Author: tlinnet
Date: Mon Sep 1 23:25:36 2014
New Revision: 25519
URL: http://svn.gna.org/viewcvs/relax?rev=25519&view=rev
Log:
Further extended the script for analysing errors with Jacobian.
Now loops over the dimension.
task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR
estimation from Jacobian and Co-variance matrix of dispersion models.
Modified:
branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py
Modified:
branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py
URL:
http://svn.gna.org/viewcvs/relax/branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py?rev=25519&r1=25518&r2=25519&view=diff
==============================================================================
--- branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py
(original)
+++ branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py
Mon Sep 1 23:25:36 2014
@@ -38,9 +38,9 @@
from pipe_control.minimise import assemble_scaling_matrix
from pipe_control.mol_res_spin import generate_spin_string, spin_loop
from specific_analyses.relax_disp.checks import check_model_type
-from specific_analyses.relax_disp.data import average_intensity,
is_r1_optimised, loop_exp_frq_offset_point, loop_time, return_cpmg_frqs,
return_offset_data, return_param_key_from_data, return_r1_data,
return_r1_err_data, return_r2eff_arrays, return_spin_lock_nu1
+from specific_analyses.relax_disp.data import average_intensity,
generate_r20_key, is_r1_optimised, loop_exp_frq_offset,
loop_exp_frq_offset_point, loop_time, return_cpmg_frqs, return_offset_data,
return_param_key_from_data, return_r1_data, return_r1_err_data,
return_r2eff_arrays, return_spin_lock_nu1
from specific_analyses.relax_disp.parameters import assemble_param_vector,
disassemble_param_vector, param_num
-from specific_analyses.relax_disp.variables import MODEL_CR72,
MODEL_R2EFF, MODEL_TSMFK01
+from specific_analyses.relax_disp.variables import MODEL_CR72,
MODEL_R2EFF, MODEL_TSMFK01, PARAMS_R20
from target_functions.chi2 import chi2_rankN, dchi2
from target_functions.relax_disp import Dispersion
@@ -243,8 +243,53 @@
jacobian = tfunc.jacobian(param_vector)
weights = 1. / tfunc.errors**2
- # Get the co-variance
- pcov = multifit_covar(J=jacobian, weights=weights)
+ # Get the shape of the data.
+ NJ, NE, NS, NM, NO, ND = jacobian.shape
+ if NS != 1:
+ raise RelaxError("The number of spins does not fit.")
+
+ # Get the parameters fitted in the model.
+ params = cur_spin.params
+
+ # Set the spin index to 0.
+ si = 0
+ # Loop over the data.
+ for exp_type, frq, offset, ei, mi, oi in
loop_exp_frq_offset(return_indices=True):
+ param_key = generate_r20_key(exp_type=exp_type, frq=frq)
+
+ # Extract weights.
+ cur_weights = weights[ei, si, mi, oi]
+
+ # Extract every column/row from the first to last columns. Is
this correct?
+ cur_jacobian = jacobian[0:NJ:1, ei, si, mi, oi]
+
+ # Get the co-variance
+ pcov = multifit_covar(J=cur_jacobian, weights=cur_weights)
+
+ # To compute one standard deviation errors on the parameters,
take the square root of the diagonal covariance.
+ param_vector_error = sqrt(diag(pcov))
+
+ # Loop over params.
+ for i, param in enumerate(params):
+ # Set the param error name
+ param_err = param + '_err'
+
+ # If param in PARAMS_R20, values are stored in with
parameter key.
+ if param in PARAMS_R20:
+ # Copy parameter attribute to error attribute, if not
in spin Class.
+ if not hasattr(cur_spin, param_err):
+ setattr(cur_spin, param_err,
deepcopy(getattr(cur_spin, param)))
+
+ # Set error.
+ getattr(cur_spin, param_err)[param_key] =
deepcopy(param_vector_error[i])
+
+ else:
+ # Copy parameter attribute to error attribute, if not
in spin Class.
+ if not hasattr(cur_spin, param_err):
+ setattr(cur_spin, param_err,
deepcopy(getattr(cur_spin, param)))
+
+ # Set error.
+ setattr(cur_spin, param_err, param_vector_error[i])
#### This class is only for testing.
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Re: r25519 - /branches/est_par_error/specific_analyses/relax_disp/estimate_r2eff.py