def find_mode(vec, x=None,SDk = 0.2,max_iter=1e5):
"""Finds first empirical mode of the vector `vec`
returns mode if it can, returns ``None`` if no local extrema can be found
Parameters:
- `vec`: an input 1D vector
- `x`: an optional vector, `vec=f(x)`
- `SDk`: tolerance
- `max_iter`: maximum number of iterations
Returns:
- `h`: a mode estimate
"""
h_prev = imf_candidate(vec,x)
if h_prev is None:
return None
for k in xrange(long(max_iter)):
h1 = imf_candidate(h_prev, x)
if h1 is None:
return h_prev
sd = np.sum((h1-h_prev)**2)/np.sum(h_prev**2)
xf,yf,der,mx,mn = lib.locextr(h1)
zc = np.where(np.diff(np.sign(h1)) > 0 )[0]
print len(zc), len(mx), len(mn)
if (abs(len(zc) - len(mx)) < 2) and \
(abs(len(mx) - len(mn)) < 2) and \
(abs(len(zc) - len(mn)) < 2) and \
sd < SDk:
return h1
h_prev = h1
print "No convergence after %d iterations" % max_iter
return h1
def _(v1,v2): v1n = lib.DFoSD(v1)/len(v1) xcorr = np.correlate(v1n, lib.DFoSD(v2), mode='full') xcorr = atrous.smooth(xcorr, 2) maxima = lib.locextr(xcorr, x=tv1, output='max') k = np.argmax([x[1] for x in maxima]) return maxima[k]
def find_mode(vec, x=None, SDk=0.2, max_iter=1e5):
"""Finds first empirical mode of the vector `vec`
returns mode if it can, returns ``None`` if no local extrema can be found
Parameters:
- `vec`: an input 1D vector
- `x`: an optional vector, `vec=f(x)`
- `SDk`: tolerance
- `max_iter`: maximum number of iterations
Returns:
- `h`: a mode estimate
"""
h_prev = imf_candidate(vec, x)
if h_prev is None:
return None
for k in xrange(long(max_iter)):
h1 = imf_candidate(h_prev, x)
if h1 is None:
return h_prev
sd = np.sum((h1 - h_prev)**2) / np.sum(h_prev**2)
xf, yf, der, mx, mn = lib.locextr(h1)
zc = np.where(np.diff(np.sign(h1)) > 0)[0]
print len(zc), len(mx), len(mn)
if (abs(len(zc) - len(mx)) < 2) and \
(abs(len(mx) - len(mn)) < 2) and \
(abs(len(zc) - len(mn)) < 2) and \
sd < SDk:
return h1
h_prev = h1
print "No convergence after %d iterations" % max_iter
return h1
def envelopes(vec, x=None):
"""
Given vector `vec` and optional `x`, return lower and upper envelopes defined
by local extrema in vector vec. Variable `x` defines the abscissa for
vector `vec`
"""
from scipy.interpolate import splrep,splev
xfit,yfit,_,maxlocs,minlocs = lib.locextr(vec,x,sort_values=False)
if (len(maxlocs) < 1) or (len(minlocs) < 1):
return []
if x is None: x = np.arange(len(vec))
minlocs = np.concatenate([[0],minlocs,[-1]])
maxlocs = np.concatenate([[0],maxlocs,[-1]])
lower = splev(x,splrep(xfit[minlocs],yfit[minlocs]))
upper = splev(x,splrep(xfit[maxlocs],yfit[maxlocs]))
return lower, upper
def envelopes(vec, x=None):
"""
Given vector `vec` and optional `x`, return lower and upper envelopes defined
by local extrema in vector vec. Variable `x` defines the abscissa for
vector `vec`
"""
from scipy.interpolate import splrep, splev
xfit, yfit, _, maxlocs, minlocs = lib.locextr(vec, x, sort_values=False)
if (len(maxlocs) < 1) or (len(minlocs) < 1):
return []
if x is None: x = np.arange(len(vec))
minlocs = np.concatenate([[0], minlocs, [-1]])
maxlocs = np.concatenate([[0], maxlocs, [-1]])
lower = splev(x, splrep(xfit[minlocs], yfit[minlocs]))
upper = splev(x, splrep(xfit[maxlocs], yfit[maxlocs]))
return lower, upper
