# graph_tool.correlations.combined_corr_hist#

graph_tool.correlations.combined_corr_hist(g, deg1, deg2, bins=[[0, 1], [0, 1]], float_count=True)[source]#

Obtain the single-vertex combined correlation histogram for the given graph.

Parameters:
g`Graph`

Graph to be used.

deg1string or `VertexPropertyMap`

first degree type (“in”, “out” or “total”) or vertex property map.

deg2string or `VertexPropertyMap`

second degree type (“in”, “out” or “total”) or vertex property map.

binslist of lists (optional, default: [[0, 1], [0, 1]])

A list of bin edges to be used for the first and second degrees. If any list has size 2, it is used to create an automatically generated bin range starting from the first value, and with constant bin width given by the second value.

float_countbool (optional, default: True)

If True, the bin counts are converted float variables, which is useful for normalization, and other processing. It False, the bin counts will be unsigned integers.

Returns:
bin_counts`numpy.ndarray`

Two-dimensional array with the bin counts.

first_bins`numpy.ndarray`

First degree bins

second_bins`numpy.ndarray`

Second degree bins

`assortativity`

assortativity coefficient

`scalar_assortativity`

scalar assortativity coefficient

`corr_hist`

vertex-vertex correlation histogram

`combined_corr_hist`

combined single-vertex correlation histogram

`avg_neighbor_corr`

average nearest-neighbor correlation

`avg_combined_corr`

average combined single-vertex correlation

Notes

If enabled during compilation, this algorithm runs in parallel.

Examples

```>>> def sample_k(max):
...     accept = False
...     while not accept:
...         i = np.random.randint(1, max + 1)
...         j = np.random.randint(1, max + 1)
...         accept = np.random.random() < (sin(i / pi) * sin(j / pi) + 1) / 2
...     return i,j
...
>>> g = gt.random_graph(10000, lambda: sample_k(40))
>>> h = gt.combined_corr_hist(g, "in", "out")
>>> clf()
>>> xlabel("In-degree")
Text(...)
>>> ylabel("Out-degree")
Text(...)
>>> imshow(h.T, interpolation="nearest", origin="lower")
<...>
>>> colorbar()
<...>
>>> savefig("combined_corr.svg")
```