def model(self, p, t, x, y):
"""Sum of two Gaussians in time, modulated by a 2D spatial Gaussian
returns a vector of voltage values v of same length as t. x and y are
vectors of x and y coordinates of each channel's spatial location. Output
of this should be an (nchans, nt) matrix of modelled voltage values v"""
return np.outer(g2(p[6], p[7], p[8], p[8], x, y),
p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t))
def model(self, p, t, x, y):
"""Sum of two Gaussians in time, modulated by a 2D spatial Gaussian
returns a vector of voltage values v of same length as t. x and y are
vectors of x and y coordinates of each channel's spatial location. Output
of this should be an (nchans, nt) matrix of modelled voltage values v"""
return np.outer(g2(p[6], p[7], p[8], p[8], x, y),
p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t))
def __init__(self, p0, t, v, min1=50, min2=50, dmurange=(100, 500)):
self.p0 = p0
self.t = t
self.v = v
self.min1 = min1
self.min2 = min2
self.dmurange = dmurange
#cons = (self.con1, self.con2, self.con3, self.con4)
self.cons = self.con0
self.calc()
figure()
plot(t, v, 'k.-')
p = self.p
plot(t, p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t), 'r-')
def __init__(self, p0, t, v, min1=50, min2=50, dmurange=(100, 500)):
self.p0 = p0
self.t = t
self.v = v
self.min1 = min1
self.min2 = min2
self.dmurange = dmurange
#cons = (self.con1, self.con2, self.con3, self.con4)
self.cons = self.con0
self.calc()
figure()
plot(t, v, 'k.-')
p = self.p
plot(t, p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t), 'r-')
def plot(self):
t = self.t
p = self.p
for (V, x, y) in zip(self.V, self.x, self.y):
figure()
title('x, y = %r um' % ((x, y), ))
plot(t, V, 'k.-')
plot(t,
g2(p[6], p[7], p[8], p[8], x, y) * p[0] * g(p[1], p[2], t),
'r-')
plot(t,
g2(p[6], p[7], p[8], p[8], x, y) * p[3] * g(p[4], p[5], t),
'g-')
plot(
t,
g2(p[6], p[7], p[8], p[8], x, y) *
(p[0] * g(p[1], p[2], t) + p[3] * g(p[4], p[5], t)), 'b-')
gca().autoscale_view(tight=True, scalex=True,
scaley=False) # tight fit to timepoints
gca().set_ylim(-100, 100)
figure()
title('x, y = %r (model origin in space)' % ((p[6], p[7]), ))
plot(t, p[0] * g(p[1], p[2], t) + p[3] * g(p[4], p[5], t), 'm-')
gca().autoscale_view(tight=True, scalex=True,
scaley=False) # tight fit to timepoints
gca().set_ylim(-100, 100)
def plot(self):
t = self.t
p = self.p
for (V, x, y) in zip(self.V, self.x, self.y):
figure()
title('x, y = %r um' % ((x, y),))
plot(t, V, 'k.-')
plot(t,
g2(p[6], p[7], p[8], p[8], x, y) * p[0]*g(p[1], p[2], t),
'r-')
plot(t,
g2(p[6], p[7], p[8], p[8], x, y) * p[3]*g(p[4], p[5], t),
'g-')
plot(t,
g2(p[6], p[7], p[8], p[8], x, y) * (p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t)),
'b-')
gca().autoscale_view(tight=True, scalex=True, scaley=False) # tight fit to timepoints
gca().set_ylim(-100, 100)
figure()
title('x, y = %r (model origin in space)' % ((p[6], p[7]),))
plot(t, p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t), 'm-')
gca().autoscale_view(tight=True, scalex=True, scaley=False) # tight fit to timepoints
gca().set_ylim(-100, 100)
def model(self, p, t):
"""Sum of two Gaussians, returns a vector of v values"""
return p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t)
def model(self, p, t):
"""Sum of two Gaussians, returns a vector of v values"""
return p[0]*g(p[1], p[2], t) + p[3]*g(p[4], p[5], t)