def clean(matrix, colormap=None, row_labels=None, column_labels=None):
"""
Visualize a dense matrix or table as a heat map.
.. image:: matrix.png
Parameters
----------
matrix : array-like (n,m)
Two-dimensional array of matrix data
row_labels : array-like (n,)
Array of rows to label columns
column_labels : array-like (m,)
Array of strings to label columns
colormap : string
Specification of color map, only colorbrewer types supported
numbers : boolean, optional, default=True
Whether to show numbers on cells
"""
matrix = mat_to_array(matrix)
outdict = {'matrix': matrix}
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, row_labels, 'rowLabels')
outdict = add_property(outdict, column_labels, 'columnLabels')
return outdict
def clean(series, color=None, label=None, size=None):
"""
Create a browsable array of line plots.
.. image:: linestacked.png
Parameters
----------
series : array-like, (n,m)
Input data for lines, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
label : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, label, 'label')
return outdict
def clean(x, y, series, color=None, label=None, size=None, alpha=None):
"""
Create a joint scatter / line plot.
.. image:: scatterline.png
Parameters
----------
x, y : array-like, each (n,)
Input data for scatter plot as x,y coordinates
t : array-like
Input data for line plot
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set point colors
label : array-like, optional, singleton or (n,)
Single integer or array to set point colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
"""
points = vecs_to_points(x, y)
series = array_to_lines(series)
outdict = {'points': points, 'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
return outdict
def clean(x, y, series, color=None, label=None, size=None, alpha=None):
"""
Create a joint scatter / line plot.
.. image:: scatterline.png
Parameters
----------
x, y : array-like, each (n,)
Input data for scatter plot as x,y coordinates
t : array-like
Input data for line plot
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set point colors
label : array-like, optional, singleton or (n,)
Single integer or array to set point colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
"""
points = vecs_to_points(x, y)
series = array_to_lines(series)
outdict = {'points': points, 'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
return outdict
def clean(series, color=None, label=None, size=None):
"""
Create a browsable array of line plots.
.. image:: linestacked.png
Parameters
----------
series : array-like, (n,m)
Input data for lines, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
label : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, label, 'label')
return outdict
def clean(regions, values, colormap=None):
"""
Create a chloropleth map of the world or united states.
.. image:: map.png
Parameters
----------
regions : string or list
String identifiers for map regions, either length two strings (for states
in a US map) or length three strings (for countries in a world map)
weights : scalar or list
Values to use to color each region
colormap : string
Specification of color map, only colorbrewer types supported
"""
regions = list_to_regions(regions)
outdict = {'regions': regions}
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, colormap, 'colormap')
return outdict
def clean(x, y, z, color=None, group=None, alpha=None, size=None):
"""
Plot three-dimensional data as points.
.. image:: scatter3.png
Parameters
----------
x, y, z : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
"""
points = vecs_to_points_three(x, y, z)
outdict = {'points': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
return outdict
def clean(x, y, z, color=None, group=None, alpha=None, size=None):
"""
Plot three-dimensional data as points.
.. image:: scatter3.png
Parameters
----------
x, y, z : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
"""
points = vecs_to_points_three(x, y, z)
outdict = {"points": points}
outdict = add_property(outdict, color, "color")
outdict = add_property(outdict, group, "group")
outdict = add_property(outdict, size, "size")
outdict = add_property(outdict, alpha, "alpha")
return outdict
def clean(x, y, z, color=None, label=None, alpha=None, size=None):
"""
Plot three-dimensional data as points.
.. image:: scatter3.png
Parameters
----------
x, y, z : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
"""
points = vecs_to_points_three(x, y, z)
outdict = {'points': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
return outdict
def clean(matrix, color=None, label=None, size=None):
"""
Create a force-directed network from a connectivity matrix.
.. image:: force.png
Parameters
----------
matrix : array-like, (n,n)
Input data with connectivity matrix. Can be binary or continuous-valued
for weighted edges.
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
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
links = mat_to_links(matrix)
nodes = list(range(0, matrix.shape[0]))
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
return outdict
def clean(regions, values, colormap=None):
"""
Create a chloropleth map of the world or united states.
