Author: bugman
Date: Thu Jun 12 18:47:36 2014
New Revision: 23886
URL: http://svn.gna.org/viewcvs/relax?rev=23886&view=rev
Log:
Simplification and clean up of the RDC and PCS flags in the frame order
target functions.
The per-alignment flags have been removed and replaced by a global flag for
all data. This
accidentally fixes a bug when only RDCs are present, as the calc_vectors()
method was being called
when it should not have been.
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=23886&r1=23885&r2=23886&view=diff
==============================================================================
--- branches/frame_order_cleanup/target_functions/frame_order.py
(original)
+++ branches/frame_order_cleanup/target_functions/frame_order.py Thu
Jun 12 18:47:36 2014
@@ -152,33 +152,33 @@
self.num_align = len(pcs)
# Set the RDC and PCS flags (indicating the presence of data).
- self.rdc_flag = [True] * self.num_align
- self.pcs_flag = [True] * self.num_align
+ rdc_flag = [True] * self.num_align
+ pcs_flag = [True] * self.num_align
for align_index in range(self.num_align):
if rdcs == None or len(rdcs[align_index]) == 0:
- self.rdc_flag[align_index] = False
+ rdc_flag[align_index] = False
if pcs == None or len(pcs[align_index]) == 0:
- self.pcs_flag[align_index] = False
- self.rdc_flag_sum = sum(self.rdc_flag)
- self.pcs_flag_sum = sum(self.pcs_flag)
+ pcs_flag[align_index] = False
+ self.rdc_flag = sum(rdc_flag)
+ self.pcs_flag = sum(pcs_flag)
# Default translation vector (if not optimised).
self._translation_vector = zeros(3, float64)
# Some checks.
- if self.rdc_flag_sum and (rdc_vect == None or not len(rdc_vect)):
+ if self.rdc_flag and (rdc_vect == None or not len(rdc_vect)):
raise RelaxError("The rdc_vect argument " + repr(rdc_vect) + "
must be supplied.")
- if self.pcs_flag_sum and (atomic_pos == None or not len(atomic_pos)):
+ if self.pcs_flag and (atomic_pos == None or not len(atomic_pos)):
raise RelaxError("The atomic_pos argument " + repr(atomic_pos) +
" must be supplied.")
# The total number of spins.
self.num_spins = 0
- if self.pcs_flag_sum:
+ if self.pcs_flag:
self.num_spins = len(pcs[0])
# The total number of interatomic connections.
self.num_interatom = 0
- if self.rdc_flag_sum:
+ if self.rdc_flag:
self.num_interatom = len(rdcs[0])
# Set up the alignment data.
@@ -186,7 +186,7 @@
to_tensor(self.A_3D[align_index],
self.full_tensors[5*align_index:5*align_index+5])
# PCS errors.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
err = False
for i in range(len(pcs_errors)):
for j in range(len(pcs_errors[i])):
@@ -199,7 +199,7 @@
self.pcs_error = 0.1 * 1e-6 * ones((self.num_align,
self.num_spins), float64)
# RDC errors.
- if self.rdc_flag_sum:
+ if self.rdc_flag:
err = False
for i in range(len(rdc_errors)):
for j in range(len(rdc_errors[i])):
@@ -212,18 +212,18 @@
self.rdc_error = ones((self.num_align, self.num_interatom),
float64)
# Missing data matrices (RDC).
- if self.rdc_flag_sum:
+ if self.rdc_flag:
self.missing_rdc = zeros((self.num_align, self.num_interatom),
uint8)
# Missing data matrices (PCS).
- if self.pcs_flag_sum:
+ if self.pcs_flag:
self.missing_pcs = zeros((self.num_align, self.num_spins), uint8)
# Clean up problematic data and put the weights into the errors..
- if self.rdc_flag_sum or self.pcs_flag_sum:
+ if self.rdc_flag or self.pcs_flag:
for align_index in range(self.num_align):
# Loop over the RDCs.
- if self.rdc_flag_sum:
+ if self.rdc_flag:
for j in range(self.num_interatom):
if isNaN(self.rdc[align_index, j]):
