def peak_minima(x, s):
s0 = p.ps(s, p0=x[0] * 18000, p1=x[1] * 18000)
s = np.real(s0).flatten()
i = np.argmax(s)
peak = s[i]
mina = np.min(s[i - 100:i])
minb = np.min(s[i:i + 100])
return np.abs(mina - minb)
def peak_minima(x, s):
s0 = p.ps(s, p0=x[0]*18000, p1=x[1]*18000)
s = np.real(s0).flatten()
i = np.argmax(s)
peak = s[i]
mina = np.min(s[i - 100:i])
minb = np.min(s[i:i + 100])
return np.abs(mina - minb)
def min_entropy(x, data):
if data.ndim > 1:
return gmean([min_entropy(x, row) for row in data])
_data = p.ps(data, p0=x[0] * 18000, p1=x[1] * 18000).real
drv = np.absolute(np.diff(_data))
hst = drv / sum(drv)
g = np.ptp(data.real)
penalty = np.sum(_data[_data < 0]**2) * g
entropy = np.sum(hst * np.log(hst)) + penalty
return entropy
def min_entropy(x, data):
if data.ndim > 1:
return gmean([min_entropy(x, row) for row in data])
_data = p.ps(data,p0=x[0]*18000, p1=x[1]*18000).real
drv = np.absolute(np.diff(_data))
hst = drv / sum(drv)
g = np.ptp(data.real)
penalty = np.sum( _data[_data<0]**2 ) * g
entropy = np.sum(hst * np.log(hst)) + penalty
return entropy
def whiten(x, data):
a = np.abs(p.ps(data, p0=x[0] * 18000, p1=x[1] * 18000).real)
t = float(np.mean(a))
return np.sum(a > t)
def min_point(x, data):
obj = -p.ps(data, p0=x[0] * 18000, p1=x[1] * 18000).real.min()
return obj
def max_integ(x, data):
integ = np.trapz(p.ps(data, p0=x[0] * 18000, p1=x[1] * 18000).real)
for i in range(1, data.udic['ndim']):
integ = np.trapz(integ)
return -integ
def whiten(x, data):
a = np.abs(p.ps(data, p0=x[0]*18000, p1=x[1]*18000).real)
t = float(np.mean(a))
return np.sum(a > t)
def min_point(x, data):
obj = -p.ps(data,p0=x[0]*18000, p1=x[1]*18000).real.min()
return obj
def max_integ(x, data):
integ = np.trapz(p.ps(data, p0=x[0]*18000, p1=x[1]*18000).real)
for i in range(1,data.udic['ndim']): integ = np.trapz(integ)
return -integ
