semvec/pypi-packages huggingface dataset:
TreeMethods 1.0.3
Creating a neighbour joining tree.
Requires Python: >=3.6

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
python-sqlite-cache 0.0.2
SQLite cache for Python.
Keywords: sqlite cache

# python-sqlite-cache

SQlite Cache module

## Requirements

- Python 3.6+

## Install

~~~
$ pip install python-sqlite-cache
~~~

## Usage

### Initialize

#### In Memory

~~~
> from sqlite_cache.sqlite_cache import SqliteCache
> sql_cache = SqliteCache()
~~~

#### On Disk

~~~
> from sqlite_cache.sqlite_cache import SqliteCache
> sql_cache = SqliteCache(path_cache_dir)
~~~

### Usage

~~~
> sql_cache.set('some_key')
> print(sql_cache_get('some_key', 'some_value'))
some_value
~~~