# Set the flag.
@@ -239,11 +239,11 @@
rdc_weights[align_index, j] = 1.0
# The RDC weights.
- if self.rdc_flag_sum:
+ if self.rdc_flag:
self.rdc_error[align_index, j] =
self.rdc_error[align_index, j] / sqrt(rdc_weights[align_index, j])
# Loop over the PCSs.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
for j in range(self.num_spins):
if isNaN(self.pcs[align_index, j]):
# Set the flag.
@@ -259,11 +259,11 @@
pcs_weights[align_index, j] = 1.0
# The PCS weights.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
self.pcs_error[align_index, j] =
self.pcs_error[align_index, j] / sqrt(pcs_weights[align_index, j])
# The paramagnetic centre vectors and distances.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Initialise the data structures.
self.paramag_unit_vect = zeros(atomic_pos.shape, float64)
self.paramag_dist = zeros(self.num_spins, float64)
@@ -281,14 +281,14 @@
self.pcs_const[align_index] =
pcs_constant(self.temp[align_index], self.frq[align_index], 1.0) * 1e30
# PCS function, gradient, and Hessian matrices.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
self.pcs_theta = zeros((self.num_align, self.num_spins), float64)
self.pcs_theta_err = zeros((self.num_align, self.num_spins),
float64)
self.dpcs_theta = zeros((self.total_num_params, self.num_align,
self.num_spins), float64)
self.d2pcs_theta = zeros((self.total_num_params,
self.total_num_params, self.num_align, self.num_spins), float64)
# RDC function, gradient, and Hessian matrices.
- if self.rdc_flag_sum:
+ if self.rdc_flag:
self.rdc_theta = zeros((self.num_align, self.num_interatom),
float64)
self.drdc_theta = zeros((self.total_num_params, self.num_align,
self.num_interatom), float64)
self.d2rdc_theta = zeros((self.total_num_params,
self.total_num_params, self.num_align, self.num_interatom), float64)
@@ -415,10 +415,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -429,7 +429,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
pcs_numeric_int_double_rotor(points=self.sobol_angles,
sigma_max=sigma_max, sigma_max_2=sigma_max_2, 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=R_eigen_full, 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)
@@ -492,10 +492,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -506,7 +506,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -570,10 +570,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -584,7 +584,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -650,10 +650,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -664,7 +664,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -727,10 +727,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -741,7 +741,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -801,10 +801,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -815,7 +815,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
pcs_numeric_int_pseudo_ellipse_qrint(points=self.sobol_angles,
theta_x=cone_theta_x, theta_y=cone_theta_y, sigma_max=cone_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)
@@ -875,10 +875,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -889,7 +889,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -949,10 +949,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -963,7 +963,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
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)
@@ -1008,10 +1008,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -1021,8 +1021,10 @@
# Calculate and sum the single alignment chi-squared value
(for the RDC).
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
- # PCS.
- if self.pcs_flag[align_index]:
+ # PCS.
+ if self.pcs_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the PCSs.
for j in range(self.num_spins):
# The back calculated PCS.
@@ -1096,10 +1098,10 @@
# Initial chi-squared (or SSE) value.
chi2_sum = 0.0
- # Loop over each alignment.
- for align_index in range(self.num_align):
- # RDCs.
- if self.rdc_flag[align_index]:
+ # RDCs.
+ if self.rdc_flag:
+ # Loop over each alignment.
+ for align_index in range(self.num_align):
# Loop over the RDCs.
for j in range(self.num_interatom):
# The back calculated RDC.
@@ -1110,7 +1112,7 @@
chi2_sum = chi2_sum + chi2(self.rdc[align_index],
self.rdc_theta[align_index], self.rdc_error[align_index])
# PCS via numerical integration.
- if self.pcs_flag_sum:
+ if self.pcs_flag:
# Numerical integration of the PCSs.
pcs_numeric_int_rotor_qrint(points=self.sobol_angles,
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)
r23886 - /branches/frame_order_cleanup/target_functions/frame_order.py