Metadata-Version: 1.1
Name: brainSimulator
Version: 0.5.4
Summary: Nuclear brain imaging synthesis with python
Home-page: https://github.com/SiPBA/brainSimulator
Author: SIPBA@UGR
Author-email: sipba@ugr.es
License: GPL-3.0+
Download-URL: https://github.com/SiPBA/brainSimulator/archive/0.5.1.tar.gz
Description-Content-Type: UNKNOWN
Description: brainSimulator
        ==============
        
        |DOI|
        
        Functional brain image synthesis using the KDE or MVN distribution.
        Currently in beta. Python code. Find the documentation at
        http://brainsimulator.readthedocs.io/
        
        ``brainSimulator`` is a brain image synthesis procedure intended to
        generate a new image set that share characteristics with an original
        one. The system focuses on nuclear imaging modalities such as PET or
        SPECT brain images. It analyses the dataset by applying PCA to the
        original dataset, and then model the distribution of samples in the
        projected eigenbrain space using a Probability Density Function (PDF)
        estimator. Once the model has been built, anyone can generate new
        coordinates on the eigenbrain space belonging to the same class, which
        can be then projected back to the image space.
        
        Use
        ---
        
        With the new version, the whole interface has been switched to an
        object. This allows to train the model once and then perform as many
        sample drawings as required.
        
        .. code:: python
        
            #navigate to the folder where simulator.py is located
            import brainSimulator as sim
        
            simulator = sim.BrainSimulator(algorithm='PCA', method='mvnormal')
            simulator.fit(original_dataset, labels) 
            images, classes = simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
        
        Cite
        ----
        
        F.J. Martinez-Murcia et al (2017). “Functional Brain Imaging Synthesis
        Based on Image Decomposition and Kernel Modelling: Application to
        Neurodegenerative Diseases.” Frontiers in neuroinformatics (online).
        DOI: 10.3389/fninf.2017.00065
        
        Safeguards
        ----------
        
        As in the paper, it is best to use MVN modelling, but it is fundamental
        to test the number of components (L) used in the modelling, otherwise it
        will lead to overfitting. The KDE modelling works better \`out of the
        box’, but the results may be more disperse.
        
        License
        -------
        
        This code is released under the license
        `GPL-3.0+ <https://choosealicense.com/licenses/gpl-3.0/>`__.
        
        .. |DOI| image:: https://zenodo.org/badge/85931767.svg
           :target: https://zenodo.org/badge/latestdoi/85931767
        
Keywords: brain,image,synthesis,PCA,neuroimaging,ICA
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
