Echelon Neuron C User Manual

Neuron

C Programmer’s Guide

0 7 8 - 0 0 0 2 -02H

Echelon, LONWORKS, LONMARK, NodeBuild er, LonTalk, Neuron, 3120, 3150, ShortStack, LonMaker, and the Echelon logo are trademarks of Echelon Corporation registered in the United States and other countries. 3170 is a trademark of the Echelon Corporation.

Other brand and product names are trademarks or registered trademarks of their respective holders.

Neuron Chips and other OEM Products were not designed for use in equipment or systems, which involve danger to human health or safety, or a risk of property damage and Echelon assumes no responsibility or liability for use of the Neuron Chips in such applications.

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Echelon Corporation

www.echelon.com

Welcome

This guide describes how to write programs using the Neuron® C Version 2.2 language. Neuron C is a programming language based on ANSI C that is designed for Neuron Chips and Smart Transceivers. It includes network communication, I/O, and event-handling extensions to ANSI C, which make it a powerful tool for the development of L programming with Neuron C are explained through the use of specific code examples and diagrams. A general methodology for designing and implementing

ONWORKS application is also presented.

a L

A subset of the Neuron C language is also used to describe the interoperable interface of host-based applications that are designed with the ShortStack® Developer’s Kit, FTXL™ Developer’s Kit, or the interoperable interface is contained within a file called a contains Neuron C declarations and definitions for the device interface. In addition to describing the Neuron C Version 2.2 language, this guide also provides a brief introduction to model file compilation, and points out syntactical differences between compilation for Neuron-hosted devices and compilation for host-based devices that use a model file.

ONWORKS

applications. Key concepts in

.LON® SmartServer. This

model file

, which

The Neuron C Programmer’s Guide

• Outlines a recommended general approach to developing a L

• Explains key concepts of programming in Neuron C through the use of

Audience

The

Neuron C Programmer’s Guide

are developing L familiar with the ANSI C programming language, and have some C programming experience.

For a complete description of ANSI C, consult the following references:

• —. 1989.

• —. 2007.

• Harbison, Samuel P. and Guy L. Steele, Jr. 2002.

application, and

code fragments and examples.

is intended for application programmers who

ONWORKS applications. Readers of this guide are assumed to be

American National Standard for Information Systems

Programming Language C

NY: American National Standards Institute.

. Standard number X3.159-1989. New York,

International Standard ISO/IEC 9899:1999. Programming

languages – C

Standardization.

Manual

. Geneva, Switzerland: International Organization for

, 5th edition. Upper Saddle River, NJ: Prentice Hall, Inc.

ONWORKS

C: A Reference

• Kernighan, Brian W. and Dennis M. Ritchie. 1988.

Language

• Plauger, P.J. and Jim Brodie. 1989.

Reference Series

• Plauger, P.J. and Jim Brodie. 1992.

Programmer's Reference

Neuron C Programmer’s Guide iii

, 2nd edition. Upper Saddle River, NJ: Prentice Hall, Inc.

Standard C: Programmer’s Quick

. Buffalo, NY: Microsoft Press.

ANSI and ISO Standard C

. Buffalo, NY: Microsoft Press.

The C Programming

Typographic Conventions for Syntax

Table 1 on page v lists the typographic conventions used in this manual for displaying Neuron C syntax.

PDF format. To view the

, which you can

Table 1. Typographic Conventions

Typeface or Symbol Used for Example

boldface type keywords

literal characters

italic type

[square brackets] optional fields [

| vertical bar a choice between two

Example: The syntax for declaring a network variable is:

network input | output [

abstract elements

elements

netvar modifier] [class] type [bind-info] identifier

• You type the keywords network, input, and output as shown

• You replace the abstract elements

identifier

and identifier for the network variable

• The declaration must include either input or output, but not both

with the actual modifier, class, type, bind information, and

netvar modifier, class, type, bind-info

network

{

identifier

bind-info

input | output

]

• The elements

When a particular element or expression includes punctuation, such as quotation marks, parentheses, and semicolons (but not including square brackets and vertical bars), you must type that punctuation as shown.

Code examples appear in the monospace Courier font:

#include <mem.h>

unsigned array1[40], array2[40];

// See if array1 matches array2 if (memcmp(array1, array2, 40) != 0) { // The contents of the two areas do not match }

netvar modifier, class

, and

bind-info

are all optional

Neuron C Programmer’s Guide v

Table of Contents

Welcome.........................................................................................................iii

Audience ........................................................................................................iii

Related Documentation ................................................................................iv

Typographic Conventions for Syntax...........................................................iv

Chapter 1. Overview .......................................................................................... 1

What Is Neuron C?......................................................................................... 2

Comparing Neuron C Version 2 to Version 1 ........................................ 2

Unique Aspects of Neuron C .........................................................................3

Neuron C Integer Constants................................................................... 4

Neuron C Variables ................................................................................. 5

Neuron C Variable Types .................................................................5

Neuron C Storage Classes ................................................................ 6

Variable Initialization ......................................................................7

Neuron C Declarations .....................................................................7

Network Variables, SNVTs, and UNVTs............................................... 8

Configuration Properties......................................................................... 9

Functional Blocks and Functional Profiles.......................................... 10

Data-Driven Compared with Command-Driven Protocols........... 11

Event-Driven Scheduling or Polled Scheduling .................................. 11

Low-Level Messaging ............................................................................11

I/O Devices ............................................................................................. 11

Neuron-Hosted and Host-Based Compilation ............................................ 12

Differences between Neuron C and ANSI C............................................... 13

Chapter 2. Focusing on a Single Device.......................................................... 15

What Happens on a Single Device? ............................................................ 16

The Scheduler............................................................................................... 16

When Clauses ........................................................................................16

When Statement .................................................................................... 17

Types of Events Used in When Clauses............................................... 18

Predefined Events ................................................................................. 18

Event Processing............................................................................. 20

Reset Event ..................................................................................... 21

User-Defined Events .............................................................................22

Scheduling of When Clauses................................................................. 22

Priority When Clauses ..........................................................................23

Interrupts............................................................................................... 24

Function Prototypes ..............................................................................24

Timers ........................................................................................................... 25

Declaring Timers ................................................................................... 25

Examples .........................................................................................26

The timer_expires Event....................................................................... 26

Input/Output ................................................................................................27

I/O Object Types ....................................................................................28

Declaring I/O Objects in Neuron C....................................................... 29

Device Self-Documentation .........................................................................30

Examples ......................................................................................................30

Example 1: Thermostat Interface........................................................ 31

Example 2: Simple Light Dimmer Interface....................................... 33

Example 3: Seven-Segment LED Display Interface........................... 35

Input Clock Frequency and Timer Accuracy.............................................. 36

