def adp_iso_analytical_gradients(weight, average_power, u_isos):
"""
um = (u1 + u2)/2
r=w*((u1-um)^2+(u2-um)^2)/um^mp
D[r,u1]
um = (u1 + u2 + u3)/3
r=w*((u1-um)^2+(u2-um)^2+(u3-um)^2)/um^mp
D[r,u1]
um = (u1 + u2 + u3 + u4)/4
r=w*((u1-um)^2+(u2-um)^2+(u3-um)^2+(u4-um)^2)/um^mp
D[r,u1]
"""
n = u_isos.size()
u_ave = flex.sum(u_isos) / n
u_diff = u_isos - u_ave
n_ave_pow = n*u_ave**average_power
return weight * ( 2/n_ave_pow*(u_diff*n-flex.sum(u_diff))
- average_power/(n_ave_pow*u_ave)*flex.sum_sq(u_diff))
def adp_iso_analytical_gradients(weight, average_power, u_isos):
"""
um = (u1 + u2)/2
r=w*((u1-um)^2+(u2-um)^2)/um^mp
D[r,u1]
um = (u1 + u2 + u3)/3
r=w*((u1-um)^2+(u2-um)^2+(u3-um)^2)/um^mp
D[r,u1]
um = (u1 + u2 + u3 + u4)/4
r=w*((u1-um)^2+(u2-um)^2+(u3-um)^2+(u4-um)^2)/um^mp
D[r,u1]
"""
n = u_isos.size()
u_ave = flex.sum(u_isos) / n
u_diff = u_isos - u_ave
n_ave_pow = n * u_ave**average_power
return weight * (2 / n_ave_pow *
(u_diff * n - flex.sum(u_diff)) - average_power /
(n_ave_pow * u_ave) * flex.sum_sq(u_diff))
def adp_iso_residual_sum(weight, average_power, u_isos):
n = u_isos.size()
u_ave = flex.sum(u_isos) / n
u_diff = u_isos - u_ave
return weight * flex.sum_sq(u_diff) / u_ave**average_power
def calculate_rms(self):
length = self.ref_model1.unit_cell().length
self.shortest_distances = flex.double(
[length(d) for d in self.calculate_shortest_diffs()])
self.rms = math.sqrt(
flex.sum_sq(self.shortest_distances) / len(self.pairs))
def adp_iso_residual_sum(weight, average_power, u_isos): n = u_isos.size() u_ave = flex.sum(u_isos) / n u_diff = u_isos - u_ave return weight * flex.sum_sq(u_diff) / u_ave**average_power
def log_p_obs_given_gamma(self, gamma):
x_gamma = (gamma * self.delta_fc2.data() - self.delta_fo2.data()) \
/ self.delta_fo2.sigmas()
if self.probability_plot_slope is not None:
x_gamma /= self.probability_plot_slope
return -0.5 * flex.sum_sq(x_gamma)
def log_p_obs_given_gamma(self, gamma):
x_gamma = (gamma * self.delta_fc2.data() - self.delta_fo2.data()) \
/ self.delta_fo2.sigmas()
if self.probability_plot_slope is not None:
x_gamma /= self.probability_plot_slope
return -0.5 * flex.sum_sq(x_gamma)
def calculate_rms(self):
length = self.ref_model1.unit_cell().length
self.shortest_distances = flex.double([
length(d) for d in self.calculate_shortest_diffs() ])
self.rms = math.sqrt(flex.sum_sq(self.shortest_distances)/len(self.pairs))
