bokehlab 0.2.3

Interactive plotting with familiar syntax in Jupyter notebooks.

bokehlab

Bolehlab is an interactive plotting library with the familiar matplotlib/matlab syntax.
Built upon bokeh visualization library. Works in both classic jupyter-notebook and JupyterLab.

Installation:

pip install bokehlab

To load this extension in jupyter notebook (both classic jupyter and jupyter lab):

%load_ext bokehlab

Or even shorter:

%bokehlab

To make the short syntax working, either run

python -m bokehlab.install_magic

Or manually copy bokelab_magic.py from the distribution directory to ~\.ipython\profile_default\startup.

Basic plotting:

plot([1,4,9])             # dots 
plot([1,4,9], '.-')       # line and dots 
plot([1,2,3], [1,4,9])    # x and y 
plot([1,2,3], [1,4,9], '.-')    # x, y and line style

Several plots in one figure:

Interactive controls:

click and drag = pan
mouse wheel = zoom, 
wheel on x axis = zoom horizontally
wheel on y axis = zoom vertically

Multiple plots syntax (equivalent ways to draw it):

x = [1,5,10]
y1 = [1,4,9]
y2 = [1,8,27]

- plot(x, y1, '.-')        # solid line with dots
  plot(x, y2, '.-g')       # the second plot is green

- plot([y1, y2])           # auto x, auto colors       

- plot(x, [y1, y2])

- plot([y1, y2], '.-bg')   # blue and green

- plot([y1, y2], style=['.', '.-'], color=['b', 'g'])

- plot(x, y1, '.-', x, y2, '.-g')

The following markers are supported so far:

'.' dots
'-' line
'.-' dots+line

The following colors are supported so far:

'b' blue
'g' green
'r' red
'O' orange  (capital O to avoid clashes with 'o' for open dots)

NB The color specifier must go after the marker if both are present.

Legend:

- plot([1,2,3], [1,4,9], label='plot1')
  plot([1,2,3], [2,5,10], label='plot2')

- plot([y1, y2], label=['y1', 'y2'])

Legend location:

- plot([1,2,3], [1,4,9], label='plot1', legend_loc='top_left')
  plot([1,2,3], [2,5,10], label='plot2')

Other legend locations: https://docs.bokeh.org/en/latest/docs/user_guide/styling.html#location

Axes labels:

- plot([1,2,3], xlabel='time', ylabel='value')
- xlabel('time'); ylabel('value')
- xylabels('time', 'value')

Other uses:

• semilogx(), semilogy() and loglog() show (semi)logarithmic plots with the same syntax as plot().

• hist(x) displays a histogram of x

• plot(x, y, hover=True) displays point coordinates on mouse hover.

• plot(x, y, vline=1, hline=1.5, vline_color='red') in addition to the (x, y) plot displays an infinite vertical line with x=1 and custom red color and an infinite horizontal line with y=1.5 and the default pink color.

Visualizing Pandas Dataframes

• plot(df) plots all columns of the dataframe as separate lines on the same figure with column names displayed in the legend and with index taken as the x axis values. If the legend grows too long, it can be hidden with legend_loc='hide' (new in v0.1.13):

• show_df(df) displays pandas dataframe as a table (new in v0.1.14):

Displaying Images

• imshow(a) displays an array as an image:

Complete list of colormaps: https://matplotlib.org/3.5.0/tutorials/colors/colormaps.html

• imshow(im1, im2, ...) shows several images side by side with linked panning and zooming (link=False to disable):

• imshow([[im1, im2, ...], [im3, im4, ... ], ...]) displays a matrix of images with panning and zooming linked row-wise:

See also a contour plot example in the bokeh gallery page.

Comparison to bokeh

Bokehlab is a thin wrapper over the excellent library bokeh primarily aimed at cutting down the amount of boilerplate code.

The following commands are equivalent: