Author: bugman
Date: Wed Sep 17 17:11:18 2014
New Revision: 25866
URL: http://svn.gna.org/viewcvs/relax?rev=25866&view=rev
Log:
Implemented the Sobol' sequence oversampling in the frame order target function
class.
Modified:
branches/frame_order_cleanup/target_functions/frame_order.py
Modified: branches/frame_order_cleanup/target_functions/frame_order.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_cleanup/target_functions/frame_order.py?rev=25866&r1=25865&r2=25866&view=diff
==============================================================================
--- branches/frame_order_cleanup/target_functions/frame_order.py
(original)
+++ branches/frame_order_cleanup/target_functions/frame_order.py Wed Sep
17 17:11:18 2014
@@ -58,7 +58,7 @@
class Frame_order:
"""Class containing the target function of the optimisation of Frame Order
matrix components."""
- def __init__(self, model=None, init_params=None, full_tensors=None,
full_in_ref_frame=None, rdcs=None, rdc_errors=None, rdc_weights=None,
rdc_vect=None, dip_const=None, pcs=None, pcs_errors=None, pcs_weights=None,
atomic_pos=None, temp=None, frq=None, paramag_centre=zeros(3),
scaling_matrix=None, num_int_pts=500, com=None, ave_pos_pivot=zeros(3),
pivot=None, pivot_opt=False):
+ def __init__(self, model=None, init_params=None, full_tensors=None,
full_in_ref_frame=None, rdcs=None, rdc_errors=None, rdc_weights=None,
rdc_vect=None, dip_const=None, pcs=None, pcs_errors=None, pcs_weights=None,
atomic_pos=None, temp=None, frq=None, paramag_centre=zeros(3),
scaling_matrix=None, sobol_max_points=200, sobol_oversample=100, com=None,
ave_pos_pivot=zeros(3), pivot=None, pivot_opt=False):
"""Set up the target functions for the Frame Order theories.
@keyword model: The name of the Frame Order model.
@@ -95,8 +95,10 @@
@type paramag_centre: numpy rank-1, 3D array or rank-2, Nx3 array
@keyword scaling_matrix: The square and diagonal scaling matrix.
@type scaling_matrix: numpy rank-2 array
- @keyword num_int_pts: The number of points to use for the
numerical integration technique.
- @type num_int_pts: int
+ @keyword sobol_max_points: The maximum number of Sobol' points to use
for the numerical PCS integration technique.
+ @type sobol_max_points: int
+ @keyword sobol_oversample: The oversampling factor Ov used for the
total number of points N * Ov * 10**M, where N is the maximum number of Sobol'
points and M is the number of dimensions or torsion-tilt angles for the system.
+ @type sobol_oversample: int
@keyword com: The centre of mass of the system. This is
used for defining the rotor model systems.
@type com: numpy 3D rank-1 array
@keyword ave_pos_pivot: The pivot point to rotate all atoms about
to the average domain position. In most cases this will be the centre of mass
of the moving domain. This pivot is shifted by the translation vector.
@@ -128,7 +130,8 @@
self.temp = temp
self.frq = frq
self.total_num_params = len(init_params)
- self.num_int_pts = num_int_pts
+ self.sobol_max_points = sobol_max_points
+ self.sobol_oversample = sobol_oversample
self.com = com
self.pivot_opt = pivot_opt
@@ -300,33 +303,33 @@
# The Sobol' sequence data and target function aliases (quasi-random
integration).
if model == MODEL_PSEUDO_ELLIPSE:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi',
'sigma'])
+ self.create_sobol_data(dims=['theta', 'phi', 'sigma'])
self.func = self.func_pseudo_ellipse
elif model == MODEL_PSEUDO_ELLIPSE_TORSIONLESS:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi'])
+ self.create_sobol_data(dims=['theta', 'phi'])
self.func = self.func_pseudo_ellipse_torsionless
elif model == MODEL_PSEUDO_ELLIPSE_FREE_ROTOR:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi',
'sigma'])
+ self.create_sobol_data(dims=['theta', 'phi', 'sigma'])
self.func = self.func_pseudo_ellipse_free_rotor
elif model == MODEL_ISO_CONE:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi',
'sigma'])
+ self.create_sobol_data(dims=['theta', 'phi', 'sigma'])
self.func = self.func_iso_cone
elif model == MODEL_ISO_CONE_TORSIONLESS:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi'])
+ self.create_sobol_data(dims=['theta', 'phi'])
self.func = self.func_iso_cone_torsionless
elif model == MODEL_ISO_CONE_FREE_ROTOR:
- self.create_sobol_data(n=self.num_int_pts, dims=['theta', 'phi',
'sigma'])
+ self.create_sobol_data(dims=['theta', 'phi', 'sigma'])
self.func = self.func_iso_cone_free_rotor
elif model == MODEL_ROTOR:
- self.create_sobol_data(n=self.num_int_pts, dims=['sigma'])
+ self.create_sobol_data(dims=['sigma'])
self.func = self.func_rotor
elif model == MODEL_RIGID:
self.func = self.func_rigid
elif model == MODEL_FREE_ROTOR:
- self.create_sobol_data(n=self.num_int_pts, dims=['sigma'])
+ self.create_sobol_data(dims=['sigma'])
self.func = self.func_free_rotor
elif model == MODEL_DOUBLE_ROTOR:
- self.create_sobol_data(n=self.num_int_pts, dims=['sigma',
'sigma2'])
+ self.create_sobol_data(dims=['sigma', 'sigma2'])
self.func = self.func_double_rotor
@@ -1185,31 +1188,35 @@
self.r_inter_pivot = pivot - pivot2
- def create_sobol_data(self, n=10000, dims=None):
+ def create_sobol_data(self, dims=None):
"""Create the Sobol' quasi-random data for numerical integration.
This uses the external sobol_lib module to create the data. The
algorithm is that modified by Antonov and Saleev.
- @keyword n: The number of points to generate.
- @type n: int
@keyword dims: The list of parameters.
@type dims: list of str
"""
+ # Printout (useful to see how long this takes!).
+ print("Generating the torsion-tilt angle sampling via the Sobol'
sequence for numerical PCS integration.")
+
# The number of dimensions.
m = len(dims)
+ # The total number of points.
+ total_num = int(self.sobol_max_points * self.sobol_oversample * 10**m)
+
# Initialise.
- self.sobol_angles = zeros((n, m), float64)
- self.Ri_prime = zeros((n, 3, 3), float64)
- self.Ri2_prime = zeros((n, 3, 3), float64)
+ self.sobol_angles = zeros((total_num, m), float64)
+ self.Ri_prime = zeros((total_num, 3, 3), float64)
+ self.Ri2_prime = zeros((total_num, 3, 3), float64)
# The Sobol' points.
- points = i4_sobol_generate(m, n, 0)
+ points = i4_sobol_generate(m, total_num, 0)
# Loop over the points.
- for i in range(n):
+ for i in range(total_num):
# Loop over the dimensions, converting the points to angles.
theta = None
phi = None
@@ -1287,6 +1294,9 @@
self.Ri_prime[i, 1, 1] = c_sigma
self.Ri_prime[i, 2, 2] = 1.0
+ # Printout (useful to see how long this takes!).
+ print(" Oversampled to %s points." % total_num)
+
def reduce_and_rot(self, ave_pos_alpha=None, ave_pos_beta=None,
ave_pos_gamma=None, daeg=None):
"""Reduce and rotate the alignments tensors using the frame order
matrix and Euler angles.
_______________________________________________
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