theme_glyphs#
This example shows how to create and use your custom theme in a Bokeh plot with JSON.
Details
- Bokeh APIs:
- More info:
- Keywords:
theme
import numpy as np
from bokeh.io import curdoc, show
from bokeh.models import (BasicTicker, BasicTickFormatter, ColumnDataSource, Ellipse,
HBar, Line, LinearAxis, Plot, Scatter, Text, Title)
from bokeh.themes import Theme
from bokeh.transform import dodge
theme_json = {
"attrs": {
"Plot": {"width": 400, "height": 400, "background_fill_color": "#eeeeee"},
"Grid": {"visible": False},
"Title": {"text": "Demo of Themes"},
"LinearAxis": {
"axis_line_color": "#ffffff",
"major_tick_line_color": "#ffffff",
"axis_label_text_font_size": "10pt",
"axis_label_text_font_style": "bold",
},
"LineGlyph": {"line_color": "#ee33ee", "line_width": 2},
"FillGlyph": {"fill_color": "orange"},
"HatchGlyph": {"hatch_pattern": "@", "hatch_alpha": 0.8},
"TextGlyph": {
"text_color": "red",
"text_font_style": "bold",
"text_font": "Helvetica",
},
"Ellipse": {"fill_color": "green", "line_color": "yellow", "fill_alpha": 0.2},
},
}
curdoc().theme = Theme(json=theme_json)
x = np.linspace(1, 5, 100)
y = x + np.sin((x - 1) * np.pi)
x2 = np.linspace(1.5, 5.5, 5)
z = x2 + 2 * np.cos((x2 - 1) * np.pi)
source1 = ColumnDataSource({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 4, 5], "who": ["a", "b", "c", "d", "e"]})
source2 = ColumnDataSource({"x": x, "y": y})
source3 = ColumnDataSource({"x": x2, "y": z})
source4 = ColumnDataSource({"y": [2.5], "x": [0.5]})
plot = Plot(width=300, height=300)
plot.title = Title(text="Themed glyphs")
xaxis = LinearAxis(ticker=BasicTicker(), formatter=BasicTickFormatter())
yaxis = LinearAxis(ticker=BasicTicker(), formatter=BasicTickFormatter())
plot.add_layout(xaxis, "below")
plot.add_layout(yaxis, "left")
plot.add_glyph(source1, Scatter(x="x", y="y", marker="diamond", size=20))
plot.add_glyph(source1, Text(x=dodge("x", -0.2), y=dodge("y", 0.1), text="who"))
plot.add_glyph(source2, Line(x="x", y="y"))
plot.add_glyph(source3, Ellipse(x="x", y="y", width=0.2, height=0.3, angle=-0.7))
plot.add_glyph(source4, glyph=HBar(y="y", right="x", height=1.5))
show(plot)