Neuron C Programmer’s Guide vii

Fixed Timers .......................................................................................... 36

Scaled Timers and I/O Objects ............................................................. 37

Calculating Accuracy for Software Timers .......................................... 37

Accuracy of Millisecond Timers ..................................................... 37

Accuracy of Second Timers............................................................. 40

Delay Functions..................................................................................... 40

EEPROM Write Timer .......................................................................... 41

Chapter 3. How Devices Communicate Using Network Variables ................ 43

Major Topics ................................................................................................. 44

Overview ....................................................................................................... 45

Behavior of Writer and Reader Devices ............................................... 46

When Updates Occur............................................................................. 47

Declaring Network Variables ...................................................................... 47

Examples of Network Variable Declarations....................................... 48

Connecting Network Variables ................................................................... 49

Use of the is_bound( ) Function ............................................................ 50

Network Variable Events ............................................................................51

The nv_update_occurs Event ................................................................ 51

The nv_update_succeeds and nv_update_fails Events ....................... 52

The nv_update_completes Event .......................................................... 53

Synchronous Network Variables................................................................. 53

Declaring Synchronous Network Variables......................................... 54

Synchronous vs. Nonsynchronous Network Variables........................ 54

Updating Synchronous Network Variables .........................................54

Preemption Mode ............................................................................ 55

Processing Completion Events for Network Variables.............................. 55

Partial Completion Event Testing........................................................ 55

Comprehensive Completion Event Testing ......................................... 55

Tradeoffs................................................................................................. 56

Polling Network Variables .......................................................................... 56

Declaring an Input Network Variable as Polled .................................58

Declaring an Output Network Variable as Polled............................... 58

Explicit Propagation of Network Variables................................................ 60

Initial Value Updates for Input Network Variables.................................. 62

Monitoring Network Variables.................................................................... 65

Authentication.............................................................................................. 66

Setting Up Devices to Use Authentication .......................................... 66

Declaring Authenticated Variables and Messages ....................... 67

Specifying the Authentication Key ................................................ 67

How Authentication Works................................................................... 67

Changeable-Type Network Variables ......................................................... 68

Processing Changes to a SCPTnvType CP .......................................... 71

Validating a Type Change .............................................................. 72

Processing a Type Change.............................................................. 73

Processing a Size Change ...............................................................74

Rejecting a Type Change ................................................................75

Changeable-Type Example ...................................................................75

Chapter 4. Using CPs to Configure Device Behavior ..................................... 83

Overview ....................................................................................................... 84

Declaring Configuration Properties............................................................ 84

Declaration of Configuration Network Variables................................ 85

Declaring Configuration Properties within Files ................................ 86

viii

Instantiation of Configuration Properties .................................................. 88

Device Property Lists ............................................................................ 89

Network Variable Property Lists ......................................................... 90

Accessing Property Values from a Program............................................... 91

Advanced Configuration Property Features .............................................. 92

Configuration Properties Applying to Arrays...................................... 92

Initialization of Configuration Properties at Instantiation ................ 95

Sharing of Configuration Properties .................................................... 96

Configuration Property Sharing Rules ................................................97

Type-Inheriting Configuration Properties ........................................... 98

Type-Inheriting CPs for NVs of Changeable Type........................ 99

Chapter 5. Using Functional Blocks to Implement a Device Interface.........101

Overview ..................................................................................................... 102

Functional Block Declarations ..................................................................104

Functional Block Property Lists......................................................... 107

Shared Functional Block Properties .................................................. 108

Scope Rules................................................................................................. 109

Accessing Members and Properties of an FB from a Program................ 110

Accessing Members and Properties of an FB from a Network Tool .......112

The Director Function................................................................................ 113

Sharing of Configuration Properties......................................................... 115

Chapter 6. How Devices Communicate Using Application Messages...........117

Introduction to Application Messages ......................................................118

Layers of Neuron Software........................................................................ 119

Implicit Messages: Network Variables .................................................... 119

Application Messages................................................................................. 120

Constructing a Message............................................................................. 120

The msg_out Object Definition ........................................................... 121

Message Tags ................................................................................122

Message Codes .............................................................................. 123

Block Transfers of Data ...................................................................... 124

Sending a Message..................................................................................... 125

Receiving a Message ..................................................................................126

The msg_arrives Event ....................................................................... 126

The msg_receive( ) Function ............................................................... 127

Format of an Incoming Message......................................................... 127

Importance of a Default When Clause ............................................... 128

Application Message Examples................................................................. 129

Lamp Program ..................................................................................... 129

Switch Program ................................................................................... 129

Connecting Message Tags ................................................................... 130

Explicit Addressing.................................................................................... 130

Sending a Message with the Acknowledged Service ............................... 131

Message Completion Events ...............................................................131

Processing Completion Events for Messages ..............................133

Preemption Mode and Messages............................................................... 134

Asynchronous and Direct Event Processing............................................. 135

Using the Request/Response Mechanism ................................................. 136

Constructing a Response.....................................................................137

Sending a Response............................................................................. 138

Receiving a Response ..........................................................................138

The resp_arrives Event................................................................. 138

Neuron C Programmer’s Guide ix

The resp_receive( ) Function ........................................................ 139

Format of a Response.................................................................... 139

Request/Response Examples............................................................... 140

Comparison of resp_arrives and msg_succeeds ................................. 141

Idempotent Versus Non-Idempotent Requests.................................. 141

Application Buffers ....................................................................................142

Allocating Application Buffers............................................................ 143

Chapter 7. Additional Features......................................................................145

The Scheduler............................................................................................. 146

Scheduler Reset Mechanism ..................................................................... 146

Scheduler Example..............................................................................148

Bypass Mode............................................................................................... 149

The post_events( ) Function................................................................ 149

Watchdog Timer ......................................................................................... 150

Additional Predefined Events ...................................................................151

Going Offline in Bypass Mode ............................................................152

Wink Event ..........................................................................................152

Interrupts ...................................................................................................153

Interrupt Sources ................................................................................ 153

I/O Interrupts ................................................................................ 154

Timer/Counter Interrupts ............................................................ 154

Periodic System Timer Interrupts ............................................... 154

Defining an Interrupt Task ................................................................ 155

Defining an I/O Interrupt Task.................................................... 155

Defining a Timer/Counter Interrupt Task ..................................156

Defining a System Timer Interrupt Task.................................... 158

Controlling Interrupts.........................................................................159

Sharing Data with an Interrupt Task................................................ 161

Interrupt Latency ................................................................................162

Debugging Interrupt Tasks................................................................. 164

Restrictions for Using Interrupts ....................................................... 165

Sleep Mode.................................................................................................. 165

