def get_meg_data(per_condition=True):
time_range = range(_addon().get_max_time_steps())
brain_obj = bpy.data.objects['Brain']
if per_condition:
meg_data, meg_colors = OrderedDict(), OrderedDict()
rois_objs = bpy.data.objects['Cortex-lh'].children + bpy.data.objects['Cortex-rh'].children
for roi_obj in rois_objs:
if roi_obj.animation_data:
meg_data_roi, meg_colors_roi = mu.evaluate_fcurves(roi_obj, time_range)
meg_data.update(meg_data_roi)
meg_colors.update(meg_colors_roi)
else:
meg_data, meg_colors = mu.evaluate_fcurves(brain_obj, time_range)
return meg_data, meg_colors
def get_meg_data(per_condition=True):
time_range = range(PlayPanel.addon.get_max_time_steps())
brain_obj = bpy.data.objects['Brain']
if per_condition:
meg_data, meg_colors = OrderedDict(), OrderedDict()
rois_objs = bpy.data.objects['Cortex-lh'].children + bpy.data.objects['Cortex-rh'].children
for roi_obj in rois_objs:
if roi_obj.animation_data:
meg_data_roi, meg_colors_roi = mu.evaluate_fcurves(roi_obj, time_range)
meg_data.update(meg_data_roi)
meg_colors.update(meg_colors_roi)
else:
meg_data, meg_colors = mu.evaluate_fcurves(brain_obj, time_range)
return meg_data, meg_colors
def get_connectivity_data():
time_range = range(_addon().get_max_time_steps())
parent_name = _addon().get_connections_parent_name()
parent_obj = bpy.data.objects.get(parent_name)
if not parent_obj is None:
return mu.evaluate_fcurves(parent_obj, time_range)
else:
return None, None
def capture_graph_data(per_condition):
parent_obj = bpy.data.objects.get(PARENT_OBJ, None)
if parent_obj:
time_range = range(0, ConnectionsPanel.addon.get_max_time_steps(), bpy.context.scene.play_dt)
if per_condition:
#todo: implement
pass
else:
data, colors = mu.evaluate_fcurves(parent_obj, time_range)
return data, colors
else:
return {}, {}
def get_electrodes_data(per_condition=True):
if bpy.context.scene.selection_type == 'spec_cond' and bpy.context.scene.conditions_selection == '':
print('You must choose the condition first!')
return None, None
elecs_data, elecs_colors = OrderedDict(), OrderedDict()
time_range = range(_addon().get_max_time_steps())
if per_condition:
for obj_name in PlayPanel.electrodes_names:
if bpy.data.objects.get(obj_name) is None:
continue
elec_obj = bpy.data.objects[obj_name]
if elec_obj.hide or elec_obj.animation_data is None:
continue
curr_cond = bpy.context.scene.conditions_selection if \
bpy.context.scene.selection_type == 'spec_cond' else None
data, colors = mu.evaluate_fcurves(elec_obj, time_range, curr_cond)
elecs_data.update(data)
elecs_colors.update(colors)
else:
parent_obj = bpy.data.objects['Deep_electrodes']
elecs_data, elecs_colors = mu.evaluate_fcurves(parent_obj, time_range)
return elecs_data, elecs_colors
def get_electrodes_data(per_condition=True):
if bpy.context.scene.selection_type == 'spec_cond' and bpy.context.scene.conditions_selection == '':
print('You must choose the condition first!')
return None, None
elecs_data, elecs_colors = OrderedDict(), OrderedDict()
time_range = range(PlayPanel.addon.get_max_time_steps())
if per_condition:
for obj_name in PlayPanel.electrodes_names:
if bpy.data.objects.get(obj_name) is None:
continue
elec_obj = bpy.data.objects[obj_name]
if elec_obj.hide or elec_obj.animation_data is None:
continue
curr_cond = bpy.context.scene.conditions_selection if \
bpy.context.scene.selection_type == 'spec_cond' else None
data, colors = mu.evaluate_fcurves(elec_obj, time_range, curr_cond)
elecs_data.update(data)
elecs_colors.update(colors)
else:
parent_obj = bpy.data.objects['Deep_electrodes']
elecs_data, elecs_colors = mu.evaluate_fcurves(parent_obj, time_range)
return elecs_data, elecs_colors
def capture_graph_data(per_condition):
parent_obj = bpy.data.objects.get(PARENT_OBJ, None)
if parent_obj:
time_range = range(0, ConnectionsPanel.addon.get_max_time_steps(),
bpy.context.scene.play_dt)
if per_condition:
#todo: implement
pass
else:
data, colors = mu.evaluate_fcurves(parent_obj, time_range)
return data, colors
else:
return {}, {}
def get_fmri_data():
time_range = range(_addon().get_max_time_steps())
brain_obj = bpy.data.objects['fMRI']
data, colors = mu.evaluate_fcurves(brain_obj, time_range)
return data, colors