laydi/fluents/lib/blmfuncs.py

"""This module contains bilinear models(Functions)
"""
import os
import pygtk
import gtk
import gtk.glade
import fluents
from fluents.workflow import Function, OptionsDialog, Options
from fluents.dataset import Dataset
from fluents import plots, dataset, workflow, logger
import scipy
from engines import *
from cx_stats import leverage, variances, hotelling
from cx_utils import mat_center
from validation import *
import blmplots
import engines
import copy

class Model(Function):
    """Base class of bilinear models.
    """
    def __init__(self,id='johndoe',name='JohnDoe'):
        Function.__init__(self,id,name)
        self.name = name
        self._options = None
        self._data = {}
        self._dataset = {}
        self._packers = {}
        self.model = {}
        
    def clear(self):
        """ Clears model paramters
        """
        self.model = {}
        self._data = {}
        self._packers = {}
    

class PCA(Model):
    def __init__(self,id='pca',name='PCA'):
        Model.__init__(self,id,name)
        self._options = PcaOptions()
        
    def validation(self, amax, cv_val_sets, pert_val_sets, cv_val_method, pert_val_method):
        """Model validation and estimate of optimal numer of components. 
        """
        if self._options['calc_cv']:
            if cv_val_method == 'random':
                sep, aopt = pca_cv_val(self.model['E0'], amax, cv_val_sets)
                self.model['sep'] = sep

        if self._options['calc_pert']:
            if pert_val_method == 'random_diag':
                sep, aopt = pca_alter_val(self.model['E0'], amax, pert_val_sets)
                self.model['sep'] = sep
            
        if self._options['calc_cv']==False and self._options['calc_pert']==False:
            self.model['sep'] = None
            aopt = self._options['amax']
        
        if self._options['auto_aopt']:
            logger.log("notice", "Auto aopt: " + str(aopt))
            self._options['aopt'] = aopt
        if aopt==1:
            logger.log('notice', 'Aopt at first component!')
        
    def confidence(self, aopt, n_sets, alpha, p_center,
                   crot, strict, cov_center ):
        """Returns a confidence measure for model parameters.
        Based on aopt.
        """
        if aopt<2:
            aopt = 2
            logger.log('notice','Hotellings T2 needs more than 1 comp.\n switching to 2!!')
        
        jk_segments = pca_jkP(self.model['E0'], aopt, n_sets)
        Pcal = self.model['P'][:,:aopt]
        # add the scale to P
        tnorm = scipy.apply_along_axis(norm, 0, self.model['T'][:,:aopt])
        Pcal = Pcal*tnorm
        tsq = hotelling(jk_segments, Pcal, p_center,
                        cov_center, alpha, crot, strict)
        self.model['p_tsq'] = tsq

    def make_model(self, amax, mode, scale):
        """Model on optimal number of components.
        """
        dat = pca(self.model['E0'], amax, scale, mode)
        
        # explained variance
        var_x, exp_var_x = variances(self.model['E0'], dat['T'], dat['P'])
        dat['var_x'] = var_x
        dat['exp_var_x'] = exp_var_x

        #fixme###
        do_lev_s = False
        do_lev_v = False
        #####
        if do_lev_s:
            # sample leverages
            tnorm = scipy.apply_along_axis(norm, 0, dat['T']) # norm of Ts
            s_lev = leverage(amax, tnorm)
            dat['s_lev'] = s_lev
        if do_lev_v:
            # variable leverages
            v_lev = leverage(amax, dat['P'])
            dat['v_lev'] = v_lev
        
        self.model.update(dat)

