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Added the ability for users to access the optional arguments of NetworkX layout functions #439

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Jul 28, 2023
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Added the ability for users to access the optional arguments of NetworkX layout functions #439

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8 changes: 4 additions & 4 deletions xgi/drawing/draw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,8 +112,8 @@ def draw(
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, False.
aspect : {"auto", "equal"} or float, optional
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
description at
https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_aspect.html
**kwargs : optional args
Expand Down Expand Up @@ -1283,8 +1283,8 @@ def draw_hypergraph_hull(
radius : float, optional
Radius of the convex hull in the vicinity of the nodes, by default 0.05.
aspect : {"auto", "equal"} or float, optional
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
description at
https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_aspect.html
**kwargs : optional args
Expand Down
56 changes: 43 additions & 13 deletions xgi/drawing/layout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
]


def random_layout(H, center=None, dim=2, seed=None):
def random_layout(H, center=None, seed=None):
"""Position nodes uniformly at random in the unit square.

For every node, a position is generated by choosing each of dim coordinates
Expand All @@ -36,8 +36,6 @@ def random_layout(H, center=None, dim=2, seed=None):
center : array-like, optional
Coordinate pair around which to center the layout.
If None (default), does not center the positions.
dim : int, optional
Dimension of layout, by default 2.
seed : int, optional
Set the random state for deterministic node layouts.
If int, `seed` is the seed used by the random number generator,
Expand Down Expand Up @@ -76,15 +74,15 @@ def random_layout(H, center=None, dim=2, seed=None):
if seed is not None:
np.random.seed(seed)

H, center = nx.drawing.layout._process_params(H, center, dim)
pos = np.random.rand(len(H), dim) + center
H, center = nx.drawing.layout._process_params(H, center, 2)
pos = np.random.rand(len(H), 2) + center
pos = pos.astype(np.float32)
pos = dict(zip(H, pos))

return pos


def pairwise_spring_layout(H, seed=None):
def pairwise_spring_layout(H, seed=None, k=None, **kwargs):
"""
Position the nodes using Fruchterman-Reingold force-directed
algorithm using the graph projection of the hypergraph
Expand All @@ -99,6 +97,13 @@ def pairwise_spring_layout(H, seed=None):
If int, `seed` is the seed used by the random number generator,
If None (default), random numbers are sampled from the
numpy random number generator without initialization.
k : float
The spring constant of the links. When k=None (default),
k = 1/sqrt(N). For more information, see the documentation
for the NetworkX spring_layout() function.
kwargs :
Optional arguments for the NetworkX spring_layout() function.
See https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.spring_layout.html

Returns
-------
Expand Down Expand Up @@ -131,7 +136,7 @@ def pairwise_spring_layout(H, seed=None):
if isinstance(H, SimplicialComplex):
H = convert.from_max_simplices(H)
G = convert.convert_to_graph(H)
pos = nx.spring_layout(G, seed=seed)
pos = nx.spring_layout(G, seed=seed, k=k, **kwargs)
return pos


Expand Down Expand Up @@ -190,7 +195,9 @@ def _augmented_projection(H, weighted=False):
return G


def barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
def barycenter_spring_layout(
H, return_phantom_graph=False, seed=None, k=None, **kwargs
):
"""
Position the nodes using Fruchterman-Reingold force-directed
algorithm using an augmented version of the the graph projection
Expand All @@ -211,6 +218,14 @@ def barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
If int, `seed` is the seed used by the random number generator,
If None (default), random numbers are sampled from the
numpy random number generator without initialization.
k : float
The spring constant of the links. When k=None (default),
k = 1/sqrt(N). For more information, see the documentation
for the NetworkX spring_layout() function.
kwargs :
Optional arguments for the NetworkX spring_layout() function.
See https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.spring_layout.html


Returns
-------
Expand Down Expand Up @@ -241,7 +256,7 @@ def barycenter_spring_layout(H, return_phantom_graph=False, seed=None):

# Creating a dictionary for the position of the nodes with the standard spring
# layout
pos_with_phantom_nodes = nx.spring_layout(G, seed=seed)
pos_with_phantom_nodes = nx.spring_layout(G, seed=seed, k=k, **kwargs)

# Retaining only the positions of the real nodes
pos = {k: pos_with_phantom_nodes[k] for k in list(H.nodes)}
Expand All @@ -252,7 +267,9 @@ def barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
return pos


def weighted_barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
def weighted_barycenter_spring_layout(
H, return_phantom_graph=False, seed=None, k=None, **kwargs
):
"""Position the nodes using Fruchterman-Reingold force-directed algorithm.

This uses an augmented version of the the graph projection of the hypergraph (or
Expand All @@ -275,6 +292,14 @@ def weighted_barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
If int, `seed` is the seed used by the random number generator,
If None (default), random numbers are sampled from the
numpy random number generator without initialization.
k : float
The spring constant of the links. When k=None (default),
k = 1/sqrt(N). For more information, see the documentation
for the NetworkX spring_layout() function.
kwargs :
Optional arguments for the NetworkX spring_layout() function.
See https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.spring_layout.html


Returns
-------
Expand Down Expand Up @@ -305,7 +330,9 @@ def weighted_barycenter_spring_layout(H, return_phantom_graph=False, seed=None):
G = _augmented_projection(H, weighted=True)

# Creating a dictionary for node position with the standard spring layout
pos_with_phantom_nodes = nx.spring_layout(G, weight="weight", seed=seed)
pos_with_phantom_nodes = nx.spring_layout(
G, weight="weight", seed=seed, k=k, **kwargs
)

# Retaining only the positions of the real nodes
pos = {k: pos_with_phantom_nodes[k] for k in list(H.nodes)}
Expand Down Expand Up @@ -452,7 +479,7 @@ def spiral_layout(H, center=None, resolution=0.35, equidistant=False):
return pos


def barycenter_kamada_kawai_layout(H, return_phantom_graph=False):
def barycenter_kamada_kawai_layout(H, return_phantom_graph=False, **kwargs):
"""Position nodes using Kamada-Kawai path-length cost-function
using an augmented version of the the graph projection
of the hypergraph (or simplicial complex), where phantom nodes
Expand All @@ -467,6 +494,9 @@ def barycenter_kamada_kawai_layout(H, return_phantom_graph=False):
return_phantom_graph: bool (default=False)
If True the function returns also the augmented version of the
the graph projection of the hypergraph (or simplicial complex).
kwargs :
Optional arguments for the NetworkX spring_layout() function.
See https://networkx.org/documentation/stable/reference/generated/networkx.drawing.layout.kamada_kawai_layout.html

Returns
-------
Expand All @@ -479,7 +509,7 @@ def barycenter_kamada_kawai_layout(H, return_phantom_graph=False):
G = _augmented_projection(H)

# Creating a dictionary for the position of the nodes with the standard spring layout
pos_with_phantom_nodes = nx.kamada_kawai_layout(G)
pos_with_phantom_nodes = nx.kamada_kawai_layout(G, **kwargs)

# Retaining only the positions of the real nodes
pos = {k: pos_with_phantom_nodes[k] for k in list(H.nodes)}
Expand Down