Author: tlinnet
Date: Mon Sep 1 14:54:48 2014
New Revision: 25501
URL: http://svn.gna.org/viewcvs/relax?rev=25501&view=rev
Log:
Yet another try to implement constrained method in
verify_estimate_r2eff_err_compare_mc.
Modified:
trunk/test_suite/system_tests/relax_disp.py
Modified: trunk/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/system_tests/relax_disp.py?rev=25501&r1=25500&r2=25501&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Mon Sep 1 14:54:48 2014
@@ -37,11 +37,13 @@
from lib.errors import RelaxError
from lib.io import get_file_path
from pipe_control.mol_res_spin import generate_spin_string, return_spin,
spin_loop
+from pipe_control.minimise import assemble_scaling_matrix
from specific_analyses.relax_disp.checks import check_missing_r1
from specific_analyses.relax_disp.estimate_r2eff import estimate_r2eff
-from specific_analyses.relax_disp.data import average_intensity,
check_intensity_errors, generate_r20_key, get_curve_type,
has_exponential_exp_type, has_r1rho_exp_type, loop_exp_frq,
loop_exp_frq_offset_point, loop_exp_frq_offset_point_time, loop_time,
return_grace_file_name_ini, return_param_key_from_data
+from specific_analyses.relax_disp.data import average_intensity,
check_intensity_errors, generate_r20_key, get_curve_type,
has_exponential_exp_type, has_r1rho_exp_type, loop_exp_frq,
loop_exp_frq_offset_point, loop_exp_frq_offset_point_time, loop_time,
return_grace_file_name_ini, return_param_key_from_data, spin_ids_to_containers
from specific_analyses.relax_disp.data import INTERPOLATE_DISP,
INTERPOLATE_OFFSET, X_AXIS_DISP, X_AXIS_W_EFF, X_AXIS_THETA, Y_AXIS_R2_R1RHO,
Y_AXIS_R2_EFF
from specific_analyses.relax_disp.model import models_info, nesting_param
+from specific_analyses.relax_disp.parameters import linear_constraints
from specific_analyses.relax_disp.variables import EXP_TYPE_CPMG_DQ,
EXP_TYPE_CPMG_MQ, EXP_TYPE_CPMG_PROTON_MQ, EXP_TYPE_CPMG_PROTON_SQ,
EXP_TYPE_CPMG_SQ, EXP_TYPE_CPMG_ZQ, EXP_TYPE_LIST, EXP_TYPE_R1RHO,
MODEL_B14_FULL, MODEL_CR72, MODEL_CR72_FULL, MODEL_DPL94, MODEL_IT99,
MODEL_LIST_ANALYTIC_CPMG, MODEL_LIST_FULL, MODEL_LIST_NUMERIC_CPMG, MODEL_LM63,
MODEL_M61, MODEL_M61B, MODEL_MP05, MODEL_NOREX, MODEL_NS_CPMG_2SITE_3D_FULL,
MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_NS_CPMG_2SITE_STAR_FULL,
MODEL_NS_R1RHO_2SITE, MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR,
MODEL_PARAMS, MODEL_R2EFF, MODEL_TP02, MODEL_TAP03
from status import Status; status = Status()
from test_suite.system_tests.base_classes import SystemTestCase
@@ -7999,13 +8001,25 @@
# Set algorithm.
min_algor = 'Newton'
- constraints = True
+ constraints = False
if constraints:
min_options = ('%s'%(min_algor),)
#min_algor = 'Log barrier'
min_algor = 'Method of Multipliers'
+ scaling_matrix = assemble_scaling_matrix(scaling=True)
+
+ # Collect spins
+ all_spin_ids = []
+ for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
+ all_spin_ids.append([spin_id])
+
+ spins = spin_ids_to_containers(all_spin_ids[:1])
+
+ # Get constraints
+ A, b = linear_constraints(spins=spins,
scaling_matrix=scaling_matrix)
else:
min_options = ()
+ A, b = None, None
min_options = ()
sim_boot = 200
scaling_list = [1.0, 1.0]
@@ -8087,7 +8101,10 @@
x0 = [r2eff, i0]
setup(num_params=len(x0), num_times=len(times),
values=I_err, sd=errors, relax_times=times, scaling_matrix=scaling_list)
- params_minfx_sim_j, chi2_minfx_sim_j, iter_count, f_count,
g_count, h_count, warning = generic_minimise(func=func, dfunc=dfunc,
d2func=d2func, args=(), x0=x0, min_algor=min_algor, min_options=min_options,
full_output=True, print_flag=0)
+ # Ref input.
+ #def generic_minimise(func=None, dfunc=None, d2func=None,
args=(), x0=None, min_algor=None, min_options=None, func_tol=1e-25,
grad_tol=None, maxiter=1e6, A=None, b=None, l=None, u=None, c=None, dc=None,
d2c=None, print_flag=0, print_prefix="", full_output=False):
+
+ params_minfx_sim_j, chi2_minfx_sim_j, iter_count, f_count,
g_count, h_count, warning = generic_minimise(func=func, dfunc=dfunc,
d2func=d2func, args=(), x0=x0, min_algor=min_algor, min_options=min_options,
A=A, b=b, full_output=True, print_flag=0)
R_m_sim_j, I0_m_sim_j = params_minfx_sim_j
R_m_sim_l.append(R_m_sim_j)
I0_m_sim_l.append(I0_m_sim_j)
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