r25347 - /trunk/user_functions/relax_disp.py -- August 27, 2014 - 20:06

Author: tlinnet
Date: Wed Aug 27 20:06:23 2014
New Revision: 25347

URL: http://svn.gna.org/viewcvs/relax?rev=25347&view=rev
Log:
Added front-end to the new user function relax_disp.r2eff_err_estimate(), 
which will estimate the R2eff errors
from a pipe and spins with optimised values of R2eff and i0.

The co-variance matrix can be calculated from the optimised parameters, and 
the Jacobian.

Big care should be taken not to directly trust these results, since the 
errors are quite different compared to the Monte-Carlo simulations.

This implementation, will reach the exact same error estimation as 
scipy.optimize.leastsq.

But with much better control over the data, and insight into the calculations.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    trunk/user_functions/relax_disp.py

Modified: trunk/user_functions/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/user_functions/relax_disp.py?rev=25347&r1=25346&r2=25347&view=diff
==============================================================================
--- trunk/user_functions/relax_disp.py  (original)
+++ trunk/user_functions/relax_disp.py  Wed Aug 27 20:06:23 2014
@@ -40,6 +40,7 @@
 from pipe_control.mol_res_spin import get_spin_ids
 from specific_analyses.relax_disp.catia import catia_execute, catia_input
 from specific_analyses.relax_disp.cpmgfit import cpmgfit_execute, 
cpmgfit_input
+from specific_analyses.relax_disp.estimate_r2eff import estimate_r2eff_err
 from specific_analyses.relax_disp.data import cpmg_setup, insignificance, 
plot_disp_curves, plot_exp_curves, r2eff_read, r2eff_read_spin, relax_time, 
set_exp_type, r20_from_min_r2eff, spin_lock_field, spin_lock_offset, 
write_disp_curves
 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.nessy import nessy_input
@@ -627,6 +628,50 @@
 uf.gui_icon = "oxygen.status.object-locked"
 uf.wizard_size = (800, 500)
 uf.wizard_image = ANALYSIS_IMAGE_PATH + 'relax_disp_200x200.png'
+
+
+# The relax_disp.r2eff_err_estimate user function.
+uf = uf_info.add_uf('relax_disp.r2eff_err_estimate')
+uf.title = "Estimate R2eff errors by the Jacobian matrix."
+uf.title_short = "Estimate R2eff errors."
+uf.add_keyarg(
+    name = "spin_id",
+    py_type = "str",
+    arg_type = "spin ID",
+    desc_short = "spin ID to restrict value setting to",
+    desc = "The spin ID string to restrict value setting to.",
+    can_be_none = True
+)
+uf.add_keyarg(
+    name = "epsrel",
+    py_type = "float",
+    default = 0.0,
+    desc_short = "parameter to remove linear-dependent columns.",
+    desc = "The parameter to remove linear-dependent columns when J is rank 
deficient.",
+    can_be_none = False
+)
+uf.add_keyarg(
+    name = "verbosity",
+    default = 1,
+    py_type = "int",
+    desc_short = "amount of information to print.",
+    desc = "The higher the value, the greater the verbosity.",
+    can_be_none = False
+)
+# Description.
+uf.desc.append(Desc_container())
+uf.desc[-1].add_paragraph("This is a new experimental feature from version 
3.3, and should only be tried out with big care.")
+uf.desc[-1].add_paragraph("This will estimate R2eff errors by using the 
exponential decay Jacobian matrix 'J' to compute the covariance matrix of the 
best-fit parameters.")
+uf.desc[-1].add_paragraph("This can be an huge time saving step, when 
performing model fitting in R1rho.  Errors of R2eff values, are normally 
estimated by time-consuming Monte-Carlo simulations.")
+uf.desc[-1].add_paragraph("This method is inspired from the GNU Scientific 
Library (GSL).")
+uf.desc[-1].add_paragraph("The covariance matrix is given by: covar = Qxx = 
(J^T J)^{-1}.")
+uf.desc[-1].add_paragraph("Qxx is computed by QR decomposition, Qxx=QR, 
Qxx^-1=R^-1 Q^T.  The columns of R which satisfy: |R_{kk}| <= epsrel |R_{11}| 
are considered linearly-dependent and are excluded from the covariance matrix 
(the corresponding rows and columns of the covariance matrix are set to 
zero).")
+uf.desc[-1].add_paragraph("The parameter 'epsrel' is used to remove 
linear-dependent columns when J is rank deficient.")
+uf.backend = estimate_r2eff_err
+uf.menu_text = "&r2eff_err_estimate"
+uf.gui_icon = "relax.relax_fit"
+uf.wizard_size = (800, 800)
+uf.wizard_image = ANALYSIS_IMAGE_PATH + sep + 'blank_150x150.png'
 
 
 # The relax_disp.r2eff_read user function.