Flushing the Neuron Chip or Smart Transceiver ............................. 166

The flush( ) and flush_cancel( ) Functions ..................................166

flush_completes Event .................................................................. 166

Putting the Device to Sleep................................................................. 166

Forced Sleep......................................................................................... 168

Error Handling........................................................................................... 168

Resetting the Device............................................................................ 169

Restarting the Application.................................................................. 169

Taking an Application Offline ............................................................170

Disabling a Functional Block.............................................................. 170

Changing Functional Block Status..................................................... 171

Logging Application Errors.................................................................171

System Errors ......................................................................................171

Access to Device Status and Statistics .....................................................172

Chapter 8. Memory Management...................................................................173

Memory Use................................................................................................ 174

RAM Use .............................................................................................. 174

EEPROM Use ......................................................................................174

Using Neuron Chip Memory .....................................................................176

Chips with Off-Chip Memory.............................................................. 176

Chips without Off-Chip Memory ........................................................ 177

Memory Regions ..................................................................................177

Memory Areas...................................................................................... 178

Default Memory Usage ....................................................................... 180

Controlling Non-Default Memory Usage ........................................... 181

eeprom Keyword (for Functions and Data Declarations) ........... 181

far Keyword (for Data Declarations) ........................................... 182

offchip Keyword (for Functions and Data Declarations) ............183

onchip Keyword (for Functions and Data Declarations) ............183

ram Keyword (for Functions) .......................................................183

uninit Keyword (for Data Declarations) ......................................184

Compiler Directives ............................................................................. 184

When the Program Is Relinked ..........................................................184

Use of Flash Memory........................................................................... 184

Use of Flash Memory for Series 3100 Chips ............................... 185

Use of Flash Memory for Series 5000 Chips ............................... 186

The eeprom_memcpy( ) Function .......................................................187

Reallocating On-Chip EEPROM ............................................................... 188

Address Table ......................................................................................188

Alias Table ...........................................................................................189

Domain Table....................................................................................... 190

Allocating Buffers ...................................................................................... 190

Buffer Size............................................................................................ 191

Application Buffer Size................................................................. 191

Network Buffer Size...................................................................... 192

Errors............................................................................................. 192

Buffer Counts.......................................................................................192

Compiler Directives for Buffer Allocation.......................................... 193

Outgoing Application Buffers....................................................... 194

Outgoing Network Buffers ...........................................................194

Incoming Network Buffers ...........................................................195

Incoming Application Buffers....................................................... 195

Number of Receive Transactions ................................................. 195

Usage Tip for Memory-Mapped I/O ..........................................................198

What to Try When a Program Does Not Fit on a Neuron Chip .............. 199

Reduce the Size of the Configuration Property Template File ......... 200

Reduce the Number of Address Table Entries .................................. 200

Remove Self-Identification Data if Not Needed ................................ 200

Remove Network Variable Names if Not Needed ............................. 201

Declare Constant Data Properly ........................................................ 201

Use Efficient Constant Values............................................................ 202

Take Advantage of Neuron Firmware Default Initialization ...........202

Use Neuron C Utility Functions Effectively ...................................... 202

Be Aware of Library Usage................................................................. 203

Use More Efficient Data Types........................................................... 203

Observe Declaration Order ................................................................. 204

Use the Optional fastaccess Feature.................................................. 205

Eliminate Common Sub-Expressions................................................. 205

Use Function Calls Liberally ..............................................................206

Use the Alternate Initialization Sequence......................................... 207

Use C Operators Effectively................................................................ 207

Use Neuron C Extensions Effectively ................................................208

Using the Link Map ................................................................................... 209

Neuron C Programmer’s Guide xi

Appendix A.

Neuron C Tools Stand-Alone Use..............................................211

Stand-Alone Tools ...................................................................................... 212

Common Stand-Alone Tool Use .......................................................... 212

Common Syntax ............................................................................212

Common Set of Basic Commands ................................................ 214

Command Switches for Stand-alone Tools ............................................... 215

Neuron C Compiler.............................................................................. 215

Neuron Assembler ............................................................................... 216

Neuron Linker .....................................................................................216

Neuron Exporter.................................................................................. 217

Neuron Librarian................................................................................. 219

Appendix B. Neuron C Function Libraries ....................................................221

Definitions ..................................................................................................222

NodeBuilder Support for Libraries ........................................................... 222

Tradeoffs, Advantages, and Disadvantages ............................................. 223

Advantages of a Library...................................................................... 223

Disadvantages of a Library................................................................. 223

Library Construction Using the Librarian............................................... 224

Performing Neuron C Functions from Libraries...................................... 225

Appendix C. Neuron C Custom System Images ............................................227

Definitions ..................................................................................................228

NodeBuilder Use of Custom System Images............................................ 229

Tradeoffs, Advantages, and Disadvantages ............................................. 230

Advantages of a Custom System Image............................................. 230

Disadvantages of a Custom System Image........................................ 230

Constructing a Custom System Image ..................................................... 231

Providing a Large RAM Space ..................................................................234

Performing Neuron C Functions ............................................................... 234

Appendix D. Neuron C Language Implementation Characteristics .............237

Neuron C Language Implementation Characteristics ............................ 238

Translation (J.3.1) ............................................................................... 238

Environment (J.3.2)............................................................................. 239

Identifiers (J.3.3) .................................................................................239

Identifiers (F.3.3 of ANSI C Standard)........................................ 239

Characters (J.3.4) ................................................................................240

Characters (F.3.4 of ANSI C Standard)....................................... 240

Integers (J.3.5)..................................................................................... 241

Integers (F.3.5 of ANSI C Standard) ...........................................241

Floating Point (J.3.6)........................................................................... 242

Arrays and Pointers (J.3.7) ................................................................. 242

Arrays and Pointers (F.3.7 of ANSI C Standard) .......................242

Hints (J.3.8) .........................................................................................243

Structures, Unions, Enumerations, and Bit-Fields (J.3.9) ............... 243

Structures, Unions, Enumerations, and Bit-Fields (F.3.9) ........ 243

Qualifiers (J.3.10) ................................................................................244

Declarators (F.3.11 of ANSI C Standard)....................................244

Statements (F.3.12 of ANSI C Standard) .................................... 244

Preprocessing Directives (J.3.11) ....................................................... 244

Library Functions (J.3.12) ..................................................................245

Index................................................................................................................247

xii

Overview

This chapter introduces the Neuron C Version 2.2 programming language. It describes the basic aspects of the language and provides an overview to using the L platform and the Neuron C programming language to construct interoperable devices and systems. The chapter also introduces key concepts of Neuron C such as eventdriven scheduling, network variables, configuration properties, and functional blocks (which are implementations of functional profiles).

