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A module to generate data logs from C++ and plot them from Python
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README.md

This module contains a library to be used from C++ that generates YAML files for data logs.

A Python script is then used to plot the files with various options.

Compilation and installation

The module can be compiled as a ROS package or standalone. When compiled as a ROS package, numerical logged data can also be published to provide online analysis through rqt_plot or other tools.

As a ROS package

Just clone the repository in your ROS workspace and build it with catkin: catkin build log2plot

As a standalone library

Go to the non_ros folder and perform a classical CMake build:

  • mkdir build
  • cd build
  • cmake ..
  • make or make install

The library can then be found through CMake find_package.

Use from C++ code

Examples can be found in the examples folder. The main class is log2plot::Logger and should be instanciated with the desired data file path and prefix: log2plot::Logger logger(fileprefix). If no fileprefix is given then the files will be created in the /tmp folder. Shipped examples use the examples path at compile time.

The logged variables have to be containers of some sort, as long as the following member functions are available:

  • operator[] to get the value at a given index
  • size() to get the length of the logged container

Besides these two points, all kind of data can be saved, but of course they will not be plottable if not numerical.

Three types of data may be logged:

  • Iteration-based data will use the index as the X-axis for the plots.
    • logger.save(v, name, legend, ylabel)
    • legend should be a YAML-style list and may be using Latex: "[v_x, \\omega_z]"
  • Time-based data has to be given a time and will use it as the X-axis.
    • logger.setTime(t, "s"); where t is a double
    • logger.saveTimed(v, name, legend, ylabel)
    • legend should be a YAML-style list and may be using Latex: "[v_x, \\omega_z]"
  • 3D pose data has to be given a 6-components pose vector (as in translation + angle-axis representation).
    • logger.save3Dpose(v, name, trajectory_name, invert_pose)
    • trajectory_name should be a single string
    • invert_pose (default false) allows to log a pose whom inverse will be actually plotted. This can be useful typically when working with a world-to-camera pose but we still want to display the camera-to-world pose afterwards.

This will log data into the file: fileprefix + name + .yaml

ROS users may use the log2plot::LogPublisher class with the exact same syntax. It will also publish the logged data as Float32MultiArray's on the log2plot/<name> topics. Trying to publish data that cannot be cast to double will lead to undefined behavior. In this case a Logger can be instanciated to log particular data while a LogPublisher will log and publish numerical data.

General options

Log is actually done when calling logger.update();, typically from inside a loop. Two parameters can be changed:

  • Subsampling to log only once every n updates: logger.setSubSampling(n) (default 1)
  • Buffer size before writing to the file: logger.setBuffer(b) (default 10)
  • The plot can be done directly from C++ if needed: logget.plot(script_path), where script_path is the path to the Python script. The default value is the path at library compile time.

Options should be given before calling the first update().

Iteration or time-based options

The following commands will be applied to the last added variable:

  • Units: logger.setUnits("[unit1, unit2, unit2]"); will save the units for the 3 first components
  • Line styles: logger.setLineType(["b, g, r--]");, line styles have to be defined in Matplotlib styles (color + line style)

3D pose options

The following commands will be applied to the last added variable:

  • Display an object following the logged pose: logger.showMovingObject(nodes, graph, desired_pose)
    • The object is displayed in wireframe from nodes (3D positions of points) and graph (segments linking 2 nodes)
    • Nodes should be a vector<vector<double> > of dimension 3xn where n in the number of points
    • Graph should be a string indicating the links between the node indices: "[[0,1],[1,2],[2,0]]" for a triangle
    • Desired pose is a 6-dimensional pose given as a std::vector. The argument can thus be passed as {0,1,2,3,4,5}
    • Built-in function allow to show a moving camera and a moving box
  • Display a fixed object: logger.showFixedObject(nodes, graph, color)
    • With the same syntax for nodes and graph, will display this wireframe object in the given Matplotlib color.
  • Change built-in colors: logger.setLineType("[b, g, r, k--]") allows to define the color and line style of the 3D elements:
    • First element is the trajectory line style (solid blue here)
    • Second element is the moving object (solid green)
    • Third one is for the initial and final poses (solid red)
    • Fourth one is for the desided pose (dashed black)

Python syntax

The Python module used to plot the files is in the src folder and requires matplotlib, YAML, and argparse. It may be useful to re-plot a file with different options. The script can be called from the command line or with the rosrun syntax if compiled with ROS:

  • python plot <file.yaml>
  • rosrun log2plot plot <file.yaml>

Many options are available from the command line, call plot -h to have a list. Several files can be plotted at the same time, in this case if they have the same y-label their y-axis will be at the same scale. By default they will be plotted in different subplots, but can be plotted in the same plot with the -g option.

Videos can be created using the -v <subsampling> option. ffmpeg or avconv will be used to create a mp4 file showing the plot evolution.

Examples

In the examples folder are shipped 4 use cases:

  • std_container uses std::vectors and shows iteration-based, time-based and 3D pose plots
  • std_publisher shows how to publish data to ROS topics when logging
  • visp_containers uses containers from the ViSP library (vpColVector and vpPoseVector) and logs an inverted 3D pose
  • eigen_containers uses containers from the Eigen library (Eigen::Vector3d)
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