bqplot is a plotting system for the Jupyter notebook.

• provide a unified framework for 2d visualizations with a pythonic API.
• provide a sensible API for adding user interations (panning, zooming, selection, etc)
• pip installable

Two main APIs are provided

• Users can build a custom visualization using our object model, that is inspired by the constructs of the Grammar of Graphics (figure, marks, axes, scales), and enrich their visualization with our Interaction Layer.
• Or they can use the context-based API similar to Matplotlib's pyplot, which provides sensible default choices for most parameters.

This package depends on the following packages:

• numpy
• IPython (version >=4.0)

1. Installing bqplot:

   $ pip install bqplot
   

   or for a development installation, close this repository and run

   $ pip install -e ./bqplot
   

2. Next, we need to install the JavaScript code.

   • Linux users:

   $ ipython -c "import bqplot; bqplot.install_nbextension(symlink=True)"
   

   • Windows users:

   $ ipython -c "import bqplot; bqplot.install_nbextension(symlink=False)"
   

3. Note for developers: the symlink=True argument in Linux allows one to modify the JavaScript code inplace. This feature is not available with Windows.

# In an IPython shell
$ ipython
In [1]: import bqplot as bq

That's it! You're ready to go!

1. Using the bqplot internal object model

   import bqplot as bq
   import numpy as np
   from IPython.display import display
   
   n = 10
   x_data = np.arange(n)
   y_data = np.cumsum(np.random.randn(n)) * 100.0
   y_data_2 = np.cumsum(np.random.randn(n)) * 100.0
   y_data_3 = np.cumsum(np.random.randn(n)) * 100.0
   
   sc_ord = bq.OrdinalScale()
   sc_y = bq.LinearScale()
   sc_y_2 = bq.LinearScale()
   
   ord_ax = bq.Axis(scale=sc_ord,
                    label='Test X',
                    tick_format='0.0f',
                    grid_lines='none')
   
   y_ax = bq.Axis(scale=sc_y,
                  label='Test Y',
                  tick_format='0.2f',
                  grid_lines='solid',
                  orientation='vertical')
   
   y_ax_2 = bq.Axis(label='Test Y 2',
                    scale=sc_y_2,
                    orientation='vertical',
                    side='right',
                    tick_format='0.0f',
                    grid_lines='dashed')
   
   line_chart = bq.Lines(x=x_data,
                         y=[y_data, y_data_2, y_data_3],
                         scales={
                             'x': sc_ord,
                             'y': sc_y
                         },
                         stroke_width=3,
                         labels=['Line1', 'Line2', 'Line3'],
                         display_legend=True)
   
   bar_chart = bq.Bars(x=x_data,
                       y=[y_data, y_data_2, y_data_3],
                       scales={
                           'x': sc_ord,
                           'y': sc_y_2
                       },
                       opacity=0.5,
                       labels=['Bar1', 'Bar2', 'Bar3'],
                       display_legend=True)
   
   fig = bq.Figure(axes=[ord_ax, y_ax, y_ax_2],
                   marks=[bar_chart, line_chart],
                   legend_location='top-left')
   
   display(fig)

2. Using the pyplot API

   from bqplot import pyplot as plt
   import numpy as np
   
   plt.figure(1)
   np.random.seed(0)
   n = 200
   x = np.linspace(0.0, 10.0, n)
   y = np.cumsum(np.random.randn(n))
   plt.plot(x,y, axes_options={'y': {'grid_lines': 'dashed'}})
   plt.show()

This software is licensed under the Apache 2.0 license. See the LICENSE file for details.