    def as_dataset(self, param, dtype='dataset'):
        """Return model parameter as Dataset.
        """
        if not param in self.model.keys():
            return
        DX = self._dataset['X'] #input dataset
        dim_name_0, dim_name_1 = DX.get_dim_name()
        # samples
        ids_0 = [dim_name_0, DX.get_identifiers(dim_name_0, sorted=True)]
        # vars
        ids_1 = [dim_name_1, DX.get_identifiers(dim_name_1, sorted=True)]
        # components (hidden)
        pc_ids = ['_amax', map(str,range(self._options['amax'])) ]
        pc_ids_opt = ['_aopt', map(str, range(self._options['aopt'])) ] 
        zero_dim = ['_doe', ['0']] # null dim, vector (hidden)
        match_ids = {'E':[ids_0, ids_1],
                     'E0':[ids_0, ids_1],
                     'P':[ids_1, pc_ids],
                     'T':[ids_0, pc_ids], 
                     'W':[ids_1, pc_ids],
                     'p_tsq':[ids_1, zero_dim],
                     'rmsep':[pc_ids, zero_dim],
                     'var_leverages':[ids_1, zero_dim],
                     'sample_leverages':[pc_ids, zero_dim],
                     'exp_var_x': [pc_ids, zero_dim],
                     'var_x': [pc_ids, zero_dim],
                     }
        
        out = Dataset(self.model[param], match_ids[param], name=param)
        return out

    def get_out_plots(self, options):
        out=[]
        for plt in options['out_plots']:
            #try:
            out.append(plt(self))
            #except:
            #    logger.log('debug', 'Plot: %s failed') %str(plt)
        return out
    
    def run_o(self, data):
        """Run pca with present options. 
        """
        self.clear()
        options = self._options
        for item in options.items():
            print item
        self._dataset['X'] = data
        self._data['X'] = data.asarray().astype('<f8')
        if options['center']:
            center = options['center_mth']
            self.model['E0'] = center(self._data['X'])
        else:
            self.model['E0'] = data.asarray()

        self.validation(**options.validation_options())
        self.model['aopt'] = self._options['aopt']
        self.make_model(**options.make_model_options())
        if options['calc_conf']:
            self.confidence(**options.confidence_options())
            
        out = [self.as_dataset(p) for p in options['out_data']]
        for plt in self.get_out_plots(options):
            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'):
        Model.__init__(self, id, name)
        self._options = PlsOptions()
        
    def validation(self, amax, n_sets, cv_val_method):
        """Returns rmsec,rmsep for model.
        """
        m, n = self.model['E0'].shape
        if m>n:
            val_engine = w_pls_cv_val
        else:
            val_engine = pls_val
        if self._options['calc_cv']==True:
            rmsep, aopt = val_engine(self.model['E0'], self.model['F0'],
                                     amax, n_sets)
            self.model['rmsep'] = rmsep[:,:-1]
            self.model['aopt'] = aopt
        else:
            self.model['rmsep'] = None
            self.model['aopt'] = self._options['aopt']
        
    def confidence(self, aopt, n_sets, alpha, p_center,
                   crot, strict, cov_center ):
        """Returns a confidence measure for model parameters
        Supported parameters: W
        """
        aopt = self.model['aopt']
        if self._options['calc_conf']:
            jk_segments = pls_jkW(self.model['E0'], self.model['F0'],
                                  aopt, n_sets)
            Wcal = self.model['W'][:,:aopt]
            # ensure that Wcal is scaled
            tnorm = scipy.apply_along_axis(norm, 0, self.model['T'][:,:aopt])
            Wcal = Wcal*tnorm
            tsq = hotelling(jk_segments, Wcal, p_center,
                            alpha, crot, strict, cov_center)
            self.model['w_tsq'] = tsq
        else:
            self.model['w_tsq'] = None

    def permutation_confidence(self, a, b, aopt, reg, n_iter, algo,
                               sim_method):
        """Estimates cut off on significant vars by controlling fdr."""

