Metadata-Version: 2.1
Name: TreeMethods
Version: 1.0.3
Summary: Creating a neighbour joining tree.
Home-page: https://github.com/BradBalderson/TreeMethods
Author: Brad Balderson
Author-email: brad.balderson@uqconnect.edu.au
License: UNKNOWN
Description: This repository stores a basic implementation for creating a neighbour joining tree from a given distance or similarity matrix.
        
        Generating a distance matrix (A good way to do this is to use sklearn.DistanceMetrics with real data):
        
            from sklearn.neighbors import DistanceMetric
        
            dist = DistanceMetric.get_metric('euclidean')
            X = [[0, 1, 2],
                 [3, 4, 5],
                 [2, 3, 1],
                 [0, 2, 1]]
            dist_mat = dist.pairwise(X)
        
        Now that we have our distance matrix, we can now use it to construct a neighbour joining tree, 
        giving some labels for the different samples:
        
            import numpy
            import TreeMethods.TreeBuild as TB
        
            tree = TB.njTree(dist_mat, numpy.array(['A', 'B', 'C', 'D']))
        
        We can then use ete3 to construct this into a tree object:
        
            from ete3 import Tree
        
            tree = Tree(tree)
            print(tree)
        
               /-B
              |
              |   /-D
            --|--|
              |   \-A
              |
               \-C
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
