Visualization Nodes

Analog signal visualizers

class pyacq.viewers.QOscilloscope(**kargs)

Continuous, multi-channel oscilloscope based on Qt and pyqtgraph.

class pyacq.viewers.QTriggeredOscilloscope(**kargs)

Spectral visualizers

class pyacq.viewers.QTimeFreq(**kargs)

Class for visualizing the frequency spectrogram with a Morlet continuous wavelet transform.

This allows better visualization than the standard FFT spectrogram because it provides better temporal resolution for high-frequency signals without sacrificing frequency resolution for low-frequency signals. See https://en.wikipedia.org/wiki/Morlet_wavelet

This class internally uses one TimeFreqWorker per channel, which allows multiple signals to be transformed in parallel.

The node operates in one of 2 modes:

• Each TimeFreqWorker lives in the same QApplication as the QTimeFreq node (nodegroup_friends=None).
• Each TimeFreqWorker is spawned in another NodeGroup to distribute the load (nodegroup_friends=[some_list_of_nodegroup]).

This viewer needs manual tuning for performance: small refresh_interval, high number of freqs, hight f_stop, and high xsize can all lead to heavy CPU load.

This node requires its input stream to use:

• transfermode==sharedarray
• axisorder==[1,0]

If the input stream does not meet these requirements, then a StreamConverter will be created to proxy the input.

QTimeFreq can be configured on the fly by changing QTimeFreq.params and QTimeFreq.by_channel_params. By default, double-clicking on the viewer will open a GUI dialog for these parameters.

Usage:

viewer = QTimeFreq()
viewer.configure(with_user_dialog=True, nodegroup_friends=None)
viewer.input.connect(somedevice.output)
viewer.initialize()
viewer.show()
viewer.start()

viewer.params['nb_column'] = 4
viewer.params['refresh_interval'] = 1000

Image visualizers

class pyacq.viewers.ImageViewer(**kargs)

A simple image viewer using pyqtgraph.