pls options added
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parent
088f180b5d
commit
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@ -1,9 +1,10 @@
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"""This module contains bilinear models(Functions)
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"""
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import os
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import pygtk
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import gtk
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import gtk.glade
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import fluents
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from fluents.workflow import Function, OptionsDialog, Options
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from fluents.dataset import Dataset
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from fluents import plots, dataset, workflow, logger
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@ -75,6 +76,9 @@ class PCA(Model):
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jk_segments = pca_jkP(self.model['E0'], aopt, n_sets)
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Pcal = self.model['P'][:,:aopt]
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# add the scale to P
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tnorm = scipy.apply_along_axis(norm, 0, self.model['T'])
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Pcal = Pcal*tnorm
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tsq = hotelling(jk_segments, Pcal, p_center,
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cov_center, alpha, crot, strict)
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self.model['p_tsq'] = tsq
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@ -95,7 +99,7 @@ class PCA(Model):
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#####
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if do_lev_s:
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# sample leverages
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tnorm = scipy.apply_along_axis(norm, 0, dat['T']) # norm of T-columns
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tnorm = scipy.apply_along_axis(norm, 0, dat['T']) # norm of Ts
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s_lev = leverage(amax, tnorm)
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dat['s_lev'] = s_lev
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if do_lev_v:
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@ -191,13 +195,22 @@ class PLS(Model):
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Model.__init__(self, id, name)
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self._options = PlsOptions()
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def pre_validation(self, amax, n_sets, val_engine):
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def validation(self, amax, n_sets, cv_val_method):
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"""Returns rmsec,rmsep for model.
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"""
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rmsep, aopt = val_engine(self.model['E0'], self.model['F0'],
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amax, n_sets)
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self.model['rmsep'] = rmsep.mean(0)
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self.model['aopt'] = aopt
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m, n = self.model['E0'].shape
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if m>n:
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val_engine = w_pls_cv_val
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else:
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val_engine = pls_val
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if self._options['calc_cv']==True:
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rmsep, aopt = val_engine(self.model['E0'], self.model['F0'],
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amax, n_sets)
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self.model['rmsep'] = rmsep[:,:-1]
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self.model['aopt'] = aopt
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else:
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self.model['rmsep'] = None
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self.model['aopt'] = self._options['aopt']
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def confidence(self, aopt, n_sets, alpha, p_center,
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crot, strict, cov_center ):
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@ -205,21 +218,35 @@ class PLS(Model):
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Supported parameters: W
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"""
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aopt = self.model['aopt']
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jk_segments = pls_jkW(self.model['E0'], self.model['F0'],
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aopt, n_sets)
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Wcal = self.model['W'][:,:aopt]
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tsq = hotelling(jk_segments, Wcal, p_center,
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alpha, crot, strict, cov_center)
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self.model['w_tsq'] = tsq
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if self._options['calc_conf']:
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jk_segments = pls_jkW(self.model['E0'], self.model['F0'],
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aopt, n_sets)
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Wcal = self.model['W'][:,:aopt]
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# ensure that Wcal is scaled
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tnorm = scipy.apply_along_axis(norm, 0, self.model['T'][:,:aopt])
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Wcal = Wcal*tnorm
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tsq = hotelling(jk_segments, Wcal, p_center,
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alpha, crot, strict, cov_center)
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self.model['w_tsq'] = tsq
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else:
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self.model['w_tsq'] = None
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def permutation_confidence(self, a, b, aopt, reg, n_iter, algo,
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sim_method):
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"""Estimates sign. vars by controlling fdr."""
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"""Estimates cut off on significant vars by controlling fdr."""
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qvals_sorted, qvals = pls_qvals(a, b, aopt=None,
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alpha=.4, n_iter=20, algo='pls',
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sim_method='shuffle', )
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if self._options['calc_qvals']==True:
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qvals_sorted, qvals = pls_qvals(a, b,
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aopt=None,
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alpha=reg,
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n_iter=n_iter,
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algo='pls',
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sim_method=sim_method)
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self.model['qval'] = qvals
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self.model['qval_sorted'] = qvals_sorted
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else:
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self.model['qval'] = None
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self.model['qval_sorted'] = None
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def make_model(self, a, b, amax, scale, mode, engine):
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"""Make model on amax components.
