clusters/halo: Remove *_avg and *_fraction methods

1 files changed, 0 insertions, 50 deletions

diff --git a/fg21sim/extragalactic/clusters/halo.py b/fg21sim/extragalactic/clusters/halo.py
index ed73a47..458c1d0 100644
--- a/fg21sim/extragalactic/clusters/halo.py
+++ b/fg21sim/extragalactic/clusters/halo.py
@@ -852,56 +852,6 @@ class RadioHaloAM(RadioHalo1M):
         rate = (mass0 - mass1) / (t0 - t1)
         return (mass1 + rate * (t - t1))
 
-    @property
-    def duration_turb_avg(self):
-        """
-        Calculate the time-averaged turbulence acceleration active time
-        within the period from ``age_begin`` to ``age_obs``.
-
-        Unit: [Gyr]
-        """
-        dt = self.time_step
-        xt = np.arange(self.age_begin, self.age_obs+dt/2, step=dt)
-        t_turb = np.array([self.duration_turb(t) for t in xt])
-        return np.sum(t_turb * dt) / (len(xt) * dt)
-
-    @property
-    def mach_turbulence_avg(self):
-        """
-        Calculate the time-averaged turbulence Mach number within the
-        period from ``age_begin`` to ``age_obs``.
-        """
-        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])
-        return np.sum(mach * dt) / (len(xt) * dt)
-
-    @property
-    def tau_acceleration_avg(self):
-        """
-        Calculate the time-averaged turbulence acceleration timescale
-        (i.e., efficiency) within the period from ``age_begin`` to
-        ``age_obs``.
-
-        Unit: [Gyr]
-        """
-        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])
-        return np.sum(tau * dt) / (len(xt) * dt)
-
-    @property
-    def time_acceleration_fraction(self):
-        """
-        Calculate the fraction of time within the period from
-        ``age_begin`` to ``age_obs`` that the turbulence acceleration
-        is active.
-        """
-        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)
-        return active.mean()
-
 
 class RadioHalo:
     """