def contraction_categorical(input_attribute=None,
graph=None,
id_nodes=None,
separator='.'):
"""contraction_categorical."""
vals = sorted([_serialize_list(graph.node[v].get(input_attribute, 'N/A'))
for v in id_nodes])
return _serialize_list(vals, separator=separator)
def contraction_categorical(input_attribute=None,
graph=None,
id_nodes=None,
separator='.'):
"""contraction_categorical."""
vals = sorted([
_serialize_list(graph.node[v].get(input_attribute, 'N/A'))
for v in id_nodes
])
return _serialize_list(vals, separator=separator)
def draw_graph(graph,
vertex_label='label',
secondary_vertex_label=None,
edge_label='label',
secondary_edge_label=None,
vertex_color=None,
vertex_alpha=0.6,
vertex_border=False,
vertex_size=600,
edge_color=None,
edge_alpha=0.5,
dark_edge_color=None,
dark_edge_dotted=True,
dark_edge_alpha=0.3,
size=10,
size_x_to_y_ratio=1,
font_size=9,
layout='graphviz',
prog='neato',
pos=None,
colormap='YlOrRd',
vmin=0,
vmax=1,
invert_colormap=False,
verbose=True,
file_name=None,
title_key='id',
ignore_for_layout="edge_attribute",
logscale=False):
"""Plot graph layout."""
if size is not None:
size_x = size
size_y = int(float(size) / size_x_to_y_ratio)
plt.figure(figsize=(size_x, size_y))
plt.grid(False)
plt.axis('off')
if vertex_label is not None:
if secondary_vertex_label:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label1 = _serialize_list(d.get(vertex_label, 'N/A'))
label2 = _serialize_list(d.get(secondary_vertex_label, 'N/A'))
vertex_labels[u] = '%s\n%s' % (label1, label2)
else:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label = d.get(vertex_label, 'N/A')
vertex_labels[u] = _serialize_list(label)
edges_normal = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is False]
edges_nesting = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is True]
if edge_label is not None:
if secondary_edge_label:
edge_labels = dict([((u, v,), '%s\n%s' %
(d.get(edge_label, ''),
d.get(secondary_edge_label, '')))
for u, v, d in graph.edges(data=True)])
else:
edge_labels = dict([((u, v,), d.get(edge_label, ''))
for u, v, d in graph.edges(data=True)])
if vertex_color is None:
node_color = 'white'
elif vertex_color in ['_labels_', '_label_', '__labels__', '__label__']:
node_color = []
for u, d in graph.nodes(data=True):
label = d.get('label', '.')
node_color.append(hash(_serialize_list(label)))
color_set = set(node_color)
color_map = {c: i for i, c in enumerate(color_set)}
node_color = [color_map[c] for c in node_color]
else:
if invert_colormap:
node_color = [- d.get(vertex_color, 0)
for u, d in graph.nodes(data=True)]
else:
node_color = [d.get(vertex_color, 0)
for u, d in graph.nodes(data=True)]
if logscale is True:
log_threshold = 0.01
node_color = [math.log(c) if c > log_threshold
else math.log(log_threshold)
for c in node_color]
if edge_color is None:
edge_colors = 'black'
elif edge_color in ['_labels_', '_label_', '__labels__', '__label__']:
edge_colors = [hash(str(d.get('label', '.')))
for u, v, d in graph.edges(data=True)
if 'nesting' not in d]
else:
if invert_colormap:
edge_colors = [- d.get(edge_color, 0)
for u, v, d in graph.edges(data=True)
if 'nesting' not in d]
else:
edge_colors = [d.get(edge_color, 0)
for u, v, d in graph.edges(data=True)
if 'nesting' not in d]
if dark_edge_color is None:
dark_edge_colors = 'black'
else:
dark_edge_colors = [d.get(dark_edge_color, 0)
for u, v, d in graph.edges(data=True)
if 'nesting' in d]
tmp_edge_set = [(a, b, d)
for (a, b, d) in graph.edges(data=True)
if ignore_for_layout in d]
graph.remove_edges_from(tmp_edge_set)
if pos is None:
if layout == 'graphviz':
graph_copy = graph.copy()
# remove all attributes for graphviz layout
for u, d in graph_copy.nodes(data=True):
graph_copy.node[u] = {}
for u, v, d in graph_copy.edges(data=True):
graph_copy.edge[u][v] = {}
pos = nx.graphviz_layout(graph_copy, prog=prog, args="-Gmode=KK")
elif layout == "RNA":
import RNA
rna_object = RNA.get_xy_coordinates(graph.graph['structure'])
pos = {i: (rna_object.get(i).X, rna_object.get(i).Y)
for i in range(len(graph.graph['structure']))}
elif layout == 'circular':
pos = nx.circular_layout(graph)
elif layout == 'random':
pos = nx.random_layout(graph)
elif layout == 'spring':
pos = nx.spring_layout(graph)
elif layout == 'shell':
pos = nx.shell_layout(graph)
elif layout == 'spectral':
pos = nx.spectral_layout(graph)
else:
raise Exception('Unknown layout format: %s' % layout)
if vertex_border is False:
linewidths = 0.001
else:
linewidths = 1
graph.add_edges_from(tmp_edge_set)
nx.draw_networkx_nodes(graph, pos,
node_color=node_color,
alpha=vertex_alpha,
node_size=vertex_size,
linewidths=linewidths,
cmap=plt.get_cmap(colormap))
nx.draw_networkx_edges(graph, pos,
edgelist=edges_normal,
width=2,
edge_color=edge_colors,
cmap=plt.get_cmap(colormap),
alpha=edge_alpha)
if dark_edge_dotted:
style = 'dotted'
else:
style = 'solid'
nx.draw_networkx_edges(graph, pos,
edgelist=edges_nesting,
width=1,
edge_color=dark_edge_colors,
style=style,
alpha=dark_edge_alpha)
if edge_label is not None:
nx.draw_networkx_edge_labels(graph,
pos,
edge_labels=edge_labels,
font_size=font_size)
if vertex_label is not None:
nx.draw_networkx_labels(graph,
pos,
vertex_labels,
font_size=font_size,
font_color='black')
if title_key:
title = str(graph.graph.get(title_key, ''))
font = FontProperties()
font.set_family('monospace')
plt.title(title, fontproperties=font)
if size is not None:
