Widget 3D Head
Source code: https://github.com/the-virtual-brain/tvb-widgets
This is part of a Pypi release: https://pypi.org/project/tvb-widgets/
tvb-widgets is also already installed in the official image released for EBRAINS lab, where you can test it directly.
Purpose
It is a Jupyter widget intended for visualization of the 3D Head data available for a patient:
- surfaces of different types (cortex, face, skull, etc)
- connectivity region centers
- sensors locations (SEEG, MEG, EEG)
Inputs
It supports the above data in the form of their corresponding TVB datatypes:
- Surface (CorticalSurface, FaceSurface, etc)
- Connectivity
- Sensors (SensorsInternal, SensorsMEG, SensorsEEG)
Installation
pip install tvb-widgets
API usage
We need to first import the widget API from tvbwidgets package, together with the TVB API and the display function:
import tvbwidgets.api as api
from tvb.simulator.lab import *
from IPython.core.display_functions import display
Then, there are 2 options to work with the widget:
- Use a file browser to load the data and automatically display it
- Use directly the API to load the data and display it
For the first option, you have to run the following 2 lines of code in a notebook cell and then just use the UI controls:
widget = api.HeadBrowser()
display(widget)
For the second option, the API is described below:
In a new cell, we instantiate the HeadWidget and a FaceSurface datatype that we want to visualize. Using the add_datatype method we add the surface to our widget and display the widget:
widget = api.HeadWidget()
face = surfaces.FaceSurface().from_file()
face.configure()
widget.add_datatype(face)
display(widget)
Next, we can continue adding other datatypes to this widget, by calling add_datatype multiple times. A maximum of 10 datatypes are supported by this widget.
The Config object can be used to tweak the display options for each datatype.
In the code below, we add a Connectivity and SEEG Sensors:
conn = connectivity.Connectivity().from_file()
conn.configure()
widget.add_datatype(conn)
seeg = sensors.SensorsInternal().from_file()
seeg.configure()
widget.add_datatype(seeg, api.HeadWidgetConfig(name='SEEG'))
We can also provide a RegionMapping to be used as colormap for a surface:
reg_map = region_mapping.RegionMapping.from_file()
config = api.HeadWidgetConfig(name='Cortex')
config.add_region_mapping_as_cmap(reg_map)
cortex = surfaces.CorticalSurface().from_file()
cortex.configure()
widget = api.HeadWidget()
widget.add_datatype(cortex, config)
display(widget)
