niscope module

Installation

As a prerequisite to using the niscope module, you must install the NI-SCOPE runtime on your system. Visit ni.com/downloads to download the driver runtime for your devices.

The nimi-python modules (i.e. for NI-SCOPE) can be installed with pip:

$ python -m pip install niscope

Or easy_install from setuptools:

$ python -m easy_install niscope

Usage

The following is a basic example of using the niscope module to open a session to a High Speed Digitizer and capture a single record of 1000 points.

import niscope
with niscope.Session("Dev1") as session:
    session.channels[0].configure_vertical(range=1.0, coupling=niscope.VerticalCoupling.AC)
    session.channels[1].configure_vertical(range=10.0, coupling=niscope.VerticalCoupling.DC)
    session.configure_horizontal_timing(min_sample_rate=50000000, min_num_pts=1000, ref_position=50.0, num_records=5, enforce_realtime=True)
    with session.initiate():
        waveforms = session.channels[0,1].fetch(num_records=5)
    for wfm in waveforms:
        print('Channel {0}, record {1} samples acquired: {2:,}\n'.format(wfm.channel, wfm.record, len(wfm.samples)))

    # Find all channel 1 records (Note channel name is always a string even if integers used in channel[])
    chan1 = [wfm for wfm in waveforms if wfm.channel == '0']

    # Find all record number 3
    rec3 = [wfm for wfm in waveforms if wfm.record == 3]

The waveform returned from fetch is a flat list of Python objects

  • Attributes:

    • relative_initial_x (float) the time (in seconds) from the trigger to the first sample in the fetched waveform

    • absolute_initial_x (float) timestamp (in seconds) of the first fetched sample. This timestamp is comparable between records and acquisitions; devices that do not support this parameter use 0 for this output.

    • x_increment (float) the time between points in the acquired waveform in seconds

    • channel (str) channel name this waveform was acquired from

    • record (int) record number of this waveform

    • gain (float) the gain factor of the given channel; useful for scaling binary data with the following formula:

      voltage = binary data * gain factor + offset

    • offset (float) the offset factor of the given channel; useful for scaling binary data with the following formula:

      voltage = binary data * gain factor + offset

    • samples (array of float) floating point array of samples. Length will be of the actual samples acquired

  • Such that all record 0 waveforms are first. For example, with a channel list of 0,1, you would have the following index values:

    • index 0 = record 0, channel 0
    • index 1 = record 0, channel 1
    • index 2 = record 1, channel 0
    • index 3 = record 1, channel 1
    • etc.

If you need more performance or need to work with SciPy, you can use the fetch_into() method instead of fetch(). This method takes an already allocated numpy array and puts the acquired samples in it. Data types supported:

  • numpy.float64
  • numpy.int8
  • numpy.in16
  • numpy.int32
voltage_range = 1.0
record_length = 2000
channels = [0, 1]
num_channels = len(channels)
num_records = 5
wfm = numpy.ndarray(num_channels * record_length, dtype=numpy.int8)
session.configure_vertical(voltage_range, niscope.VerticalCoupling.AC)
session.configure_horizontal_timing(50000000, record_length, 50.0, num_records, True)
with session.initiate():
    waveform_infos = session.channels[channels].fetch_into(wfm=wfm, num_records=num_records)

The waveform_infos returned from fetch_into is a 1D list of Python objects

  • Attributes:

    • relative_initial_x (float) the time (in seconds) from the trigger to the first sample in the fetched waveform

    • absolute_initial_x (float) timestamp (in seconds) of the first fetched sample. This timestamp is comparable between records and acquisitions; devices that do not support this parameter use 0 for this output.

    • x_increment (float) the time between points in the acquired waveform in seconds

    • channel (str) channel name this waveform was asquire from

    • record (int) record number of this waveform

    • gain (float) the gain factor of the given channel; useful for scaling binary data with the following formula:

      voltage = binary data * gain factor + offset

    • offset (float) the offset factor of the given channel; useful for scaling binary data with the following formula:

      voltage = binary data * gain factor + offset

    • samples (numpy array of datatype used) floating point array of samples. Length will be of the actual samples acquired

  • Such that all record 0 waveforms are first. For example, with a channel list of 0,1, you would have the following index values:

    • index 0 = record 0, channel 0
    • index 1 = record 0, channel 1
    • index 2 = record 1, channel 0
    • index 3 = record 1, channel 1
    • etc.

Other usage examples can be found on GitHub.

API Reference