# here we decide if we output the image.
# note: if size is not set, the canvas has been created outside
# of this function.
# we wont write on a canvas that we didn't create ourselves.
if file_name is None:
plt.show()
else:
plt.savefig(file_name,
bbox_inches='tight',
transparent=True,
pad_inches=0)
plt.close()
def draw_graph(graph,
vertex_label='label',
vertex_color=None,
vertex_color_dict=None,
vertex_alpha=0.6,
vertex_border=1,
vertex_size=600,
compact=False,
colormap='YlOrRd',
vmin=0,
vmax=1,
invert_colormap=False,
secondary_vertex_label=None,
secondary_vertex_color=None,
secondary_vertex_alpha=0.6,
secondary_vertex_border=1,
secondary_vertex_size=600,
secondary_vertex_colormap='YlOrRd',
secondary_vertex_vmin=0,
secondary_vertex_vmax=1,
edge_label='label',
secondary_edge_label=None,
edge_colormap='YlOrRd',
edge_vmin=0,
edge_vmax=1,
edge_color=None,
edge_width=None,
edge_alpha=0.5,
dark_edge_colormap='YlOrRd',
dark_edge_vmin=0,
dark_edge_vmax=1,
dark_edge_color=None,
dark_edge_dotted=True,
dark_edge_alpha=0.3,
size=10,
size_x_to_y_ratio=1,
font_size=9,
layout='graphviz',
prog='neato',
pos=None,
verbose=True,
file_name=None,
title_key='id',
ignore_for_layout="edge_attribute",
logscale=False):
"""Plot graph layout."""
if size is not None:
size_x = size
size_y = int(float(size) / size_x_to_y_ratio)
plt.figure(figsize=(size_x, size_y))
plt.grid(False)
plt.axis('off')
if vertex_label is not None:
if secondary_vertex_label:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label1 = _serialize_list(d.get(vertex_label, 'N/A'))
label2 = _serialize_list(d.get(secondary_vertex_label, 'N/A'))
vertex_labels[u] = '%s\n%s' % (label1, label2)
else:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label = d.get(vertex_label, 'N/A')
vertex_labels[u] = _serialize_list(label)
edges_normal = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is False]
edges_nesting = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is True]
if edge_label is not None:
if secondary_edge_label:
edge_labels = dict([
((
u,
v,
), '%s\n%s' %
(d.get(edge_label, ''), d.get(secondary_edge_label, '')))
for u, v, d in graph.edges(data=True)
])
else:
edge_labels = dict([((
u,
v,
), d.get(edge_label, '')) for u, v, d in graph.edges(data=True)])
if vertex_color is None:
node_color = 'white'
elif vertex_color in ['_labels_', '_label_', '__labels__', '__label__']:
node_color = []
for u, d in graph.nodes(data=True):
label = d.get('label', '.')
if vertex_color_dict is not None:
node_color.append(vertex_color_dict.get(label, 0))
else:
node_color.append(hash(_serialize_list(label)))
else:
if invert_colormap:
node_color = [
-d.get(vertex_color, 0) for u, d in graph.nodes(data=True)
]
else:
node_color = [
d.get(vertex_color, 0) for u, d in graph.nodes(data=True)
]
if logscale is True:
log_threshold = 0.01
node_color = [
math.log(c) if c > log_threshold else math.log(log_threshold)
for c in node_color
]
if edge_width is None:
widths = 1
elif isinstance(edge_width, int):
widths = edge_width
else:
widths = [
d.get(edge_width, 1) for u, v, d in graph.edges(data=True)
if 'nesting' not in d
]
if edge_color is None:
edge_colors = 'black'
elif edge_color in ['_labels_', '_label_', '__labels__', '__label__']:
edge_colors = [
hash(str(d.get('label', '.')))
