Changeset 706bb4e in sasview for src/sas/sasgui/perspectives/fitting


Ignore:
Timestamp:
Sep 18, 2017 8:20:29 AM (7 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload
Children:
60a7820
Parents:
460d3a1 (diff), cfd27dd (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.
Message:

Merge branch 'master' into ticket-887-reorg

Location:
src/sas/sasgui/perspectives/fitting
Files:
3 added
6 edited

Legend:

Unmodified
Added
Removed
  • src/sas/sasgui/perspectives/fitting/fitting.py

    r489f53a r2d9526d  
    17421742            @param unsmeared_error: data error, rescaled to unsmeared model 
    17431743        """ 
    1744  
    17451744        number_finite = np.count_nonzero(np.isfinite(y)) 
    17461745        np.nan_to_num(y) 
     
    17481747                                         data_description=model.name, 
    17491748                                         data_id=str(page_id) + " " + data.name) 
     1749        plots_to_update = [] # List of plottables that have changed since last calculation 
     1750        # Create the new theories 
    17501751        if unsmeared_model is not None: 
    1751             self.create_theory_1D(x, unsmeared_model, page_id, model, data, state, 
     1752            unsmeared_model_plot = self.create_theory_1D(x, unsmeared_model,  
     1753                                  page_id, model, data, state, 
    17521754                                  data_description=model.name + " unsmeared", 
    17531755                                  data_id=str(page_id) + " " + data.name + " unsmeared") 
     1756            plots_to_update.append(unsmeared_model_plot) 
    17541757 
    17551758            if unsmeared_data is not None and unsmeared_error is not None: 
    1756                 self.create_theory_1D(x, unsmeared_data, page_id, model, data, state, 
     1759                unsmeared_data_plot = self.create_theory_1D(x, unsmeared_data,  
     1760                                      page_id, model, data, state, 
    17571761                                      data_description="Data unsmeared", 
    17581762                                      data_id="Data  " + data.name + " unsmeared", 
    17591763                                      dy=unsmeared_error) 
    1760         # Comment this out until we can get P*S models with correctly populated parameters 
    1761         #if sq_model is not None and pq_model is not None: 
    1762         #    self.create_theory_1D(x, sq_model, page_id, model, data, state, 
    1763         #                          data_description=model.name + " S(q)", 
    1764         #                          data_id=str(page_id) + " " + data.name + " S(q)") 
    1765         #    self.create_theory_1D(x, pq_model, page_id, model, data, state, 
    1766         #                          data_description=model.name + " P(q)", 
    1767         #                          data_id=str(page_id) + " " + data.name + " P(q)") 
     1764                plots_to_update.append(unsmeared_data_plot) 
     1765        if sq_model is not None and pq_model is not None: 
     1766            sq_id = str(page_id) + " " + data.name + " S(q)" 
     1767            sq_plot = self.create_theory_1D(x, sq_model, page_id, model, data, state, 
     1768                                  data_description=model.name + " S(q)", 
     1769                                  data_id=sq_id) 
     1770            plots_to_update.append(sq_plot) 
     1771            pq_id = str(page_id) + " " + data.name + " P(q)" 
     1772            pq_plot = self.create_theory_1D(x, pq_model, page_id, model, data, state, 
     1773                                  data_description=model.name + " P(q)", 
     1774                                  data_id=pq_id) 
     1775            plots_to_update.append(pq_plot) 
     1776        # Update the P(Q), S(Q) and unsmeared theory plots if they exist 
     1777        wx.PostEvent(self.parent, NewPlotEvent(plots=plots_to_update,  
     1778                                              action='update')) 
    17681779 
    17691780        current_pg = self.fit_panel.get_page_by_id(page_id) 
  • src/sas/sasgui/perspectives/fitting/media/fitting_help.rst

    r5295cf5 rca383a0  
    195195the :ref:`Advanced_Plugin_Editor` . 
    196196 
     197**SasView version 4.2** made it possible to specify whether a plugin created with  
     198the *New Plugin Model* dialog is actually a form factor P(Q) or a structure factor  
     199S(Q). To do this, simply add one or other of the following lines under the *import*  
     200statements. 
     201 
     202For a form factor:: 
     203 
     204     form_factor = True 
     205          
     206or for a structure factor:: 
     207 
     208     structure_factor = True 
     209          
     210If the plugin is a structure factor it is *also* necessary to add two variables to  
     211the parameter list:: 
     212 
     213     parameters = [  
     214                     ['radius_effective', '', 1, [0.0, numpy.inf], 'volume', ''], 
     215                     ['volfraction', '', 1, [0.0, 1.0], '', ''], 
     216                     [...], 
     217 
     218and to the declarations of the functions Iq and Iqxy::: 
     219 
     220     def Iq(x , radius_effective, volfraction, ...): 
     221 
     222     def Iqxy(x, y, radius_effective, volfraction, ...): 
     223 
     224Such a plugin should then be available in the S(Q) drop-down box on a FitPage (once  
     225a P(Q) model has been selected). 
     226 
    197227Sum|Multi(p1,p2) 
    198228^^^^^^^^^^^^^^^^ 
     
