Area glyphs (original) (raw)

Rectangles#

Bars#

Quads#

To draw axis aligned rectangles by specifying the left, right,top, and bottom positions, use the quad() glyph function:

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3], right=[1.2, 2.5, 3.7], color="#B3DE69")

show(p)

Blocks#

To draw axis aligned rectangles by specifying the x and ycoordinates for a lower-left corner, and a width and height, use the block()glyph function:

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.block(x=[1, 2, 3], y=[1, 2, 3], width=[0.2, 0.5, 0.1], height=1.5)

show(p)

Rotatable#

To draw arbitrary rectangles by specifying center coordinates, width,height, and angle, use the rect() glyph function:

from math import pi

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.rect(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=40, color="#CAB2D6", angle=pi/3, height_units="screen")

show(p)

Directed areas#

Directed areas are filled regions between two series that share a common index. For instance, a vertical directed area has one x coordinate array and twoy coordinate arrays, y1 and y2, defining the space for Bokeh to fill.

Single areas#

To fill an area in vertical direction, use the varea() method. You can do the same in horizontal direction with harea().

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

p.varea(x=[1, 2, 3, 4, 5], y1=[2, 6, 4, 3, 5], y2=[1, 4, 2, 2, 3])

show(p)

Stacked areas#

To stack directed areas, use the varea_stack() and harea_stack() convenience methods.

from bokeh.models import ColumnDataSource from bokeh.plotting import figure, show

source = ColumnDataSource(data=dict( x=[1, 2, 3, 4, 5], y1=[1, 2, 4, 3, 4], y2=[1, 4, 2, 2, 3], )) p = figure(width=400, height=400)

p.varea_stack(['y1', 'y2'], x='x', color=("grey", "lightgrey"), source=source)

show(p)

Patches#

Single patches#

The following example generates a single polygonal patch from one-dimensional sequences of x and y points using the patch() glyph method:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

add a patch renderer with an alpha and line width

p.patch([1, 2, 3, 4, 5], [6, 7, 8, 7, 3], alpha=0.5, line_width=2)

show(p)

Multiple patches#

To plot several polygonal patches, use the patches() glyph method:

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

p.patches([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]], color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=2)

show(p)

Note

Unlike many other glyph methods, patches() accepts a list of lists of xand y positions for each line. The patches() method also expects a scalar value or a list of scalars for each patch for parameters such as color, alpha, and line width. You can similarly use a ColumnDataSourceconsisting of a list of lists of point coordinates and a list of scalar values of matching length.

Missing points#

Just as with the line() and multi_line() methods, you can pass NaN values to patch() and patches() glyphs. This produces disjointed patches with gaps for NaN values.

from math import nan

from bokeh.plotting import figure, show

p = figure(width=400, height=400)

add a patch renderer with NaN values

p.patch([1, 2, 3, nan, 4, 5, 6], [6, 7, 5, nan, 7, 3, 6], alpha=0.5, line_width=2)

show(p)

Warning

Bokeh doesn’t currently support hit testing on patch objects with NaNvalues.

Polygons#

The multi_polygons() glyph uses nesting to accept a variety of information relevant to polygons. The method duplicates the functionality of patches() but you can also use it to render holes inside polygons.

Note

Unlike many other glyph methods, multi_polygons() accepts a triple-nested lists of x and y positions for the exterior and holes composing each polygon. The multi_polygons() method also expects a scalar value or a list of scalars for each item for parameters such as color, alpha, and line width. You can similarly use a ColumnDataSource consisting of a triple- nested list of point coordinates and a list of scalars, with the top-level list of point coordinates being of equal length with the list of scalars.

Simple polygon#

The following example generates a single polygon from a triple-nested list of one-dimensional sequences of x and y points using the multi_polygons()glyph method.

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.multi_polygons(xs=[[[[1, 1, 2, 2]]]], ys=[[[[3, 4, 4, 3]]]])

show(p)

Polygon with holes#

The following example generates a single polygon with holes from three sequences of x and y points. The first sequence represents the exterior of the polygon and the following sequences represent the holes.

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.multi_polygons(xs=[[[ [1, 2, 2, 1], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ]]], ys=[[[ [3, 3, 4, 4], [3.2, 3.6, 3.2], [3.4, 3.8, 3.8] ]]])

show(p)

Multi-polygon with separate parts#

A single polygon concept can comprise multiple polygon geometries. The following example generates a multi-polygon glyph from several sequences ofx and y points. Each item in the sequence represents a part of the glyph.

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.multi_polygons(xs=[[[ [1, 1, 2, 2], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ], [ [3, 4, 3] ]]], ys=[[[ [4, 3, 3, 4], [3.2, 3.2, 3.6], [3.4, 3.8, 3.8] ], [ [1, 1, 3] ]]])

show(p)

Multiple multi-polygons#

The top-level of nesting separates each multi-polygon from the rest. You can think of each multi-polygon as a row in the data source, potentially with a corresponding label or color.

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.multi_polygons( xs=[ [[ [1, 1, 2, 2], [1.2, 1.6, 1.6], [1.8, 1.8, 1.6] ], [ [3, 3, 4] ]], [[ [1, 2, 2, 1], [1.3, 1.3, 1.7, 1.7] ]]], ys=[ [[ [4, 3, 3, 4], [3.2, 3.2, 3.6], [3.4, 3.8, 3.8] ], [ [1, 3, 1] ]], [[ [1, 1, 2, 2], [1.3, 1.7, 1.7, 1.3] ]]], color=['blue', 'red'])

show(p)

Strips#

To draw multiple horizontal or vertical strips (bars of infinite width or height respectively), use the hstrip() or vstrip() glyph methods. These methods accept either y0 and y1 or x0 and x1 coordinate components respectively. Note that these glyphs can only compute bounds in one axis, thus may require explicit range specification in the orthogonal axis, e.g. if used alone.

from bokeh.io import show from bokeh.plotting import figure

plot = figure()

plot.hstrip( y0=[45, 60, 80], y1=[50, 70, 95], line_color="pink", fill_color="purple", hatch_pattern="x", hatch_color="yellow", ) plot.vstrip( x0=[45, 60, 80], x1=[50, 70, 95], line_color="pink", fill_color="yellow", hatch_pattern="/", hatch_color="purple", )

show(plot)

Ellipses#

The ellipse() glyph method accepts the same properties as rect(), but renders ellipse shapes.

from math import pi

from bokeh.plotting import figure, show

p = figure(width=400, height=400) p.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=[0.2, 0.3, 0.1], height=0.3, angle=pi/3, color="#CAB2D6")

show(p)