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@ -243,7 +270,8 @@ class PLS(Model):
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# y vars
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ids_3 = [dim_name_3, DY.get_identifiers(dim_name_3, sorted=True)]
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# components (hidden)
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pc_ids = ['_comp', map(str, range(self.model['aopt']))]
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pc_ids = ['_comp', map(str, range(self._options['amax']))]
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pc_ids_opt = ['_comp', map(str, range(self.model['aopt']))]
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zero_dim = ['_doe',['0']] # null dim, vector (hidden)
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match_ids = {'E':[ids_0, ids_1],
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@ -257,11 +285,11 @@ class PLS(Model):
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'qval':[ids_1, zero_dim],
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'qval_sorted':[ids_1, zero_dim],
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'w_tsq':[ids_1, zero_dim],
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'rmsep':[pc_ids, zero_dim],
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'rmsep':[ids_3, pc_ids],
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}
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array = self.model[name]
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M = Dataset(array,identifiers=match_ids[name],name=name)
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M = Dataset(array, identifiers=match_ids[name], name=name)
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return M
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def get_out_plots(self, options):
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@ -274,19 +302,21 @@ class PLS(Model):
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return out
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def run_o(self, a, b):
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"""Run PLS with present options."""
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options = self._options
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self._dataset['X'] = a
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self._dataset['Y'] = b
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self._data['X'] = a.asarray()
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self._data['Y'] = b.asarray()
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if options['center']:
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self.model['E0'] = options['center_mth'](self._data['X'])
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self.model['F0'] = options['center_mth'](self._data['Y'])
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else:
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self.model['E0'] = self._data['X']
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self.model['F0'] = self._data['Y']
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self.pre_validation(**options.pre_validation_options())
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self.validation(**options.validation_options())
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self.make_model(self.model['E0'], self.model['F0'],
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**options.make_model_options())
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# variance captured
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@ -307,7 +337,7 @@ class PLS(Model):
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return out
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def run(self, a, b):
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"""Run Pls with option gui.
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"""Run PLS with option gui.
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"""
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dialog = PlsOptionsDialog([a, b], self._options)
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dialog.show_all()
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@ -317,8 +347,10 @@ class PLS(Model):
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if response == gtk.RESPONSE_OK:
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# set output data and plots
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dialog.set_output()
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#run with current data and options
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#run with current data and options
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for key, val in self._options.items():
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print (key, val)
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return self.run_o(a, b)
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class Packer:
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@ -342,7 +374,7 @@ class Packer:
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if axis == 1:
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self._array = self._array.T
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u,s,vt = svd(self._array,full_matrices=0)
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u, s, vt = svd(self._array,full_matrices=0)
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self._inflater = vt.T
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self._packed_data = u*s
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return self._packed_data
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@ -391,11 +423,12 @@ class PcaOptions(Options):
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('p_tsq', 't2', False),
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('rmsep', 'RMSEP', False)
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]
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# (class, name, sensitive, ticked)
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opt['all_plots'] = [(blmplots.PcaScorePlot, 'Scores', True),
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(blmplots.PcaLoadingPlot, 'Loadings', True),
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(blmplots.LineViewXc, 'Line view', True),
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(blmplots.PredictionErrorPlot, 'Residual Error', True)
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(blmplots.PredictionErrorPlot, 'Residual Error', False)
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]
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opt['out_data'] = ['T','P', 'p_tsq']
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@ -405,7 +438,7 @@ class PcaOptions(Options):
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def make_model_options(self):
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"""Options for make_model method."""
