# Bokeh Integration

> [Bokeh](http://bokeh.pydata.org/en/latest/) is a Python interactive visualization library that targets modern web browsers for presentation.

This document can be compiled to HTML with

    ```
    stitch ex_bokeh.txt -o ex_bokeh.html --no-self-contained
    ```

The easiest way to use stitch & Bokeh together is with the ``Bokeh.output_notebook``
method.

```{python}
from bokeh.plotting import figure, show, output_notebook

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

output_notebook()
```

```{python}
# create a new plot
p = figure(
   tools="pan,box_zoom,reset,save",
   y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
   x_axis_label='sections', y_axis_label='particles'
)

# add some renderers
p.line(x, x, legend="y=x")
p.circle(x, x, legend="y=x", fill_color="white", size=8)
p.line(x, y0, legend="y=x^2", line_width=3)
p.line(x, y1, legend="y=10^x", line_color="red")
p.circle(x, y1, legend="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend="y=10^x^2", line_color="orange", line_dash="4 4")

# show the results
show(p)
```

See also Bokeh's tools for [embedding](http://bokeh.pydata.org/en/latest/docs/user_guide/embed.html)
the HTML and javascript necessary to get a figure to show up in HTML output.