def __call__(self, theta, phi):
par = self.par
if self.backward:
theta = np.pi - theta
out = gauss_plus(theta, par[0], par[1], par[2])
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def __call__(self, theta, phi):
if self.backward:
theta = np.pi - theta
out = self.sp(theta, self.nu, grid=False)
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def __call__(self, theta, phi):
par = self.par
if self.backward:
theta = np.pi - theta
theta_eff = theta
if 170 >= self.nu >= 130:
theta_eff = theta * self.nu / 150
out = gauss_plus(theta_eff, par[0], par[1], par[2])
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def func(shapein, shapeout, extradata, extrainput):
datashape = extradata + shapeout + shapein
d = np.arange(product(datashape)).reshape(datashape)
b = DenseBlockDiagonalOperator(d,
naxesin=len(shapein),
naxesout=len(shapeout))
new_shape = broadcast_shapes(extradata, extrainput)
bdense = b.todense(shapein=new_shape + shapein)
d_ = reshape_broadcast(d, new_shape + shapeout + shapein)
d_ = d_.reshape(-1, product(shapeout), product(shapein))
expected = BlockDiagonalOperator(
[_ for _ in d_],
axisin=0).todense(shapein=product(new_shape + shapein))
assert_same(bdense, expected)
bTdense = b.T.todense(shapein=new_shape + shapeout)
assert_same(bTdense, expected.T)
def __call__(self, theta, phi):
if self.backward:
theta = np.pi - theta
s_peak = self.sigma
s_ripple = self.sigma / 1.96
coef = -0.5 / s_peak**2
out = ne.evaluate('exp(coef * theta**2)')
h = [0.01687, 0.00404] # relative heights of the first two ripples
add = np.zeros(out.shape)
for r in xrange(self.nripples):
coef = -0.5 / s_ripple**2
rh = h[r]
m = s_peak * 4.014 + s_peak * r * 2.308
add += ne.evaluate('rh * exp(coef * (theta - m)**2)')
set_trace()
out += add
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def __call__(self, theta, phi):
if self.backward:
theta = np.pi - theta
s_peak = self.sigma
s_ripple = self.sigma / 1.96
coef = -0.5 / s_peak**2
out = ne.evaluate('exp(coef * theta**2)')
h = [0.01687, 0.00404] # relative heights of the first two ripples
add = np.zeros(out.shape)
for r in xrange(self.nripples):
coef = -0.5 / s_ripple**2
rh = h[r]
m = s_peak * 4.014 + s_peak * r * 2.308
add += ne.evaluate('rh * exp(coef * (theta - m)**2)')
set_trace()
out += add
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def __call__(self, theta, phi):
if self.backward:
theta = np.pi - theta
coef = -0.5 / self.sigma**2
out = ne.evaluate('exp(coef * theta**2)')
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def __call__(self, theta, phi):
out = 1.
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
def func(shape, new_shape):
data_ = data.reshape(shape)
expected = np.empty(new_shape)
expected[...] = data_
actual = reshape_broadcast(data_, new_shape)
assert_equal(actual, expected)
def __call__(self, theta_rad, phi_rad):
out = 1.
return reshape_broadcast(out, np.broadcast(theta_rad, phi_rad).shape)
def __call__(self, theta_rad, phi_rad):
if self.backward:
theta_rad = pi - theta_rad
coef = -0.5 / self.sigma_rad**2
out = ne.evaluate('exp(coef * theta_rad**2)')
return reshape_broadcast(out, np.broadcast(theta_rad, phi_rad).shape)
def func(shape, new_shape):
data_ = data.reshape(shape)
expected = np.empty(new_shape)
expected[...] = data_
actual = reshape_broadcast(data_, new_shape)
assert_equal(actual, expected)
def __call__(self, theta, phi):
if self.backward:
theta = np.pi - theta
coef = -0.5 / self.sigma**2
out = ne.evaluate('exp(coef * theta**2)')
return reshape_broadcast(out, np.broadcast(theta, phi).shape)
