Re: r27225 - /trunk/user_functions/monte_carlo.py -- January 20, 2015

Hi Troels,

For the user function description, could you expand the STD acronym?
I guess this is the standard deviation
(https://en.wikipedia.org/wiki/Standard_deviation), but STD is not the
standard acronym for this (see https://en.wikipedia.org/wiki/STD and
https://en.wikipedia.org/wiki/Sexually_transmitted_infection).  SD is
the normal acronym.

Cheers,

Edward


On 20 January 2015 at 12:02,  <tlinnet@xxxxxxxxxxxxx> wrote:

Author: tlinnet
Date: Tue Jan 20 12:02:10 2015
New Revision: 27225

URL: http://svn.gna.org/viewcvs/relax?rev=27225&view=rev
Log:
Extended the user function 'monte_carlo.create_data', to allow for the 
defition of the STD to use in gauss distribution.

This is for creation of Monte-Carlo simulations, where one has perhaps 
gained information about the expected errors of the datapoints, which is 
not measured.

Task #7882 (https://gna.org/task/?7882): Implement Monte-Carlo simulation, 
where errors are generated with width of standard deviation or residuals.): 
Implement Monte-Carlo simulation, where errors are generated with width of 
standard deviation or residuals.

Modified:
    trunk/user_functions/monte_carlo.py

Modified: trunk/user_functions/monte_carlo.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/user_functions/monte_carlo.py?rev=27225&r1=27224&r2=27225&view=diff
==============================================================================
--- trunk/user_functions/monte_carlo.py (original)
+++ trunk/user_functions/monte_carlo.py Tue Jan 20 12:02:10 2015
@@ -87,16 +87,24 @@
     desc_short = "distribution",
     desc = "The error distribution method.",
     wiz_element_type = "combo",
-    wiz_combo_choices = ["Measured error", "Reduced chi2"],
-    wiz_combo_data = ["measured", "red_chi2"],
+    wiz_combo_choices = ["Measured error", "Reduced chi2", "Fixed error"],
+    wiz_combo_data = ["measured", "red_chi2", "fixed"],
     wiz_read_only = True
+)
+uf.add_keyarg(
+    name = "fixed_error",
+    py_type = "float",
+    default = None,
+    desc_short = "fixed error value for fixed error distribution.",
+    desc = "The fixed value to use when distribution is set to 'fixed'.",
+    can_be_none = True
 )
 # Description.
 uf.desc.append(Desc_container())
 uf.desc[-1].add_paragraph("The method can either be set to back 
calculation (Monte Carlo) or direct (bootstrapping), the choice of which 
determines the simulation type.  If the values or parameters are calculated 
rather than minimised, this option will have no effect.  Errors should only 
be propagated via Monte Carlo simulations if errors have been measured. ")
 uf.desc[-1].add_paragraph("For error analysis, the method should be set to 
back calculation which will result in proper Monte Carlo simulations.  The 
data used for each simulation is back calculated from the minimised model 
parameters and is randomised using Gaussian noise where the standard 
deviation is from the original error set.  When the method is set to back 
calculation, this function should only be called after the model is fully 
minimised.")
 uf.desc[-1].add_paragraph("The simulation type can be changed by setting 
the method to direct.  This will result in bootstrapping simulations which 
cannot be used in error analysis (and which are no longer Monte Carlo 
simulations).  However, these simulations are required for certain model 
selection techniques (see the documentation for the model selection user 
function for details), and can be used for other purposes.  Rather than the 
data being back calculated from the fitted model parameters, the data is 
generated by taking the original data and randomising using Gaussian noise 
with the standard deviations set to the original error set.")
-uf.desc[-1].add_paragraph("The errors generated per simulation can either 
be generated indidual per datapoint and drawn from a gauss distrubtion 
described by the STD of the indidual point, or it can be generated from a 
overall gauss distribution described by the STD_fit of the goodness of fit, 
where STD_fit = sqrt(chi2/(N-p)).")
+uf.desc[-1].add_paragraph("The errors generated per simulation can either 
be generated indidual per datapoint and drawn from a gauss distrubtion 
described by the STD of the indidual point, or it can be generated from a 
overall gauss distribution described by the STD_fit of the goodness of fit, 
where STD_fit = sqrt(chi2/(N-p)).  The last possibility is to supply a 
fixed value of STD, from which gauss distribution to draw errors from.")
 uf.desc.append(monte_carlo_desc)
 uf.backend = error_analysis.monte_carlo_create_data
 uf.menu_text = "&create_data"


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Re: r27225 - /trunk/user_functions/monte_carlo.py

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