Metadata-Version: 1.2
Name: Neuroharmony
Version: 0.0.1
Summary: A tool to perform Freesurfer volume Harminization in unseen scanner.
Home-page: https://github.com/garciadias/Neuroharmony
Author: Rafael Garcia-Dias
Author-email: rafaelagd@gmail.com
License: MIT license
Description: Neuroharmony: A tool for harmonizing volumetric MRI data from unseen scanners
        =============================================================================
        
        The model presented in `Garcia-Dias, et
        al. (2020) <https://www.sciencedirect.com/science/article/pii/S1053811920306133>`__.
        
        Install Neuroharmony.
        ---------------------
        
        -  Clone the repository:
        
        ::
        
           git clone https://github.com/garciadias/Neuroharmony.git
        
        -  Install:
        
        ::
        
           python setup.py install
        
        Example of use:
        ===============
        
        Pre-trained Neuroharmony model
        ------------------------------
        
        An example plot of how to load and apply pre-trained a Neuroharmony
        model.
        
        .. code:: python
        
           import matplotlib.pyplot as plt
           from neuroharmony.models.harmonization import fetch_trained_model, fetch_sample
           import seaborn as sns
        
           X = fetch_sample()
           neuroharmony = fetch_trained_model()
           x_harmonized = neuroharmony.transform(X)
        
           rois = ['Left-Hippocampus',
                   'lh_bankssts_volume',
                   'lh_posteriorcingulate_volume',
                   'lh_superiorfrontal_volume',
                   'rh_frontalpole_volume',
                   'rh_parsopercularis_volume',
                   'rh_parstriangularis_volume',
                   'rh_superiorfrontal_volume',
                   'Right-Cerebellum-White-Matter',
                   ]
           fig, axes = plt.subplots(3, 3, figsize=(10, 10))
           for roi, ax in zip(rois, axes.flatten()):
               ax.plot(neuroharmony.kde_data_[roi]['x'], neuroharmony.kde_data_[roi]['y'],
                       color='#fcb85b', ls='--', label='ComBat harmonized training set')
               sns.kdeplot(X[roi], color='#f47376', ls=':', legend=False, ax=ax, label='Original test set')
               sns.kdeplot(x_harmonized[roi], color='#00bcab', ls='-', legend=False, ax=ax, label='Harmonized test set')
               ax.set_xlabel(roi, fontsize=13)
           axes.flatten()[2].legend(ncol=3, bbox_to_anchor=(0.8, 1.175), fontsize=13)
           axes.flatten()[3].set_ylabel('Density', fontsize=15)
           plt.subplots_adjust(left=0.07, right=0.99,
                               bottom=0.05, top=0.96,
                               hspace=0.20, wspace=0.20)
           plt.savefig('test.png', dpi=200)
           plt.show()
        
Keywords: Harminization,MRI,data science
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