        if self._options['calc_qvals']==True:
            qvals_sorted, qvals = pls_qvals(a, b,
                                            aopt=None,
                                            alpha=reg,
                                            n_iter=n_iter,
                                            algo='pls',
                                            sim_method=sim_method)
            self.model['qval'] = qvals
            self.model['qval_sorted'] = qvals_sorted
        else:
           self.model['qval'] = None
           self.model['qval_sorted'] = None 

    def make_model(self, a, b, amax, scale, mode, engine):
        """Make model on amax components.
        """
        dat = engine(a, b, amax, scale, mode)
        self.model.update(dat)
        
    def as_dataset(self, name, dtype='Dataset'):
        """Return any model parameter as Dataset
        No ids matching
        """
        if name not in self.model.keys():
            return
        DX, DY = self._dataset['X'], self._dataset['Y']
        dim_name_0, dim_name_1 = DX.get_dim_name()
        dim_name_2, dim_name_3 = DY.get_dim_name()
        #samples
        ids_0 = [dim_name_0, DX.get_identifiers(dim_name_0, sorted=True)]
        # x vars
        ids_1 = [dim_name_1, DX.get_identifiers(dim_name_1, sorted=True)]
        # y vars
        ids_3 = [dim_name_3, DY.get_identifiers(dim_name_3, sorted=True)]
        # components (hidden)
        pc_ids = ['_comp', map(str, range(self._options['amax']))]
        pc_ids_opt = ['_comp', map(str, range(self.model['aopt']))]
        zero_dim = ['_doe',['0']] # null dim, vector (hidden)

        match_ids = {'E':[ids_0, ids_1],
                     'P':[ids_1, pc_ids],
                     'T':[ids_0, pc_ids],
                     'W': [ids_1, pc_ids],
                     'R': [ids_1, pc_ids],
                     'Q':[ids_3, pc_ids],
                     'F':[ids_0, ids_3],
                     'B':[ids_1, ids_3],
                     'qval':[ids_1, zero_dim],
                     'qval_sorted':[ids_1, zero_dim],
                     'w_tsq':[ids_1, zero_dim],
                     'rmsep':[ids_3, pc_ids],
                     }
        
        array = self.model[name]     
        M = Dataset(array, identifiers=match_ids[name], name=name)
        return M

    def get_out_plots(self, options):
        out=[]
        for plt in options['out_plots']:
            #try:
            out.append(plt(self))
            #except:
            #    logger.log('debug', 'Plot: %s failed' %plt)
        return out
            
    def run_o(self, a, b):
        """Run PLS with present options."""
        options = self._options
        self._dataset['X'] = a
        self._dataset['Y'] = b
        self._data['X'] = a.asarray()
        self._data['Y'] = b.asarray()

        if options['center']:
            self.model['E0'] = options['center_mth'](self._data['X'])
            self.model['F0'] = options['center_mth'](self._data['Y'])
        else:
            self.model['E0'] = self._data['X']
            self.model['F0'] = self._data['Y']
        
        self.validation(**options.validation_options())
        self.make_model(self.model['E0'], self.model['F0'],
                        **options.make_model_options())
        # variance captured
        var_x, exp_var_x = variances(self.model['E0'], self.model['T'], self.model['P'])
        self.model['var_x'] = var_x
        self.model['exp_var_x'] = exp_var_x

        var_y, exp_var_y = variances(self.model['F0'], self.model['T'], self.model['Q'])
        self.model['var_y'] = var_y
        self.model['exp_var_y'] = exp_var_y
        
        if options['calc_conf']:
            self.confidence(**options.confidence_options())

        out = [self.as_dataset(p) for p in options['out_data']]
        for plt in self.get_out_plots(options):
            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
            for key, val in self._options.items():
                print (key, val)
            return self.run_o(a, b)

class Packer:
    """A compression object used to speed up model calculations.

