def _sigma(self, u):
index = self.first_particle_index_in_slice
stats = np.zeros(self.n_slices)
for i in xrange(self.n_slices):
k = u[index[i]:index[i + 1]]
stats[i] = cp.std(k)
return stats
def _sigma(self, u):
index = self.first_particle_index_in_slice
stats = np.zeros(self.n_slices)
for i in xrange(self.n_slices):
k = u[index[i]:index[i + 1]]
stats[i] = cp.std(k)
return stats
def _set_longitudinal_cuts(self, bunch):
if self.nsigmaz == None:
z_cut_tail = bunch.z[0]
z_cut_head = bunch.z[-1 - bunch.n_macroparticles_lost]
else:
mean_z = cp.mean(bunch.z[:bunch.n_macroparticles - bunch.n_macroparticles_lost])
sigma_z = cp.std(bunch.z[:bunch.n_macroparticles - bunch.n_macroparticles_lost])
z_cut_tail = mean_z - self.nsigmaz * sigma_z
z_cut_head = mean_z + self.nsigmaz * sigma_z
return z_cut_tail, z_cut_head
def _set_longitudinal_cuts(self, bunch):
if self.nsigmaz == None:
z_cut_tail = bunch.z[0]
z_cut_head = bunch.z[-1 - bunch.n_macroparticles_lost]
else:
mean_z = cp.mean(bunch.z[:bunch.n_macroparticles -
bunch.n_macroparticles_lost])
sigma_z = cp.std(bunch.z[:bunch.n_macroparticles -
bunch.n_macroparticles_lost])
z_cut_tail = mean_z - self.nsigmaz * sigma_z
z_cut_head = mean_z + self.nsigmaz * sigma_z
return z_cut_tail, z_cut_head
def sigma_dp(self):
return cp.std(self.dp)
def sigma_y(self):
return cp.std(self.y)
def sigma_z(self):
return cp.std(self.z)
def sigma_x(self):
return cp.std(self.x)
def sigma_dp(self):
return cp.std(self.dp)
def sigma_z(self):
return cp.std(self.z)
def sigma_y(self):
return cp.std(self.y)
def sigma_x(self):
return cp.std(self.x)