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BasicProgrammingConceptsInGAML

Baptiste Lesquoy edited this page Sep 8, 2023 · 19 revisions

Basic programming concepts in GAML

In this part, we will focus on the very basic structures in GAML, such as how to declare a variable, how to use loops, or how to manipulate lists. We will overfly quickly all those basic programming concepts, admitting that you already have some basics in coding.

Index

Variables

Variables are declared very easily in GAML, starting with the keyword for the type, following by the name you want for your variable. NB: The declaration has to be inside the global, the experiment, or the species scope.

typeName myVariableName;

Basic types

All the "basic" types are present in GAML:int, float, string, bool (see the data type page for information about all the available datatype). The operator for the affectation in GAML is <- (the operator = is used to test the equality).

int integerVariable <- 3;
float floatVariable <- 2.5;
string stringVariable <- "test"; // you can also write simple ' : <- 'test'
bool booleanVariable <- true; // or false

To follow the behavior of a variable, we can write their value in the console. Let's go back to our basic skeleton of a model, and let's create a reflex in the global scope (to be short, a reflex is a procedure that is executed in each step. We will come back to this concept later). The write statement works very easily, simply writing down the keyword write and the name of the variable we want to be displayed.

model firstModel

global {
    int integerVariable <- 3;
    float floatVariable <- 2.5;
    string stringVariable <- "test"; // you can also write simple ' : <- 'test'
    bool booleanVariable <- true; // or false
    
    reflex writeDebug {
	 write integerVariable;
	 write floatVariable;
	 write stringVariable;
	 write booleanVariable;
    }
}

experiment myExperiment {}

The statement write is overloaded for each type of variable (even for the more complex type, such as containers).

Note that before being initialized, a variable has the value nil.

reflex update {
    string my_string;
    write my_string; // this will write "nil".
    int my_int;
    write my_int; // this will write "0", which is the default value for int.
}

nil is also a literal you can use to initialize your variables (you can learn more about the concept of literal in this page).

reflex update {
    string my_string <- "a string";
    my_string <- nil;
    write my_string; // this will write "nil".
    int my_int <- 6;
    my_int <- nil;
    write my_int; // this will write "0", which is the default value for int.
}

The point type

Another variable type you should know is the point type. This type of variable is used to describe coordinates. It is in fact a complex variable, composed of two float variables (or three if you are working in 3D). To declare it, you have to use the curly bracket {:

point p <- {0.2,2.4};

The first field is related to the x value, and the second one to the y value. You can easily get this value as follows:

point p <- {0.2,2.4};
write p.x; // the output will be 0.2
write p.y; // the output will be 2.4

You cannot modify directly the value. But if you want, you can do a simple operation to get what you want:

point p <- {0.2,2.4};
p <- p + {0.0,1.0};
write p.y; // the output will be 3.4

A world about dimensions

When manipulating float values, you can specify the dimension (also called unit) of your value. Dimensions are preceded by # or ° (exactly the same).

float a <- 5°m;
float b <- 4#cm;
float c <- a + b; // c is equal to 5.0399999 (it's not equal to 5.04 because it is a float value, not as precise as int)

Declare variables using facet

Facets are used to describe the behavior of a variable during its declaration, by adding the keyword facet just after the variable name, followed by the value you want for the facet (or also just after the initial value). See the page related to the variable declaration for all the facets.

type variableName <- initialValue facet1:valueForFacet1 facet2:valueForFacet2;
// or:
type variableName facet1:valueForFacet1 facet2:valueForFacet2;
variableName <- initialValue;

You can use the facet update if you want the value of your variable to change at every simulation step. For example, to increment your integer variable each step, you can do as follow:

int integerVariable <- 3 min: 0 max: 10 update: integerVariable+1;
// nb: the operator "++" doesn't exist in gaml.

You can use the facets min and max to constraint the value in a specific range of values:

int integerVariable <- 3 min: 0 max: 10 update: integerVariable+1;
// the result will be 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 10 - 10 - ...