.. image:: map.png
Parameters
----------
regions : string or list
String identifiers for map regions, either length two strings (for states
in a US map) or length three strings (for countries in a world map)
weights : scalar or list
Values to use to color each region
colormap : string
Specification of color map, only colorbrewer types supported
"""
regions = list_to_regions(regions)
outdict = {'regions': regions}
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, colormap, 'colormap')
return outdict
def clean(matrix, colormap=None, row_labels=None, column_labels=None):
"""
Visualize a dense matrix or table as a heat map.
.. image:: matrix.png
Parameters
----------
matrix : array-like (n,m)
Two-dimensional array of matrix data
row_labels : array-like (n,)
Array of rows to label columns
column_labels : array-like (m,)
Array of strings to label columns
colormap : string
Specification of color map, only colorbrewer types supported
numbers : boolean, optional, default=True
Whether to show numbers on cells
"""
matrix = mat_to_array(matrix)
outdict = {'matrix': matrix}
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, row_labels, 'rowLabels')
outdict = add_property(outdict, column_labels, 'columnLabels')
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(x,
y,
color=None,
label=None,
value=None,
colormap=None,
size=None,
alpha=None,
xaxis=None,
yaxis=None):
"""
Plot two-dimensional data as points.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
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 point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
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')
outdict = add_property(outdict, alpha, 'alpha')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
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(imagedata, coordinates=None, xy=None):
"""
Display an array as an image with polygonal regions and region drawing.
.. image:: image.png
Parameters
----------
imagedata : array-like
Image as a two dimensional (grayscale) or three dimensional (RGB) array.
coordinates : array-like
List of coordinates or list of list of coordinates. Assumes array indexing
(i.e. row/column), in the form [[r,c],[r,c]] for one region or
[[[r0,c0],[r0,c0]], [[r1,c1],[r1,c1]]] for multiple regions
xy : boolean, optional, default = None
Only if True treat coordinates as x/y positions instead of row/column indices
"""
if asarray(imagedata).ndim not in set((2, 3)):
raise Exception("Input must be two or three dimensional")
imgs = [array_to_im(imagedata)]
outdict = {'images': imgs}
outdict = add_property(outdict, coordinates, 'coordinates', xy=xy)
return outdict
def clean(imagedata, coordinates=None, xy=None):
"""
Display an array as an image with polygonal regions and region drawing.
.. image:: image.png
Parameters
----------
imagedata : array-like
Image as a two dimensional (grayscale) or three dimensional (RGB) array.
coordinates : array-like
List of coordinates or list of list of coordinates. Assumes array indexing
(i.e. row/column), in the form [[r,c],[r,c]] for one region or
[[[r0,c0],[r0,c0]], [[r1,c1],[r1,c1]]] for multiple regions
xy : boolean, optional, default = None
Only if True treat coordinates as x/y positions instead of row/column indices
"""
if asarray(imagedata).ndim not in set((2, 3)):
raise Exception("Input must be two or three dimensional")
imgs = [array_to_im(imagedata)]
outdict = {"images": imgs}
outdict = add_property(outdict, coordinates, "coordinates", xy=xy)
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(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,
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(x, y, values=None, labels=None, group=None, color=None, colormap=None, size=None, xaxis=None, yaxis=None):
"""
Create a streaming scatter plot of x and y.
Plotting once returns a visualization on which 'append' can be called to add new data
in a streaming fashion. The opacity of old and new data is automatically set
to highlight the most recent data and fade old data away.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
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,3)
Single rgb value or array to set colors
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
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')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
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(x, y, color=None, label=None, size=None, xaxis=None, yaxis=None):
"""
Create a streaming scatter plot of x and y.
Plotting once returns a visualization on which 'append' can be called to add new data
in a streaming fashion. The opacity of old and new data is automatically set
to highlight the most recent data and fade old data away.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(x, y, color=None, label=None, value=None, colormap=None, size=None, alpha=None, xaxis=None, yaxis=None):
"""
Plot two-dimensional data as points.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
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 point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
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')
outdict = add_property(outdict, alpha, 'alpha')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(x, y, matrix, color=None, label=None, size=None, imagedata=None):
"""
Create a node-link graph with bundled edges.
.. image:: graphbundled.png
Parameters
----------
x,y : array-like, each (n,)
Input data for nodes (x,y coordinates)
matrix : array, (n,n)
Input data with connectivity matrix. Can be binary or continuous-valued
(for weighted edges).
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
size : array-like, optional, singleton or (n,)
Single size or array to set node sizes
"""
points = vecs_to_points(x, y)
links = mat_to_links(matrix)
outdict = {'links': links, 'nodes': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, size, 'size')
if imagedata is not None:
images = array_to_im(imagedata)
outdict['images'] = images
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(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(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(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(spec):
"""
Create a visualization from a vega-lite spec.
.. image:: vega-lite.png
Parameters
----------
values : spec
Vega-lite spec. Can be a dictionary, string, or Altiar object
"""
outdict = {}
outdict = add_property(outdict, spec, 'spec')
return outdict
def clean(series,
index=None,
color=None,
group=None,
size=None,
xaxis=None,
yaxis=None):
"""
Plot streaming one-dimensional series data as updating lines.
Plotting once returns a visualization on which 'append' can be called
to add new data in a streaming fashion. New lines will appear on the right.
.. image:: line-streaming.png
Parameters
----------
series : array-like, (n,m)
Input data for line plot, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
index : array-like, (m,)
Specify index for the x-axis of the line plot.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
group : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group assignment
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
max_width : int, optional, default = 50
The maximum number of time points to show before plot shifts.
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, index, 'index')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(x, y, color=None, label=None, value=None, colormap=None, size=None, xaxis=None, yaxis=None):
"""
Create a streaming scatter plot of x and y.
Plotting once returns a visualization on which 'append' can be called to add new data
in a streaming fashion. The opacity of old and new data is automatically set
to highlight the most recent data and fade old data away.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
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 point sizes
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
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')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(x, y, color=None, label=None, value=None, colormap=None, size=None, xaxis=None, yaxis=None):
"""
Create a streaming scatter plot of x and y.
Plotting once returns a visualization on which 'append' can be called to add new data
in a streaming fashion. The opacity of old and new data is automatically set
to highlight the most recent data and fade old data away.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set colors
label : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
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 point sizes
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
points = vecs_to_points(x, y)
outdict = {"points": points}
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")
outdict = add_property(outdict, xaxis, "xaxis")
outdict = add_property(outdict, yaxis, "yaxis")
return outdict
def clean(values, bins=None):
"""
Create a histogram.
.. image:: histogram.png
Parameters
----------
values : list
Values to plot a histogram of
bins : number, optional
Number of bins to used in the histogram. If unspecified
will default to sqrt(len(values))
"""
outdict = {'values': values}
outdict = add_property(outdict, bins, 'bins')
return outdict
def clean(values, bins=None):
"""
Create a histogram.
.. image:: histogram.png
Parameters
----------
values : list
Values to plot a histogram of
bins : number, optional
Number of bins to used in the histogram. If unspecified
will default to sqrt(len(values))
"""
outdict = {'values': values}
outdict = add_property(outdict, bins, 'bins')
return outdict
def clean(matrix, colormap=None):
"""
Visualize a dense matrix or table as a heat map.
.. image:: matrix.png
Parameters
----------
matrix : array-like
Two-dimensional array of matrix data
colormap : string
Specification of color map, only colorbrewer types supported
"""
matrix = mat_to_array(matrix)
outdict = {'matrix': matrix}
outdict = add_property(outdict, colormap, 'colormap')
return outdict
def clean(series,
index=None,
color=None,
label=None,
size=None,
xaxis=None,
yaxis=None):
"""
Plot one-dimensional series data as lines.
.. image:: line.png
Parameters
----------
series : array-like, (n,m)
Input data for line plot, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
index : array-like, (m,)
Specify index for the x-axis of the line plot.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
label : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, label, 'label')
outdict = add_property(outdict, index, 'index')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(matrix, colormap=None):
"""
Visualize a dense matrix or table as a heat map.
.. image:: matrix.png
Parameters
----------
matrix : array-like
Two-dimensional array of matrix data
colormap : string
Specification of color map, only colorbrewer types supported
"""
matrix = mat_to_array(matrix)
outdict = {'matrix': matrix}
outdict = add_property(outdict, colormap, 'colormap')
return outdict
def clean(series, index=None, color=None, group=None, size=None, xaxis=None, yaxis=None):
"""
Plot streaming one-dimensional series data as updating lines.
