Author: tlinnet
Date: Thu Aug 21 22:57:45 2014
New Revision: 25189
URL: http://svn.gna.org/viewcvs/relax?rev=25189&view=rev
Log:
Modified intermediate systemtest:
Relax_disp.test_bug_9999_slow_r1rho_r2eff_error_with_mc
to see if the initial Grid Search for 'i0' and 'R2eff' estimation can be
skipped.
This is done by converting the exponential curve, to a linear curve, and
calculate
the best parameters by a line of best fit by least squares.
This seems like a promising method as an initial estimator of 'i0' and
'r2eff'.
For 500 to 2000 Monte-Carlo simulations, this could have an influence on the
timings,
as all grid searchs could be skipped.
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=25189&r1=25188&r2=25189&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Thu Aug 21 22:57:45 2014
@@ -23,7 +23,7 @@
# Python module imports.
from os import F_OK, access, getcwd, path, sep
-from numpy import array, median
+from numpy import array, exp, median, log, sum
import re, math
from tempfile import mkdtemp
@@ -35,7 +35,7 @@
from lib.io import get_file_path
from pipe_control.mol_res_spin import generate_spin_string, return_spin,
spin_loop
from specific_analyses.relax_disp.checks import check_missing_r1
-from specific_analyses.relax_disp.data import 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,
return_grace_file_name_ini, return_param_key_from_data
+from specific_analyses.relax_disp.data import average_intensity,
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 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.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
@@ -158,8 +158,8 @@
# Now loop over the experiments, to set the variables in relax.
exp_ids = []
- for exp in exps:
- sfrq, time_T2, ncycs, r2eff_errs = exp
+ for exp_i in exps:
+ sfrq, time_T2, ncycs, r2eff_errs = exp_i
exp_id = 'CPMG_%3.1f' % (sfrq/1E6)
exp_ids.append(exp_id)
@@ -193,8 +193,8 @@
# Now read data in.
for exp_type, frq, ei, mi in loop_exp_frq(return_indices=True):
exp_id = exp_ids[mi]
- exp = exps[mi]
- sfrq, time_T2, ncycs, r2eff_errs = exp
+ exp_i = exps[mi]
+ sfrq, time_T2, ncycs, r2eff_errs = exp_i
# Then loop over the spins.
for res_name, res_num, spin_name, params in spins:
@@ -1363,26 +1363,15 @@
# Read data.
self.interpreter.results.read(prev_data_path + sep + 'results')
- # Get initial offset, point, time
- for exp_type, frq, offset, point, time, ei, mi, oi, di, ti in
loop_exp_frq_offset_point_time(return_indices=True):
- offset_i = offset
- point_i = point
- time_i = time
- break
-
# Now count number
- graph_nr = 0
- for exp_type, frq, offset, point, time, ei, mi, oi, di, ti in
loop_exp_frq_offset_point_time(return_indices=True):
- print(exp_type, frq, offset, point, time)
-
- # If different, count 1 graph.
- if offset != offset_i or point != point_i:
- offset_i = offset
- point_i = point
- graph_nr += 1
- print("Graph %i complete\n" % graph_nr)
-
- print(graph_nr + 1)
+ graph_nr = 1
+ for exp_type, frq, offset, point in
loop_exp_frq_offset_point(return_indices=False):
+ print("\nGraph nr %i" % graph_nr)
+ for time in loop_time(exp_type=exp_type, frq=frq, offset=offset,
point=point):
+ print(exp_type, frq, offset, point, time)
+ graph_nr += 1
+
+ ## Possibly do an error analysis.
# Check if intensity errors have already been calculated by the user.
precalc = True
@@ -1425,13 +1414,111 @@
model = 'R2eff'
self.interpreter.relax_disp.select_model(model)
+ for spin, spin_id in spin_loop(return_id=True, skip_desel=True):
+ #delattr(spin, 'r2eff')
+ #delattr(spin, 'r2eff_err')
+ #delattr(spin, 'i0')
+ #delattr(spin, 'i0_err')
+ setattr(spin, 'r2eff', {})
+ setattr(spin, 'r2eff_err', {})
+ setattr(spin, 'i0', {})
+ setattr(spin, 'i0_err', {})
+
# Do Grid Search
- self.interpreter.minimise.grid_search(lower=None, upper=None,
inc=11, constraints=True, verbosity=1)
+ self.interpreter.minimise.grid_search(lower=None, upper=None,
inc=21, constraints=True, verbosity=1)
+
+ # Start dic.
+ my_dic = {}
+
+ # Loop over each spectrometer frequency and dispersion point.
+ for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
+ # Add key to dic.
+ my_dic[spin_id] = {}
+
+ # Generate spin string.
+ spin_string = generate_spin_string(spin=cur_spin,
mol_name=mol_name, res_num=resi, res_name=resn)
+
+ # Loop over the parameters.