A secondary purpose of this chapter is to introduce fundamental material on Neuron C concerning Neuron C types, storage classes, data objects, and how the Neuron C language compares to the ANSI C language.

ONWORKS

Neuron C Programmer’s Guide 1

What Is Neuron C?

Neuron C Version 2 is a programming language based on ANSI C that is designed for Neuron Chips and Smart Transceivers. It includes network communication, I/O, and event-handling extensions to ANSI C, which make it a powerful tool for the development of L few of these new

features:

ONWORKS applications. Following are a

• A new network communication model, based on

network variables

like or disparate devices.

• A new network configuration model, based on functional blocks and

configuration properties

configuration tools.

• A new type model based on standard and user

the market for interoperable devices by simplifying integration of devices from multiple manufacturers.

• An extensive built-in set of

capabilities of Neuron Chips and Smart Transceivers.

• Powerful

statements, provide easy handling of network, I/O, and timer events.

• A high-level programming model that supports application-specific

interrupt handlers and synchronization tools.

Neuron C provides a rich set of language extensions to ANSI C tailored to the unique requirements of distributed control applications. Experienced C programmers will find Neuron C a natural extension to the familiar ANSI C paradigm. Neuron C offers built-in type checking and allows the programmer to generate highly efficient code for distributed L

Neuron C omits ANSI C features that are not required by the standard for freestanding implementations. For example, certain standard C libraries are not part of Neuron C. Other differences between Neuron C and ANSI C are described in

event-driven programming

Differences between Neuron C and ANSI C

, that simplifies and promotes data sharing between

, that facilitates interoperable network

I/O models

that support the powerful I/O

extensions, based on new when

ONWORKS applications.

functional blocks

resource files

on page 13.

that expands

and

Comparing Neuron C Version 2 to Version 1

Neuron C version 2 includes major enhancements to the language that greatly simplify the development of devices that use functional blocks and configuration properties. Prior to version 2, developers had to manually construct selfdocumentation strings for each device, network variable, and configuration property. This process could be both time-consuming and error-prone. With Neuron C version 2, the Neuron C compiler can automatically create and manage these strings.

If any of the following Neuron C keywords appear in an application, the Neuron C compiler automatically generates and manages the self-documentation strings:

• fblock

"New" means relative to the ANSI Standard C language.

2 Overview

• fb_properties

• nv_properties

• device_properties

• cp

• cp_family

You can still manually create the self-documentation strings, if necessary, by avoiding the use of any of these keywords and by declaring the selfdocumentation strings using the Neuron C version 1 syntax. Using this syntax could be useful for migrating older applications (created with the NodeBuilder 1.5 or LonBuilder tools) to the NodeBuilder FX Development Tool. Applications that do not use these keywords still get the benefit of access to resource definitions contained within resource files.

Unique Aspects of Neuron C

Neuron C implements all of the basic ANSI C types, and type conversions as necessary. In addition to the ANSI C data constructs, Neuron C provides some unique data elements.

ONWORKS applications. Network variables are data constructs that have

L language and system firmware support to provide something that looks like a variable in a C program, but has additional properties for propagating across a

ONWORKS network to or from one or more other devices on that network. The

L network variables make up part of the

Network variables

device interface

are fundamental to Neuron C and

for a LONWORKS device.

Configuration properties

the device interface. Configuration properties allow the device’s behavior to be customized using a network tool such as the LonMaker tool or a customized plugin created for the device.

Neuron C also provides a way to organize the network variables and configuration properties in the device into provides a collection of network variables and configuration properties, that are used together to perform one task. These network variables and configuration properties are called the

Each network variable, configuration property, and functional block is defined by a type definition contained in a configuration properties are defined by

configuration property types profiles

Network variables, configuration properties, and functional blocks in Neuron C can use promotes the interconnection of disparate devices on a L configuration properties, the standard types are called standard configuration property types (SCPTs; pronounced standard types are called standard network variable types (SNVTs; pronounced

snivets

profiles. If you cannot find standard types or profiles that meet your requirements, Neuron C also provides full support for user network variable types (UNVTs; pronounced pronounced

(which are also called

standardized, interoperable types

). For functional blocks, the standard types are called standard functional

u-keep-its

are Neuron C data constructs that are another part of

functional blocks

functional block members

resource file

. Network variables and

network variable types

(CPTs). Functional blocks are defined by

functional profile templates

. The use of standardized data types

skip-its

u-nivets

), and user functional profiles.

), user configuration property types (UCPTs;

). For network variables, the

, each of which

(NVTs) and

functional

ONWORKS network. For

Neuron C Programmer’s Guide 3

Neuron C is designed to execute in the environment provided by the Neuron system firmware. This firmware provides an part of the Neuron C language’s run-time environment.

event-driven scheduling system

Neuron C also provides a lower-level language in addition to the network variable model, but the network variable model has the advantage of being a standardized method of information interchange, whereas the messaging service is not standardized (with the exception of its usage by the L of network variables, both standard types and user types, promotes interoperability between multiple devices from multiple vendors. The lower-level messaging service allows for proprietary solutions in addition to the file transfer protocol.

Another Neuron C data object is the manipulated like variables. When a timer expires, the system firmware automatically manages the timer events and notifies the program of those events.

Neuron C provides many built-in

objects

Chip or Smart Transceiver I/O hardware. Each I/O model fits into the eventdriven programming model. A function-call interface is provided to interact with each I/O object.

Some I/O models, all I/O pins, and a dedicated, high-resolution system timer, can also be used to trigger asynchronous interrupts.

The rest of this chapter discusses these various aspects of Neuron C in more detail, and the remaining chapters cover these aspects in greater detail, accompanied by many examples.

. These I/O models are standardized I/O “device drivers” for the Neuron

ONWORKS file transfer protocol, LW-FTP). The use

messaging service

timer

. Timers can be declared and

I/O models

, which are instantiated as

integrated into the

I/O

Neuron C Integer Constants

Negative constants are treated as a unary minus operation on a positive constant, for example, -128 is a signed long, not a signed short. Likewise, -32768 is an unsigned long, not a signed long. To construct a signed short value of -128, you must use a cast:

((signed short)(-128))

To construct a signed long value of –32768, you must also use a cast:

((signed long)(-32768))

Decimal integer constants have the following default types:

0 .. 127 signed short 128 .. 32767 signed long 32768 .. 65535 unsigned long

The default type can be modified with the u, U, l, and L suffixes. For example:

0L signed long 127U unsigned short 127UL unsigned long 256U unsigned long

Hexadecimal constants have the following default types, which can also be modified as described above with the u, U, l, and L suffixes:

4 Overview

0x0 .. 0x7F signed short 0x80 .. 0xFF unsigned short 0x100 .. 0x7FFF signed long 0x8000 .. 0xFFFF unsigned long

Octal constants have the following default types, which can also be modified as described above with the u, U, l, and L suffixes:

0 .. 0177 signed short 0200 .. 0377 unsigned short 0400 .. 077777 signed long 0100000 .. 0177777 unsigned long

Binary constants have the following default types, which can also be modified as described above with the u, U, l, and L suffixes:

0b0 .. 0b01111111 signed short 0b10000000 .. 0b11111111 unsigned short 0b0000000100000000 .. 0b0111111111111111 signed long 0b1000000000000000 .. 0b1111111111111111 unsigned long

Neuron C Variables

The following sections briefly discuss various aspects of variable declarations. Data types affect what sort of data the variable represents. Storage classes affect where the variable is stored, whether it can be modified (and if so, how often), and whether there are any device interface aspects to modifying the data.

