nidaqmx 0.6.5

NI-DAQmx Python API

Info	Contains a Python API for interacting with NI-DAQmx. See GitHub for the latest source.
Author	National Instruments

About

The nidaqmx package contains an API (Application Programming Interface) for interacting with the NI-DAQmx driver. The package is implemented in Python. The package is implemented as a complex, highly object-oriented wrapper around the NI-DAQmx C API using the ctypes Python library.

nidaqmx supports all versions of the NI-DAQmx driver that ships with the C API. The C API is included in any version of the driver that supports it. The nidaqmx package does not require installation of the C header files.

Some functions in the nidaqmx package may be unavailable with earlier versions of the NI-DAQmx driver. Visit the ni.com/downloads to upgrade your version of NI-DAQmx.

nidaqmx supports Windows and Linux operating systems where the NI-DAQmx driver is supported. Refer to NI Hardware and Operating System Compatibility for which versions of the driver support your hardware on a given operating system.

nidaqmx supports CPython 3.7+ and PyPy3.

Installation

Running nidaqmx requires NI-DAQmx or NI-DAQmx Runtime. Visit the ni.com/downloads to download the latest version of NI-DAQmx.

nidaqmx can be installed with pip:

$ python -m pip install nidaqmx

Similar Packages

There are similar packages available that also provide NI-DAQmx functionality in Python:

• daqmx (slightlynybbled/daqmx on GitHub) provides an abstraction of NI-DAQmx in the ni module.

• PyLibNIDAQmx (pearu/pylibnidaqmx on GitHub) provides an abstraction of NI-DAQmx in the nidaqmx module, which collides with this package’s module name.

Usage

The following is a basic example of using an nidaqmx.task.Task object. This example illustrates how the single, dynamic nidaqmx.task.Task.read method returns the appropriate data type.

>>> import nidaqmx
>>> with nidaqmx.Task() as task:
...     task.ai_channels.add_ai_voltage_chan("Dev1/ai0")
...     task.read()
...
-0.07476920729381246
>>> with nidaqmx.Task() as task:
...     task.ai_channels.add_ai_voltage_chan("Dev1/ai0")
...     task.read(number_of_samples_per_channel=2)
...
[0.26001373311970705, 0.37796597238117036]
>>> from nidaqmx.constants import LineGrouping
>>> with nidaqmx.Task() as task:
...     task.di_channels.add_di_chan(
...         "cDAQ2Mod4/port0/line0:1", line_grouping=LineGrouping.CHAN_PER_LINE)
...     task.read(number_of_samples_per_channel=2)
...
[[False, True], [True, True]]

A single, dynamic nidaqmx.task.Task.write method also exists.

>>> import nidaqmx
>>> from nidaqmx.types import CtrTime
>>> with nidaqmx.Task() as task:
...     task.co_channels.add_co_pulse_chan_time("Dev1/ctr0")
...     sample = CtrTime(high_time=0.001, low_time=0.001)
...     task.write(sample)
...
1
>>> with nidaqmx.Task() as task:
...     task.ao_channels.add_ao_voltage_chan("Dev1/ao0")
...     task.write([1.1, 2.2, 3.3, 4.4, 5.5], auto_start=True)
...
5

Consider using the nidaqmx.stream_readers and nidaqmx.stream_writers classes to increase the performance of your application, which accept pre-allocated NumPy arrays.

Following is an example of using an nidaqmx.system.System object.

>>> import nidaqmx.system
>>> system = nidaqmx.system.System.local()
>>> system.driver_version
DriverVersion(major_version=16L, minor_version=0L, update_version=0L)
>>> for device in system.devices:
...     print(device)
...
Device(name=Dev1)
Device(name=Dev2)
Device(name=cDAQ1)
>>> import collections
>>> isinstance(system.devices, collections.Sequence)
True
>>> device = system.devices['Dev1']
>>> device == nidaqmx.system.Device('Dev1')
True
>>> isinstance(device.ai_physical_chans, collections.Sequence)
True
>>> phys_chan = device.ai_physical_chans['ai0']
>>> phys_chan
PhysicalChannel(name=Dev1/ai0)
>>> phys_chan == nidaqmx.system.PhysicalChannel('Dev1/ai0')
True
>>> phys_chan.ai_term_cfgs
[<TerminalConfiguration.RSE: 10083>, <TerminalConfiguration.NRSE: 10078>, <TerminalConfiguration.DIFFERENTIAL: 10106>]
>>> from enum import Enum
>>> isinstance(phys_chan.ai_term_cfgs[0], Enum)
True

Bugs / Feature Requests

To report a bug or submit a feature request, please use the GitHub issues page.

Information to Include When Asking for Help

Please include all of the following information when opening an issue:

• Detailed steps on how to reproduce the problem and full traceback, if applicable.

• The python version used:

  $ python -c "import sys; print(sys.version)"

• The versions of the nidaqmx, numpy, six and enum34 packages used:

  $ python -m pip list

• The version of the NI-DAQmx driver used. Follow this KB article to determine the version of NI-DAQmx you have installed.

• The operating system and version, for example Windows 7, CentOS 7.2, …

Documentation

Documentation is available here.

Additional Documentation

Refer to the NI-DAQmx Help for API-agnostic information about NI-DAQmx or measurement concepts.

NI-DAQmx Help installs only with the full version of NI-DAQmx.

License

nidaqmx 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.