Metadata-Version: 2.1
Name: nmjanalyzer
Version: 0.0.2
Summary: NMJ Analyzer
Home-page: https://github.com/csudre/NMJ_Analyser
Author: Carole Sudre
Author-email: c.sudre@ucl.ac.uk
License: UNKNOWN
Description: # NMJ_Analyser
        The code was created using python 3.7 and the following version of packages
        ## Requirements
         - scipy 1.5.3
         - Pillow 7.2.0
         - numpy 1.19.1
         - pandas 1.1.0
         - nibabel 3.1.1
         
        In addition, the following modules are imported
         - glob
         - os
         - argparse
         - sys
        
        Performs analysis of NMJ data 
        
        ## Usage
        
        The NMJ Analyser takes as input directories where jpg files have been stored 
        
        For each subject, the input is presented as 
        SUBJ/JPEG/*jpg
        The ..... files must contain the keyword red or RED
        The ..... files must contain the keyword green or GREEN
        The slices should ordered numerically ex Mouse1_GREEN_0001.jpg.... Mouse1_GREEN_0010.jpg
        
        The following parameters are given to the system:
         - -p regular expression of the subject path
         - -dx planar resolution
         - -dz slice thickness
         - -t threshold for voxels to be considered as positives
         
        ## Output
         For each subject the following parameters are calculated for each RED connected component and intersection of GREEN on RED component
         
        ### RegionProperties
         - 'centre of mass': (self.centre_of_mass, ['CoMx',
                                                             'CoMy',
                                                             'CoMz']),
         - 'centre_abs': (self.centre_abs, ['Truex, Truey, Truez']),
         - 'volume': (self.volume,
                               ['NVoxels', 'NVolume']),
         - 'fragmentation': (self.fragmentation, ['Fragmentation']),
         - 'mean_intensity': (self.mean_int, ['MeanIntensity']),
         - 'surface': (self.surface, ['NSurface', 'Nfaces_surf',
                                               'NSurf_ext', 'Nfaces_ext']),
         - 'surface_dil': (self.surface_dil, ['surf_dil', 'surf_ero']),
         - 'surface volume ratio': (self.sav, ['sav_dil', 'sav_ero']),
         - 'compactness': (self.compactness, ['CompactNumbDil'
                                                       ]),
         - 'eigen': (self.eigen, ['eigenvalues']),
         - 'std': (self.std_values, ['std']),
         - 'quantiles': (self.quantile_values, ['quantiles']),
         - 'bounds': (self.bounds, ['bounds']),
         - 'cc': (self.connect_cc, ['N_CC']),
         - 'cc_dist': (self.dist_cc, ['MeanDistCC']),
         - 'cc_size': (self.cc_size, ['MinSize', 'MaxSize', 'MeanSize']),
         - 'max_extent': (self.max_extent, ['MaxExtent']),
         - 'shape_factor': (self.shape_factor, ['ShapeFactor',
                                                         'shapefactor_surfcount']),
         - 'skeleton_length': (self.skeleton_length, ['SkeletonLength'])
         
        ### Comparison Properties
         - 'green volume': (self.n_pos_ref, 'Volume_(Green)'),
         - 'red volume': (self.n_pos_seg, 'Volume_(Red)'),
         - 'n_intersection': (self.n_intersection, 'Intersection'),
         - 'n_union': (self.n_union, 'Union'),
         - 'IoU': Intersection of union
         - 'coverage': Overlap        
         - 'vol_diff': Volume difference
         - 'ave_dist': Average distance
         - 'haus_dist': Hausdorff distance
         - 'haus_dist95': 95% HD
         - 'com_dist': distance between centre of mass
         - 'com_ref': centre of mass RED
         - 'com_seg': centre of mass GREEN
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