Neuron C Variable Types

Neuron C supports the following C variable types. The keywords shown in square brackets are optional; if omitted, they are assumed by the Neuron C language, per the rules of the ANSI C standard.

[signed] long [int] 16-bit quantity

unsigned long [int] 16-bit quantity

signed char 8-bit quantity

[unsigned] char 8-bit quantity

[signed] [short] [int] 8-bit quantity

unsigned [short] [int] 8-bit quantity

enum 8-bit quantity (int type)

Neuron C provides some predefined enum types. One example is shown below:

typedef enum {FALSE, TRUE} boolean;

Neuron C also provides predefined objects that, in many ways, provide the look and feel of an ANSI C language variable. These objects include Neuron C timer and I/O objects. See the see the timer objects.

Timers

chapter in the

I/O Model Reference

Neuron C Reference Guide

for more details on I/O objects, and

for more details on

The extended arithmetic library also defines float_type and s32_type for IEEE 754 and signed 32-bit integer data respectively. These types are discussed in great detail in the

Neuron C Programmer’s Guide 5

Functions

chapter of the

Neuron C Reference Guide

Neuron C Storage Classes

If no class is specified and the declaration is at file scope, the data or function is global. a function or a task. Global data (including all data declared with the static keyword) is present throughout the entire execution of the program, starting from the point where the symbol was declared. Declarations using extern references can be used to provide forward references to variables, and function prototypes must be declared to provide forward references to functions.

Upon power-up or reset of a Neuron Chip or Smart Transceiver, the global data in RAM is initialized to its initial-value expression, if present, otherwise to zero (variables declared with the eeprom or config class, as well as configuration properties declared with the config_prop or cp_family keywords, are only initialized when the application image is first loaded).

Neuron C supports the following ANSI C storage classes and type qualifiers:

auto Declares a variable of local scope. Typically, this would be within a

const Declares a value that cannot be modified by the application program.

File scope

is that part of a Neuron C program that is not contained within

function body. This is the default storage class within a local scope and the keyword is normally not specified. Variables of auto scope that are not also static are not initialized upon entry to the local scope. The value of the variable is not preserved once program execution leaves the scope.

Affects self-documentation (SD) data generated by the Neuron C compiler when used in conjunction with the declaration of CP families or configuration network variables.

extern Declares a data item or function that is defined in another module, in

a library, or in the system image.

not

static Declares a data item or function which is

other modules at link time. Furthermore, if the data item is local to a function or to a when task, the data value is to be preserved between invocations, and is not made available to other functions at compile time.

In addition to the ANSI C storage classes, Neuron C provides the following classes and class modifiers:

config Can be combined only with an input network variable declaration. A

config network variable is used for application configuration. It is equivalent to const eeprom. Such a network variable is initialized only when the application image is first loaded. The config class is obsolete and is included only for legacy applications. The Neuron C compiler does not generate self-documentation data for config-class network variables. New applications should use the configuration network variable syntax described Chapter

Properties to Configure Device Behavior

network Begins a network variable declaration. See Chapter

Communicate Using Network Variables

system Used in Neuron C solely to access the Neuron firmware function

library. Do not use this keyword for data or function declarations.

to be made available to

Using Configuration

, on page 83.

How Devices

, on page 43, for more details.

6 Overview

uninit When combined with the eeprom keyword (see below), specifies that

the EEPROM variable is not initialized or altered on program load or reload over the network.

The following Neuron C keywords allow you to direct portions of application code and data to specific memory sections:

• eeprom

• far

• offchip (only for Neuron Chips and Smart Transceivers

with external memory)

• onchip

• ram (only for Neuron Chips and Smart Transceivers

with external memory)

These keywords are particularly useful on the Neuron 3150 Chip and 3150 Smart Transceivers, because a majority of the address space for these parts is mapped off chip. See description of memory usage and the use of these keywords.

Using Neuron Chip Memory

on page 176 for a more detailed

Variable Initialization

Initialization of variables occurs at different times for different classes. The

must

const variables, except for network variables, of const variables occurs when the application image is first loaded into the Neuron Chip or Smart Transceiver. The const ram variables are placed in offchip RAM that must be non-volatile. Therefore, the eeprom and config variables are also initialized at load time, except when the uninit class modifier is included in these variable definitions.

Automatic variables cannot be declared const because Neuron C does not implement initializers in declarations of automatic variables.

Global RAM variables are initialized at reset (that is, when the device is reset or powered up). By default, all global RAM variables (including static variables) are initialized to zero at this time. Initialization to zero costs no extra code space, as it is a firmware feature.

Initialization of I/O objects, input network variables (except for eeprom, config, config_prop, or const network variables), and timers also occurs at reset. Zero is the default initial value for network variables and timers.

Local variables (except static ones) are not automatically initialized, nor are their values preserved when the program execution leaves the local scope.

be initialized. Initialization

Neuron C Declarations

Both ANSI C and Neuron C support the declarations listed in Table 2.

Table 2. ANSI C and Neuron C Declarations

Declaration Example

Simple data items int a, b, c;

Neuron C Programmer’s Guide 7

Declaration Example

Data types typedef unsigned long ULONG;

Enumerations enum hue {RED, GREEN, BLUE};

Pointers char *p;

Functions int f(int a, int b);

Arrays int a[4];

Structures and unions struct s {

int field1; unsigned field2 : 3; unsigned field3 : 4; };

In addition, Neuron C Version 2 supports the declarations listed in Table 3.