    Often used in conjunction with crossvalidation and perturbations
    analysis. 
    """
    def __init__(self,array):
        self._shape = array.shape
        self._array = array
        self._packed_data = None
        
    def expand(self,a):
        if self._inflater!=None:
            return dot(self._inflater,a)
        
    def collapse(self,axis=None,mode='svd'):
        if not axis:
            axis = argmin(self._array.shape) # default is the smallest dim

        if axis == 1:
            self._array = self._array.T
        u, s, vt = svd(self._array,full_matrices=0)
        self._inflater = vt.T
        self._packed_data = u*s
        return self._packed_data

    def get_packed_data(self):
        return self._packed_data


class PcaOptions(Options):
    """Options for Principal Component Analysis.
    """
    def __init__(self):
        Options.__init__(self)
        self._set_default()
            
    def _set_default(self):
        opt = {}
        opt['algo'] = 'pca'
        opt['engine'] = engines.pca
        opt['mode'] = 'normal' # how much info to calculate
        opt['amax'] = 10
        opt['aopt'] = 100
        opt['auto_aopt'] = False
        opt['center'] = True
        opt['center_mth'] = mat_center
        opt['scale'] = 'scores'

        opt['calc_conf'] = False
        opt['n_sets'] = 5
        opt['strict'] = True
        opt['p_center'] = 'med'
        opt['alpha'] = .8
        opt['cov_center'] = 'med'
        opt['crot'] = True

        opt['calc_cv'] = False
        opt['calc_pert'] = True
        opt['pert_val_method'] = 'random_diag'
        opt['cv_val_method'] = 'random'
        opt['cv_val_sets'] = 10
        opt['pert_val_sets'] = 10

        opt['all_data'] = [('T', 'scores', True),
                           ('P', 'loadings', True),
                           ('E','residuals', False),
                           ('p_tsq', 't2', False),
                           ('rmsep', 'RMSEP', False)
                           ]

        # (class, name, sensitive, ticked)
        opt['all_plots'] = [(blmplots.PcaScorePlot, 'Scores', True),
                            (blmplots.PcaLoadingPlot, 'Loadings', True),
                            (blmplots.LineViewXc, 'Line view', True),
                            (blmplots.PredictionErrorPlot, 'Residual Error', False)
                            ]
        
        opt['out_data'] = ['T','P', 'p_tsq']
        opt['out_plots'] = [blmplots.PcaScorePlot,blmplots.PcaLoadingPlot,blmplots.LineViewXc]

        self.update(opt)
        
    def make_model_options(self):
        """Options for make_model method."""
        opt_list = ['scale', 'mode', 'amax']
        return self._copy_from_list(opt_list)   

    def confidence_options(self):
        """Options for confidence method."""
        opt_list = ['n_sets', 'aopt', 'alpha', 'p_center',
                    'strict', 'crot', 'cov_center']
        return self._copy_from_list(opt_list)  

    def validation_options(self):
        """Options for pre_validation method."""
        opt_list = ['amax', 'cv_val_sets', 'pert_val_sets',
                    'cv_val_method', 'pert_val_method']
        return self._copy_from_list(opt_list)


class PlsOptions(Options):
    """Options for Partial Least Squares Regression.
    """
    def __init__(self):
        Options.__init__(self)
        self._set_default()
            
    def _set_default(self):
        opt = {}
        opt['algo'] = 'pls'
        opt['engine'] = engines.pls
        opt['mode'] = 'normal' # how much info to calculate
        opt['amax'] = 10
        opt['aopt'] = 10
        opt['auto_aopt'] = False
        opt['center'] = True
        opt['center_mth'] = mat_center
        opt['scale'] = 'scores'

        opt['calc_conf'] = False
        opt['n_sets'] = 5
        opt['strict'] = True
        opt['p_center'] = 'med'
        opt['alpha'] = .8
        opt['cov_center'] = 'med'
        opt['crot'] = True

        opt['calc_cv'] = False
        opt['cv_val_method'] = 'random'
        opt['cv_val_sets'] = opt['n_sets']

        opt['all_data'] = [('T', 'scores', True),
                           ('P', 'loadings', True),
                           ('E','residuals', False),
                           ('p_tsq', 't2', False),
                           ('rmsep', 'RMSEP', False)
                           ]

