def fundamental_frequency(s, FS):
# TODO: review fundamental frequency to guarantee that f0 exists
# suggestion peak level should be bigger
# TODO: explain code
"""Compute fundamental frequency along the specified axes.
Parameters
----------
s: ndarray
input from which fundamental frequency is computed.
FS: int
sampling frequency
Returns
-------
f0: int
its integer multiple best explain the content of the signal spectrum.
"""
s = s - np.mean(s)
f, fs = plotfft(s, FS, doplot=False)
fs = fs[1:len(fs) / 2]
f = f[1:len(f) / 2]
cond = find(f > 0.5)[0]
bp = bigPeaks(fs[cond:], 0)
if not bp:
f0 = 0
else:
bp = bp + cond
f0 = f[min(bp)]
return f0
def max_frequency(sig, FS):
"""Compute max frequency along the specified axes.
Parameters
----------
sig: ndarray
input from which max frequency is computed.
FS: int
sampling frequency
Returns
-------
f_max: int
0.95 of max_frequency using cumsum.
"""
f, fs = plotfft(sig, FS, doplot=False)
t = np.cumsum(fs)
try:
ind_mag = np.where(t > t[-1] * 0.95)[0][0]
except:
ind_mag = np.argmax(t)
f_max = f[ind_mag]
return f_max
def median_frequency(sig, FS):
"""Compute median frequency along the specified axes.
Parameters
----------
sig: ndarray
input from which median frequency is computed.
FS: int
sampling frequency
Returns
-------
f_max: int
0.50 of max_frequency using cumsum.
"""
f, fs = plotfft(sig, FS, doplot=False)
t = np.cumsum(fs)
ind_mag = np.where(t > t[-1] * 0.50)[0][0]
f_median = f[ind_mag]
return f_median
def median_frequency(sig,FS):
"""Compute median frequency along the specified axes.
Parameters
----------
sig: ndarray
input from which median frequency is computed.
FS: int
sampling frequency
Returns
-------
f_max: int
0.50 of max_frequency using cumsum.
"""
f, fs = plotfft(sig, FS, doplot=False)
t = cumsum(fs)
ind_mag = find (t>t[-1]*0.50)[0]
f_median=f[ind_mag]
return f_median
def fundamental_frequency(s,FS):
# TODO: review fundamental frequency to guarantee that f0 exists
# suggestion peak level should be bigger
# TODO: explain code
"""Compute fundamental frequency along the specified axes.
Parameters
----------
s: ndarray
input from which fundamental frequency is computed.
FS: int
sampling frequency
Returns
-------
f0: int
its integer multiple best explain the content of the signal spectrum.
"""
s = s - mean(s)
f, fs = plotfft(s, FS, doplot=False)
#fs = smooth(fs, 50.0)
fs = fs[1:len(fs) / 2]
f = f[1:len(f) / 2]
cond = find(f > 0.5)[0]
bp = bigPeaks(fs[cond:], 0)
if bp==[]:
f0=0
else:
bp = bp + cond
f0 = f[min(bp)]
return f0