Table 3. Neuron C Version 2 Declarations

Declaration Example

I/O Objects IO_0 output oneshot relay_trigger;

(See Chapter 2)

Timers mtimer led_on_timer;

(See Chapter 2)

Network Variables network input SNVT_temp temperature;

(See Chapter 3)

Configuration Properties

Functional Blocks fblock SFPTnodeObject { … } myNode;

Message Tags msg_tag command;

cp_family SCPTdefOutput defaultOut;

(See Chapter 4)

(See Chapter 5)

(See Chapter 6)

Network Variables, SNVTs, and UNVTs

network variable

variables on one or more additional devices. A device’s network variables define its inputs and outputs from a network point of view and allow the sharing of data in a distributed application. Whenever a program writes into one of its network variables (with the exception of output network variables that are

is an object on one device that can be

connected

to network

output

8 Overview

declared with the polled modifier), the new value of the network variable is

propagated

connected to that output network variable. If the output network variable is not currently a member of any network variable connection, no transaction and no error occurs.

across the network to all devices with

input

network variables

Although the propagation of network variables occurs through L messages, these messages are sent implicitly. The application program does not require any explicit instructions for sending, receiving, managing, retrying, authenticating, or acknowledging network variable updates. A Neuron C application provides the most recent value by writing into an output network variable, and it obtains the most recent data from the network by reading an input network variable.

Example:

network input SNVT_temp nviTemperature;

network output SNVT_temp nvoTemperature;

void f(void)

{

nvoTemperature = 2 * nviTemperature;

}

Network variables greatly simplify the process of developing and installing distributed systems because devices can be defined individually, then connected and reconnected easily into many new L variables are discussed in detail in Chapter

Network Variables

Network variables promote interoperability between devices by providing a welldefined interface that devices use to communicate. Interoperability simplifies installation of devices into different types of networks by keeping the network configuration independent of the device’s application. A device can be installed in a network and logically connected to other devices in the network as long as the data types (for example, SNVT_switch or SNVT_temp_p) match. To further promote interoperability, the L profiles that define standard functional interfaces for devices, and standard network variable types (SNVTs) that define standard data encoding, scaling, and units, such as degrees C, volts, or meters. There are standard functional profiles for a variety of functions and industries. There are SNVT definitions for essentially every physical quantity, and other more abstract definitions tailored for certain industries and common applications.

, on page 43, and also in the

ONWORKS platform provides standard functional

ONWORKS applications. Network

How Devices Communicate Using

Neuron C Reference Guide

ONWORKS

You can also create your own user functional profiles and user network variable types (UNVTs). You can define resource files for your custom types and profiles to enable your devices to be used with devices from other manufacturers. The NodeBuilder Resource Editor included with the NodeBuilder tool provides a simple interface for viewing existing resources and defining your own resources.

Configuration Properties

A configuration property is a data item that, like a network variable, is part of the device interface for a device. A configuration property can be modified by a network tool. Configuration properties facilitate interoperable installation and configuration tools by providing a standardized network interface for device

Neuron C Programmer’s Guide 9

configuration data. Like network variables, configuration properties also provide a well-defined interface. Each configuration property type is defined in a resource file that specifies the data encoding, scaling, units, default value, invalid value, range, and behavior for configuration properties based on the type. A rich variety of standard configuration property types (SCPTs) are available. You can also create your own user configuration property types (UCPTs) that are defined in resource files that you create with the NodeBuilder Resource Editor.

Functional Blocks and Functional Profiles

The

device interface

network variables, and configuration properties. A of network variables and configuration properties that are used together to perform one task. These network variables and configuration properties are called the

functional block members

for a LONWORKS device consists of its functional blocks,

functional block

is a collection

Functional blocks are defined by to describe common units of functional behavior. Each functional profile defines mandatory and optional network variables and mandatory and optional configuration properties. Each functional block implements an instance of a functional profile. A functional block must implement all of the mandatory network variables and configuration properties that are defined by the functional profile, and can also implement any of the optional network variables and configuration properties that are defined by the functional profile. In addition, a functional block can implement network variables and configuration properties that are not defined by the functional profile – these are called

specific

Functional profiles are defined in profiles or you can define your own functional profiles in your own resource files by using the NodeBuilder Resource Editor. A functional profile defined in a resource file is also called a

L L requirements as specified by the

Guidelines

network variables and configuration properties.

ONMARK International provides a procedure for developers to certify devices. ONMARK interoperable devices conform to all LonTalk

, and conform to all aspects of application design, as described in the

LonMark Application Layer Interoperability Guidelines

Contact L becoming a member and certifying your devices.

You can automatically embed data within your device that identifies its device interface to network tools that are used to install the device. This data is called

self-identification

compiler generates this data based on the functional blocks, network variables, and configuration properties that you declare, as well as the resource files that you provide. You can add your own documentation to the SD data to further document your device and its interface.

ONMARK International at www.lonmark.org for more details about

(SI) data and

functional profiles

resource files

functional profile template

. A functional profile is used

. You can use standard functional

(FPT).

protocol layer 1 – 6

LonMark Layer 1 – 6 Interoperability

self-documentation

(SD) data. The Neuron C

implementation-

You can include network variable names in the SD data using the #pragma enable_sd_nv_names directive. You can also include a rate estimate in tenths-ofmessages/second and a maximum rate estimate in tenths-of-messages/second in the SD data for each network variable. The rate estimate and maximum rate estimate values are provided through the bind_info feature. (See the discussion

10 Overview

of this feature in Chapter

Variables

An application image for a device created by the Neuron C compiler contains SD information unless the #pragma disable_snvt_si directive is used. (See the

Compiler Directives

information.)

, on page 43, and also in the

How Devices Communicate Using Network

chapter of the

Neuron C Reference Guide

for more

Data-Driven Compared with CommandDriven Protocols

Network variables are used to communicate data and state information between devices. This data-driven model provides a different communication model than in command-based systems. In command-based messaging systems, designers are faced with having a large number of commands, specific to each application, that must be managed, updated, and maintained. Each device has to have knowledge of every command. This leads to ever-growing command tables and application code.

With network variables, the command or action portion of a message is not in the message. Instead, with network variables, this information is in the application program, and each application program only needs have the knowledge required to perform its function. A network integrator can add new types of devices at any time, and connect them to existing devices in the network to perform new applications not envisioned by the original designers of the devices.

Event-Driven Scheduling or Polled Scheduling

Although the Neuron C language is principally designed to make event-driven scheduling natural and easy, Neuron C also allows you to construct polled applications that implement a centralized control application. Chapter

Devices Communicate Using Network Variables

information on polling.

, on page 43, provides further

Low-Level Messaging

In addition to the functional block and network variable communication model, Neuron C also supports application messages. You can use application messages – in place of or in conjunction with the network variables approach – to implement proprietary interfaces to your devices. They are also used for the

ONWORKS file transfer protocol. Application messages are described in Chapter

L 6,

How Devices Communicate Using Application Messages

, on page 117.