        # (class, name, sensitive, ticked)
        opt['all_plots'] = [(blmplots.PlsScorePlot, 'Scores', True),
                            (blmplots.PlsLoadingPlot, 'Loadings', True),
                            (blmplots.LineViewXc, 'Line view', True),
                            (blmplots.PredictionErrorPlot, 'Residual Error', False),
                            (blmplots.RMSEPPlot, 'RMSEP', False)
                            ]
        
        opt['out_data'] = ['T','P', 'p_tsq']
        opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
        
        opt['out_data'] = None
                            
        opt['pack'] = False
        opt['calc_qvals'] = False
        opt['q_pert_method'] = 'shuffle_rows'
        opt['q_iter'] = 20        

        self.update(opt)

    def make_model_options(self):
        """Options for make_model method."""
        opt_list = ['scale','mode', 'amax', 'engine']
        return self._copy_from_list(opt_list)   

    def confidence_options(self):
        """Options for confidence method."""
        opt_list = ['n_sets', 'aopt', 'alpha', 'p_center',
                    'strict', 'crot', 'cov_center'] 
        return self._copy_from_list(opt_list)  
    
    def validation_options(self):
        """Options for pre_validation method."""
        opt_list = ['amax', 'n_sets', 'cv_val_method']
        return self._copy_from_list(opt_list)

    def permutation_confidence(self):
        opt_list = ['q_pert_method', 'q_iter']
        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)
        #glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
        glade_file = os.path.join("/home/flatberg/fluents/fluents/", 'pca_options.glade')

        notebook_name = "vbox1"
        page_name = "Options"
        self.add_page_from_glade(glade_file, notebook_name, page_name)
        # connect signals to handlers
        dic = {"on_amax_value_changed" : self.on_amax_changed,
               "on_aopt_value_changed" : self.on_aopt_changed,
               "auto_aopt_toggled" : self.auto_aopt_toggled,
               "center_toggled" : self.center_toggled,
               #"on_scale_changed" : self.on_scale_changed,
               "on_val_none" : self.val_toggled,
               "on_val_cv" : self.cv_toggled,
               "on_val_pert" : self.pert_toggled,
               "on_cv_method_changed" : self.on_cv_method_changed,
               "on_cv_sets_changed" : self.on_cv_sets_changed,
               "on_pert_sets_changed" : self.on_pert_sets_changed,
               "on_conf_toggled" : self.on_conf_toggled,
               "on_subset_loc_changed" : self.on_subset_loc_changed,
               "on_cov_loc_changed" : self.on_cov_loc_changed,
               "on_alpha_changed" : self.on_alpha_changed,
               "on_rot_changed" : self.on_rot_changed
               }
        
        self.wTree.signal_autoconnect(dic)

        # set/ensure valid default values/ranges
        #
        amax_sb = self.wTree.get_widget("amax_spinbutton")
        max_comp =  min(data[0].shape) # max num of components
        if self._options['amax']>max_comp:
            logger.log('debug', 'amax default too large ... adjusting')
            self._options['amax'] = max_comp
        amax_sb.get_adjustment().set_all(self._options['amax'], 1, max_comp, 1, 0, 0)
        # aopt spin button
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        if self._options['aopt']>self._options['amax']:
            self._options['aopt'] = self._options['amax'] + 1 - 1
        aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, self._options['amax'], 1, 0, 0)

        # scale
        # scale_cb = self.wTree.get_widget("scale_combobox")
        # scale_cb.set_active(0)

        # validation frames
        if self._options['calc_cv']==False:
            cv_frame = self.wTree.get_widget("cv_frame")
            cv_frame.set_sensitive(False)
        if self._options['calc_pert']==False:
            pert_frame = self.wTree.get_widget("pert_frame")
            pert_frame.set_sensitive(False)

        cv = self.wTree.get_widget("cv_method").set_active(0)
        pm = self.wTree.get_widget("pert_method").set_active(0)

        # confidence
        if self._options['calc_conf']==True:
            self.wTree.get_widget("subset_expander").set_sensitive(True)
        else:
            self.wTree.get_widget("subset_expander").set_sensitive(False)

        cb = self.wTree.get_widget("subset_loc")
        _m = {'med': 0, 'mean': 1, 'full_model': 2}
        cb.set_active(_m.get(self._options['p_center']))

        cb = self.wTree.get_widget("cov_loc")
        _m = {'med': 0, 'mean': 1}
        cb.set_active(_m.get(self._options['cov_center']))

        hs = self.wTree.get_widget("alpha_scale")
        hs.set_value(self._options['alpha'])
        
