Author: bugman
Date: Fri Feb 3 11:41:55 2012
New Revision: 15295
URL: http://svn.gna.org/viewcvs/relax?rev=15295&view=rev
Log:
All of the non-quadratic integration frame order target functions have been
converted to quasi-random.
This completes the update from Monte Carlo to quasi-random numerical
integration.
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=15295&r1=15294&r2=15295&view=diff
==============================================================================
--- branches/frame_order_testing/maths_fns/frame_order.py (original)
+++ branches/frame_order_testing/maths_fns/frame_order.py Fri Feb 3 11:41:55
2012
@@ -276,28 +276,36 @@
self.create_sobol_data(n=self.num_int_pts, dims=['theta',
'phi', 'sigma'])
self.func = self.func_pseudo_ellipse_qrint
elif model == 'pseudo-ellipse, torsionless':
- self.func = self.func_pseudo_ellipse_torsionless_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta',
'phi'])
+ self.func = self.func_pseudo_ellipse_torsionless_qrint
elif model == 'pseudo-ellipse, free rotor':
- self.func = self.func_pseudo_ellipse_free_rotor_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta',
'phi'])
+ self.func = self.func_pseudo_ellipse_free_rotor_qrint
elif model == 'iso cone':
- self.func = self.func_iso_cone_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta',
'sigma'])
+ self.func = self.func_iso_cone_qrint
elif model == 'iso cone, torsionless':
- self.func = self.func_iso_cone_torsionless_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta'])
+ self.func = self.func_iso_cone_torsionless_qrint
elif model == 'iso cone, free rotor':
- self.func = self.func_iso_cone_free_rotor_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta'])
+ self.func = self.func_iso_cone_free_rotor_qrint
elif model == 'line':
- self.func = self.func_line_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta',
'sigma'])
+ self.func = self.func_line_qrint
elif model == 'line, torsionless':
- self.func = self.func_line_torsionless_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta'])
+ self.func = self.func_line_torsionless_qrint
elif model == 'line, free rotor':
- self.func = self.func_line_free_rotor_mcint
+ self.create_sobol_data(n=self.num_int_pts, dims=['theta'])
+ self.func = self.func_line_free_rotor_qrint
elif model == 'rotor':
self.create_sobol_data(n=self.num_int_pts, dims=['sigma'])
self.func = self.func_rotor_qrint
elif model == 'rigid':
self.func = self.func_rigid
elif model == 'free rotor':
- self.func = self.func_free_rotor_mcint
+ self.func = self.func_free_rotor_qrint
# The target function aliases (Scipy numerical integration).
else:
@@ -440,7 +448,7 @@
return chi2_sum
- def func_free_rotor_mcint(self, params):
+ def func_free_rotor_qrint(self, params):
"""Target function for free rotor model optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -505,7 +513,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- pcs_numeric_int_rotor_qrint(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)
+ pcs_numeric_int_rotor_qrint(points=self.sobol_angles,
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):
@@ -600,7 +608,7 @@
return chi2_sum
- def func_iso_cone_mcint(self, params):
+ def func_iso_cone_qrint(self, params):
"""Target function for isotropic cone model optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -665,7 +673,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- 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)
+ pcs_numeric_int_iso_cone_qrint(points=self.sobol_angles,
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):
@@ -762,7 +770,7 @@
return chi2_sum
- def func_iso_cone_free_rotor_mcint(self, params):
+ def func_iso_cone_free_rotor_qrint(self, params):
"""Target function for free rotor isotropic cone model optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -830,7 +838,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- 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)
+ pcs_numeric_int_iso_cone_qrint(points=self.sobol_angles,
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):
@@ -924,7 +932,7 @@
return chi2_sum
- def func_iso_cone_torsionless_mcint(self, params):
+ def func_iso_cone_torsionless_qrint(self, params):
"""Target function for torsionless isotropic cone model optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -989,7 +997,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- 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)
+
pcs_numeric_int_iso_cone_torsionless_qrint(points=self.sobol_angles,
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):
@@ -1233,7 +1241,7 @@
return chi2_sum
- def func_pseudo_ellipse_free_rotor_mcint(self, params):
+ def func_pseudo_ellipse_free_rotor_qrint(self, params):
"""Target function for free_rotor pseudo-elliptic cone model
optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -1295,7 +1303,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
- 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)
+ pcs_numeric_int_pseudo_ellipse_qrint(points=self.sobol_angles,
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):
@@ -1386,7 +1394,7 @@
return chi2_sum
- def func_pseudo_ellipse_torsionless_mcint(self, params):
+ def func_pseudo_ellipse_torsionless_qrint(self, params):
"""Target function for torsionless pseudo-elliptic cone model
optimisation.
This function optimises the isotropic cone model parameters using
the RDC and PCS base data. Simple Monte Carlo integration is used for the
PCS.
@@ -1448,7 +1456,7 @@
# PCS via Monte Carlo integration.
if self.pcs_flag:
# Numerical integration of the PCSs.
-
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)
+
pcs_numeric_int_pseudo_ellipse_torsionless_qrint(points=self.sobol_angles,
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):
r15295 - /branches/frame_order_testing/maths_fns/frame_order.py