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opt_list = ['scale','mode', 'amax']
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opt_list = ['scale', 'mode', 'amax']
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return self._copy_from_list(opt_list)
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def confidence_options(self):
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@ -428,29 +461,29 @@ class PlsOptions(Options):
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Options.__init__(self)
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self._set_default()
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def _set_default(self):
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def _set_default(self):
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opt = {}
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opt['algo'] = 'pls'
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opt['engine'] = engines.pls
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opt['mode'] = 'normal' # how much info to calculate
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opt['lod'] = 'compact' # how much info to store
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opt['amax'] = 3
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opt['aopt'] = 3
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opt['amax'] = 10
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opt['aopt'] = 10
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opt['auto_aopt'] = False
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opt['center'] = True
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opt['center_mth'] = mat_center
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opt['scale'] = 'scores'
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opt['calc_conf'] = False
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opt['n_sets'] = 10
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opt['calc_conf'] = False
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opt['n_sets'] = 5
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opt['strict'] = True
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opt['p_center'] = 'med'
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opt['alpha'] = .2
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opt['alpha'] = .8
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opt['cov_center'] = 'med'
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opt['crot'] = True
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opt['calc_cv'] = True
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opt['calc_pert'] = False
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opt['val_engine'] = w_pls_cv_val
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opt['calc_cv'] = False
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opt['cv_val_method'] = 'random'
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opt['cv_val_sets'] = opt['n_sets']
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opt['all_data'] = [('T', 'scores', True),
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('P', 'loadings', True),
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@ -458,19 +491,23 @@ class PlsOptions(Options):
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('p_tsq', 't2', False),
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('rmsep', 'RMSEP', False)
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]
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# (class, name, sensitive, ticked)
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opt['all_plots'] = [(blmplots.PlsScorePlot, 'Scores', True),
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(blmplots.PlsLoadingPlot, 'Loadings', True),
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(blmplots.LineViewXc, 'Line view', True)]
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opt['out_plots'] = [blmplots.PlsScorePlot,
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blmplots.PlsLoadingPlot,
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blmplots.LineViewXc]
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(blmplots.LineViewXc, 'Line view', True),
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(blmplots.PredictionErrorPlot, 'Residual Error', False),
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(blmplots.RMSEPPlot, 'RMSEP', False)
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]
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opt['out_data'] = ['T','P', 'p_tsq']
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opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
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opt['out_data'] = None
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opt['pack'] = False
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opt['calc_qvals'] = False
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opt['q_pert_mth'] = 'shuffle_vars'
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opt['q_pert_method'] = 'shuffle_rows'
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opt['q_iter'] = 20
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self.update(opt)
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@ -485,20 +522,23 @@ class PlsOptions(Options):
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opt_list = ['n_sets', 'aopt', 'alpha', 'p_center',
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'strict', 'crot', 'cov_center']
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return self._copy_from_list(opt_list)
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def pre_validation_options(self):
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def validation_options(self):
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"""Options for pre_validation method."""
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opt_list = ['amax', 'n_sets', 'val_engine']
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return self._copy_from_list(opt_list)
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opt_list = ['amax', 'n_sets', 'cv_val_method']
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return self._copy_from_list(opt_list)
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def permutation_confidence(self):
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opt_list = ['q_pert_method', 'q_iter']
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return self._copy_from_list(opt_list)
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class PcaOptionsDialog(OptionsDialog):
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"""Options dialog for Principal Component Analysis.