    206236or:: 
    207237 
    208      Plugin Model = scale_factor * model_1 /* model_2 + background 
     238     Plugin Model = scale_factor * (model1 * model2) + background 
    209239 
    210240In the *Easy Sum/Multi Editor* give the new model a function name and brief 
    211241description (to appear under the *Details* button on the *FitPage*). Then select 
    212242two existing models, as p1 and p2, and the required operator, '+' or '*' between 
    213 them. Finally, click the *Apply* button to generate the model and then click *Close*. 
    214  
    215 Any changes to a plugin model generated in this way only become effective *after* it is re-selected from the model drop-down menu on the FitPage. 
     243them. Finally, click the *Apply* button to generate and test the model and then click *Close*. 
     244 
     245Any changes to a plugin model generated in this way only become effective *after* it is re-selected  
     246from the plugin models drop-down menu on the FitPage. If the model is not listed you can force a  
     247recompilation of the plugins by selecting *Fitting* > *Plugin Model Operations* > *Load Plugin Models*. 
     248 
     249**SasView version 4.2** introduced a much simplified and more extensible structure for plugin models  
     250generated through the Easy Sum/Multi Editor. For example, the code for a combination of a sphere model  
     251with a power law model now looks like this:: 
     252 
     253     from sasmodels.core import load_model_info 
     254     from sasmodels.sasview_model import make_model_from_info 
     255      
     256     model_info = load_model_info('sphere+power_law') 
     257     model_info.name = 'MyPluginModel' 
     258     model_info.description = 'sphere + power_law' 
     259     Model = make_model_from_info(model_info) 
     260 
     261To change the models or operators contributing to this plugin it is only necessary to edit the string  
     262in the brackets after *load_model_info*, though it would also be a good idea to update the model name  
     263and description too!!! 
     264 
     265The model specification string can handle multiple models and combinations of operators (+ or *) which  
     266are processed according to normal conventions. Thus 'model1+model2*model3' would be valid and would  
     267multiply model2 by model3 before adding model1. In this example, parameters in the *FitPage* would be  
     268prefixed A (for model2), B (for model3) and C (for model1). Whilst this might appear a little  
     269confusing, unless you were creating a plugin model from multiple instances of the same model the parameter  
     270assignments ought to be obvious when you load the plugin. 
     271 
     272If you need to include another plugin model in the model specification string, just prefix the name of  
     273that model with *custom*. For instance:: 
     274 
     275     sphere+custom.MyPluginModel 
     276 
     277To create a P(Q)*\S(Q) model use the @ symbol instead of * like this:: 
     278 
     279     sphere@hardsphere 
     280      
     281This streamlined approach to building complex plugin models from existing library models, or models  
     282available on the *Model Marketplace*, also permits the creation of P(Q)*\S(Q) plugin models, something  
     283that was not possible in earlier versions of SasView.  
    216284 
    217285.. _Advanced_Plugin_Editor: 
     
    484552.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 
    485553 
     554.. _Batch_Fit_Mode: 
     555 
    486556Batch Fit Mode 
    487557-------------- 
     
