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Extract and read data from National Instruments LabVIEW tdx/tdm files and export them as csv files

Project description


TDMtermite is a C++ based library that decodes the proprietary file format TDM/TDX for measurement data. First introduced by National Instruments, the TDM format relies on the technical data management data model and is employed by LabVIEW, LabWindows™/CVI™, Measurement Studio, SignalExpress, and DIAdem.

The Record Evolution Platform uses TDMtermite to integrate measurement data into ETL processes. The TDMtermite library is available both as a command line tool and as a Python module. The Python module of TDMtermite enables data scientists to conveniently include TDM formats in their existing data pipelines by providing access to both raw data and metadata in terms of native Python objects.



Datasets encoded in the TDM/TDX format come in pairs comprised of a .tdm (header) file and a .tdx (data) file. While the .tdm file is a human-readable file providing meta information about the dataset, the .tdx file is a binary file containing the actual data. The .tdm based on the technical data management model is an XML file. It describes what data the .tdx file contains and how to read it. The TDM data model structures the data hierarchically with respect to file, (channel) groups and channels. The file-level XML may contain any number of (channel) groups, each of which is made up of an arbitrary number of channels. Thus, the XML tree in the TDM header file looks like this:

<?xml version="1.0" encoding="UTF-8" standalone="no" ?>
<usi:tdm xmlns:usi="" version="1.0">

    <usi:exporter>National Instruments USI</usi:exporter>

  <usi:model modelName="National Instruments USI generated meta file" modelVersion="1.0">
    <usi:include nsUri=""/>

    <file byteOrder="littleEndian" url="example.tdx">
    <block byteOffset="0" id="inc0" length="1000" valueType="eFloat64Usi"/>
    <block_bm id="inc4" blockOffset="100" blockSize="7" byteOffset="0" length="4" valueType="eInt8Usi"/>



The XML tree is comprised of four main XML elements: usi:documentation, usi:model, usi:include and usi:data. The element usi:include references the data file example.tdx and reveals one of two possible orderings of the mass data (.tdx):

  1. either channel-wise (<block>) - all values of a specific channel follow subsequently
  2. or block-wise (<block_bm>) - all values of a specific measurement time follow subsequently.

The supported numerical data types are:

datatype channel datatype numeric value sequence size description
eInt16Usi DT_SHORT 2 short_sequence 2byte signed 16 bit integer
eInt32Usi DT_LONG 6 long_sequence 4byte signed 32 bit integer
eUInt8Usi DT_BYTE 5 byte_sequence 1byte unsigned 8 bit integer
eUInt16Usi DT_SHORT 2 short_sequence 2byte unsigned 16 bit integer
eUInt32Usi DT_LONG 6 long_sequence 4byte unsigned 32 bit integer
eFloat32Usi DT_FLOAT 3 float_sequence 4byte 32 bit float
eFloat64Usi DT_DOUBLE 7 double_sequence 8byte 64 Bit double
eStringUsi DT_STRING 1 string_sequence text

The XML element <usi:data> is comprised of five different types of elements that are <tdm_root>, <tdm_channelgroup>, <tdm_channel>, <localcolumn> and <submatrix>. The root element <tdm_root> describes the general properties of the dataset and lists the ids of all channel groups that belong to the dataset. The element <tdm_channelgroup> divides the channels into groups and has a unique id that is referenced by its root element. The <channels> element in <tdm_channelgroup> lists the unique ids of all channels that belong to that group. Finally, the element <tdm_channel> describes a single column of actual data including its datatype. The remaining element types are <localcolumn>

<localcolumn id="usiXY">
  <sequence_representation> ... </sequence_representation>

with a unique id, the <measurement_quantity> referring to one specific channel, the <submatrix> and its id respectively, the type of representation in <sequence_representation> - being one of explicit, implicit linear or rawlinear - and the <values> element, which refers to one value sequence, and the element <submatrix>

<submatrix id="usiXX">

that references the channel group in <measurement> to which it belongs and provides the number of rows in the channels listed in <local_columns>.


The library can be used both as a CLI-based tool and as a Python module.

CLI tool

To install the CLI tool TDMtermite, do

make install

which uses /usr/local/bin as an installation directory. On macOSX, please first build the binary locally with make and install it in your preferred location.


