Author: tlinnet
Date: Wed Aug 27 20:06:21 2014
New Revision: 25346
URL: http://svn.gna.org/viewcvs/relax?rev=25346&view=rev
Log:
Removed the user function to estimate the R2eff values and errors with
scipy.optimize.leastsq.
With the newly implemented Jacobian and Hessian of the exponential decay
function, the front-end to
scipy.optimize.leastsq does not serve a purpose.
This is because minfx is now as fast as scipy.optimize.leastsq, and can
estimate the errors from the
Jacobian to the exact same numbers as scipy.optimize.leastsq.
In addition to that, the covariance can be calculated by QR decomposition.
This adds additional feature for checking for a singular matrix.
The back-end wilÃl still be kept in place for the coming tim, but could be
removed later.
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=25346&r1=25345&r2=25346&view=diff
==============================================================================
--- trunk/user_functions/relax_disp.py (original)
+++ trunk/user_functions/relax_disp.py Wed Aug 27 20:06:21 2014
@@ -43,7 +43,6 @@
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
-from specific_analyses.relax_disp.estimate_r2eff import estimate_r2eff
from specific_analyses.relax_disp.parameters import copy
from specific_analyses.relax_disp.sherekhan import sherekhan_input
from specific_analyses.relax_disp.variables import EXP_TYPE_CPMG_DQ,
EXP_TYPE_CPMG_MQ, EXP_TYPE_CPMG_SQ, EXP_TYPE_CPMG_ZQ,
EXP_TYPE_CPMG_PROTON_MQ, EXP_TYPE_CPMG_PROTON_SQ, EXP_TYPE_R1RHO, MODEL_B14,
MODEL_B14_FULL, MODEL_CR72, MODEL_CR72_FULL, MODEL_DPL94, MODEL_IT99,
MODEL_LIST_FIT_R1, MODEL_LM63, MODEL_LM63_3SITE, MODEL_M61, MODEL_M61B,
MODEL_MMQ_CR72, MODEL_MP05, MODEL_NOREX, MODEL_NS_CPMG_2SITE_3D,
MODEL_NS_CPMG_2SITE_3D_FULL, MODEL_NS_CPMG_2SITE_EXPANDED,
MODEL_NS_CPMG_2SITE_STAR, MODEL_NS_CPMG_2SITE_STAR_FULL, MODEL_NS_MMQ_2SITE,
MODEL_NS_MMQ_3SITE, MODEL_NS_MMQ_3SITE_LINEAR, MODEL_NS_R1RHO_2SITE,
MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR, MODEL_R2EFF, MODEL_TAP03,
MODEL_TP02, MODEL_TSMFK01
@@ -628,80 +627,6 @@
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_estimate user function.
-uf = uf_info.add_uf('relax_disp.r2eff_estimate')
-uf.title = "Estimate r2eff and errors by exponential curve fitting with
scipy.optimize.leastsq."
-uf.title_short = "Estimate R2eff by scipy.optimize.leastsq."
-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 = "ftol",
- py_type = "float",
- default = 1e-15,
- desc_short = "relative error desired in the sum of squares.",
- desc = "is parsed to leastsq 'ftol'. It sets the relative error desired
in the sum of squares. 1e-15 seems to be the minimum value, and
'1.49012e-08' is the standard value.",
- can_be_none = False
-)
-uf.add_keyarg(
- name = "xtol",
- py_type = "float",
- default = 1e-15,
- desc_short = "relative error desired in the approximate solution.",
- desc = "is parsed to leastsq 'xtol'. It sets the relative error desired
in the approximate solution. 1e-15 seems to be the minimum value, and
'1.49012e-08' is the standard value.",
- can_be_none = False
-)
-uf.add_keyarg(
- name = "maxfev",
- default = 10000000,
- py_type = "int",
- min = 1,
- desc_short = "maximum number of calls to the function.",
- desc = "If zero, then 100*(N+1) is the maximum function calls. N is the
number of elements in x0=[r2eff, i0].",
- can_be_none = False
-)
-uf.add_keyarg(
- name = "factor",
- py_type = "float",
- default = 100.0,
- desc_short = "initial step bound.",
- desc = "is parsed to leastsq algorithm 'factor'. It determines the
initial step bound (''factor * || diag * x||''). Should be in the interval
(0.1, 100).",
- 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 and the associated error
by exponential curve fitting through scipy.optimize.leastsq, which is a
wrapper around the MINPACK lmdif and lmder algorithms. MINPACK is a
FORTRAN90 library which solves systems of nonlinear equations, or carries out
the least squares minimization of the residual of a set of linear or
nonlinear equations.")
-uf.desc[-1].add_paragraph("Errors are calculated by taking the square root
of the reported co-variance from leastsq.")
-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("Initial guess for the starting parameter")
-uf.desc[-1].add_verbatim("""
-x0 = [r2eff_est, i0_est],
-""")
-uf.desc[-1].add_paragraph("is by converting the exponential curve to a
linear problem. Then solving initial guess by linear least squares of")
-uf.desc[-1].add_verbatim("""
-ln(Intensity[j]) = ln(i0) - time[j] * r2eff.
-""")
-uf.backend = estimate_r2eff
-uf.menu_text = "&r2eff_estimate"
-uf.gui_icon = "relax.relax_fit"
-uf.wizard_size = (800, 800)
-uf.wizard_image = ANALYSIS_IMAGE_PATH + sep + 'dispersion' + sep +
'scipy_200x187.png'
# The relax_disp.r2eff_read user function.
r25346 - /trunk/user_functions/relax_disp.py