    636706 
    637707     Example: radius [2 : 5] , radius [10 : 25] 
    638  
    639 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 
    640  
    641 .. note::  This help document was last changed by Steve King, 10Oct2016 
     708      
     709.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 
     710 
     711Combined Batch Fit Mode 
     712----------------------- 
     713 
     714The purpose of the Combined Batch Fit is to allow running two or more batch 
     715fits in sequence without overwriting the output table of results.  This may be 
     716of interest for example if one is fitting a series of data sets where there is 
     717a shape change occurring in the series that requires changing the model part 
     718way through the series; for example a sphere to rod transition.  Indeed the 
     719regular batch mode does not allow for multiple models and requires all the 
     720files in the series to be fit with single model and set of parameters.  While 
     721it is of course possible to just run part of the series as a batch fit using 
     722model one followed by running another batch fit on the rest of the series with 
     723model two (and/or model three etc), doing so will overwrite the table of 
     724outputs from the previous batch fit(s).  This may not be desirable if one is 
     725interested in comparing the parameters: for example the sphere radius of set 
     726one and the cylinder radius of set two. 
     727 
     728Method 
     729^^^^^^ 
     730 
     731In order to use the *Combined Batch Fit*, first load all the data needed as 
     732described in :ref:`Loading_data`. Next start up two or more *BatchPage* fits 
     733following the instructions in :ref:`Batch_Fit_Mode` but **DO NOT PRESS FIT**. 
     734At this point the *Combine Batch Fit* menu item under the *Fitting menu* should 
     735be active (if there is one or no *BatchPage* the menu item will be greyed out 
     736and inactive).  Clicking on *Combine Batch Fit* will bring up a new panel, 
     737similar to the *Const & Simult Fit* panel. In this case there will be a 
     738checkbox for each *BatchPage* instead of each *FitPage* that should be included 
     739in the fit.  Once all are selected, click the Fit button on 
     740the *BatchPage* to run each batch fit in *sequence* 
     741 
     742.. image:: combine_batch_page.png 
     743 
     744The batch table will then pop up at the end as for the case of the simple Batch 
     745Fitting with the following caveats: 
     746 
     747.. note:: 
     748   The order matters.  The parameters in the table will be taken from the model 
     749   used in the first *BatchPage* of the list.  Any parameters from the 
     750   second and later *BatchPage* s that have the same name as a parameter in the 
     751   first will show up allowing for plotting of that parameter across the 
     752   models. The other parameters will not be available in the grid. 
     753.. note:: 
     754   a corralary of the above is that currently models created as a sum|multiply 
     755   model will not work as desired because the generated model parameters have a 
     756   p#_ appended to the beginning and thus radius and p1_radius will not be 
     757   recognized as the same parameter. 
     758    
     759.. image:: combine_batch_grid.png 
     760 
     761In the example shown above the data is a time series with a shifting peak. 
     762The first part of the series was fitted using the *broad_peak* model, while 
     763the rest of the data were fit using the *gaussian_peak* model. Unfortunately the 
     764time is not listed in the file but the file name contains the information. As 
     765described in :ref:`Grid_Window`, a column can be added manually, in this case 
     766called time, and the peak position plotted against time.  
     767 
     768.. image:: combine_batch_plot.png 
     769 
     770Note the discontinuity in the peak position.  This reflects the fact that the 
     771Gaussian fit is a rather poor model for the data and is not actually 
     772finding the peak. 
     773 
     774.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ 
     775 
     776.. note::  This help document was last changed by Paul Butler, 10 September 
     777   2017 
  • src/sas/sasgui/perspectives/fitting/media/plugin.rst

    r72100ee re081946  
    1818* By writing a model from scratch outside of SasView (only recommended for 
    1919  code monkeys!) 
     20 
     21**What follows below is quite technical. If you just want a helping hand to get  
     22started creating your own models see** :ref:`Adding_your_own_models`. 
    2023 
    2124Overview 
  • src/sas/sasgui/perspectives/fitting/model_thread.py

    r7432acb r0f9ea1c  
    7171                    (self.data.qy_data * self.data.qy_data)) 
    7272 
    73         # For theory, qmax is based on 1d qmax  
     73        # For theory, qmax is based on 1d qmax 
    7474        # so that must be mulitified by sqrt(2) to get actual max for 2d 
    7575        index_model = (self.qmin <= radius) & (radius <= self.qmax) 
     
    9191                self.data.qy_data[index_model] 
    9292            ]) 
    93         output = np.zeros(len(self.data.qx_data)) 
     93        # Initialize output to NaN so masked elements do not get plotted. 
     94        output = np.empty_like(self.data.qx_data) 
    9495        # output default is None 
    9596        # This method is to distinguish between masked 
    9697        #point(nan) and data point = 0. 
    97         output = output / output 
     98        output[:] = np.NaN 
    9899        # set value for self.mask==True, else still None to Plottools 
    99100        output[index_model] = value 
     
