TSDuck Version 3.26-2348 (TSDuck - The MPEG Transport Stream Toolkit)
Java and Python bindings


Starting with version 3.25, TSDuck includes Java and Python bindings to some high-level features.

Although subject to enhancements, these bindings will never aim at supporting the full TSDuck feature set since this would be too large. Only a small subset of TSDuck high-level features are targeted.

The Java classes are documented in the Java bindings reference section.

The Python classes are documented in the Python bindings reference section.

Sample Java and Python applications are available in the TSDuck source tree.

Currently, the TSDuck Java and Python bindings provide access to the following features. Equivalences are provided between C++, Java, Python and command line tools.

The first three classes implement high-level features which have direct counterparts as command line tools. The others are support classes which are only required to use the high-level classes.

Command line C++ class Java class Python class
tsp ts::TSProcessor io.tsduck.TSProcessor tsduck.TSProcessor
tsswitch ts::InputSwitcher io.tsduck.InputSwitcher tsduck.InputSwitcher
tstabcomp ts::SectionFile io.tsduck.SectionFile tsduck.SectionFile
n/a ts::DuckContext io.tsduck.DuckContext tsduck.DuckContext
n/a ts::Report io.tsduck.AbstractSyncReport tsduck.AbstractSyncReport
n/a ts::AsyncReport io.tsduck.AbstractAsyncReport tsduck.AbstractAsyncReport
n/a ts::SystemMonitor io.tsduck.SystemMonitor tsduck.SystemMonitor
n/a ts::PluginEventHandlerInterface io.tsduck.AbstractPluginEventHandler tsduck.AbstractPluginEventHandler
n/a ts::PluginEventContext io.tsduck.PluginEventContext tsduck.PluginEventContext

Support classes

TSDuck execution context

The DuckContext class is used to define and accumulate regional or operator preferences. In the TSDuck C++ programming guide, it is referred to as TSDuck execution context. Most of the time, using the default state of a new instance is sufficient.

The application sample Japanese tables, available in Java and Python, demonstrates how it can be necessary to override the defaults in specific cases.

Reporting classes

The reporting classes (ts::Report C++ class hierarchy) are used to report logs, errors and debug. They are consistently used all over TSDuck and are required to use the high level features. There is a large hierarchy of classes in the three languages which can be classified according to two sets of criteria:

  • Synchronous vs. asynchronous:
    • Synchronous report classes log messages in the same thread as the caller. They are usually not thread-safe.
    • Asynchronous report classes, on the other hand, can be used in a multi-threaded environment and the actual message logging (such as writing in a log file) is performed in a separate thread. As a consequence, an asynchronous report instance must be explicitly terminated. An asynchronous report class is required when using heavily multi-threaded classes such as TSProcessor or InputSwitcher.
  • Native vs. abstract:
    • Native classes are the C++ classes which are used in all the TSDuck command line tools. They are typically used to report to standard output, standard error, files or dropping the logs. They can be used from Java and Python directly but cannot be derived or customized. They are typically used when predefined error logging is sufficient.
    • Abstract classes are pure Java or Python base classes which are designed to be derived in applications. Such application-defined classes shall override the method logMessageHandler (Java) or log (Python) to intercept and process the message lines.

The asynchronous abstract classes can be useful to collect events, tables and sections in XML, JSON or binary / hexadecimal form in Java or Python applications when using TSProcessor or InputSwitcher. Some of the sample Java and Python applications illustrate this mechanism.

Category C++ class Java class Python class
Synchronous, native ts::CerrReport io.tsduck.ErrReport tsduck.StdErrReport
ts::NullReport io.tsduck.NullReport tsduck.NullReport
Asynchronous, native ts::AsyncReport io.tsduck.AsyncReport tsduck.AsyncReport
Synchronous, abstract ts::Report io.tsduck.AbstractSyncReport tsduck.AbstractSyncReport
Asynchronous, abstract ts::AsyncReport io.tsduck.AbstractAsyncReport tsduck.AbstractAsyncReport

Resource monitoring

The SystemMonitor class is available in all languages, C++, Java and Python. It can be used at the top-level of an application to implement the --monitor option as found in tsp and tsswitch. An instance of a thread-safe Report class is used to report monitoring messages.

The SystemMonitor class is very simple to used. Examples are available in Java and Python.

Plugin events

For developers, TSDuck plugins can signal events which can be handled by the application. Each event is signalled with a user-defined 32-bit event code. An application can register event handlers in the ts::TSProcessor instance (see the class ts::PluginEventHandlerRegistry, knowing that ts::TSProcessor is a subclass of ts::PluginEventHandlerRegistry). The event handler registration can include various selection criteria such as event code value or originating plugin (see the inner class ts::PluginEventHandlerRegistry::Criteria).

C++ developers who create their own plugins can signal any kind of event that they later handle in their application. This is illustrated in the C++ sample custom application. In this sample code, everything is customized in the application: the plugin, the event it signals, the associated event data, the application handling of the event.

Since developing a TSDuck plugin is only possible in C++, Java and Python developers have more limited options. Some standard TSDuck plugins such as tables, psi or mpe provide the option --event-code. Using this option, the plugins signal event using the specified event code for each data they handle (sections or MPE datagrams depending on the plugin).

Java and Python applications can derive from class AbstractPluginEventHandler to define and register their own event handlers. Thus, binary sections or MPE datagrams can be handled directly from the plugin to the Java or Python application.

