Useful Functions

This document outlines some of the useful functions available in MARTe2-Python for a application.

MARTe2Application

The MARTe2Application instance is the first place to start, not only is it the central class of this repository and how to construct a MARTe2 Application, it contains very useful functions which are described further below.

The pythonic object representation of a MARTe2 application, how to build it and ultimately write this into it’s string configuration format.

CPU Configurator

The CPU Configurator function is fairly useful, simply copy and paste the below code into your python to convert numeric CPU configuration to their hexadecimal equivalent.

CPU_OFFSET_FROM_ONE = 0  # 0 to start at cpu 1
def cpu_thread_gen(x):
    ''' Generate a cpu core to used based on thread number x and pre-defined offset from core 1. '''
    return str(hex(2 ** (x + CPU_OFFSET_FROM_ONE)))

GAM Functions

gam_functions.py contains functions that create or maniuplate gams

martepy.functions.gam_functions.addAlias(signal: tuple)

If an Alias is not set, set it to the basic name

martepy.functions.gam_functions.addDimensions(signal: tuple)

If a signal does not contain elements or dimensions, add them

martepy.functions.gam_functions.assignUniqueName(signal: tuple, signals_list: list)

Ensures that a signal is uniquely named compared to a list of signals

martepy.functions.gam_functions.consolidate(thread_0_functions, gam_type, config_name, inputs=[], outputs=[], prepend=False)
Parameters:

  • thread_0_functions (list[]) – list of references to rtcc1blocks specific class

  • gam_type (str) – type of gam

  • config_name (str) – SDN config

  • inputs (list[tuple[str, dict[]]]) – input signal

  • outputs (list[tuple[str, dict[]]]) – output signal

Return gam:

marte2 gam

Return type:

marte2 gam

Add a GAM which creates a constant signal or reads a signal from SDN but check first if a similarly named GAM of that type exists, and if it does simply add the signals provided to this method into the pre-existing GAM.

martepy.functions.gam_functions.findSignalInFunctionList(signal, functions)

Will return the signal that matches a given signal in a list of functions

martepy.functions.gam_functions.getAlias(signal_key)

Return the alias of a signal

martepy.functions.gam_functions.getDatasource(signal)

Simplification of getting a signals datasource

martepy.functions.gam_functions.getGamByName(name: str, function_list)

Returns a GAM based on the name in a given function list

martepy.functions.gam_functions.getKeyAttribute(signal, key)

Given a specific MARTe2Config key, retrieve the value

martepy.functions.gam_functions.getParameterName(parameter_name: str)

This takes the serialized name of a parameter and returns it’s attribute name

martepy.functions.gam_functions.isMatchingSignal(signal, other_signal)

Here we say a signal is the same if it has the same alias and datasource where the signal exists.

martepy.functions.gam_functions.removeKeysFromConfig(signal: tuple, keys: list)

Safely Delete keys from a signal

martepy.functions.gam_functions.setDatasource(signal, datasource)

Simplification of setting a signals datasource

martepy.functions.gam_functions.setKeyAttribute(signal, key, value)

Set a attribute of the MARTe Config

Extra Functions

extra_functions.py contains additional useful functions

exception martepy.functions.extra_functions.ConfigGeneratorError

Generic exception for errors encountered during configuration generation

martepy.functions.extra_functions.calculateStackSize(signals)
Parameters:

signals (list[tuple[str, dict[]]]) – inputs and outputs

Return stacksize:

total size of variable types

Return type:

int

Given a function, how big is the input and output signal stack size of the variables

martepy.functions.extra_functions.findIndexByDictKey(list_i, key, value)

This will search through a list of dictionaries and find a key value matching pair # this is intended for where dictionaries are identified by a unique key/value.

martepy.functions.extra_functions.form(sig_name, type_, num=None, datasource='DDB0', alias=None, default=None)
Parameters:

  • sig_name (str) – signal name

  • type (str) – signal type

  • num (int) – number of elements

  • datasource (str) – MARTe datasource

  • alias (str) – signal alias

Returns:

output signals formatted

Return type:

list[tuple[str, dict[]]]

Formats the input/output signals

martepy.functions.extra_functions.getname(object_cls)

Return the name of an object, omitting the +

martepy.functions.extra_functions.isfloat(value)

Is our value a valid float value?

martepy.functions.extra_functions.isint(value)

Is our value a valid integer?

martepy.functions.extra_functions.normalizeSignal(signal)

Checks that a signal has elements and dimensions and removes these if not otherwise it ensures they are both set. It also handles defaults in this context, if a default is with zero dimensions/elements, it will remove the {} encapsulation of default.

Bin2CSV

The Bin2CSV tool can be used to parse a binary file generated in MARTe2 into a CSV file. It currently supports arrays and singles of int32, uint32, float32, uint64, float64, uint16, uint8.

Note: User specific types cannot be incorporated into the FileReader and need to be split out

Usage:

conv = Bin2CSV(source_binary, output_csv)
conv.main()