Author: bugman
Date: Wed Feb 1 11:39:57 2012
New Revision: 15285
URL: http://svn.gna.org/viewcvs/relax?rev=15285&view=rev
Log:
Renamed the number of numerical integration points for the switch of
numerical integration methods.
This generalisation is to switch from Monte Carlo integration to quasi-random
methods.
Modified:
branches/frame_order_testing/maths_fns/frame_order.py
Modified: branches/frame_order_testing/maths_fns/frame_order.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order_testing/maths_fns/frame_order.py?rev=15285&r1=15284&r2=15285&view=diff
==============================================================================
--- branches/frame_order_testing/maths_fns/frame_order.py (original)
+++ branches/frame_order_testing/maths_fns/frame_order.py Wed Feb 1 11:39:57
2012
@@ -50,7 +50,7 @@
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, rdc_const=None, pcs=None, pcs_errors=None, pcs_weights=None,
pcs_atoms=None, temp=None, frq=None, paramag_centre=None,
scaling_matrix=None, mcint_num=500, pivot=None, pivot_opt=False, mcint=True):
+ 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, rdc_const=None, pcs=None, pcs_errors=None, pcs_weights=None,
pcs_atoms=None, temp=None, frq=None, paramag_centre=None,
scaling_matrix=None, num_int_pts=500, pivot=None, pivot_opt=False,
mcint=True):
"""Set up the target functions for the Frame Order theories.
@keyword model: The name of the Frame Order model.
@@ -87,8 +87,8 @@
@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 mcint_num: The number of samplings to use for the
Monte Carlo integration technique.
- @type mcint_num: int
+ @keyword num_int_pts: The number of points to use for the
numerical integration technique.
+ @type num_int_pts: int
@keyword pivot: The pivot point for the ball-and-socket
joint motion. This is needed if PCS or PRE values are used.
@type pivot: numpy rank-1, 3D array or None
@keyword pivot_opt: A flag which if True will allow the
pivot point of the motion to be optimised.
@@ -119,7 +119,7 @@
self.frq = frq
self.paramag_centre = paramag_centre
self.total_num_params = len(init_params)
- self.mcint_num = mcint_num
+ self.num_int_pts = num_int_pts
self._param_pivot = pivot
self.pivot_opt = pivot_opt
@@ -507,7 +507,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_rotor_mcint(N=self.mcint_num, sigma_max=pi,
c=self.pcs_const, full_in_ref_frame=self.full_in_ref_frame,
r_pivot_atom=self.r_pivot_atom, r_pivot_atom_rev=self.r_pivot_atom_rev,
r_ln_pivot=self.r_ln_pivot, A=self.A_3D, R_eigen=self.R_eigen,
RT_eigen=RT_eigen, Ri_prime=self.Ri_prime, pcs_theta=self.pcs_theta,
pcs_theta_err=self.pcs_theta_err, missing_pcs=self.missing_pcs,
error_flag=False)
+ pcs_numeric_int_rotor_mcint(N=self.num_int_pts, sigma_max=pi,
c=self.pcs_const, full_in_ref_frame=self.full_in_ref_frame,
r_pivot_atom=self.r_pivot_atom, r_pivot_atom_rev=self.r_pivot_atom_rev,
r_ln_pivot=self.r_ln_pivot, A=self.A_3D, R_eigen=self.R_eigen,
RT_eigen=RT_eigen, Ri_prime=self.Ri_prime, pcs_theta=self.pcs_theta,
pcs_theta_err=self.pcs_theta_err, missing_pcs=self.missing_pcs,
error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -667,7 +667,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_iso_cone_mcint(N=self.mcint_num,
theta_max=cone_theta, sigma_max=sigma_max, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+ pcs_numeric_int_iso_cone_mcint(N=self.num_int_pts,
theta_max=cone_theta, sigma_max=sigma_max, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -832,7 +832,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_iso_cone_mcint(N=self.mcint_num,
theta_max=theta_max, sigma_max=pi, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+ pcs_numeric_int_iso_cone_mcint(N=self.num_int_pts,
theta_max=theta_max, sigma_max=pi, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -991,7 +991,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_iso_cone_torsionless_mcint(N=self.mcint_num,
theta_max=cone_theta, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+ pcs_numeric_int_iso_cone_torsionless_mcint(N=self.num_int_pts,
theta_max=cone_theta, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -1297,7 +1297,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_pseudo_ellipse_mcint(N=self.mcint_num,
theta_x=cone_theta_x, theta_y=cone_theta_y, sigma_max=pi, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+ pcs_numeric_int_pseudo_ellipse_mcint(N=self.num_int_pts,
theta_x=cone_theta_x, theta_y=cone_theta_y, sigma_max=pi, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -1450,7 +1450,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
-
pcs_numeric_int_pseudo_ellipse_torsionless_mcint(N=self.mcint_num,
theta_x=cone_theta_x, theta_y=cone_theta_y, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+
pcs_numeric_int_pseudo_ellipse_torsionless_mcint(N=self.num_int_pts,
theta_x=cone_theta_x, theta_y=cone_theta_y, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
@@ -1678,7 +1678,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_rotor_mcint(N=self.mcint_num,
sigma_max=sigma_max, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
+ pcs_numeric_int_rotor_mcint(N=self.num_int_pts,
sigma_max=sigma_max, c=self.pcs_const,
full_in_ref_frame=self.full_in_ref_frame, r_pivot_atom=self.r_pivot_atom,
r_pivot_atom_rev=self.r_pivot_atom_rev, r_ln_pivot=self.r_ln_pivot,
A=self.A_3D, R_eigen=self.R_eigen, RT_eigen=RT_eigen, Ri_prime=self.Ri_prime,
pcs_theta=self.pcs_theta, pcs_theta_err=self.pcs_theta_err,
missing_pcs=self.missing_pcs, error_flag=False)
# Calculate and sum the single alignment chi-squared value (for
the PCS).
for i in xrange(self.num_align):
r15285 - /branches/frame_order_testing/maths_fns/frame_order.py