for u, v, d in graph.edges(data=True) if 'nesting' not in d
]
else:
if invert_colormap:
edge_colors = [
-d.get(edge_color, 0) for u, v, d in graph.edges(data=True)
if 'nesting' not in d
]
else:
edge_colors = [
d.get(edge_color, 0) for u, v, d in graph.edges(data=True)
if 'nesting' not in d
]
if dark_edge_color is None:
dark_edge_colors = 'black'
else:
dark_edge_colors = [
d.get(dark_edge_color, 0) for u, v, d in graph.edges(data=True)
if 'nesting' in d
]
tmp_edge_set = [(a, b, d) for (a, b, d) in graph.edges(data=True)
if ignore_for_layout in d]
graph.remove_edges_from(tmp_edge_set)
if pos is None:
if layout == 'graphviz':
graph_copy = graph.copy()
pos = nx.nx_pydot.graphviz_layout(graph_copy, prog=prog)
elif layout == "RNA":
import RNA # this is part of the vienna RNA package
rna_object = RNA.get_xy_coordinates(graph.graph['structure'])
pos = {
i: (rna_object.get(i).X, rna_object.get(i).Y)
for i in range(len(graph.graph['structure']))
}
elif layout == 'circular':
pos = nx.circular_layout(graph)
elif layout == 'random':
pos = nx.random_layout(graph)
elif layout == 'spring':
pos = nx.spring_layout(graph)
elif layout == 'shell':
pos = nx.shell_layout(graph)
elif layout == 'spectral':
pos = nx.spectral_layout(graph)
elif layout == 'KK':
pos = KKEmbedder().transform(graph)
else:
raise Exception('Unknown layout format: %s' % layout)
if vertex_border is False:
linewidths = 0.001
else:
linewidths = vertex_border
graph.add_edges_from(tmp_edge_set)
if secondary_vertex_color is not None:
if secondary_vertex_border is False:
secondary_linewidths = 0.001
else:
secondary_linewidths = secondary_vertex_border
secondary_node_color = [
d.get(secondary_vertex_color, 0) for u, d in graph.nodes(data=True)
]
secondary_nodes = nx.draw_networkx_nodes(
graph,
pos,
node_color=secondary_node_color,
alpha=secondary_vertex_alpha,
node_size=secondary_vertex_size,
linewidths=secondary_linewidths,
cmap=plt.get_cmap(secondary_vertex_colormap),
vmin=secondary_vertex_vmin,
vmax=secondary_vertex_vmax)
secondary_nodes.set_edgecolor('k')
if compact:
nodes = nx.draw_networkx_nodes(graph,
pos,
node_color='w',
alpha=1,
node_size=vertex_size,
linewidths=linewidths)
nodes.set_edgecolor('k')
nx.draw_networkx_nodes(graph,
pos,
node_color=node_color,
alpha=vertex_alpha,
node_size=vertex_size,
linewidths=None,
cmap=plt.get_cmap(colormap),
vmin=vmin,
vmax=vmax)
else:
nodes = nx.draw_networkx_nodes(graph,
pos,
node_color=node_color,
alpha=vertex_alpha,
node_size=vertex_size,
linewidths=linewidths,
cmap=plt.get_cmap(colormap),
vmin=vmin,
vmax=vmax)
nodes.set_edgecolor('k')
nx.draw_networkx_edges(graph,
pos,
edgelist=edges_normal,
width=widths,
edge_color=edge_colors,
edge_cmap=plt.get_cmap(edge_colormap),
edge_vmin=edge_vmin,
edge_vmax=edge_vmax,
alpha=edge_alpha)
if dark_edge_dotted:
style = 'dotted'
else:
style = 'solid'
nx.draw_networkx_edges(graph,
pos,
edgelist=edges_nesting,
width=1,
edge_cmap=plt.get_cmap(dark_edge_colormap),
edge_vmin=dark_edge_vmin,
edge_vmax=dark_edge_vmax,
edge_color=dark_edge_colors,
style=style,
alpha=dark_edge_alpha)
if edge_label is not None:
nx.draw_networkx_edge_labels(graph,
pos,
edge_labels=edge_labels,
font_size=font_size)
if vertex_label is not None:
nx.draw_networkx_labels(graph,
pos,
vertex_labels,
font_size=font_size,
font_weight='normal',
font_color='black')
if title_key:
title = str(graph.graph.get(title_key, ''))
font = FontProperties()
font.set_family('monospace')
plt.title(title, fontproperties=font)
if size is not None:
# here we decide if we output the image.
# note: if size is not set, the canvas has been created outside
# of this function.
# we wont write on a canvas that we didn't create ourselves.
if file_name is None:
plt.show()
else:
plt.savefig(file_name,
bbox_inches='tight',
transparent=True,
pad_inches=0)
plt.close()