                    
    def on_amax_changed(self, sb):
        logger.log("debug", "amax changed: new value: %s" %sb.get_value_as_int())
        amax = sb.get_value_as_int()
        # update aopt if needed
        if amax<self._options['aopt']:
            self._options['aopt'] = amax
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, amax, 1, 0, 0)
        self._options['amax'] = sb.get_value_as_int()
        
    def on_aopt_changed(self, sb):
        aopt = sb.get_value_as_int()
        self._options['aopt'] = aopt
        
    def auto_aopt_toggled(self, tb):
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        if tb.get_active():
            self._options['auto_aopt'] = True
            aopt_sb.set_sensitive(False)
        else:
            self._options['auto_aopt'] = False
            aopt_sb.set_sensitive(True)

    def center_toggled(self, tb):
        if tb.get_active():
            self._options['center'] = True
        else:
            logger.log("debug", "centering set to False")
            self._options['center'] = False

    #def on_scale_changed(self, cb):
    #    scale = cb.get_active_text()
    #    if scale=='Scores':
    #        self._options['scale'] = 'scores'
    #    elif scale=='Loadings':
    #        self._options['scale'] = 'loads'
    #    else:
    #        raise IOError

    def val_toggled(self, tb):
        """Callback for validation: None. """
        cv_frame = self.wTree.get_widget("cv_frame")
        pert_frame = self.wTree.get_widget("pert_frame")
        cv_tb = self.wTree.get_widget("cv_toggle")
        p_tb = self.wTree.get_widget("pert_toggle")
        if tb.get_active():
            self._options['calc_cv'] = False
            self._options['calc_pert'] = False
            cv_frame.set_sensitive(False)
            pert_frame.set_sensitive(False)
            cv_tb.set_sensitive(False)
            p_tb.set_sensitive(False)
        else:
            p_tb.set_sensitive(True)
            cv_tb.set_sensitive(True)
            if p_tb.get_active(): 
                pert_frame.set_sensitive(True)
                self._options['calc_pert'] = True
            if cv_tb.get_active():
                cv_frame.set_sensitive(True)
                self._options['calc_cv'] = True

    def cv_toggled(self, tb):
        cv_frame = self.wTree.get_widget("cv_frame")
        if tb.get_active():
            cv_frame.set_sensitive(True)
            self._options['calc_cv'] = True
        else:
            cv_frame.set_sensitive(False)
            self._options['calc_cv'] = False

    def pert_toggled(self, tb):
        pert_frame = self.wTree.get_widget("pert_frame")
        if tb.get_active():
            pert_frame.set_sensitive(True)
            self._options['calc_pert'] = True
        else:
            pert_frame.set_sensitive(False)
            self._options['calc_pert'] = False


    def on_cv_method_changed(self, cb):
        method = cb.get_active_text()
        if method == 'Random':
            self._options['cv_val_method'] = 'random'
            
    def on_pert_method_changed(self, cb):
        method = cb.get_active_text()
        if method == 'Random diags':
            self._options['pert_val_method'] = 'random_diag'

    def on_cv_sets_changed(self, sb):
        val = sb.get_value_as_int()
        self._options['cv_val_sets'] = val
        
    def on_pert_sets_changed(self, sb):
        val = sb.get_value_as_int()
        self._options['pert_val_sets'] = val

    def on_conf_toggled(self, tb):
        if tb.get_active():
            self._options['calc_conf'] = False
            self.wTree.get_widget("subset_expander").set_sensitive(False)
        else:
            self._options['calc_conf'] = True
            self.wTree.get_widget("subset_expander").set_sensitive(True)

