I tried to install PySurfer and followed the steps given on the GitHub page. After installation, I just ran the demo code for displaying the aparac.2009 parcellations. I just get a blank screen with a colorbar. The parcellations for gyri and sulci are not displayed. I tried to run the code in the Jupyter notebook and also in the python terminal, but I get the same issue. I have set the environment variable SUBJECTS_DIR as well.
Please find the below code and attached screenshot of the output:
import os
import numpy as np
import nibabel as nib
from surfer import Brain
"""
Make a vector containing the data point at each vertex.
"""
vtx_data = roi_data[labels]
"""
Handle vertices that are not defined in the annotation.
"""
vtx_data[labels == -1] = -1
"""
Display these values on the brain. Use a sequential colormap (assuming
these data move from low to high values), and add an alpha channel so the
underlying anatomy is visible.
"""
brain.add_data(vtx_data, .5, .75, thresh=0, colormap="rocket", alpha=.8)
Hello Team,
I tried to install PySurfer and followed the steps given on the GitHub page. After installation, I just ran the demo code for displaying the aparac.2009 parcellations. I just get a blank screen with a colorbar. The parcellations for gyri and sulci are not displayed. I tried to run the code in the Jupyter notebook and also in the python terminal, but I get the same issue. I have set the environment variable SUBJECTS_DIR as well.
Please find the below code and attached screenshot of the output:
import os import numpy as np import nibabel as nib from surfer import Brain
print(doc)
subject_id = "fsaverage" hemi = "lh" surf = "inflated" os.environ["SUBJECTS_DIR"] = "/home/ubuntu/freesurfer/subjects"
""" Bring up the visualization. """ brain = Brain(subject_id, hemi, surf, background="white")
""" Read in the automatic parcellation of sulci and gyri. """ aparc_file = os.path.join(os.environ["SUBJECTS_DIR"], subject_id, "label", hemi + ".aparc.a2009s.annot") labels, ctab, names = nib.freesurfer.read_annot(aparc_file)
""" Make a random vector of scalar data corresponding to a value for each region in the parcellation.
""" rs = np.random.RandomState(4) roi_data = rs.uniform(.5, .8, size=len(names))
""" Make a vector containing the data point at each vertex. """ vtx_data = roi_data[labels]
""" Handle vertices that are not defined in the annotation. """ vtx_data[labels == -1] = -1
""" Display these values on the brain. Use a sequential colormap (assuming these data move from low to high values), and add an alpha channel so the underlying anatomy is visible. """ brain.add_data(vtx_data, .5, .75, thresh=0, colormap="rocket", alpha=.8)
Can someone direct me on resolving this issue?
Thank you, Leema