    198199            output[index] = self.model.evalDistribution(self.data.x[index]) 
    199200 
     201        x=self.data.x[index] 
     202        y=output[index] 
    200203        sq_values = None 
    201204        pq_values = None 
    202         s_model = None 
    203         p_model = None 
    204205        if isinstance(self.model, MultiplicationModel): 
    205206            s_model = self.model.s_model 
    206207            p_model = self.model.p_model 
    207         elif hasattr(self.model, "get_composition_models"): 
    208             p_model, s_model = self.model.get_composition_models() 
    209  
    210         if p_model is not None and s_model is not None: 
    211             sq_values = np.zeros((len(self.data.x))) 
    212             pq_values = np.zeros((len(self.data.x))) 
    213             sq_values[index] = s_model.evalDistribution(self.data.x[index]) 
    214             pq_values[index] = p_model.evalDistribution(self.data.x[index]) 
     208            sq_values = s_model.evalDistribution(x) 
     209            pq_values = p_model.evalDistribution(x) 
     210        elif hasattr(self.model, "calc_composition_models"): 
     211            results = self.model.calc_composition_models(x) 
     212            if results is not None: 
     213                pq_values, sq_values = results 
     214 
    215215 
    216216        elapsed = time.time() - self.starttime 
    217217 
    218         self.complete(x=self.data.x[index], y=output[index], 
     218        self.complete(x=x, y=y, 
    219219                      page_id=self.page_id, 
    220220                      state=self.state, 
  • src/sas/sasgui/perspectives/fitting/simfitpage.py

    r959eb01 ra9f9ca4  
    11""" 
    2     Simultaneous fit page 
     2    Simultaneous or Batch fit page 
    33""" 
     4# Note that this is used for both Simultaneous/Constrained fit AND for  
     5# combined batch fit.  This is done through setting of the batch_on parameter. 
     6# There are the a half dozen or so places where an if statement is used as in  
     7# if not batch_on: 
     8#     xxxx 
     9# else: 
     10#     xxxx 
     11# This is just wrong but dont have time to fix this go. Proper approach would be 
     12# to strip all parts of the code that depend on batch_on and create the top 
     13# level class from which a contrained/simultaneous fit page and a combined  
     14# batch page inherit. 
     15# 
     16#            04/09/2017   --PDB 
     17 
    418import sys 
    519from collections import namedtuple 
     
    400414        # General Help button 
    401415        self.btHelp = wx.Button(self, wx.ID_HELP, 'HELP') 
    402         self.btHelp.SetToolTipString("Simultaneous/Constrained Fitting help.") 
     416        if self.batch_on: 
     417            self.btHelp.SetToolTipString("Combined Batch Fitting help.") 
     418        else: 
     419            self.btHelp.SetToolTipString("Simultaneous/Constrained Fitting help.") 
    403420        self.btHelp.Bind(wx.EVT_BUTTON, self._on_help) 
    404421 
     
    527544    """ 
    528545        _TreeLocation = "user/sasgui/perspectives/fitting/fitting_help.html" 
    529         _PageAnchor = "#simultaneous-fit-mode" 
    530         _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation, 
     546        if not self.batch_on: 
     547            _PageAnchor = "#simultaneous-fit-mode" 
     548            _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation, 
    531549                                          _PageAnchor, 
    532550                                          "Simultaneous/Constrained Fitting Help") 
     551        else: 
     552            _PageAnchor = "#combined-batch-fit-mode" 
     553            _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation, 
     554                                          _PageAnchor, 
     555                                          "Combined Batch Fit Help") 
    533556 
    534557    def set_manager(self, manager): 
  • src/sas/sasgui/perspectives/fitting/models.py

    rb1c2011 r12f7f24  
    1414import py_compile 
    1515import shutil 
     16from sasmodels.sasview_model import load_custom_model, load_standard_models 
    1617# Explicitly import from the pluginmodel module so that py2exe 
    1718# places it in the distribution. The Model1DPlugin class is used 
    1819# as the base class of plug-in models. 
     20from sas.sasgui import get_user_dir 
    1921from sas.sascalc.fit.pluginmodel import Model1DPlugin 
    2022from sas.sasgui.guiframe.CategoryInstaller import CategoryInstaller 
    21 from sasmodels.sasview_model import load_custom_model, load_standard_models 
    2223 
    2324logger = logging.getLogger(__name__) 
     
    2526 
    2627PLUGIN_DIR = 'plugin_models' 
    27 PLUGIN_LOG = os.path.join(os.path.expanduser("~"), '.sasview', PLUGIN_DIR, 
    28                           "plugins.log") 
     28PLUGIN_LOG = os.path.join(get_user_dir(), PLUGIN_DIR, "plugins.log") 
    2929PLUGIN_NAME_BASE = '[plug-in] ' 
    3030 
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