np.logical_not(np.isnan(frCenter))
+    frCenter = frCenter[ind]
+    power = power[ind]
+    # print(f'{frCenter=} {power=}')
+    return frCenter,  np.sqrt( power )
+
-- 
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