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. This package was created and is supported by NI. 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 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
There are similar packages available that also provide NI-DAQmx functionality in Python:
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
Support / Feedback
The nidaqmx package is supported by NI. For support for nidaqmx, open a request through the NI support portal at ni.com.
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 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.