    def on_subset_loc_changed(self, cb):
        method = cb.get_active_text()
        if method=='Full model':
            self._options['p_center'] = 'full_model'
        elif method=='Median':
            self._options['p_center'] = 'med'
        elif method=='Mean':
            self._options['p_center'] = 'mean'

    def on_cov_loc_changed(self, cb):
        method = cb.get_active_text()
        if method=='Median':
            self._options['cov_center'] = 'med'
        elif method=='Mean':
            self._options['cov_center'] = 'mean'

    def on_alpha_changed(self, hs):
        self._options['alpha'] = hs.get_value()

    def on_rot_changed(self, rg):
        proc, strict = rg
        if proc.get_active():
            self._options['crot'] = True
        else:
            self._options['crot'] = True
            self._options['strict'] = True 


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)
        #glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
        glade_file = os.path.join("/home/flatberg/fluents/fluents/", 'pls_options.glade')

        notebook_name = "vbox1"
        page_name = "Options"
        self.add_page_from_glade(glade_file, notebook_name, page_name)
        # connect signals to handlers
        dic = {"on_amax_value_changed" : self.on_amax_changed,
               "on_aopt_value_changed" : self.on_aopt_changed,
               "auto_aopt_toggled" : self.auto_aopt_toggled,
               "center_toggled" : self.center_toggled,
               #"on_scale_changed" : self.on_scale_changed,
               "on_val_none" : self.val_toggled,
               "on_val_cv" : self.cv_toggled,
               "on_cv_method_changed" : self.on_cv_method_changed,
               "on_cv_sets_changed" : self.on_cv_sets_changed,
               "on_conf_toggled" : self.conf_toggled,
               "on_subset_loc_changed" : self.on_subset_loc_changed,
               "on_cov_loc_changed" : self.on_cov_loc_changed,
               "on_alpha_changed" : self.on_alpha_changed,
               "on_rot_changed" : self.on_rot_changed,
               "on__toggled" : self.conf_toggled,
               "on_qval_changed" : self.on_qval_changed,
               "on_iter_changed" : self.on_iter_changed
               }
        
        self.wTree.signal_autoconnect(dic)

        # set/ensure valid default values/ranges
        #
        amax_sb = self.wTree.get_widget("amax_spinbutton")
        max_comp =  min(data[0].shape) # max num of components
        if self._options['amax']>max_comp:
            logger.log('debug', 'amax default too large ... adjusting')
            self._options['amax'] = max_comp
        amax_sb.get_adjustment().set_all(self._options['amax'], 1, max_comp, 1, 0, 0)
        # aopt spin button
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        if self._options['aopt']>self._options['amax']:
            self._options['aopt'] = self._options['amax'] + 1 - 1
        aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, self._options['amax'], 1, 0, 0)

        # scale
        # scale_cb = self.wTree.get_widget("scale_combobox")
        # scale_cb.set_active(0)

        # validation frames
        if self._options['calc_cv']==False:
            cv_frame = self.wTree.get_widget("cv_frame")
            cv_frame.set_sensitive(False)

        cv = self.wTree.get_widget("cv_method").set_active(0)

        # confidence
        if self._options['calc_conf']==True:
            self.wTree.get_widget("subset_expander").set_sensitive(True)
        else:
            self.wTree.get_widget("subset_expander").set_sensitive(False)

        cb = self.wTree.get_widget("subset_loc")
        _m = {'med': 0, 'mean': 1, 'full_model': 2}
        cb.set_active(_m.get(self._options['p_center']))

        cb = self.wTree.get_widget("cov_loc")
        _m = {'med': 0, 'mean': 1}
        cb.set_active(_m.get(self._options['cov_center']))

        hs = self.wTree.get_widget("alpha_scale")
        hs.set_value(self._options['alpha'])

        tb = self.wTree.get_widget("qvals")
        tb.set_sensitive(True)
        
