black formatter

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)