def mkgraph(cmat, threshold=0.0, threshold2=None):
"""Make a weighted graph object out of an adjacency matrix.
The values in the original matrix cmat can be thresholded out. If only one
threshold is given, all values below that are omitted when creating edges.
If two thresholds are given, then values in the th2-th1 range are
ommitted. This allows for the easy creation of weighted graphs with
positive and negative values where a range of weights around 0 is omitted.
Parameters
----------
cmat : 2-d square array
Adjacency matrix.
threshold : float
First threshold.
threshold2 : float
Second threshold.
Returns
-------
G : a NetworkX weighted graph object, to which a dictionary called
G.metadata is appended. This dict contains the original adjacency matrix
cmat, the two thresholds, and the weights
"""
# Input sanity check
nrow, ncol = cmat.shape
if nrow != ncol:
raise ValueError("Adjacency matrix must be square")
row_idx, col_idx, vals = threshold_arr(cmat, threshold, threshold2)
# Also make the full thresholded array available in the metadata
cmat_th = np.empty_like(cmat)
if threshold2 is None:
cmat_th.fill(threshold)
else:
cmat_th.fill(-np.inf)
cmat_th[row_idx, col_idx] = vals
# Next, make a normalized copy of the values. For the 2-threshold case, we
# use 'folding' normalization
if threshold2 is None:
vals_norm = minmax_norm(vals)
else:
vals_norm = minmax_norm(vals, 'folding', [threshold, threshold2])
# Now make the actual graph
G = nx.Graph(weighted=True)
G.add_nodes_from(list(range(nrow)))
# To keep the weights of the graph to simple values, we store the
# normalize ones in a separate dict that we'll stuff into the graph
# metadata.
normed_values = {}
for i, j, val, nval in zip(row_idx, col_idx, vals, vals_norm):
if i == j:
# no self-loops
continue
G.add_edge(i, j, weight=val)
normed_values[i, j] = nval
# Write a metadata dict into the graph and save the threshold info there
G.metadata = dict(threshold1=threshold,
threshold2=threshold2,
cmat_raw=cmat,
cmat_th=cmat_th,
vals_norm=normed_values,
)
return G
def mkgraph(cmat, threshold=0.0, threshold2=None):
"""Make a weighted graph object out of an adjacency matrix.
The values in the original matrix cmat can be thresholded out. If only one
threshold is given, all values below that are omitted when creating edges.
If two thresholds are given, then values in the th2-th1 range are
ommitted. This allows for the easy creation of weighted graphs with
positive and negative values where a range of weights around 0 is omitted.
Parameters
----------
cmat : 2-d square array
Adjacency matrix.
threshold : float
First threshold.
threshold2 : float
Second threshold.
Returns
-------
G : a NetworkX weighted graph object, to which a dictionary called
G.metadata is appended. This dict contains the original adjacency matrix
cmat, the two thresholds, and the weights
"""
# Input sanity check
nrow, ncol = cmat.shape
if nrow != ncol:
raise ValueError("Adjacency matrix must be square")
row_idx, col_idx, vals = threshold_arr(cmat, threshold, threshold2)
# Also make the full thresholded array available in the metadata
cmat_th = np.empty_like(cmat)
if threshold2 is None:
cmat_th.fill(threshold)
else:
cmat_th.fill(-np.inf)
cmat_th[row_idx, col_idx] = vals
# Next, make a normalized copy of the values. For the 2-threshold case, we
# use 'folding' normalization
if threshold2 is None:
vals_norm = minmax_norm(vals)
else:
vals_norm = minmax_norm(vals, 'folding', [threshold, threshold2])
# Now make the actual graph
G = nx.Graph(weighted=True)
G.add_nodes_from(range(nrow))
# To keep the weights of the graph to simple values, we store the
# normalize ones in a separate dict that we'll stuff into the graph
# metadata.
normed_values = {}
for i, j, val, nval in zip(row_idx, col_idx, vals, vals_norm):
if i == j:
# no self-loops
continue
G.add_edge(i, j, weight=val)
normed_values[i, j] = nval
# Write a metadata dict into the graph and save the threshold info there
G.metadata = dict(
threshold1=threshold,
threshold2=threshold2,
cmat_raw=cmat,
cmat_th=cmat_th,
vals_norm=normed_values,
)
return G