I/O Devices

A Neuron Chip or Smart Transceiver can be connected to one or more physical I/O devices. Examples of simple I/O devices include temperature and position sensors, valves, switches, and LED displays. Neuron Chips and Smart Transceivers can also be connected to other microprocessors. The Neuron firmware implements numerous devices for a Neuron C application. I/O models are discussed in detail in Chapter 2,

Focusing on a Single Device

I/O models

, on page 15, and in the

that manage the interface to these

I/O Model Reference

How

Neuron C Programmer’s Guide 11

Neuron-Hosted and Host-Based Compilation

Compilation for Neuron-hosted devices, that is, devices based on a Neuron Chip or Smart Transceiver as the main processor, use Neuron C to define all aspects of the device’s application:

• The application’s interoperable interface, including its set of network

variables, configuration properties, and functional blocks, and the selfidentification and self-documentation data

• Device configuration information, such as buffer configuration

• Application code including, when tasks, interrupt tasks, I/O device

declarations, and functions

Compilation for host-based devices, that is, devices that implement the application on a different processor (the Neuron C language to model the device’s interoperable interface. For these devices, the Neuron C source code acts as a model for the finished application, and the primary source file is called the devices uses Neuron C to define only the following aspect of the device’s application:

• The application’s interoperable interface, including its set of network

variables, configuration properties, and functional blocks, and the selfidentification and self-documentation data

The ShortStack Developer’s Kit, FTXL Developer’s Kit, and the SmartServer all use model files. See the related product documentation for information about compiling a model file, and about implementing the device’s application (including the device configuration information and application code).

host processor

model file

), often use a subset of the

. Compilation for host-based

.LON

Syntactically, a Neuron C application and a model file differ in the following ways:

• Declaration of the interoperable interface is largely identical, however, a

few exceptions exist which are identified throughout this book, as necessary.

• Device configuration is typically adjusted using a product-specific tool,

such as the LonTalk Interface Developer utility, and is not specified in a model file. When encountered within a model file, device configuration constructs can trigger a compiler error or warning.

• Application code, including I/O device declarations, application timer

declarations and any form of executable code, is ignored in a model file. Such code results in a warning for model file compilation, and the code has no effect.

You can use conditional compilation to prepare the same source code for use as Neuron C application and a model file. The _MODEL_FILE preprocessor symbol is automatically predefined for model file compilation, and is not predefined for application compilation. See the about predefined preprocessor symbols.

Neuron C Reference Guide

for more information

12 Overview

Differences between Neuron C and ANSI C

Neuron C adheres closely to the ANSI C language standard; however, Neuron C is not a "conforming implementation" of Standard C, as defined by the American National Standards Institute committee X3-J11.

The following list outlines the major differences between Neuron C and ANSI C:

• Neuron C does not support floating-point computation with C syntax or

operators. However, a floating-point library is provided to allow use of floating-point data that conforms to the IEEE 754 standard.

• ANSI C defines a short int as 16 bits or more and a long int as 32 bits or

more. Neuron C defines a short int as 8 bits and a long int as 16 bits. In Neuron C, int defaults to a short int. A 32-bit signed integer library is available to allow use of 32-bit quantities.

• Neuron C does not support the register or volatile classes. These storage

classes can be specified but are ignored.

• Neuron C does not implement initializers in declarations of automatic

variables.

• Neuron C does not support structures or unions as procedure parameters

or as function return values.

• Neuron C does not support declaration of bitfields as members of unions.

However, an equivalent declaration can be accomplished by defining a structure as a member of the union, where the structure contains the bitfields.

• Network variable structures cannot contain pointers. Configuration

property structures also cannot contain pointers.

• Pointers to timers, to message tags, or to I/O objects are not supported.

• Pointers to network variables, configuration properties, and EEPROM

variables are treated as pointers to constants (that is, the contents of the variable referenced by the pointer can be read, but not modified). Under special circumstances, and with certain restrictions, the pointers can be used to modify the memory. See the discussion of the eeprom_memcpy( )

function in Chapter

Functions

discussion of the #pragma relaxed_casting_on compiler directive in the

chapter of the

Compiler Directives

• Macro arguments are not rescanned until after the macro is expanded,

thus the macro operators # and ## might not yield results as defined in the ANSI C standard when they occur in nested macro expansions.

• Names of network variables and message tags are limited to 16

characters. Names of functional blocks are limited to 16 characters unless they are declared using the external_name feature, in which case the external name is limited to 16 characters, and the internal name of the functional block is limited to 64 characters.

Memory Management

Neuron C Reference Guide

chapter of the

Neuron C Reference Guide

, on page 173, and also in the

. Also refer to the

• A few ANSI C library functions are included in Neuron C, such as

memcpy( ) and memset( ). A string and byte operation library is provided to allow use of a subset of the ANSI C functions defined in the <string.h>

Neuron C Programmer’s Guide 13

include file. Other ANSI C library functions, such as file I/O and storage allocation functions, are not included in Neuron C. Consult the

Reference Guide

• The Neuron C implementation includes three ANSI include files:

<stddef.h>, <stdlib.h>, and <limits.h>.

• Neuron C requires use of the function prototype feature whenever a call

to the function precedes the function definition (see Chapter

on a Single Device

• Neuron C does not support the use of the ellipsis (...) in function

prototypes or definitions.

• Neuron C contains additional reserved words and syntax not found in

ANSI C. See the the list of reserved words.

• Neuron C supports binary constants in addition to octal and hexadecimal.

Binary constants are specified as 0b 0b1101 equals decimal 13.

• Neuron C supports the // comment style from C++, in addition to the

traditional /* */ C coment style. In the // style, two slashes (//) begin a comment. The comment is terminated by the end of the line, without further punctuation.

C code /* An ANSI C and NEURON C comment */ C code // A line-style NEURON C comment

for a complete and detailed list.

, on page 15).

Neuron C Reference Guide

for the syntax summary and

<binary_number>

. For example,

Neuron C

Focusing

• The main( ) construct is not used. Instead, a Neuron C program’s

executable objects consist of when statements in addition to functions. A thread of execution always begins with a when statement, as described in

Chapter

• Neuron C does not support multiple source files in separate compilation

units (however, the #include directive is supported).

• The ANSI C preprocessor directives #if, #elif, and #line are not supported.

However, #ifdef, #ifndef, #else, and #endif are supported.

• The Neuron C implementation of the #error directive requires a double-

quoted string for the error message; the ANSI C directive does not.

See Appendix 237 for a description of how the Neuron C language conforms to “implementationspecific” aspects of the ISO and ANSI C language definitions.

Focusing on a Single Device

Neuron C Language Implementation Characteristics

, on page 15.

