<jupyter_start><jupyter_text>Plot coordinates on brain<jupyter_code>coords = dict(green = [[-15,-66,54], #Hutchinson 2014 SPL (task>specific)
                      [-9,-72,57], #Hutchinson 2015 (decision uncertainty)
                      [-11,-57,52], #Fleck 2006 (low>high conf)
                      ],
                red = [[-30,-54,39], #Hutchinson 2014 (item only > cr)
                       [-39,-54,45], #Hutchinson 2015 (linear memory strength)
                       [-39,-55,36], #Wheeler/Buckner 2004 (K>N)
                       [-38,-76,28], #Daselaar 2006 (increase famil)
                       [-33,-60,36], #Yonelinas 2005 (increase famil)
                       [-3,-76,45], #Montaldi 2006 (increase famil)
                       [-39,-47,47], #Montaldi 2006 (increase famil)],
                       [-37,-59,42], #Sharot 2004 (K>N)
                       [-36,-80,33], #Sharot 2004 (K>N)
                       ],
                yellow = [[-39,-63,39], #Hutchinson 2014 (source>item)
                          [-50,-41,25], #Eldridge etal 2000 (R>K)
                          [-30,-79,43], #Fenker etal 2005 (R>K)
                          [-42,-72,39], #Henson etal 1999 (R>K)
                          [-43,-67,40], #Wheeler/Buckner 2004 (R>K)
                          [-53,-25,18], #Yonelinas 2005 (R>K)
                          [-42,-68,31], #Montaldi 2006 (R>K)
                          [-10,-68,29], #Sharot 2004 (R>K, R>N)
                          [-52,-59,24], #Sharot 2004 (R>K, R>N)
                         ],
                orange = [[-39,-30,45], #Vilberg/Rugg 2007 (famil (K>M, exclusive R>K))
                          [-30,-63,45]]) #Vilberg/Rugg 2007 (famil (K>M, exclusive R>K))])
hemi='lh'
surf='semi7'

b = Brain(subject_id, hemi, surf, background='white')

for color in coords.keys():
    for coord in coords[color]:
        b.add_foci(coord, map_surface="white", 
                   color=color, alpha=.8, scale_factor=.5)
    
b.show_view('parietal')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16535433070866143, 'fraction_numerical': 0.11368110236220473, 'mean_word_length': 1.4582829504232164, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 26, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '564181', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 843, 'n_tokens_neox': 652, 'n_words': 143, 'path': 'notebooks/.ipynb_checkpoints/Visualize results on surface-checkpoint.ipynb', 'repo_name': 'sgagnon/felix', 'size': '2032'}