class BaseMachineLearningView(BaseTaskView): title = "Machine Learning View" def create_widgets(self): self.input_models = [] self.input_models.append( FileInputModel("BOLD/fMRI File", "The BOLD data for the story", [('NIFTI files', '*.nii;*nii.gz'), ('HDR files', '*.hdr'), ('All files', '*')], file_handler.open_nifti)) self.input_models.append( FileInputModel("Mask", "The mask to remove useless brain data", [('HDR files', '*.hdr'), ('NIFTI files', '*.nii;*nii.gz'), ('All files', '*')], file_handler.open_nifti)) self.input_models.append( FileInputModel("Experiment Metadata", "The story data points", [('MATLAB files', '*.mat'), ('All files', '*')], file_handler.open_matlab)) self.input_models.append( FileInputModel("Target Trajectory", "The story data points", [('PICKLE files', '*.pkl'), ('All files', '*')], file_handler.open_pickle)) super(BaseMachineLearningView, self).create_widgets() self.result_view = GraphPlotView(self) self.result_view.grid(row = 0, column = 1, rowspan = 3, columnspan = 1, sticky = W+E+N+S) def update_ui_from_processing(self, *args): self.status_text.set(self.processing_model.progress.get() + " [" + self.processing_model.state.get() + "]") self.update_ui() if self.processing_model.state.get() == self.processing_model.FINISHED: self.is_processing_active = False accuracy = "%.2f" % self.processing_model.accuracy self.status_text.set(self.processing_model.progress.get() + " [Accuracy: " + accuracy + "%]") self.display_results() #enable the button to stop multiple self.check_processing_possible() def display_results(self): self.result_view.plot_results(self.processing_model.original, self.processing_model.result)
def create_widgets(self): self.input_models = [] self.input_models.append( FileInputModel("BOLD/fMRI File", "The BOLD data for the story", [('NIFTI files', '*.nii;*nii.gz'), ('HDR files', '*.hdr'), ('All files', '*')], file_handler.open_nifti)) self.input_models.append( FileInputModel("Mask", "The mask to remove useless brain data", [('HDR files', '*.hdr'), ('NIFTI files', '*.nii;*nii.gz'), ('All files', '*')], file_handler.open_nifti)) self.input_models.append( FileInputModel("Experiment Metadata", "The story data points", [('MATLAB files', '*.mat'), ('All files', '*')], file_handler.open_matlab)) self.input_models.append( FileInputModel("Target Trajectory", "The story data points", [('PICKLE files', '*.pkl'), ('All files', '*')], file_handler.open_pickle)) super(BaseMachineLearningView, self).create_widgets() self.result_view = GraphPlotView(self) self.result_view.grid(row = 0, column = 1, rowspan = 3, columnspan = 1, sticky = W+E+N+S)