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Live timeseries analysis on your desktop!


Lognplot is a graphical viewer for time series data. Unlike many other projects in this area, lognplot is not a hosted web application. It is a desktop application which visualizes your data from your application. This can be an embedded, robotic, mobile, PC or PLC application.


  • Plot data live while staying responsive
  • Send data over TCP/IP link to GUI.
  • Two GUI implementations:
    • python GUI implementation (based on PyQt5)
    • rust GUI implementation (based on gtk-rs / cairo)
  • Client libraries for:
  • Export data to HDF5
  • Data adapters for:
    • ADS
    • MQTT
    • ROS2


These are recorded videos of the tool:


This is an example screenshot of the lognplot python application, visualizing 10 million datapoints. Note that zooming is still smoothly performed.


This is an example screenshot of the GTK gui implemented with rust, visualizing also 10 million datapoints.


This is an example of the plot window, when zoomed out. Note that not all points are displayed, but aggregates of the data are visualized as min/max/mean/stddev lines.


When zooming into the data, the individual data points come into picture.



Note that at this moment, you will want to grab the latest git version from github.

First clone this repository:

$ git clone

For python, follow this steps:

$ cd lognplot/python
$ pip install .
$ python -m lognplot

For rust, follow these steps:

$ cd lognplotgtk
$ cargo run --release

Packages are released for rust and python

Rust crate:

Python package:


For the GTK variant of the tool, you need the following to be installed:

  • cairo-gobject-devel
  • pango-devel
  • atk-devel
  • gdk-pixbuf2-devel
  • gtk3-devel
  • hdf5-devel


To use the python side of this code, start as a demo the softscope:

$ cd python
$ python

This will popup a plot window. Zooming and panning can be done with the keyboard keys w,a,s,d and i,j,k,l. Press space or enter to autofit. The data is a 10 kHz generated signal.

Another demo is the softscope server. This will open a TCP/IP port which can receive data.

$ cd python
$ python -m lognplot

The softscope is now ready to receive streaming data via network.

Next, start the demo datasource, which will send data via TCP to this GUI:

$ cd demo
$ python

Another server demo is the rust side of the code. Start the GUI like this:

$ cd lognplotgtk
$ cargo run

This application will be able to receive data via TCP/IP.

Send data from C code

To send data from C-code, refer to the demo in demo/c. This demo uses the clognplot rust crate, which is a static library which can be used from C. The resulting C program will connect over TCP/IP and send its data to the plot tool using parts of the rust crates.

Export data to HDF5

To be able to further process the data in, for example, a python script, you can use File->Save to save all captured data as a HDF5 file.

Example usage of this saved HDF5 file:

import h5py
from matplotlib import pyplot as plt

f = h5py.File('datorz.h5', 'r')
group = f['my_datorz']
signal = group['My_signal']
plt.plot(signal[:,0], signal[:,1])


Documentation for python users can be found here: Documentation for rust users can be found here:


This is a list of things to do:

  • PyQt5 implementation
  • gtk-rs implementation

Requirements for live data visualization

  • Append data structure to enable appending new data
  • Data point aggregation for zooming out and showing min/max/mean lines

Similar projects

There is an interesting list of similar projects. Do you know of another project? Please submit a pull request or an issue!


To optimize the GUI experience, you can profile the rust lognplot gui by the following method.

Modify the Cargo.toml file to include this snippet:

debug = true

This will build in release mode, but include debug symbols.

Now, build in release mode:

$ cargo build --release

Next up, use the linux perf tool to profile the application:

$ perf record -F 99 --call-graph dwarf target/release/lognplotgtk

Now perform some intensive work. When done, close the gui.

Analyze the perf results:

$ perf report