Data-Visualization

Visualization of data:

  • ts : Interactive analysis of time-series data (1D and 3D).

  • orientation : Visualization of 3D orientations as animated triangle.

  • Orientation_OGLMuch faster 3D orientation viewer, based

    on OpenGL

1-dimensional and 3-dimensional data can be viewed. It also allows to inspect the variables of the current workspace.

_images/viewer_large.png

Interactively analyze time-series data …

Functions

_images/viewer_ts3.png

Improved viewability of 3D data.

_images/orientation_viewer.png

Class

view.Orientation_OGL([quat_in, rate, ...])

Orientation viewer utilizing OpenGL

Details

This module includes two functions:

  • An interactive viewer for time-series data (“view.ts”)

  • An animation of 3D orientations, expressed as quaternions (“view.orientation”)

For the time-series viewer, variable types that can in principle be plotted are:

  • np.ndarray

  • pd.core.frame.DataFrame

  • pd.core.series.Series

Viewer can be used to inspect a single variable, or to select one from the current workspace.

Notable aspects:

  • Based on Tkinter, to ensure that it runs on all Python installations.

  • Resizable window.

  • Keyboard-based interaction.

  • Logging of marked events.

view.orientation(quats, out_file=None, title_text=None, deltaT=100)[source]

Calculates the orienation of an arrow-patch used to visualize a quaternion. Uses “_update_func” for the display.

Parameters:

  • quats (array [(N,3) or (N,4)]) – Quaterions describing the orientation.

  • out_file (string) – Path- and file-name of the animated out-file (“.mp4”). [Default=None]

  • title_text (string) – Name of title of animation [Default=None]

  • deltaT (int) – interval between frames [msec]. Smaller numbers make faster animations.

Example

To visualize a rotation about the (vertical) z-axis:

>>> # Set the parameters
>>> omega = np.r_[0, 10, 10]     # [deg/s]
>>> duration = 2
>>> rate = 100
>>> q0 = [1, 0, 0, 0]
>>> out_file = 'demo_patch.mp4'
>>> title_text = 'Rotation Demo'
>>>
>>> # Calculate the orientation
>>> num_rep = duration*rate
>>> omegas = np.tile(omega, [num_rep, 1])
>>> q = skin.quat.calc_quat(omegas, q0, rate, 'sf')
>>>
>>> orientation(q, out_file, 'Well done!', deltaT=1000./rate)

Note

Seems to be slow. So unless you need a movie, better use “Orientation_OGL”.

view.ts(data=None)[source]

Show the given time-series data. In addition to the (obvious) GUI-interactions, the following options are available:

Keyboard interaction:

  • f … forward (+ 1/2 frame)

  • n … next (+ 1 frame)

  • b … back ( -1/2 frame)

  • p … previous (-1 frame)

  • z … zoom (x-frame = 10% of total length)

  • a … all (adjust x- and y-limits)

  • x … exit

Optimized y-scale:

Often one wants to see data symmetrically about the zero-axis. To facilitate this display, adjusting the “Upper Limit” automatically sets the lower limit to the corresponding negative value.

Logging:

When “Log” is activated, right-mouse clicks are indicated with vertical bars, and the corresponding x-values are stored into the users home-directory, in the file “[varName].log”. Since the name of the first value is unknown the first events are stored into “data.log”.

Load:

Pushing the “Load”-button shows you all the plottable variables in your namespace. Plottable variables are:

  • ndarrays

  • Pandas DataFrames

  • Pandas Series

Examples

To view a single plottable variable:

>>> x = np.random.randn(100,3)
>>> view.ts(x)

To select a plottable variable from the workspace

>>> x = np.random.randn(100,3)
>>> t = np.arange(0,10,0.1)
>>> y = np.sin(x)
>>> view.ts(locals)