+ #print("Grid optimised parameters for spin: %s" % (spin_string))
+
+ for exp_type, frq, offset, point in loop_exp_frq_offset_point():
+ # Generate the param_key.
+ param_key = return_param_key_from_data(exp_type=exp_type,
frq=frq, offset=offset, point=point)
+
+ # Add key to dic.
+ my_dic[spin_id][param_key] = {}
+
+ # Get the value.
+ R2eff_value = getattr(cur_spin, 'r2eff')[param_key]
+ i0_value = getattr(cur_spin, 'i0')[param_key]
+
+ # Save to dic.
+ my_dic[spin_id][param_key]['R2eff_value_grid'] = R2eff_value
+ my_dic[spin_id][param_key]['i0_value_grid'] = i0_value
+
+ ## Now try do a line of best fit by least squares.
+ # The peak intensities, errors and times.
+ values = []
+ errors = []
+ times = []
+ for time in loop_time(exp_type=exp_type, frq=frq,
offset=offset, point=point):
+ values.append(average_intensity(spin=cur_spin,
exp_type=exp_type, frq=frq, offset=offset, point=point, time=time,
sim_index=None))
+ errors.append(average_intensity(spin=cur_spin,
exp_type=exp_type, frq=frq, offset=offset, point=point, time=time,
error=True))
+ times.append(time)
+
+ # y= A exp(x * k)
+ # w[i] = ln(y[i])
+ # int[i] = i0 * exp( - times[i] * r2eff);
+ w = log(array(values))
+ x = - array(times)
+ n = len(times)
+
+ b = (sum(x*w) - 1./n * sum(x) * sum(w) ) / ( sum(x**2) -
1./n * (sum(x))**2 )
+ a = 1./n * sum(w) - b * 1./n * sum(x)
+ R2eff_est = b
+ i0_est = exp(a)
+
+ my_dic[spin_id][param_key]['R2eff_est'] = R2eff_est
+ my_dic[spin_id][param_key]['i0_est'] = i0_est
+
+ # Print value.
+ #print("%-10s %-6s %-6s %3.1f : %3.1f" % ("Parameter:",
'R2eff', "Value : Estimated:", R2eff_value, R2eff_est))
+ #print("%-10s %-6s %-6s %3.1f : %3.1f" % ("Parameter:",
'i0', "Value: Estimated:", i0_value, i0_est))
+
# Do minimisation.
- set_func_tol = 1e-11
- set_max_iter = 10000
+ set_func_tol = 1e-25
+ set_max_iter = int(1e7)
self.interpreter.minimise.execute(min_algor='simplex',
func_tol=set_func_tol, max_iter=set_max_iter, constraints=True, scaling=True,
verbosity=1)
+
+ # Loop over each spectrometer frequency and dispersion point.
+ for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
+ # Generate spin string.
+ spin_string = generate_spin_string(spin=cur_spin,
mol_name=mol_name, res_num=resi, res_name=resn)
+
+ # Loop over the parameters.
+ print("Optimised parameters for spin: %s" % (spin_string))
+
+ for exp_type, frq, offset, point in loop_exp_frq_offset_point():
+ # Generate the param_key.
+ param_key = return_param_key_from_data(exp_type=exp_type,
frq=frq, offset=offset, point=point)
+
+ # Get the value.
+ R2eff_value = getattr(cur_spin, 'r2eff')[param_key]
+ i0_value = getattr(cur_spin, 'i0')[param_key]
+
+ # Extract from dic.
+ R2eff_value_grid =
my_dic[spin_id][param_key]['R2eff_value_grid']
+ i0_value_grid = my_dic[spin_id][param_key]['i0_value_grid']
+ R2eff_est = my_dic[spin_id][param_key]['R2eff_est']
+ i0_est = my_dic[spin_id][param_key]['i0_est']
+
+ # Print value.
+ #print("%-10s %-6s %-6s %3.1f : %3.1f" % ("Parameter:",
'R2eff', "Value : Estimated:", R2eff_value, R2eff_est))
+ #print("%-10s %-6s %-6s %3.1f : %3.1f" % ("Parameter:",
'i0', "Value: Estimated:", i0_value, i0_est))
+
+ print("%-10s %-6s %-6s %3.1f : %3.1f: %3.1f" %
("Parameter:", 'R2eff', "Grid : Min : Estimated:", R2eff_value_grid,
R2eff_value, R2eff_est))
+ print("%-10s %-6s %-6s %3.1f : %3.1f: %3.1f" %
("Parameter:", 'i0', "Grid : Min : Estimated:", i0_value_grid, i0_value,
i0_est))
def test_check_missing_r1(self):
r25189 - /trunk/test_suite/system_tests/relax_disp.py