def _convolution_in_point(t_val,
f,
g,
n_integral=100,
inverse_time=None,
return_log=False):
'''
evaluates int_tau f(t+tau)g(tau) or int_tau f(t-tau)g(tau) if inverse time is TRUE
'''
if inverse_time is None:
raise Exception("Inverse time argument must be set!")
# determine integration boundaries:
if inverse_time:
## tau>g.xmin and t-tau<f.xmax
tau_min = max(t_val - f.xmax, g.xmin)
## tau<g.xmax and t-tau>f.xmin
tau_max = min(t_val - f.xmin, g.xmax)
else:
## tau>g.xmin and t+tau>f.xmin
tau_min = max(f.xmin - t_val, g.xmin)
## tau<g.xmax and t+tau<f.xmax
tau_max = min(f.xmax - t_val, g.xmax)
#print(tau_min, tau_max)
if tau_max <= tau_min + ttconf.TINY_NUMBER:
if return_log:
return ttconf.BIG_NUMBER
else:
return 0.0 # functions do not overlap
else:
# create the tau-grid for the interpolation object in the overlap region
if inverse_time:
tau = np.unique(
np.concatenate((g.x, t_val - f.x, [tau_min, tau_max])))
else:
tau = np.unique(
np.concatenate((g.x, f.x - t_val, [tau_min, tau_max])))
tau = tau[(tau > tau_min - ttconf.TINY_NUMBER)
& (tau < tau_max + ttconf.TINY_NUMBER)]
if len(tau) < 10:
tau = np.linspace(tau_min, tau_max, 10)
if inverse_time: # add negative logarithms
fg = f(t_val - tau) + g(tau)
else:
fg = f(t_val + tau) + g(tau)
# create the interpolation object on this grid
FG = Distribution(tau, fg, is_log=True, kind='linear')
#integrate the interpolation object, return log, make neg_log
#print('FG:',FG.xmin, FG.xmax, FG(FG.xmin), FG(FG.xmax))
res = -FG.integrate(
a=FG.xmin, b=FG.xmax, n=n_integral, return_log=True)
if return_log:
return res
else:
return np.exp(-res)
