Skip to main content

Create real-time plots in Jupyter Notebooks.

Project description

jupyterplot

Create real-time plots in Jupyter notebooks.

What is it?

This is a library to generate real-time plots in Jupyter notebooks with a tqdm-like interface. It is largely based on the python-lrcurve library by Andreas Madsen.

single-plot

Install

pip install jupyterplot

How to use

Single plot

Creating a simple real-time plot in a Jupyter notebook is as easy as easy as the following line:

from jupyterplot import ProgressPlot
import numpy as np

pp = ProgressPlot()
for i in range(1000):
    pp.update(np.sin(i/100))
pp.finalize()

single-plot

Note: The pp.finalize() statement is necessary to make the plots persistent between notebook sessions.

Custom range

By default, the x and y range adapt to new data points. If the scale is known beforehand, it might steadier to set it beforehand:

pp = ProgressPlot(x_lim=[0,1000],y_lim=[-1.5,1.5])
for i in range(1000):
    pp.update(np.sin(i/100))
pp.finalize()

single-plot

Multiple lines

One can also plot several lines in parallel by specifying the line names in the constructor and passing all values in a list.

pp = ProgressPlot(line_names=['lin', 'log', 'cos', 'sin'], x_lim=[0, 1000], y_lim=[-1,4])
for i in range(1000):
    pp.update([[i/250, np.log10(i+1), np.cos(i/100), np.sin(i/100)]])
pp.finalize()

single-plot

Note: The data is fed with two brackets [[y1, y2, y3]]. The first list corresponds the plots, wheras the second list to each line of each plot as we will also see in the next example.

Multiple plots

pp = ProgressPlot(plot_names=['cos', 'sin'], line_names=['data', 'delayed-data'], x_lim=[0, 1000], y_lim=[-1,1])
for i in range(1000):
    pp.update([[np.cos(i/100), np.cos((i+20)/100)], [np.sin(i/100), np.sin((i+20)/100)]])
pp.finalize()

single-plot

Custom x-values

Finally, if the x values should not be incremented by 1 at every update one can set the x_iterator=False. This requires passing two values to the update(x, y), where x is an int/float and y follows the same format as in the previous examples.

pp = ProgressPlot(x_iterator=False, x_label='custom-x', x_lim=[0,10000], y_lim=[0, 10])
for i in range(1000):
    pp.update(10*i, i/100)
pp.finalize()

single-plot

Input format

Single plot, single line

If a the progress plot consists of a single plot with a single line one can pass the y-updates as int/floats.

Multiple plots, multiple lines

If multiple plots or lines are used, the y-updates can either be lists or dicts:

y_update_list = [[y_plot_1_line_1, y_plot_1_line_2],
                 [y_plot_2_line_1, y_plot_2_line_2]]

y_update_dict = {'plot_name_1': {'line_name_1': y_plot_1_line_1,
                                 'line_name_2': y_plot_1_line_2},
                 'plot_name_2': {'line_name_1': y_plot_2_line_1,
                                 'line_name_2': y_plot_2_line_2}}

Limitations

  • Only one ProgressPlot() object can be used at a time.
  • Each subplot must have the same number of lines.
  • The same color cycle for each subplot is used.

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for jupyterplot, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size jupyterplot-0.0.1-py3-none-any.whl (10.1 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size jupyterplot-0.0.1.tar.gz (11.6 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page