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(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(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(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, 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(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, 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(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(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(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