Added Optionsdialog for pca and pls

fluents/lib/blmfuncs.py

@@ -1,10 +1,8 @@
 """This module contains bilinear models(Functions)
 """
-import sys
+
-# add library
+import gtk
-sys.path.append('/home/flatberg/fluents/fluents/lib')
+from fluents.workflow import Function, OptionsDialog, Options
-import time
-from fluents.workflow import Function
 from fluents.dataset import Dataset
 from fluents import plots, dataset, workflow, logger
 import scipy
@@ -127,7 +125,7 @@ class PCA(Model):
                 #logger.log('debug', 'Plot: %s failed') %plt
         return out
     
-    def run(self, data):
+    def run_o(self, data):
         """Run pca with present options. 
         """
         self.clear()
@@ -150,6 +148,21 @@ class PCA(Model):
             out.append(plt)
         return out
         
+    def run(self, data):
+        """Run Pca with option gui.
+        """
+        dialog = PcaOptionsDialog([data], self._options)
+        dialog.show_all()
+        response = dialog.run()
+        dialog.hide()
+
+        if response == gtk.RESPONSE_OK:
+            # set output data and plots
+            dialog.set_output()
+
+            #run with current data and options 
+            return self.run_o(data)
+    
 
 class PLS(Model):
     def __init__(self, id='pls', name='PLS'):
@@ -238,7 +251,7 @@ class PLS(Model):
             #    logger.log('debug', 'Plot: %s failed' %plt)
         return out
             
-    def run(self,a,b):
+    def run_o(self, a, b):
         options = self._options
         self._dataset['X'] = a
         self._dataset['Y'] = b
@@ -271,6 +284,20 @@ class PLS(Model):
             out.append(plt)
         return out
 
+    def run(self, a, b):
+        """Run Pls with option gui.
+        """
+        dialog = PlsOptionsDialog([a, b], self._options)
+        dialog.show_all()
+        response = dialog.run()
+        dialog.hide()
+
+        if response == gtk.RESPONSE_OK:
+            # set output data and plots
+            dialog.set_output()
+
+            #run with current data and options 
+            return self.run_o(a, b)
 
 class Packer:
     """A compression object used to speed up model calculations.
@@ -302,18 +329,6 @@ class Packer:
         return self._packed_data
 
 
-class Options(dict):
-    """Options base class.
-    """
-    def __init__(self, *args,**kw):
-        dict.__init__(self, *args, **kw)
-
-    def _copy_from_list(self, key_list):
-        d = {}
-        for key in key_list:
-            d[key] = self.get(key,None)
-        return d
-
 class PcaOptions(Options):
     """Options for Principal Component Analysis.
     """
@@ -344,9 +359,17 @@ class PcaOptions(Options):
         opt['val_engine'] = pca_alter_val
         opt['val_n_sets'] = 10
 
-        opt['all_data'] = ['T','P','E','p_tsq','rmsep']
+        opt['all_data'] = [('T', 'scores', True),
-        opt['all_plots'] = ['PcaScorePlot', 'PcaLoadingPlot',
+                           ('P', 'loadings', True),
-                            'PcaRmsepPlot']
+                           ('E','residuals', False),
+                           ('p_tsq', 't2', False),
+                           ('rmsep', 'root mean square error of prediction', False)
+                           ]
+        
+        opt['all_plots'] = [(blmplots.PcaScorePlot, 'Scores', True),
+                            (blmplots.PcaLoadingPlot, 'Loadings', True),
+                            (blmplots.LineViewXc, 'Line view', True)
+                            ]
         
         opt['out_data'] = ['T','P', 'p_tsq']
         opt['out_plots'] = [blmplots.PcaScorePlot,blmplots.PcaLoadingPlot,blmplots.LineViewXc]
@@ -399,20 +422,27 @@ class PlsOptions(Options):
         
         opt['val_engine'] = w_pls_cv_val
 
-        opt['all_data'] = ['T','P','E','p_tsq','rmsep']
+        opt['all_data'] = [('T', 'scores', True),
-        opt['all_plots'] = ['PcaScorePlot', 'PcaLoadingPlot',
+                           ('P', 'loadings', True),
-                            'PcaRmsepPlot']
+                           ('E','residuals', False),
+                           ('p_tsq', 't2', False),
+                           ('rmsep', 'root mean square error of prediction', False)
+                           ]
+        
+        opt['all_plots'] = [(blmplots.PlsScorePlot, 'Scores', True),
+                            (blmplots.PlsLoadingPlot, 'Loadings', True),
+                            (blmplots.LineViewXc, 'Line view', True)]
                             
-        opt['out_data'] = []
         opt['out_plots'] = [blmplots.PlsScorePlot,
                             blmplots.PlsLoadingPlot,
                             blmplots.LineViewXc]
-                            #blmplots.PlsQvalScatter]
+        opt['out_data'] = None
                             
         opt['pack'] = False
         opt['calc_qvals'] = False
         opt['q_pert_mth'] = 'shuffle_vars'
         opt['q_iter'] = 20        
+
         self.update(opt)
 
     def make_model_options(self):
@@ -430,3 +460,17 @@ class PlsOptions(Options):
         """Options for pre_validation method."""
         opt_list = ['amax', 'n_sets', 'val_engine']
         return self._copy_from_list(opt_list)  
+
+
+class PcaOptionsDialog(OptionsDialog):
+    """Options dialog for Principal Component Analysis.
+    """
+    def __init__(self, data, options, input_names=['X']):
+        OptionsDialog.__init__(self, data, options, input_names)
+
+
+class PlsOptionsDialog(OptionsDialog):
+    """Options dialog for Partial Least Squares Regression.
+    """
+    def __init__(self, data, options, input_names=['X', 'Y']):
+        OptionsDialog.__init__(self, data, options, input_names)