def find_intersection(x1,y1,x2,y2):
#http://stackoverflow.com/questions/8094374/python-matplotlib-find-intersection-of-lineplots
import scipy.interpolate as interpolate
import scipy.optimize as optimize
p1=interpolate.PiecewisePolynomial(x1,y1[:,np.newaxis])
p2=interpolate.PiecewisePolynomial(x2,y2[:,np.newaxis])
def pdiff(x):
return p1(x)-p2(x)
xs=np.r_[x1,x2]
xs.sort()
x_min=xs.min()
x_max=xs.max()
x_mid=xs[:-1]+np.diff(xs)/2
roots=set()
for val in x_mid:
root,infodict,ier,mesg = optimize.fsolve(pdiff,val,full_output=True)
# ier==1 indicates a root has been found
if ier==1 and x_min<root<x_max:
roots.add(root[0])
roots=np.array(list(roots))
return roots,p1(roots)
def catmullrom(self):
derivs = self.find_derivatives()
u = numpy.arange(0.0, self.x.size)
polys = []
polys.append(
interp.PiecewisePolynomial(u,
zip(self.x, derivs[0]),
orders=3,
direction=None))
polys.append(
interp.PiecewisePolynomial(u,
zip(self.y, derivs[1]),
orders=3,
direction=None))
return polys, u
def half_amp_dur(self, cluster):
"""
Half amplitude duration of a spike
Parameters
----------
A: ndarray
An nSpikes x nElectrodes x nSamples array
Returns
-------
had: float
The half-amplitude duration for the channel (electrode) that has
the strongest (highest amplitude) signal. Units are ms
"""
from scipy import interpolate, optimize
waveforms = self.waveforms[self.cut == cluster, :, :]
best_chan = np.argmax(np.max(np.mean(waveforms, 0), 1))
mn_wvs = np.mean(waveforms, 0)
wvs = mn_wvs[best_chan, :]
half_amp = np.max(wvs) / 2
half_amp = np.zeros_like(wvs) + half_amp
t = np.linspace(0, 1 / 1000., 50)
# create functions from the data using PiecewisePolynomial
p1 = interpolate.PiecewisePolynomial(t, wvs[:, np.newaxis])
p2 = interpolate.PiecewisePolynomial(t, half_amp[:, np.newaxis])
xs = np.r_[t, t]
xs.sort()
x_min = xs.min()
x_max = xs.max()
x_mid = xs[:-1] + np.diff(xs) / 2
roots = set()
for val in x_mid:
root, infodict, ier, mesg = optimize.fsolve(
lambda x: p1(x) - p2(x), val, full_output=True)
if ier == 1 and x_min < root < x_max:
roots.add(root[0])
roots = list(roots)
if len(roots) > 1:
r = np.abs(np.diff(roots[0:2]))[0]
else:
r = np.nan
return r
def pla(data, period=15):
N = int(len(data)/period)
orig_x = range(0,len(data))
tck = splrep(orig_x, data,s=0)
test_xs = np.linspace(0,len(data),N)
spline_ys = splev(test_xs, tck)
spline_yps = splev(test_xs, tck, der=1)
xi = np.unique(tck[0])
yi = [[splev(x, tck, der=j) for j in xrange(3)] for x in xi]
P = interpolate.PiecewisePolynomial(xi,yi,orders=1)
test_ys = P(test_xs)
#inter_y = interp0(test_xs, test_ys, orig_x)
inter_y = interp1(test_xs, test_ys, orig_x)
mae = sqrt(mean_absolute_error(inter_y, data))
# mae = np.var(inter_y-data)
return mae