The facet among can also be useful (that can be seen as an enum):

string fruits <- "banana" among: ["pear","apple","banana"];

Operators in GAMA

In GAML language, you can use a lot of different operators. An operator is a function, i.e. a way to get the result of a computation. All of them are listed in this page, but here are the most useful ones:

Mathematical operators

The basic arithmetical operators, such as +(add), -(substract), *(multiply), /(divide), ^(power) are used this way:

// FirstOperand BinaryOperator SecondOperand 
//   --> ex: 5 * 3; // return 15

int fif <- 5 * 3;

Some other operators, such as cos(cosinus), sin(sinus), tan(tangent), sqrt(square root), round(rounding) etc... are used this way:

// UnaryOperator(Operand) 
//   --> ex: sqrt(49); // return 7.0

float sq <- sqrt(49);

Logical operators

Logical operators such as and(and), or(inclusive or) are used the same way as basic arithmetical operators. The operator !(negation) has to be placed just before the operand. They return a boolean result.

// FirstOperand Operator SecondOperand 
//   --> ex: true or false; // return true

// NegationOperator Operand 
//   --> ex: !(true or false); // return false

Comparison operators

The comparison operators !=(different than), <(smaller than), <=(smaller of equal), =(equal), >(bigger than), >=(bigger or equal) are used the same way as basic arithmetical operators:

// FirstOperand Operator SecondOperand 
//   --> ex: 5 < 3; // return false

bool cmp <- 5 < 3;

Type casting operators

You can cast an operand to a special type using casting operator:

// Operator(Operand); 
//  --> ex: int(2.1); // return 2

int intTwo <- int(2.1);

Other operators

A lot of other operators exist in GAML. The standard way to use those operators is as followed:

Operator(FirstOperand,SecondOperand,...) --> ex: rnd(1,8);

Some others are used in a more intuitive way:

FirstOperand Operator SecondOperand --> ex: 2[6,4,5] contains(5);

Conditional structures

You can write conditionals with if/else in GAML:

if (integerVariable<0) {
    write "my value is negative !! The exact value is " + integerVariable;
}
else if (integerVariable>0) {
    write "my value is positive !! The exact value is " + integerVariable;
}
else if (integerVariable=0) {
    write "my value is equal to 0 !!";
}
else {
    write "hey... This is not possible, right ?";
}

GAML also accepts ternary operator:

stringVariable <- (booleanVariable) ? "booleanVariable = true" : "booleanVariable = false";

Loop

Loops in GAML are designed by the keyword loop. As for variables, a loop have multiple facets to determine its behavior:

  • The facet times, to repeat a fixed number of times a set of statements:
loop times: 2 {
    write "helloWorld";
} 
// the output will be helloWorld - helloWorld
  • The facet while, to repeat a set of statements while a condition is true:
loop while: true {
}
// infinity loop
  • The facet from / to, to repeat a set of statements while an index iterates over a range of values with a fixed step of 1:
loop i from: 0 to: 5 { 
    write i;
}
// the output will be 0 - 1 - 2 - 3 - 4 - 5
  • The facet from / to combine with the facet step to choose the step:
loop i from: 0 to: 5 step: 2 {
    write i;
}
// the output will be 0 - 2 - 4
  • The facet over to browse containers, as we will see in the next part.
loop i over: [0, 2, 4] {
    write i;
}
// the output will be 0 - 2 - 4

Nb: you can interrupt a loop at any time by using the break statement.

Manipulate containers

We saw in the previous parts "simple" types of variable. You also have multiple containers types, such as list, matrix, map, pair... In this section, we will only focus on the container list (you can learn the other by reading the section about datatypes).

How to declare a list?

To declare a list, you can either or not specify the type of the data of its elements:

list<int> listOfInt <- [5,4,9,8];
list listWithoutType <- [2,4.6,"oij",["hoh",0.0]];

How to know the number of elements of a list?

To know the number of elements of a list, you can use the operator length that returns the number of elements (note that this operator also works with strings).

int numberOfElements <- length([12,13]); // will return 2
int numberOfElements <- length([]); // will return 0
int numberOfElements <- length("stuff"); // will return 5

There is another operator, empty, that returns you a boolean telling you if the list is empty or not.

bool isEmpty <- empty([12,13]); // will return false
bool isEmpty <- empty([]); // will return true
bool isEmpty <- empty("stuff"); // will return false

How to get an element from a list?

