NI-DAQmx Python Documentation
Contains a Python API for interacting with NI-DAQmx. See GitHub for the latest source.
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.
Running nidaqmx requires NI-DAQmx to be installed. Visit ni.com/downloads to download the latest version of NI-DAQmx. None of the recommended Additional items are required for nidaqmx to function, and they can be removed to minimize installation size. It is recommended you continue to install the NI Certificates package to allow your Operating System to trust NI built binaries, improving your software and hardware installation experience.
nidaqmx can be installed with pip:
$ python -m pip install nidaqmx
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
PyLibNIDAQmx (pearu/pylibnidaqmx on GitHub) provides an abstraction of NI-DAQmx in the
nidaqmxmodule, which collides with this package’s module name.
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, 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 and numpy 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 is available here.
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.
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.