def create_widgets(self):
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("Anatomy", "The story data points", [('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(CorrelationTaskView, self).create_widgets()
self.result_view = CorrelationGraphView(self)
self.result_view.grid(row = 0, column = 1, rowspan = 3, columnspan = 1, sticky = W+E+N+S)
class CorrelationTaskView(BaseTaskView):
title = "Correlation"
def create_widgets(self):
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("Anatomy", "The story data points", [('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(CorrelationTaskView, self).create_widgets()
self.result_view = CorrelationGraphView(self)
self.result_view.grid(row = 0, column = 1, rowspan = 3, columnspan = 1, sticky = W+E+N+S)
def start_processing(self):
super(CorrelationTaskView, self).start_processing()
CorrelationTaskProcessor(self.queue, self.processing_model).start()
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
self.display_results()
#enable the button to stop multiple
self.check_processing_possible()
def display_results(self):
#load the high-res anatomy - this needs to be downsampled to fit the resolution of our data
original_anatomy = nibabel.load(self.processing_model.processing_request_model.anatomy_location)
#we will use the first volumne/sample from the bold data to
bold_data_sample = image.index_img(self.processing_model.processing_request_model.bold_location, 0)
#resample the original anatomy to meet the bold data
resampled_anatomy = resample_img(original_anatomy, target_affine = bold_data_sample.get_affine(), target_shape=bold_data_sample.shape)
mri_args = {
'background' : resampled_anatomy,
'cmap_bg' : 'gray',
'cmap_overlay' : 'PiYG', # YlOrRd_r # pl.cm.autumn
'interactive' : cfg.getboolean('examples', 'interactive', True),
}
self.result_view.plot_results(self.processing_model.result, mri_args)