To get an element from a list by its index, you have to use the operator at (nb: it is indeed an operator and not a facet, so no ":" after the keyword).

int theFirstElementOfTheList <- [5,4,9,8] at 0; // this will return 5
int theThirdElementOfTheList <- [5,4,9,8] at 2; // this will return 9

How to know the index of an element of a list?

You can know the index of the first occurrence of a value in a list using the operator index_of. You can know the index of the last occurrence of a value in a list using the operator last_index_of.

int result <- [4,2,3,4,5,4] last_index_of 4;  // result equals 5
int result <- [4,2,3,4,5,4] index_of 4;  // result equals 0

How to know if an element exists in a list?

You can use the operator contains (return a boolean):

bool result <- [{1,2}, {3,4}, {5,6}] contains {3,4};  // result equals true

How to insert/remove an element to/from a list?

For those operation, you can use dedicated statements. The statements add and put are used to insert/modify an element, while the statement remove is used to remove an element. Here are some examples of how to use those 3 statements with the most common facets:

list<int> list_int <- [1,5,7,6,7];

remove from:list_int index:1; // remove the 2nd element of the list
write list_int; // the output is : [1,7,6,7]
remove item:7 from:list_int; // remove the 1st occurrence of 7
write list_int; // the output is : [1,6,7]

add item:9 to: list_int at: 2; // add 9 in the 3rd position
write list_int; // the output is : [1,6,9,7]
add 0 to: list_int; // add 0 in the last position
write list_int; // the output is : [1,6,9,7,0]

put 3 in: list_int at: 0; // put 3 in the 1st position
write list_int; // the output is : [3,6,9,7,0]
put 2 in: list_int key: 2; // put 2 in the 3rd position
write list_int; // the output is : [3,6,2,7,0]

Note that the + and - operators can be used to add an element at the end of a list and to remove the last element of a list:

list<int> list_int <- [1,5,7,6,7];

list_int <- list_int + 8;
write list_int;  // the output is : [1,5,7,6,7,8]
list_int <- list_int - 7;
write list_int;  // the output is : [1,5,7,6,8]

How to add 2 lists?

You can add 2 lists by creating a third one and browsing the 2 first one, but you can do it much easily by using the operator + :

list<int> list_int1 <- [1,5,7,6,7];
list<int> list_int2 <- [6,9];
list<int> list_int_result <- list_int1 + list_int2;

How to browse a list?

You can use the facet over of a loop:

list<int> exampleOfList <- [4,2,3,4,5,4];
loop i over: exampleOfList {
	write i;
}
// the output will be 4 - 2 - 3 - 4 - 5 - 4

How to filter a list?

If you want to get all the elements of a list that fulfill a particular condition, you need the operator where. In the condition, you can design all the elements of a particular list by using the pseudo-variable each as followed:

list<int> exampleOfList <- [4,2,3,4,5,4] where (each <= 3); 
// the list is now [2,3]

Other useful operators for the manipulation of lists:

Here are some other operators which can be useful to manipulate lists: sort, sort_by, shuffle, reverse, collect, accumulate, among. Please read the GAML Reference if you want to know more about those operators.

Random values

When you will implement your model, you will have to manipulate some random values quite often.

To get a random value in a range of value, use the operator rnd. You can use this operator in many ways:

int var0 <- rnd (2);    // var0 equals 0, 1 or 2
float var1 <- rnd (1000) / 1000;    // var1 equals a float between 0 and 1 with a precision of 0.001
float var2 <- rnd(3.4);     // var4 equals a random float between 0.0 and 3.4
point var3 <- rnd ({2.0, 4.0}, {2.0, 5.0, 10.0}, 1);    // var2 equals a point with x = 2.0, y equal to 2.0, 3.0 or 4.0 and z between 0.0 and 10.0 every 1.0
float var4 <- rnd (2.0, 4.0, 0.5);  // var3 equals a float number between 2.0 and 4.0 every 0.5
int var5 <- rnd (2, 4);     // var7 equals 2, 3 or 4
int var6 <- rnd (2, 12, 4);     // var5 equals 2, 6 or 10
point var7 <- rnd ({2.5,3, 0.0});   // var6 equals {x,y} with x in [0.0,2.0], y in [0.0,3.0], z = 0.0
point var8 <- rnd ({2.0, 4.0}, {2.0, 5.0, 10.0});   // var8 equals a point with x = 2.0, y between 2.0 and 4.0 and z between 0.0 and 10.0
float var9 <- rnd (2.0, 4.0);   // var9 equals a float number between 2.0 and 4.0

Use the operator flip if you want to pick a boolean value with a certain probability:

bool result <- flip(0.2); // result will have 20% of chance to be true

You can use randomness in list, by using the operator shuffle, or also by using the operator among to pick randomly one (or several) element of your list:

list TwoRandomValuesFromTheList <- 2 among [5,4,9,8];
// the list will be for example [5,9].