Plotting once returns a visualization on which 'append' can be called
to add new data in a streaming fashion. New lines will appear on the right.
.. image:: line-streaming.png
Parameters
----------
series : array-like, (n,m)
Input data for line plot, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
index : array-like, (m,)
Specify index for the x-axis of the line plot.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
group : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group assignment
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
max_width : int, optional, default = 50
The maximum number of time points to show before plot shifts.
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, index, 'index')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(series, index=None, color=None, label=None, size=None, xaxis=None, yaxis=None):
"""
Plot streaming one-dimensional series data as updating lines.
Plotting once returns a visualization on which 'append' can be called
to add new data in a streaming fashion. New lines will appear on the right.
.. image:: line.png
Parameters
----------
series : array-like, (n,m)
Input data for line plot, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
index : array-like, (m,)
Specify index for the x-axis of the line plot.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
label : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group labels
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
"""
series = array_to_lines(series)
outdict = {"series": series}
outdict = add_property(outdict, color, "color")
outdict = add_property(outdict, size, "size")
outdict = add_property(outdict, label, "label")
outdict = add_property(outdict, index, "index")
outdict = add_property(outdict, xaxis, "xaxis")
outdict = add_property(outdict, yaxis, "yaxis")
return outdict
def clean(series, index=None, color=None, group=None, size=None, xaxis=None, yaxis=None):
"""
Plot one-dimensional series data as lines.
.. image:: line.png
Parameters
----------
series : array-like, (n,m)
Input data for line plot, typically n series each of length m.
Can also pass a list where each individual series is of a different length.
index : array-like, (m,)
Specify index for the x-axis of the line plot.
color : array-like, optional, singleton or (n,3)
Single rgb value or array to set line colors
group : array-like, optional, singleton or (n,)
Single integer or array to set line colors via group assignment
size : array-like, optional, singleton or (n,)
Single size or array to set line thickness
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
zoom : boolean, optional, default=True
Whether to allow zooming
"""
series = array_to_lines(series)
outdict = {'series': series}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, index, 'index')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(matrix, label=None):
"""
Visualize a sparse adjacency matrix.
.. image:: adjacency.png
Parameters
----------
matrix : array-like
Two-dimensional array of matrix data
colormap : string
Specification of color map, only colorbrewer types supported
"""
links = mat_to_links(matrix)
nodes = list(range(0, matrix.shape[0]))
outdict = {'links': links, 'nodes': nodes}
outdict = add_property(outdict, label, 'label')
return outdict
def clean(x, y, labels=None, values=None, color=None, group=None, colormap=None,
size=None, alpha=None, xaxis=None, yaxis=None):
"""
Plot two-dimensional data as points.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
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,3)
Single rgb value or array to set colors
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
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
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
def clean(x, y, labels=None, values=None, color=None, group=None, colormap=None,
size=None, alpha=None, xaxis=None, yaxis=None):
"""
Plot two-dimensional data as points.
.. image:: scatter.png
Parameters
----------
x, y : array-like, each (n,)
Input data
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,3)
Single rgb value or array to set colors
group : array-like, optional, singleton or (n,)
Single integer or array to set colors via groups
colormap : string
Specification of color map, only colorbrewer types supported
size : array-like, optional, singleton or (n,)
Single size or array to set point sizes
alpha : array-like, optional, singleton or (n,)
Single alpha value or array to set fill and stroke opacity
xaxis : str, optional, default = None
Label for x-axis
yaxis : str, optional, default = None
Label for y-axis
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
"""
points = vecs_to_points(x, y)
outdict = {'points': points}
outdict = add_property(outdict, color, 'color')
outdict = add_property(outdict, group, 'group')
outdict = add_property(outdict, labels, 'labels')
outdict = add_property(outdict, values, 'values')
outdict = add_property(outdict, colormap, 'colormap')
outdict = add_property(outdict, size, 'size')
outdict = add_property(outdict, alpha, 'alpha')
outdict = add_property(outdict, xaxis, 'xaxis')
outdict = add_property(outdict, yaxis, 'yaxis')
return outdict