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"""
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def __init__(self, data, options, input_names=['X']):
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OptionsDialog.__init__(self, data, options, input_names)
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glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
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#glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
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glade_file = os.path.join("/home/flatberg/fluents/fluents/", 'pca_options.glade')
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notebook_name = "vbox1"
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page_name = "Options"
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@ -508,19 +548,24 @@ class PcaOptionsDialog(OptionsDialog):
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"on_aopt_value_changed" : self.on_aopt_changed,
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"auto_aopt_toggled" : self.auto_aopt_toggled,
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"center_toggled" : self.center_toggled,
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"on_scale_changed" : self.on_scale_changed,
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#"on_scale_changed" : self.on_scale_changed,
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"on_val_none" : self.val_toggled,
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"on_val_cv" : self.cv_toggled,
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"on_val_pert" : self.pert_toggled,
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"on_cv_method_changed" : self.on_cv_method_changed,
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"on_cv_sets_changed" : self.on_cv_sets_changed,
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"on_pert_sets_changed" : self.on_pert_sets_changed,
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"on_conf_toggled" : self.on_conf_toggled
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"on_conf_toggled" : self.on_conf_toggled,
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"on_subset_loc_changed" : self.on_subset_loc_changed,
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"on_cov_loc_changed" : self.on_cov_loc_changed,
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"on_alpha_changed" : self.on_alpha_changed,
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"on_rot_changed" : self.on_rot_changed
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}
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self.wTree.signal_autoconnect(dic)
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# set/ensure valid default values/ranges
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#
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amax_sb = self.wTree.get_widget("amax_spinbutton")
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max_comp = min(data[0].shape) # max num of components
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if self._options['amax']>max_comp:
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@ -534,8 +579,8 @@ class PcaOptionsDialog(OptionsDialog):
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aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, self._options['amax'], 1, 0, 0)
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# scale
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scale_cb = self.wTree.get_widget("scale_combobox")
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scale_cb.set_active(0)
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# scale_cb = self.wTree.get_widget("scale_combobox")
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# scale_cb.set_active(0)
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# validation frames
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if self._options['calc_cv']==False:
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@ -550,11 +595,22 @@ class PcaOptionsDialog(OptionsDialog):
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# confidence
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if self._options['calc_conf']==True:
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self.wTree.get_widget("subset_frame").set_sensitive(True)
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self.wTree.get_widget("subset_expander").set_sensitive(True)
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else:
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self.wTree.get_widget("subset_frame").set_sensitive(False)
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self.wTree.get_widget("subset_expander").set_sensitive(False)
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cb = self.wTree.get_widget("subset_loc")
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_m = {'med': 0, 'mean': 1, 'full_model': 2}
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cb.set_active(_m.get(self._options['p_center']))
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cb = self.wTree.get_widget("cov_loc")
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_m = {'med': 0, 'mean': 1}
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cb.set_active(_m.get(self._options['cov_center']))
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hs = self.wTree.get_widget("alpha_scale")
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hs.set_value(self._options['alpha'])
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def on_amax_changed(self, sb):
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logger.log("debug", "amax changed: new value: %s" %sb.get_value_as_int())
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amax = sb.get_value_as_int()
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@ -585,14 +641,14 @@ class PcaOptionsDialog(OptionsDialog):
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logger.log("debug", "centering set to False")
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self._options['center'] = False
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def on_scale_changed(self, cb):
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scale = cb.get_active_text()
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if scale=='Scores':
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self._options['scale'] = 'scores'
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elif scale=='Loadings':
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self._options['scale'] = 'loads'
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else:
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raise IOError
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#def on_scale_changed(self, cb):
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# scale = cb.get_active_text()
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# if scale=='Scores':
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# self._options['scale'] = 'scores'
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# elif scale=='Loadings':
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# self._options['scale'] = 'loads'
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# else:
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# raise IOError
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def val_toggled(self, tb):
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"""Callback for validation: None. """
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@ -657,17 +713,242 @@ class PcaOptionsDialog(OptionsDialog):
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def on_conf_toggled(self, tb):
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if tb.get_active():
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self._options['calc_conf'] = False
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self.wTree.get_widget("subset_frame").set_sensitive(False)
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self.wTree.get_widget("subset_expander").set_sensitive(False)
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else:
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self._options['calc_conf'] = True
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self.wTree.get_widget("subset_frame").set_sensitive(True)
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self.wTree.get_widget("subset_expander").set_sensitive(True)
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def on_subset_loc_changed(self, cb):
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method = cb.get_active_text()
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if method=='Full model':
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self._options['p_center'] = 'full_model'
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elif method=='Median':
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self._options['p_center'] = 'med'
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elif method=='Mean':
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self._options['p_center'] = 'mean'
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def on_cov_loc_changed(self, cb):
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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.
|
||||
"""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'
|
||||
|
||||
|
||||
|
|
|
|||
Reference in New Issue