ASCEE/lasp
ASCEE/lasp
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Fixed a bug in the new smoothing algorithm

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This commit is contained in:
Casper Jansen 2021-10-18 08:23:42 +02:00
parent 417c1bd3d8
commit 82fc38a990
1 changed files with 21 additions and 12 deletions
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33
lasp/tools/tools.py
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@ -108,7 +108,7 @@ def smoothSpectralData(freq, M, sw: SmoothingWidth,
# Safety # Safety
MM = copy.deepcopy(M) MM = copy.deepcopy(M)
Noct = sw.value[0] Noct = sw.value[0]
assert M, "Smoothing function: input array is empty" assert len(M) > 0, "Smoothing function: input array is empty" # not sure if this works
assert Noct > 0, "'Noct' must be absolute positive" assert Noct > 0, "'Noct' must be absolute positive"
if Noct < 1: if Noct < 1:
raise Warning('Check if \'Noct\' is entered correctly') raise Warning('Check if \'Noct\' is entered correctly')
@ -141,17 +141,26 @@ def smoothSpectralData(freq, M, sw: SmoothingWidth,
for x in range(x0, L): for x in range(x0, L):
# Find indices of data points to calculate current (smoothed) magnitude # Find indices of data points to calculate current (smoothed) magnitude
# #
# Indices beyond [0, L] point to non-existing data. This occurs when # Indices beyond [0, L] point to non-existing data. Beyond 0 does not
# the smoothing windows nears the beginning or end of the series. # occur in this implementation. Beyond L occurs when the smoothing
# Optional: if one end of the window is clipped, the other end # window nears the end of the series.
# could be clipped as well, to prevent an error on magnitude data with # If one end of the window is truncated, the other end
# a slope. It however results in unsmoothed looking data at the ends. # could be truncated as well, to prevent an error on magnitude data
# # with a slope. It however results in unsmoothed looking data at the
# Implicitly, the window is truncated in this implementation, because # end.
# of the bisect search
fc = freq[x] # center freq. of smoothing window fc = freq[x] # center freq. of smoothing window
fl = fc / np.sqrt(2**(tr/Noct)) # lower cutoff fl = fc / np.sqrt(2**(tr/Noct)) # lower cutoff
fu = fc * np.sqrt(2**(tr/Noct)) # upper cutoff fu = fc * np.sqrt(2**(tr/Noct)) # upper cutoff
# If the upper (frequency) side of the window is truncated because
# there is no data beyond the Nyquist frequency, also truncate the
# other side to keep it symmetric in a log(frequency) scale.
# So: fu / fc = fc / fl
fNq = freq[-1]
if fu > fNq:
fu = fNq # no data beyond fNq
fl = fc**2 / fu # keep window symmetric
xl = bisect.bisect_left(f, fl) # index corresponding to fl xl = bisect.bisect_left(f, fl) # index corresponding to fl
xu = bisect.bisect_left(f, fu) xu = bisect.bisect_left(f, fu)
@ -292,14 +301,14 @@ if __name__ == "__main__":
""" """
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
# Initialize # Initialize
Noct = 6 # Noct = 6 for 1/6 oct. smoothing Noct = 2 # Noct = 6 for 1/6 oct. smoothing
# Create dummy data set 1: noise # Create dummy data set 1: noise
fmin = 3e3 # [Hz] min freq fmin = 3e3 # [Hz] min freq
fmax = 24e3 # [Hz] max freq fmax = 24e3 # [Hz] max freq
Ndata = 200 # number of data points Ndata = 200 # number of data points
f = np.linspace(fmin, fmax, Ndata) # frequency points f = np.linspace(fmin, fmax, Ndata) # frequency points
M = abs(1+0.4*np.random.normal(size=(Ndata,)))+0.01 # M = abs(0.4*np.random.normal(size=(Ndata,)))+0.01 #
M = 20*np.log10(M) M = 20*np.log10(M)
# # Create dummy data set 2: dirac delta # # Create dummy data set 2: dirac delta
@ -308,7 +317,7 @@ if __name__ == "__main__":
# Ndata = 200 # number of data points # Ndata = 200 # number of data points
# f = np.linspace(fmin, fmax, Ndata) # frequency points # f = np.linspace(fmin, fmax, Ndata) # frequency points
# M = 0 * abs(1+0.4*np.random.normal(size=(Ndata,))) + 0.01 # # M = 0 * abs(1+0.4*np.random.normal(size=(Ndata,))) + 0.01 #
# M[int(Ndata/5)] = 1 # M[int(100)] = 1
# M = 20*np.log10(M) # M = 20*np.log10(M)
# Apply function # Apply function