.. Graph Clustering Toolkit documentation master file, created by
   sphinx-quickstart on Wed Nov 14 09:59:53 2018.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Usage
~~~~~~~~~~~~~~


Evnronment variables
---------------------

*Graph Clustering Toolkit(gct)* uses two environment variables: 

#. *$GCT_HOME* where the gct code is, default to *$HOME/graph_clustering_toolkit*
#. *$GCT_DATA* where the data, graph or clustering result locate, default to *$HOME/.gct*, It is safe to delete this folder.

For docker users, the two variables are already set, where $GCT_DATA is set to /data. 
It is better to map the folder to a host folder so that it does not vanish when container shutdown.

Quick Code
---------------------

.. code-block:: python

    import gct
    from gct.dataset import random_dataset
    #create a random graph use LFR generator
    ds=random_dataset.generate_undirected_unweighted_random_graph_LFR(name="random_graph", \
                                           N=128, k=16, maxk=32, mu=0.2, minc=32)
    
    # run pScan graph algorithm
    pscan_clustering=gct.scan_pScan("get_start_pscan", ds)


Cheats
---------------------

* list available algorithms

.. code-block:: python

    import gct
    gct.list_algorithms()

* list available (created) graphs

.. code-block:: python

    import gct
    gct.list_dataset()
    
* delete a graph or graphs by pattern

.. code-block:: python

    import gct
    gct.remove_data("name or pattern", dry_run=False)
    
* create a graph using edge list

.. code-block:: python

    from gct.dataset.dataset import Dataset
    edgelist=.... #edge tuples or edge triples with weights
    data =Dataset("name", edgesObj=edge_list)

* run clustering algorithm by name

.. code-block:: python

    import gct
    gct.alg.run_alg(runname, data, algname, **algparams)
    
* create a graph from/to networkx/igraph/snap/graph_tool

.. code-block:: python

    from gct.dataset import convert 
    G=... #networkx graph
    ds = convert.from_networkx(....) 
    G2 = convert.to_networkx(....)
    #other converters are similar
    
* use graph libraries like networkx/igraph/snap/graph_tool  directly

.. code-block:: python

    #just import them
    import networkx
    import igraph
    import snap
    import graph_tool
 


Notebooks
---------------------

See the following notebooks for further usage.

.. toctree::
    :maxdepth: 1

    notebook/1_get_start