def clean(conn, label=None):
"""
Visualize a sparse adjacency matrix.
.. image:: adjacency.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, label, 'label')
return outdict
def clean(conn, label=None):
"""
Visualize a sparse adjacency matrix.
.. image:: adjacency.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, label, 'label')
return outdict
def clean(x, y, conn, values=None, labels=None,color=None, group=None, colormap=None, size=None):
"""
Create a node-link graph from spatial points and their connectivity.
.. image:: graph.png
Parameters
----------
x,y : array-like, each (n,)
Input data for nodes (x,y coordinates)
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
values : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
labels : array-like, optional, (n,)
Array of text labels to set tooltips
color : array-like, optional, singleton or (n,) or (n,3)
Single rgb value or array to set node colors
group : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group assignment
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
tooltips : boolean, optional, default=True
Whether to show tooltips
zoom : boolean, optional, default=True
Whether to allow zooming
brush : boolean, optional, default=True
Whether to support brushing
"""
links = parse_links(conn)
nodes = vecs_to_points(x, y)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
return outdict
def clean(x, y, conn, values=None, labels=None,color=None, group=None, colormap=None, size=None):
"""
Create a node-link graph from spatial points and their connectivity.
.. image:: graph.png
Parameters
----------
x,y : array-like, each (n,)
Input data for nodes (x,y coordinates)
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
values : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
labels : array-like, optional, (n,)
Array of text labels to set tooltips
color : array-like, optional, singleton or (n,) or (n,3)
Single rgb value or array to set node colors
group : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group assignment
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
tooltips : boolean, optional, default=True
Whether to show tooltips
zoom : boolean, optional, default=True
Whether to allow zooming
brush : boolean, optional, default=True
Whether to support brushing
"""
links = parse_links(conn)
nodes = vecs_to_points(x, y)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
return outdict
def clean(x,
y,
conn,
color=None,
label=None,
value=None,
colormap=None,
size=None,
imagedata=None):
"""
Create a node-link graph from spatial points and their connectivity.
.. image:: graph.png
Parameters
----------
x,y : array-like, each (n,)
Input data for nodes (x,y coordinates)
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
color : array-like, optional, singleton or (n,) or (n,3)
Single rgb value or array to set node colors
label : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group labels
value : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
links = parse_links(conn)
nodes = vecs_to_points(x, y)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, value, 'value')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
if imagedata is not None:
images = array_to_im(imagedata)
outdict['images'] = images
return outdict
def clean(x, y, conn, color=None, label=None, value=None, colormap=None, size=None, imagedata=None):
"""
Create a node-link graph from spatial points and their connectivity.
.. image:: graph.png
Parameters
----------
x,y : array-like, each (n,)
Input data for nodes (x,y coordinates)
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
color : array-like, optional, singleton or (n,) or (n,3)
Single rgb value or array to set node colors
label : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group labels
value : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
links = parse_links(conn)
nodes = vecs_to_points(x, y)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, value, 'value')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
if imagedata is not None:
images = array_to_im(imagedata)
outdict['images'] = images
return outdict
def clean(conn, group=None, color=None, labels=None):
"""
Create a circular graph from connectivity data.
.. image:: circle.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
group : array-like, optional, (m,n) or (n,)
Hierarchical group assignments, where m is
the number of groups
color : array-like, optional, singleton or (k,3)
Single rgb value or array to set colors of top-level group,
where k is the number of unique elements in the top-level group
labels : array-like, optional, (n,)
Array of text labels to label nodes
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, color, 'color')
if group is not None:
if isinstance(group, ndarray):
group = group.tolist()
if isinstance(group, list):
if not isinstance(group[0], list):
if isinstance(group[0], ndarray):
group = [g.tolist() for g in group]
else:
group = [group]
else:
raise ValueError('group must be list or nested list')
outdict['group'] = group
return outdict
def clean(conn, group=None, color=None, labels=None):
"""
Create a circular graph from connectivity data.
.. image:: circle.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
group : array-like, optional, (m,n) or (n,)
Hierarchical group assignments, where m is
the number of groups
color : array-like, optional, singleton or (k,3)
Single rgb value or array to set colors of top-level group,
where k is the number of unique elements in the top-level group
labels : array-like, optional, (n,)
Array of text labels to label nodes
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, color, 'color')
if group is not None:
if isinstance(group, ndarray):
group = group.tolist()
if isinstance(group, list):
if not isinstance(group[0], list):
if isinstance(group[0], ndarray):
group = [g.tolist() for g in group]
else:
group = [group]
else:
raise ValueError('group must be list or nested list')
outdict['group'] = group
return outdict
def clean(conn,
color=None,
label=None,
value=None,
colormap=None,
size=None):
"""
Create a force-directed network from connectivity.
.. image:: force.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set node colors
label : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group labels
value : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, value, 'value')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
return outdict
def clean(conn, color=None, label=None, value=None, colormap=None, size=None):
"""
Create a force-directed network from connectivity.
.. image:: force.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set node colors
label : array-like, optional, singleton or (n,)
Single integer or array to set node colors via group labels
value : array-like, optional, singleton or (n,)
Values to set node colors via a linear scale
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, value, 'value')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
return outdict
def clean(conn, labels=None, group=None):
"""
Visualize a sparse adjacency matrix.
.. image:: adjacency.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
labels : array-like, (n,)
Text labels for each item (will label rows and columns)
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
sort : str, optional, default='group'
What to sort by, options are 'group' | 'degree'
numbers : boolean, optional, default=False
Whether to show numbers on cells
symmetric : boolean, optional, default=True
Whether to make links symmetrical
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, group, 'group')
return outdict
def clean(conn, labels=None, group=None):
"""
Visualize a sparse adjacency matrix.
.. image:: adjacency.png
Parameters
----------
conn : array-like, (n,n) or (n,3) or (n,2)
Input connectivity data as either a matrix or a list of links.
Matrix can be binary or continuous valued. Links should contain
either 2 elements per link (source, target),
or 3 elements (source, target, value).
labels : array-like, (n,)
Text labels for each item (will label rows and columns)
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
sort : str, optional, default='group'
What to sort by, options are 'group' | 'degree'
numbers : boolean, optional, default=False
Whether to show numbers on cells
symmetric : boolean, optional, default=True
Whether to make links symmetrical
"""
links = parse_links(conn)
nodes = parse_nodes(conn)
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, group, 'group')
return outdict