Some plugins are even dedicated to application developers and are useless on tsp command lines. This is the case of the memory plugin (both an input and an output plugin). This plugin, when used in a TSProcessor instance, performs direct transport stream input and output from and to the application using memory buffers. The memory buffers are signalled using plugin events. The memory input plugin is an example of an application-defined event handler returning data to the plugin. See sample code in the TSDuck source code tree.

Communication between Java or Python applications and their plugins

At high level, Java and Python applications can only run TSProcessor or InputSwitcher sessions, just like a shell-script would do with commands tsp and tsswitch.

The communication from the Java and Python applications to the plugins is performed using plugin options. These options may contain file names or UDP ports which can be created by the application.

More effectively, most file contents can be provided directly on the command line, avoiding the burden of creating temporary files. For instance, wherever an input XML file name is expected, it is possible to use the XML content instead. Any "XML file name" which starts with "<?xml", it is considered as inline XML content. Similarly, if an input JSON file name starts with "{" or "[", it is considered as inline JSON content.

On reverse side, there is some limited form of communication from the plugins to the Java or Python application. There are basically two ways to handle plugin information in the application: the logging system and plugin events.

Using the logging system: Some plugins support options such as --log-xml-line, --log-json-line or --log-hexa-line. With these options, the extracted data (table, section, MPE datagram) are "displayed" as one single line in the designated format on the logging system. Using user-defined Java or Python asynchronous abstract reporting classes, the application receives all logged lines and can filter and manipulate the data which were extracted and logged by the plugins.

Using plugin events: Some plugins support the option --event-code. With this option, the extracted data are signalled by the plugin as an event. Using and registering user-defined Java or Python plugin event handlers, the application is directly notified of the data.

Which mechanism, logging system or plugin events, should be used depends on the application.

  • Logging system:
    • Pros:
      • The log lines are asynchronously processed in the context of the low-priority logging thread. Any lengthy processing in the Java or Python application does not hurt the dynamics of the plugins.
    • Cons:
      • If the application needs to process binary data, the additional serialization process in the log line adds some useless overhead.
      • Because the logging system is non-intrusive by design, log messages may be lost if there are more messages than the logging thread can process without making plugin threads wait. This can be mitigated using the synchronous log option in the AbstractAsyncReport consttructor.
  • Plugin events:
    • Pros:
      • The binary data are directly passed from the plugin to the application without any serialization, logging or multi-threading overhead.
    • Cons:
      • The application-defined event handlers execute in the context of the plugin thread. Any lengthy processing at this stage slows down the plugin.

The following sample applications can be used as a starting point:

Communication type Java Python
Logging (XML) SampleAnalyzeSDT sample-analyze-sdt.py
Logging (JSON) SampleAnalyzeTS sample-analyze-ts.py
Logging (bin/hexa) SampleFilterTablesLog sample-filter-tables-log.py
Plugin events (sections) SampleFilterTablesEvent sample-filter-tables-event.py
Plugin events (MPE datagrams) SampleMPE sample-mpe.py
Plugin events (input/output) SampleMemoryPlugins sample-memory-plugins.py

Using TSDuck Java bindings

All TSDuck Java classes are defined in a package named io.tsduck.

A few examples are provided in the directory sample/sample-java in the TSDuck source code package.


The TSDuck Java bindings are installed with TSDuck in /usr/share/tsduck/java. All classes are in a JAR file named tsduck.jar. Simply add this JAR in the environment variable CLASSPATH to use TSDuck from any Java application:

export CLASSPATH="/usr/share/tsduck/java/tsduck.jar:$CLASSPATH"


This is similar to Linux, except that /usr/local/share is used instead of /usr/share.

export CLASSPATH="/usr/local/share/tsduck/java/tsduck.jar:$CLASSPATH"


On Windows, Java bindings are optional components of the TSDuck installer. When they are selected for installation, they are installed in the TSDuck area and the environment variable CLASSPATH is modified at system level to include the JAR file of the TSDuck Java bindings. Thus, any Java program can use TSDuck directly.

Using TSDuck Python bindings

All TSDuck bindings are defined in a module named tsduck. All Python programs using TSDuck shall consequently start with:

import tsduck

A few examples are provided in the directory sample/sample-python in the TSDuck source code package.


The Python bindings are installed with TSDuck in /usr/share/tsduck/python. Simply add this directory in the environment variable PYTHONPATH to use TSDuck from any Python application:

export PYTHONPATH="/usr/share/tsduck/python:$PYTHONPATH"


This is similar to Linux, except that /usr/local/share is used instead of /usr/share.

export PYTHONPATH="/usr/local/share/tsduck/python:$PYTHONPATH"


On Windows, Python bindings are optional components of the TSDuck installer. When they are selected for installation, they are installed in the TSDuck area and the environment variable PYTHONPATH is modified at system level to include the root directory of the TSDuck Python bindings. Thus, any Python program can use TSDuck directly.

Python prerequisites

The code was initially tested with Python 3.7 and higher. Python 2.x is not supported. Intermediate versions may work but without guarantee.

Implementation notes

There are usually two ways to call C/C++ from Python:

  • Using the predefined ctypes Python module to call C functions,
  • Implementating a full native Python module in C/C++.

The second option is usually more flexible and more generic. However, the generated binary depends on the version of Python. If such an option is used, the binary installation of TSDuck would require a specific version of Python (or a specific set of versions of it). But each system has it own requirements on Python and it is difficult for a product like TSDuck to impose a specific version of Python.

Consequently, the less flexible ctypes approach was chosen. The TSDuck binary library contains C++ wrapper functions to some features of TSDuck and these carefully crafted functions are directly called from Python code using ctypes, regardless of the version of Python. Note, however, that these C++ functions are hidden inside the Python bindings and invisible to the C++ application developer.