, on page

14 Overview

Focusing on a Single Device

This chapter describes the Neuron C event scheduler and I/O objects. The concepts of introduced. I/O and timer objects, and I/O functions.

Objects that can be defined for each Neuron C application include

timers variables

in Chapter 4;

application messages

and

, described in Chapter 3;

predefined events

Code examples in this chapter illustrate the use of events,

input/output (I/O) objects

functional blocks

, described in Chapter 6.

and

user-defined events

, described here;

configuration properties

, described in Chapter 5; and

network

, described

are

Neuron C Programmer’s Guide 15

What Happens on a Single Device?

In this chapter, you begin to learn about programming a Neuron Chip or Smart Transceiver by focusing first on a single device. Each Neuron Chip and each Smart Transceiver has standard firmware, called the hardware support that implement a scheduler, timers, and I/O device drivers and interfaces. Series 5000 chips also provide hardware support for interrupts; see

Interrupts

The Neuron C language includes predefined objects that provide access to these firmware features. These objects are described briefly here, and in more detail later in this chapter:

on page 153 for more information.

Neuron firmware

, and

• The Neuron firmware's

application program. This chapter explains how to use the Neuron C language to define events and tasks, how the scheduler evaluates nonpriority events, and how you can define priority events.

• The Neuron C language offers two types of

second timers. These timers can be used to affect the scheduling of tasks, as described in

• A number of

ANSI C. These I/O objects, as well as related I/O functions and events, are described in

The Scheduler

The scheduling of application program tasks is event driven: when a given condition becomes TRUE, a body of code (called a condition is executed. The scheduler allows you to define tasks that run as the result of certain events, such as a change in the state of an input pin, receiving a new value for a network variable, or the expiration of a timer. You can also specify certain tasks as priority

Priority When Clauses

(see specify interrupt tasks that are serviced independently of the scheduler; see

Interrupts

on page 153 for more information.

event scheduler

Timers

I/O objects

on page 25.

can be declared using Neuron C extensions to

Input/Output

on page 23). Series 5000 chips also allow you to

on page 27.

tasks, so that they receive preferential service

handles task scheduling for the

timer

objects: millisecond and

task

) associated with that

When Clauses

Events are defined through when clauses. A when clause contains an expression

task

that, if evaluated as TRUE, causes the body of code (the expression to be executed to completion. Multiple when clauses can be associated with a single task. A simple when clause and its associated task are shown below. The when clause or clauses and the associated task are frequently referred to as one entity known as a

when (timer_expires(led timer))

when task

or a

when statement

{

// Turn off the LED

io_out(io_led, OFF);

}

16 Focusing on a Single Device

) following the

when clause

task

In this example above, when the led_timer application timer (definition not shown in this example) expires, the body of code (the task) that follows the when clause is executed to turn off the specified I/O object, io_led (also defined elsewhere in the program). After this task has been executed, the timer_expires event is automatically cleared. Its task is then ignored until the LED timer expires again and the when clause again evaluates to TRUE.

The following examples demonstrate various ways of using tasks and events.

More information about tasks and events can be found in Chapter

Features

The when clauses cannot be nested. For example, the following nested when clause is not valid:

, on page 145, and Figure 14 on page 147.

when (reset)

when (io_changes(io_switch))

when (!timer_expires)

when (flush_completes && (y == 5))

when (x == 3)

{

// Turn on the LED and start the timer

. . .

}

when (io_changes(io_switch))

{

when (x == 3) { // Can't nest!

...

}

Additional

An equivalent result may be achieved by testing the event with an if statement:

when (io_changes(io_switch))

{

if (x == 3) {

...

}

When Statement

The syntax for a when statement (the when clause or clauses plus the associated task) is:

when-clause

[when-clause ... ]

task

The syntax for

[priority] [preempt_safe] when (

priority Forces evaluation of the following when clause each time the

preempt_safe Allows the scheduler to execute the associated when task

when-clause

scheduler runs. See

even if the application is in preemption mode. See the discussions on preemption mode in Chapter

Application Messages

is:

event

)

Priority When Clauses

How Devices Communicate Using

, on page 117.

on page 23.

Neuron C Programmer’s Guide 17

event

This expression is either a predefined event (see the following section)

or any valid Neuron C expression (which can contain a predefined event). Predefined events as well as expressions are enclosed in parentheses. One or more when clauses can be associated with the same task.

task

A Neuron C compound statement, consisting of a series of Neuron C

declarations and statements, enclosed in braces, which are identical to those found in a Neuron C function definition. The task is identical to the body of a void function (that is, it cannot return a value). A return statement can be used to terminate execution of the task but is not required.

Types of Events Used in When Clauses

The events defined in a when clause fall into two general categories: predefined events and user-defined events. compiler. Examples of predefined events include input pin state changes, network variable changes, timer expiration, and message reception.

defined events

to a Boolean value.

The distinction between user-defined events and predefined events is not critical. Use predefined events whenever possible, because they require less code space.

There is one exception to the statement that a when clause can be any valid C expression. The offline, online, and wink predefined events must appear by themselves if used. All other predefined events may be combined into any arbitrary expressions. This restriction only applies to when clauses.

can be any valid Neuron C expression that evaluates or converts

Predefined events

use keywords built into the

User-

Examples:

when (msg_arrives) // O.K.

when (msg_arrives && flag == TRUE) // O.K.

when (online) // O.K.

when (online && flag == TRUE) // Not permitted.

Predefined Events

The timer_expires event shown earlier is one type of predefined event. Table 4 lists other predefined events that are represented by unique keywords.

Predefined Event Where Described in This Manual

flush_completes Chapter 7

io_changes this chapter

io_in_ready this chapter

io_out_ready this chapter

io_update_occurs this chapter

Table 4. Predefined Events

18 Focusing on a Single Device

+ 238 hidden pages

Echelon Neuron C User Manual

Specifications and Main Features

Frequently Asked Questions

User Manual

Welcome

Audience

Related Documentation

Typographic Conventions for Syntax

Overview

What Is Neuron C?

Comparing Neuron C Version 2 to Version 1

Unique Aspects of Neuron C

Neuron C Integer Constants

Neuron C Variables

Neuron C Variable Types

Neuron C Storage Classes

Variable Initialization

Neuron C Declarations

Network Variables, SNVTs, and UNVTs

Configuration Properties

Functional Blocks and Functional Profiles

Event-Driven Scheduling or Polled Scheduling

Low-Level Messaging

I/O Devices

Neuron-Hosted and Host-Based Compilation

Differences between Neuron C and ANSI C

Focusing on a Single Device

What Happens on a Single Device?

The Scheduler

When Clauses

When Statement

Types of Events Used in When Clauses

Predefined events