diff options
| -rw-r--r-- | fg21sim/extragalactic/clusters/halo.py | 26 |
1 files changed, 8 insertions, 18 deletions
diff --git a/fg21sim/extragalactic/clusters/halo.py b/fg21sim/extragalactic/clusters/halo.py index a94eb84..4e1b0bf 100644 --- a/fg21sim/extragalactic/clusters/halo.py +++ b/fg21sim/extragalactic/clusters/halo.py @@ -708,10 +708,7 @@ class RadioHalo: return False t_merger = self._merger_time(t) t_turb = self.time_turbulence(t_merger) - if (t >= t_merger) and (t <= t_merger + t_turb): - return True - else: - return False + return (t >= t_merger) and (t <= t_merger + t_turb) def _energy_loss(self, gamma, t): """ @@ -801,11 +798,8 @@ class RadioHaloAM(RadioHalo): Determine the beginning time of the merger event within which the given time is located. """ - try: - idx = self._merger_idx(t) - return self.age_merger[idx] - except IndexError: - return None + idx = self._merger_idx(t) + return self.age_merger[idx] def _merger(self, idx): """ @@ -880,8 +874,7 @@ class RadioHaloAM(RadioHalo): dt = self.time_step xt = np.arange(self.age_begin, self.age_obs+dt/2, step=dt) t_turb = np.array([self.time_turbulence(t) for t in xt]) - avg = np.sum(t_turb * dt) / (len(xt) * dt) - return avg + return np.sum(t_turb * dt) / (len(xt) * dt) @property def mach_turbulence_avg(self): @@ -892,8 +885,7 @@ class RadioHaloAM(RadioHalo): dt = self.time_step xt = np.arange(self.age_begin, self.age_obs+dt/2, step=dt) mach = np.array([self.mach_turbulence(t) for t in xt]) - avg = np.sum(mach * dt) / (len(xt) * dt) - return avg + return np.sum(mach * dt) / (len(xt) * dt) @property def tau_acceleration_avg(self): @@ -907,8 +899,7 @@ class RadioHaloAM(RadioHalo): dt = self.time_step xt = np.arange(self.age_begin, self.age_obs+dt/2, step=dt) tau = np.array([self.tau_acceleration(t) for t in xt]) - avg = np.sum(tau * dt) / (len(xt) * dt) - return avg + return np.sum(tau * dt) / (len(xt) * dt) @property def time_acceleration_fraction(self): @@ -917,8 +908,7 @@ class RadioHaloAM(RadioHalo): ``age_begin`` to ``age_obs`` that the turbulence acceleration is active. """ - dt = self.time_step + dt = self.fpsolver.tstep xt = np.arange(self.age_begin, self.age_obs+dt/2, step=dt) active = np.array([self._is_turb_active(t) for t in xt], dtype=int) - fraction = active.mean() - return fraction + return active.mean() |