<jupyter_start><jupyter_text>intra subject  Training set - interactive viewer<jupyter_code>hdf5_file = load_r2_intra(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
r2_training = mean_r2(hdf5_file['training'], ref_img=mask_img)
plotting.view_img(r2_training, threshold=0.1, vmax=1)<jupyter_output><empty_output><jupyter_text>Training set - static viewer (all subjects)<jupyter_code>n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_intra(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
    r2_training = mean_r2(hdf5_file['training'], ref_img=mask_img)
    plotting.plot_stat_map(r2_training, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, colorbar=False)<jupyter_output><empty_output><jupyter_text>Validation set - interactive viewer<jupyter_code>hdf5_file = load_r2_intra(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
r2_training = mean_r2(hdf5_file['validation'], ref_img=mask_img)
plotting.view_img(r2_training, threshold=0.1, vmax=1)
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_intra(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
    r2_training = mean_r2(hdf5_file['validation'], ref_img=mask_img)
    plotting.plot_stat_map(r2_training, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, colorbar=False)<jupyter_output><empty_output><jupyter_text>Inter-subject  interactive viewer<jupyter_code>hdf5_file = load_r2_inter(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
r2_inter = mean_inter_r2(hdf5_file['inter'], ref_img=mask_img)
plotting.view_img(r2_inter, threshold=0.1, vmax=1)<jupyter_output><empty_output><jupyter_text>static viewer (all subjects)<jupyter_code>n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_inter(subject=subject, root_data=root_data, fwhm=fwhm, cluster=cluster, state=state)
    r2_inter = mean_inter_r2(hdf5_file['inter'], ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, colorbar=False)<jupyter_output><empty_output><jupyter_text>Group atlases  Schaefer<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'schaefer'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
    r2_inter = mean_inter_r2(hdf5_file, ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output><jupyter_text>MIST444<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'mist444'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
    r2_inter = mean_inter_r2(hdf5_file, ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output><jupyter_text>MIST197<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'mist197'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
    r2_inter = mean_inter_r2(hdf5_file, ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output><jupyter_text>Smith<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'smith'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
    r2_inter = mean_inter_r2(hdf5_file, ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output><jupyter_text>DIFUMO 1024 fwhm-5<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'difumo1024'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
    r2_inter = mean_inter_r2(hdf5_file, ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output><jupyter_text>DIFUMO 512 fwhm-5<jupyter_code>#list_atlas = ['mist197', 'mist444', 'schaefer', 'smith']
atlas = 'difumo512'
n_comp = 6
width_fig = 10
fig = plt.figure(figsize=(width_fig, n_comp*3))
hdf5_file = load_r2_other(atlas=atlas, root_data=root_data, fwhm=fwhm)
for ind_subject in range(1,7):
    subject = f'sub-0{ind_subject}'
    print(subject)
    r2_inter = mean_r2(hdf5_file[subject], ref_img=mask_img)
    plotting.plot_stat_map(r2_inter, cut_coords=[2, -30, 15], display_mode='ortho', 
        axes=plt.subplot(n_comp, 1, ind_subject), threshold=0.1, vmax=1, title=subject, 
        colorbar=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.139502973014179, 'fraction_numerical': 0.04406159475529806, 'mean_word_length': 6.7176470588235295, 'pattern_counts': {'":': 0, '<': 45, '<?xml version=': 0, '>': 45, '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': '11', 'dir': 'jupyter-structured-clean-dedup', 'id': '330682', 'license': 'MIT', 'n_tokens_mistral': 2861, 'n_tokens_neox': 2704, 'n_words': 395, 'path': 'notebooks/fig_r2_friends-s01_maps.ipynb', 'repo_name': 'courtois-neuromod/cneuromod_embeddings', 'size': '6559'}