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Plotting and logging of real-time data for desktop.

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Live timeseries analysis on your desktop!

# About

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.

Features:
- 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:
    - Python
    - [C](demo/c/README.md)
    - [C++](cpp/README.md)
    - [C#](dotnet/README.md)
    - Rust
- Export data to HDF5
- [Data adapters](adapters/README.md) for:
    - ADS
    - MQTT
    - ROS2

# Video

These are recorded videos of the tool:

- [lognplot @ FOSDEM 2020](https://fosdem.org/2020/schedule/event/ema_lognplot/)

# Screenshots

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

![screenshot3](https://raw.githubusercontent.com/windelbouwman/lognplot/master/screenshots/screenshot3.png)

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

![screenshot4](https://raw.githubusercontent.com/windelbouwman/lognplot/master/screenshots/screenshot4.png)

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.

![screenshot1](https://raw.githubusercontent.com/windelbouwman/lognplot/master/screenshots/screenshot1.png)

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

![screenshot2](https://raw.githubusercontent.com/windelbouwman/lognplot/master/screenshots/screenshot2.png)

# Installation

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

First clone this repository:

$ git clone https://github.com/windelbouwman/lognplot.git

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: https://crates.io/crates/lognplot

Python package: https://pypi.org/project/lognplot/

## Requirements

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

# Usage

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

$ cd python
    $ python softscope.py

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 noize_source.py

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:

```python

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])
plt.show()

```

# Documentation

Documentation for python users can be found here: https://lognplot.readthedocs.io/en/latest/
Documentation for rust users can be found here: https://docs.rs/lognplot

# Plan

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!

- [Embedded debugger](https://github.com/DEMCON/EmbeddedDebugger)
- [getcurve.io](https://getcurve.io/)
- [grafana](https://grafana.com/)
    - Web based
- [KST plot](https://kst-plot.kde.org/)
    - Can view large CSV files
- [plot juggler](https://github.com/facontidavide/PlotJuggler)
    - Qt interface
    - Interfaces with ROS
- [Plc-Lab](http://plc-lab.nl/)
    - Fancy GUI
- [RQT](https://wiki.ros.org/rqt)
    - Comes with ROS
- [sigrok](https://sigrok.org)
    - Slick Qt interface
    - Interfacing with many logic analyzers and oscilloscopes
- [speedscope](https://www.speedscope.app/)
    - Callstack tracer
- [trace compass](https://www.eclipse.org/tracecompass/)
    - Eclipse based
    - Supports many trace formats
- [tracy profiler](https://bitbucket.org/wolfpld/tracy)

# Profiling

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:

```toml
[profile.release]
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

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