def get_tau(self, x):
""" Uses a precomputed spline to find tau at x """
#Hack to work with the ode solver that sometimes wants an x value
#later than today
if isinstance(x, np.ndarray):
x[x > self.x_array[-1]] = self.x_array[-1]
else:
if x > self.x_array[-1]:
x = self.x_array[-1]
return np.exp(interpolation.splint(self.x_array, np.log(self.tau_array), self.tau_spline, x))
def get_tau_primed(self, x):
""" Uses a spline of the derivative to find dtau/dx at x """
#Hack to work with the ode solver that sometimes wants an x value
#later than today
if isinstance(x, np.ndarray):
x[x > self.x_array[-1]] = self.x_array[-1]
else:
if x > self.x_array[-1]:
x = self.x_array[-1]
return -np.exp(interpolation.splint(self.x_array, np.log(-self.tau_der_array), self.tau_der_spline, x))
def get_eta(self, x):
""" Using input arrays, produces eta for a given value of x = ln a. """
if self.eta_spline is None:
if self.eta_array is None:
#Initialize the eta array based on the scale factor values.
#We need to append to an array with a single entry because
#cumtrapz returns an array with one less value than the input
#array
self.eta_array = np.append(np.array([self.eta_init]), s_o_l * cumtrapz(1 / (get_H_p(self.x_array, self.bg_params)), self.x_array))
self.eta_spline = interpolation.cubic_spline(self.x_array, self.eta_array)
# self.eta_splint = scipy.interpolate.interp1d(self.a_array, self.eta_array, kind='cubic')
if isinstance(x, np.ndarray):
x[x > self.x_array[-1]] = self.x_array[-1]
elif isinstance(x, float):
if x > self.x_array[-1]:
x = self.x_array[-1]
return interpolation.splint(self.x_array, self.eta_array, self.eta_spline, x)
def get_g_double_primed(self, x):
""" Uses precomputed information to compute the double derivative of the visibility function. """
return interpolation.splint(self.x_array, self.g_spline, self.g2_spline, x)
def get_g(self, x):
""" Uses precomputed information to compute the visibility function. """
return interpolation.splint(self.x_array, self.g_array, self.g_spline, x)
def get_ne(self, x):
""" Uses a precomputed spline to find the electron density at x. """
return np.exp(interpolation.splint(self.x_array, np.log(self.ne_array), self.ne_spline, x))