You can use probabilistic laws, using operators such as gauss, poisson, binomial, or truncated_gauss (we invite you to read the documentation for those operators).

  1. What's new (Changelog)
  1. Installation and Launching
    1. Installation
    2. Launching GAMA
    3. Updating GAMA
    4. Installing Plugins
  2. Workspace, Projects and Models
    1. Navigating in the Workspace
    2. Changing Workspace
    3. Importing Models
  3. Editing Models
    1. GAML Editor (Generalities)
    2. GAML Editor Tools
    3. Validation of Models
  4. Running Experiments
    1. Launching Experiments
    2. Experiments User interface
    3. Controls of experiments
    4. Parameters view
    5. Inspectors and monitors
    6. Displays
    7. Batch Specific UI
    8. Errors View
  5. Running Headless
    1. Headless Batch
    2. Headless Server
    3. Headless Legacy
  6. Preferences
  7. Troubleshooting
  1. Introduction
    1. Start with GAML
    2. Organization of a Model
    3. Basic programming concepts in GAML
  2. Manipulate basic Species
  3. Global Species
    1. Regular Species
    2. Defining Actions and Behaviors
    3. Interaction between Agents
    4. Attaching Skills
    5. Inheritance
  4. Defining Advanced Species
    1. Grid Species
    2. Graph Species
    3. Mirror Species
    4. Multi-Level Architecture
  5. Defining GUI Experiment
    1. Defining Parameters
    2. Defining Displays Generalities
    3. Defining 3D Displays
    4. Defining Charts
    5. Defining Monitors and Inspectors
    6. Defining Export files
    7. Defining User Interaction
  6. Exploring Models
    1. Run Several Simulations
    2. Batch Experiments
    3. Exploration Methods
  7. Optimizing Model Section
    1. Runtime Concepts
    2. Optimizing Models
  8. Multi-Paradigm Modeling
    1. Control Architecture
    2. Defining Differential Equations
  1. Manipulate OSM Data
  2. Diffusion
  3. Using Database
  4. Using FIPA ACL
  5. Using BDI with BEN
  6. Using Driving Skill
  7. Manipulate dates
  8. Manipulate lights
  9. Using comodel
  10. Save and restore Simulations
  11. Using network
  12. Headless mode
  13. Using Headless
  14. Writing Unit Tests
  15. Ensure model's reproducibility
  16. Going further with extensions
    1. Calling R
    2. Using Graphical Editor
    3. Using Git from GAMA
  1. Built-in Species
  2. Built-in Skills
  3. Built-in Architecture
  4. Statements
  5. Data Type
  6. File Type
  7. Expressions
    1. Literals
    2. Units and Constants
    3. Pseudo Variables
    4. Variables And Attributes
    5. Operators [A-A]
    6. Operators [B-C]
    7. Operators [D-H]
    8. Operators [I-M]
    9. Operators [N-R]
    10. Operators [S-Z]
  8. Exhaustive list of GAMA Keywords
  1. Installing the GIT version
  2. Developing Extensions
    1. Developing Plugins
    2. Developing Skills
    3. Developing Statements
    4. Developing Operators
    5. Developing Types
    6. Developing Species
    7. Developing Control Architectures
    8. Index of annotations
  3. Introduction to GAMA Java API
    1. Architecture of GAMA
    2. IScope
  4. Using GAMA flags
  5. Creating a release of GAMA
  6. Documentation generation

  1. Predator Prey
  2. Road Traffic
  3. 3D Tutorial
  4. Incremental Model
  5. Luneray's flu
  6. BDI Agents

  1. Team
  2. Projects using GAMA
  3. Scientific References
  4. Training Sessions

Resources

  1. Videos
  2. Conferences
  3. Code Examples
  4. Pedagogical materials
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