def p_resp(self):
r""" Returns the sustained parvocellular response to flicker stimulus."""
p_resp = fftconvolve(self.flickers,self.p[:,np.newaxis])
p_resp = utils.normalize(p_resp,-1,1)
return p_resp
def generate_m_resp(self, tau):
m_rf = self.m_rf(tau)
m_resp = fftconvolve(self.flickers, m_rf[:, np.newaxis])
# m_resp[:,0] = utils.normalize(m_resp[:,0],-1,1)
# m_resp[:,1] = utils.normalize(m_resp[:,1],-1,1)
m_resp = utils.normalize(m_resp, -1, 1)
return m_resp
def test_simulate_sinflicker_bar():
# no blanks
bar = simulate_sinflicker_bar(100,100,50,10,[0],1,10,5,1,1,60)
y = utils.normalize(bar[1,1,:],0,1)
t = np.linspace(0,1,60)
yhat = utils.normalize(np.sin(2 * np.pi * t),0,1)
nt.assert_almost_equal(np.sum(yhat-y),0)
# blanks
bar = simulate_sinflicker_bar(100,100,50,10,[0,-1],1,10,5,1,1,60)
y = np.round(utils.normalize(bar[1,1,:],0,1),2)
t = np.linspace(0,1,60)
yhat = utils.normalize(np.sin(2 * np.pi * t),0,1)
yhat = np.append(yhat,np.repeat(0.5,60))
nt.assert_almost_equal(np.sum(yhat-np.round(y,2)),0,1)
def m_resp(self):
r""" Returns the transient magnocellular response to flicker stimulus."""
m_resp = fftconvolve(self.flickers,self.m[:,np.newaxis])
m_resp= utils.normalize(m_resp,-1,1)
return m_resp
def m_resp(self):
m_resp = fftconvolve(self.flickers, self.m[:, np.newaxis])
# m_resp[:,0] = utils.normalize(m_resp[:,0],-1,1)
# m_resp[:,1] = utils.normalize(m_resp[:,1],-1,1)
m_resp = utils.normalize(m_resp, -1, 1)
return m_resp
def p_resp(self):
r""" Returns the sustained parvocellular response to flicker stimulus."""
p_resp = fftconvolve(self.flickers,self.p[:,np.newaxis])
p_resp = utils.normalize(p_resp,-1,1)
return p_resp
def m_resp(self):
r""" Returns the transient magnocellular response to flicker stimulus."""
m_resp = fftconvolve(self.flickers,self.m[:,np.newaxis])
m_resp= utils.normalize(m_resp,-1,1)
return m_resp
def test_normalize():
# 1D
arr = np.linspace(0, 1, 100)
lo = 100.0
hi = 200.0
arr_new = utils.normalize(arr, lo, hi)
npt.assert_equal(np.min(arr_new), lo)
npt.assert_equal(np.max(arr_new), hi)
# 2D
arr = np.tile(np.linspace(0, 1, 100), (10, 1))
lo = np.repeat(lo, 10)
hi = np.repeat(hi, 10)
arr_new = utils.normalize(arr, lo, hi)
npt.assert_equal(np.min(arr_new), lo)
npt.assert_equal(np.max(arr_new), hi)
def test_normalize():
# 1D
arr = np.linspace(0,1,100)
lo = 100.0
hi = 200.0
arr_new = utils.normalize(arr, lo, hi)
npt.assert_equal(np.min(arr_new), lo)
npt.assert_equal(np.max(arr_new), hi)
# 2D
arr = np.tile(np.linspace(0,1,100),(10,1))
lo = np.repeat(lo,10)
hi = np.repeat(hi,10)
arr_new = utils.normalize(arr, lo, hi)
npt.assert_equal(np.min(arr_new), lo)
npt.assert_equal(np.max(arr_new), hi)
def generate_p_resp(self, tau):
r""" Returns the sustained parvocellular response to flicker stimulus.
Parameters
----------
tau : float
The temporal dispersion of the sustained temporal receptive field.
Returns
-------
p_resp : ndarray
The temporal response of the flickering visual stimulus convolved
with the sustained parvocellular temporal receptive field.
"""
p_rf = self.p_rf(tau)
p_resp = fftconvolve(self.flickers, p_rf[:, np.newaxis])
p_resp = utils.normalize(p_resp, -1, 1)
return p_resp
def generate_p_resp(self, tau):
r""" Returns the sustained parvocellular response to flicker stimulus.
Parameters
----------
tau : float
The temporal dispersion of the sustained temporal receptive field.
Returns
-------
p_resp : ndarray
The temporal response of the flickering visual stimulus convolved
with the sustained parvocellular temporal receptive field.
"""
p_rf = self.p_rf(tau)
p_resp = fftconvolve(self.flickers,p_rf[:,np.newaxis])
p_resp = utils.normalize(p_resp,-1,1)
return p_resp
def generate_p_resp(self, tau):
p_rf = self.p_rf(tau)
p_resp = fftconvolve(self.flickers, p_rf[:, np.newaxis])
p_resp = utils.normalize(p_resp, -1, 1)
return p_resp
def p_resp(self):
p_resp = fftconvolve(self.flickers, self.p[:, np.newaxis])
p_resp = utils.normalize(p_resp, -1, 1)
return p_resp
def generate_m_resp(self, tau):
m_rf = self.m_rf(tau)
m_resp = fftconvolve(self.flickers, m_rf[:, np.newaxis])
m_resp = utils.normalize(m_resp, -1, 1)
return m_resp
def m_resp(self):
m_resp = fftconvolve(self.flickers, self.m[:, np.newaxis])
m_resp = utils.normalize(m_resp, -1, 1)
return m_resp
def generate_p_resp(self, tau):
p_rf = self.p_rf(tau)
p_resp = fftconvolve(self.flickers, p_rf[:, np.newaxis])
p_resp = utils.normalize(p_resp, -1, 1)
return p_resp
def p_resp(self):
p_resp = fftconvolve(self.flickers, self.p[:, np.newaxis])
p_resp = utils.normalize(p_resp, -1, 1)
return p_resp