                    
    def on_amax_changed(self, sb):
        logger.log("debug", "amax changed: new value: %s" %sb.get_value_as_int())
        amax = sb.get_value_as_int()
        # update aopt if needed
        if amax<self._options['aopt']:
            self._options['aopt'] = amax
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, amax, 1, 0, 0)
        self._options['amax'] = sb.get_value_as_int()
        
    def on_aopt_changed(self, sb):
        aopt = sb.get_value_as_int()
        self._options['aopt'] = aopt
        
    def auto_aopt_toggled(self, tb):
        aopt_sb = self.wTree.get_widget("aopt_spinbutton")
        if tb.get_active():
            self._options['auto_aopt'] = True
            aopt_sb.set_sensitive(False)
        else:
            self._options['auto_aopt'] = False
            aopt_sb.set_sensitive(True)

    def center_toggled(self, tb):
        if tb.get_active():
            self._options['center'] = True
        else:
            logger.log("debug", "centering set to False")
            self._options['center'] = False

    #def on_scale_changed(self, cb):
    #    scale = cb.get_active_text()
    #    if scale=='Scores':
    #        self._options['scale'] = 'scores'
    #    elif scale=='Loadings':
    #        self._options['scale'] = 'loads'
    #    else:
    #        raise IOError

    def val_toggled(self, tb):
        """Callback for validation: None. """
        cv_frame = self.wTree.get_widget("cv_frame")
        cv_tb = self.wTree.get_widget("cv_toggle")
        if tb.get_active():
            self._options['calc_cv'] = False
            cv_frame.set_sensitive(False)
            cv_tb.set_sensitive(False)
        else:
            cv_tb.set_sensitive(True)
            if cv_tb.get_active():
                cv_frame.set_sensitive(True)
                self._options['calc_cv'] = True

    def cv_toggled(self, tb):
        cv_frame = self.wTree.get_widget("cv_frame")
        val_tb = self.wTree.get_widget("val_none_toggle")
        if tb.get_active():
            cv_frame.set_sensitive(True)
            self._options['calc_cv'] = True
        else:
            cv_frame.set_sensitive(False)
            self._options['calc_cv'] = False

    def on_cv_method_changed(self, cb):
        method = cb.get_active_text()
        if method == 'Random':
            self._options['cv_val_method'] = 'random'

    def on_cv_sets_changed(self, sb):
        val = sb.get_value_as_int()
        self._options['cv_val_sets'] = val

    def conf_toggled(self, tb):
        if tb.get_active():
            self._options['calc_conf'] = False
            self.wTree.get_widget("subset_expander").set_sensitive(False)
        else:
            self._options['calc_conf'] = True
            self.wTree.get_widget("subset_expander").set_sensitive(True)

    def on_subset_loc_changed(self, cb):
        method = cb.get_active_text()
        if method=='Full model':
            self._options['p_center'] = 'full_model'
        elif method=='Median':
            self._options['p_center'] = 'med'
        elif method=='Mean':
            self._options['p_center'] = 'mean'

    def on_cov_loc_changed(self, cb):
        method = cb.get_active_text()
        if method=='Median':
            self._options['cov_center'] = 'med'
        elif method=='Mean':
            self._options['cov_center'] = 'mean'

    def on_alpha_changed(self, hs):
        self._options['alpha'] = hs.get_value()

    def on_rot_changed(self, rg):
        proc, strict = rg
        if proc.get_active():
            self._options['crot'] = True
        else:
            self._options['crot'] = True
            self._options['strict'] = True 

    def qval_toggled(self, tb):
        if tb.get_active():
            self._options['calc_qval'] = False
            print "Setting not sens"
            self.wTree.get_widget("qval_method").set_sensitive(False)
            self.wTree.get_widget("q_iter").set_sensitive(False)
        else:
            self._options['calc_qval'] = True
            self.wTree.get_widget("qval_method").set_sensitive(True)
            self.wTree.get_widget("q_iter").set_sensitive(True)

    def on_iter_changed(self, sb):
        self._options['q_iter'] = sb.get_value()

    def on_qval_changed(self, cb):
        q_method = cb.get_active_text()
        if method=='Shuffle rows':
            self._options['q_pert_method'] = 'shuffle'