In order to build a Python module from the C++ code base, the Cython package must be available. It may be installed via python3 -m pip install cython . The Numpy package is recommended to pass arrays of data from the C++ kernel to Python. The makefile provides the target make cython-requirements to install all required Python modules. Finally, to build the Python extension tdm_termite locally or install it, the targets make cython-build and make cython-install are provided. To install the Python module on the system, simply do

make cython-requirements
make cython-install

which makes the module available for import by import tdm_termite .

Installation with pip

The package is also available via the Python Package Index at TDMtermite. To install the latest version simply do

python3 -m pip install TDMtermite

Note, that python3_setuptools and gcc version >= 10.2.0 are required to successfully install and use it.


CLI tool

The usage of the CLI tool is sufficiently clarified by its help message displayed by tdmtermite --help. To extract the data decoded in the pair of files samples/SineData.tdm and samples/SineData.tdx into the directory /home/jack/data/:

tdmtermite samples/SineData.tdm samples/SineData.tdx --output /home/jack/data

The tool can also be used to list the available objects in the TDM dataset, which are i.a. channels, channelgroups and TDX blocks. To list all channels and channelgroups (without writing any file output):

tdmtermite samples/SineData.tdm samples/SineData.tdx --listgroups --listchannels

The user may also submit a filenaming rule to control the names of the files the channel(group)s are written to. To this end, the magic flags %G %g, %C and %c representing the group id, group name, channel index and channel name are defined. The default filenaming option is:

tdmtermite samples/SineData.tdm samples/SineData.tdx --output /home/jack/data --filenames channelgroup_%G.csv

This makes the tool write all channels grouped into files according to their group association, while all channelgroup filenames obey the pattern channelgroup_%G.csv, with %G being replaced by the group id. The filenaming rule also enables the user to extract only a single channel(group) by providing a particular channel(group) id in the filenaming flag. For example,

tdmtermite samples/SineData.tdm samples/SineData.tdx --output /home/jack/data -f channel_usi16_%c.csv --includemeta

This will write the single channel with the id usi16 to the file /home/jack/data/channel_usi16_A4.csv, including its meta-data as a file header.


To be able to use the Python module tdm_termite, it first has to be built locally or installed on the system. In the Python interpreter, simply do:

import TDMtermite

This will import the module. The TDM files are provided by creating an instance of the tdmtermite class:

# create 'tdmtermite' instance object
try :
    jack = TDMtermite.tdmtermite(b'samples/SineData.tdm',b'samples/SineData.tdx')
except RuntimeError as e:
    print("failed to load/decode TDM files: " + str(e))

After initializing the tdmtermite object, it can be used to extract any of the available data. For instance, to list the included channelgroups and channels:

# list ids of channelgroups
grpids = jack.get_channelgroup_ids()

# list ids of channels
chnids = jack.get_channel_ids()

As a use case, we have a look at listing the ids of all channelgroups and printing their data to separate files:

import TDMtermite
import re

# create 'tdmtermite' instance object
try :
    jack = TDMtermite.tdmtermite(b'samples/SineData.tdm',b'samples/SineData.tdx')
except RuntimeError as e :
    print("failed to load/decode TDM files: " + str(e))

# list ids of channelgroups
grpids = jack.get_channelgroup_ids()
grpids = [x.decode() for x in grpids]
print("list of channelgroups: ",grpids)

for grp in grpids :

    # obtain meta data of channelgroups
    grpinfo = jack.get_channelgroup_info(grp.encode())
    print( json.dumps(grpinfo,sort_keys=False,indent=4) )

    # write this channelgroup to file
    try :
        grpname = re.sub('[^A-Za-z0-9]','',grpinfo['name'])
        grpfile = "channelgroup_" + str(grp) + "_" + str(grpname) + ".csv"
        jack.print_channelgroup(grp.encode(),      # id of group to be printed
                                grpfile.encode(),  # filename
                                True,              # include metadata as fileheader
                                ord(' ')           # delimiter char
    except RuntimeError as e :
        print("failed to print channelgroup: " + str(grp) + " : " + str(e))

For details, see this extensive example and the absolute minimal example minimal usage. In order to simply extract all data of the TDM datatset and dump it to files in a given (existing!) directory, do

import TDMtermite
jack = TDMtermite.tdmtermite(b'samples/SineData.tdm',b'samples/SineData.tdx')

The interface allows you to construct customized file/column headers from any meta-data and provide these headers for usage in file output (see this example).



IEEE Standard and datatypes




C/C++ Extensions


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