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add function description #105

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20 changes: 16 additions & 4 deletions docs/source/create-network.rst
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Expand Up @@ -8,8 +8,6 @@ Minimum Spanning Tree
---------------------
- why a connected graph?

Thresholding
------------


The BrainNetwork Class
Expand All @@ -19,10 +17,24 @@ This is a very lightweight subclass of the :class:`networkx.Graph` class. This m

All :class:`scona.BrainNetwork` **methods** have a corresponding ``scona`` **function**. So while the :class:`scona.BrainNetwork` methods can only be applied to :class:`scona.BrainNetwork` objects, you can find the equivalent function in ``scona`` which can be used on a regular :class:`networkx.Graph` object.

For example, if ``G`` is a :class:`scona.BrainNetwork` object, you can threshold it by typing ``G.threshold(10)``. If ``nxG`` is a :class:`Networkx.Graph` you can use ``scona.threshold_graph(nxG, 10)`` to perform the same function.
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I would like to leave this line where it is to illustrate the line above. This example is more about showing that class methods and functions come in pairs, rather than explaining the threshold function.


A :class:`BrainNetwork` can be initialised from a :class:`networkx.Graph` or from a correlation matrix represented as a :class:`pandas.DataFrame` or :class:`numpy.array`.

Threshold
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------------
if ``G`` is a :class:`scona.BrainNetwork` object, you can threshold it by typing ``G.threshold(10)``. If ``nxG`` is a :class:`Networkx.Graph` you can use ``scona.threshold_graph(nxG, 10)`` to perform the same function.

This function creates a binary graph by thresholding weighted graph ``G``.

First creates a spanning forest that is a union of the spanning trees for each connected component of the graph.

Then adds in edges according to their connection strength up to cost.


The GraphBundle Class
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----------------------

This is is a subclass of :class:`dict` containing :class:`str`: :class:`BrainNetwork` pairs.


Resources
---------
6 changes: 3 additions & 3 deletions scona/make_graphs.py
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Expand Up @@ -258,12 +258,12 @@ def is_anatomical_match(G,

def weighted_graph_from_matrix(M, create_using=None):
'''
Create a weighted graph from an correlation matrix.
Create a weighted graph from a correlation matrix.
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Parameters
----------
M : :class:`numpy.array`
an correlation matrix
a correlation matrix
create_using : :class:`networkx.Graph`, optional
Use specified graph for result. The default is Graph()

Expand Down Expand Up @@ -369,7 +369,7 @@ def threshold_graph(G, cost, mst=True):
germ.remove_edges_from(G.edges)

if mst:
# Calculate minimum spanning tree
# Calculate minimum spanning trees
germ.add_edges_from(nx.minimum_spanning_edges(H))

# Make a list of the germ graph's edges
Expand Down