LDAQ 1.0.3

Data acquisition and generation with live visualization.

LDAQ

Data acquisition and generation with live visualization.

Installation

The package can be installed from PyPI using pip: .. code-block:

pip install LDAQ

Getting started

Create the acquisition object

The first step to starting the measurement is to create an acquisition object. Depending on your measurement hardware, you can select the appropriate acquisition class.

In this example, we use the LDAQ.national_instruments.NIAcquisition class, which is a wrapper for the National Instruments DAQmx driver. The class accepts the name of the input task as an argument:

acq = LDAQ.national_instruments.NIAcquisition(input_task_name, acquisition_name='DataSource')

If the acquisition_name argument is not specified, the name of the acquisition object will be set to the value of input_task_name.

The acquisition_name argument is important when using multiple acquisition objects in the same measurement, and when specifying the layout of the live visualization.

Create the Core object

The acq object can now be added to the LDAQ.Core class:

ldaq = LDAQ.Core(acq)

Note

To add live visualization of the measurement, the visualization object can be added to the LDAQ.Core object:

ldaq = LDAQ.Core(acq, visualization=vis)

Read how to prepare the vis object in the visualization section.

Set the trigger

Often the measurement is started when one of the signal excedes a certain level. This can be achieved by setting the trigger on one of the data sources by calling the set_trigger method:

ldaq.set_trigger(
    source='DataSource',
    level=100,
    channel=0,
    duration=11,
    presamples=10
)

Where:

• source: the name of the acquisition object on which the trigger is set.

• level: the trigger level.

• channel: the channel on which the trigger is set.

• duration: the duration of the trigger in seconds.

• presamples: the number of samples to be acquired before the trigger is detected.

Note

The LDAQ.Core may seem unnecessary when using a single acquisition source. However, it enables the simultaneous usage of signal generation, live visualization and multiple acquisition/generation sources.

Run the measurement

The measurement can now be started by calling the run method:

ldaq.run()

Save the measurement

After the measurement is completed, the data can be saved by calling:

ldaq.save_measurement(
    name='my_measurement',
    root=path_to_save_folder,
    timestamp=True,
    comment='my comment'
)

Where:

• name: required, the name of the measurement, without extension (.pkl is added automatically).

• root: optional, the path to the folder where the measurement will be saved. If it is not given, the measurement will be saved in the current working directory.

• timestamp: optional, add a timestamp at the beginning of the file name.

• comment: optional, a comment to be saved with the measurement.

What else can I do with LDAQ?

• Add generation to the LDAQ.Core object (see generation).

• Apply virtual channels to acquisition objects, to perform calculations on the acquired data (see virtual channels).

• Add visualization to the LDAQ.Core object (see visualization).

• Apply functions to measured data in real-time visualization (see visualization).

• Add multiple acquisition and signal generation objects to LDAQ.Core (see multiple sources).

• Define a NI Task in your program and use it with LDAQ (see NI Task).

• Currently the package supports a limited set of devices from National Instruments, Digilent, FLIR, Basler and devices using serial communication (see supported devices).

• Create your own acquisition class by overriding just few methods (see custom acquisition).