niscope 1.3.0

NI-SCOPE Python API

Overall Status

master branch status
GitHub status

Info	Python bindings for NI Modular Instrument drivers. See GitHub for the latest source.
Author	National Instruments

About

The nimi-python repository generates Python bindings (Application Programming Interface) for interacting with the Modular Instrument drivers. The following drivers are supported:

• NI-DCPower (Python module: nidcpower)
• NI-Digital Pattern Driver (Python module: nidigital)
• NI-DMM (Python module: nidmm)
• NI-FGEN (Python module: nifgen)
• NI-ModInst (Python module: nimodinst)
• NI-SCOPE (Python module: niscope)
• NI Switch Executive (Python module: nise)
• NI-SWITCH (Python module: niswitch)
• NI-TClk (Python module: nitclk)

It is implemented as a set of Mako templates and per-driver metafiles that produce a Python module for each driver. The driver is called through its public C API using the ctypes Python library.

nimi-python supports all the Operating Systems supported by the underlying driver.

nimi-python follows Python Software Foundation support policy for different versions. At this time this includes Python 3.5 and above using CPython.

NI-SCOPE Python API Status

NI-SCOPE (niscope)
Driver Version Tested Against	20.0.0
PyPI Version
Supported Python Version
Open Issues
Open Pull Requests

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~=1.3.0

Or easy_install from setuptools:

$ python -m easy_install niscope

Contributing

We welcome contributions! You can clone the project repository, build it, and install it by following these instructions.

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

    Note

    Python 3 only

• 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.

Note

When using Python 2, the waveform_infos objects do not include the waveform for that record. Instead, samples are in the waveform passed into the function using the following layout:

• index 0 = record 0, channel 0
• index x = record 0, channel 1
• index 2x = record 1, channel 0
• index 3x = record 1, channel 1
• etc.
• Where x = the record length

Additional examples for NI-SCOPE are located in src/niscope/examples/ directory.

Support / Feedback

The packages included in nimi-python package are supported by NI. For support, open a request through the NI support portal at ni.com.

Bugs / Feature Requests

To report a bug or submit a feature request specific to NI Modular Instruments Python bindings (nimi-python), please use the GitHub issues page.

Fill in the issue template as completely as possible and we will respond as soon as we can.

For hardware support or any other questions not specific to this GitHub project, please visit NI Community Forums.

Documentation

Documentation is available here.

License

nimi-python is licensed under an MIT-style license (see LICENSE). Other incorporated projects may be licensed under different licenses. All licenses allow for non-commercial and commercial use.