subj = 'SEEG76'
subj_dir = 'd:\\\\CloudEpi\\Subjects\\\\'

from IPython.display import display, Markdown, Latex, Image, HTML
# %autosave 0
%matplotlib inline

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
''')

from pyepi.tools import inout, plots
from time import time
import pandas as pd
from surfer import Brain
import os
import datetime
tstart = time()

def Images(images, header=None, width="100%"): # to match Image syntax
    if type(width)==type(1): width = "{}px".format(width)
    html = ["<table style='width:{}; border: 0px solid black;'><tr>".format(width)]
    if header is not None:
        html += ["<th align='center'>{}</th>".format(h) for h in header] + ["</tr><tr>"]

    for image in images:
        html.append("<td><img src='{}' /></td>".format(image))
    html.append("</tr></table>")
    display(HTML(''.join(html)))

display(Markdown('##' + subj ))

SEEG76

Implantation scheme

3D rendering of the brain's pial surface with SEEG electrodes.

from mayavi import mlab
mlab.init_notebook('png',600,600, local=False)

Notebook initialized with png backend.

coords = pd.read_excel(os.path.join(subj_dir, subj, 'Contact_coordinates.xlsx'))
brain = plots.implantation_scheme(subj, subj_dir, fig_size=(500, 500), brain_alpha=0.3,electrode_label_size=3)
view_orientations=plots.get_views(coords)

list_of_labels =['COR',
                 'SAG',
                 'AX' ]
# list_of_views = [os.path.join(subj_dir, subj, 'reports', 'implantation_scheme_' + label+ '.png') 
#                  for label in list_of_labels]
list_of_views = ['implantation_scheme_' + label+ '.png' 
                 for label in list_of_labels]
for v in range(0,len(view_orientations)):
    brain.show_view(view_orientations[v])
    img = brain.save_image(filename=list_of_views[v])
    
# img = brain.save_montage(os.path.join(subj_dir, subj, 'reports', 'implantation_scheme.png'),
#                          order = view_orientations, border_size=0)

# Image(os.path.join(subj_dir, subj, 'reports', 'implantation_scheme.png'))

# Images(list_of_views, header=list_of_labels, width="100%")
Images(list_of_views, header=None, width="100%")

Effective Connectivity inferred by single pulse electrical stimulation

Outbound connectivity

try:
    brain, circle, spes = plots.spes_responses_by_contacts(subj, subj_dir, fig_size=(500, 500),
                                                       flow='outbound', max_linewidth=10,
                                                       hide_buttons=True)
except:
    print("""
        The connectome can not be generated at this time. 
        Possible causes include missing SPES.xls file, contact name mismatch, 
        incorrect split of intracranial and scalp data, etc. 
        Check the raw data try again.""")
    

Inbound connectivity

try:
    brain2, circle2, spes2 = plots.spes_responses_by_contacts(subj, subj_dir, fig_size=(500, 500),
                                                       flow='inbound', max_linewidth=10,
                                                       hide_buttons=True)
except:
    print("""
        The connectome can not be generated at this time. 
        Possible causes include missing SPES.xls file, contact name mismatch, 
        incorrect split of intracranial and scalp data, etc. 
        Check the raw data try again.""")
    

current_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print('Last update: ' + current_date + '. Took ' + str(time()-tstart) + ' seconds')

Last update: 2018-11-11 19:58:00. Took 30.225134134292603 seconds