abs function

abs(X)

Returns the absolute value of X - i.e. ignores its sign

Input: numeric scalar or numeric array

Result: numeric scalar or array

Examples:

abs(3) --> 3

abs(-3) --> 3

abs([2,-3,4,-5]) --> [2,3,4,5]

In: Contents >> Working with equations >> Functions >> Built-in functions

pow function

pow(X,Y)

Returns X raised to the power Y

Input: numeric, numeric

Result: numeric

Comment:

This is equivalent to the use of the ^ operator: i.e.

pow(5,2)

is the same as

5^2

The latter should be used of preference, as it is the more familiar notation.

In: Contents >> Working with equations >> Functions >> Built-in functions

min function

min(X,Y)

Returns lesser of X and Y; i.e. it returns X if X<=Y, otherwise it returns Y.

Inputs: numeric, numeric

Result: numeric

Comments:

The min function is a useful way of ensuring that some value does not go above some threshold. For example, if b increase as a increases, but does not exceed 20, then the equation for b could be:

b = min(20, 0.2*a)

This avoids the use of a cumbersome if…then… else… construction

In: Contents >> Working with equations >> Functions >> Built-in functions

max function

max(X,Y)

Returns greater of X and Y; i.e. it returns X if X>=Y, otherwise it returns Y.

Inputs: numeric, numeric

Result: numeric

Comments:

The max function is a useful way of ensuring that some value does not go below some threshold. For example, if b declines as a increases, but does not go below zero, then the equation for b could be:

b = max(0,10-0.2*a)

This avoids the use of a cumbersome if...then...else... construction.

In: Contents >> Working with equations >> Functions >> Built-in functions

sqrt function

sqrt(X)

Returns the square root of X

Input: numeric

Result: numeric

In: Contents >> Working with equations >> Functions >> Built-in functions

round function

round(X)

Rounds X up or down to the nearest whole number

Input: numeric, or array of numeric values

Result: numeric, or array of numeric value

Examples:

round(1.9) --> 2

round(1.1) --> 1

round([1,2.1,3.9]) --> [1,2,4]

In: Contents >> Working with equations >> Functions >> Built-in functions

log10 function

log10(X)

Returns base-10 logarithm of X

Input: numeric

Result: numeric

In: Contents >> Working with equations >> Functions >> Built-in functions

log function

log(X)

Returns natural logarithm of X

Input: numeric; or an array of numeric values

Result: numeric; or an array of numeric values

In: Contents >> Working with equations >> Functions >> Built-in functions

ceil function

ceil(X)

Rounds up X to the next whole number (stands for 'ceiling')

Input: numeric, or array of numeric values

Result: numeric, or array of numeric value

Examples:

ceil(1.9) --> 2

ceil(1.1) --> 2

ceil([1,2.1,3.9]) --> [1,3,4]

In: Contents >> Working with equations >> Functions >> Built-in functions

exp function

exp(X)

Returns e (the base of natural logarithms) to the power X

Input: numeric, or an array of numeric values

Result: numeric, or an array of numeric values

Example:

The exponential growth of a population is given by the formula

N_t = N₀e^r.t

where:

N_t is the population size at time t,

N₀ is the initial population size,

e is the base of natural logarithms,

r is the intrinsic growth rate, and

t is current time
 

We can represent this in Simile using a single variable (called N), with its equation being

N = 10*exp(0.1*time(1))

assuming that the initial population size = 10 and the value of r is 0.1.
 

In: Contents >> Working with equations >> Functions >> Built-in functions

floor function

floor(X)

Rounds X down to a whole number.

Input: numeric, or an array of numeric values

Result: numeric, or an array of numeric values

Examples:

floor(3.1) --> 3

floor(3.99) --> 3

floor([1.1,2.4,3.7,4.9]) --> [1,2,3,4]

In: Contents >> Working with equations >> Functions >> Built-in functions

int function

int(X)

Returns integer part of X

Input: numeric

Result: numeric

In: Contents >> Working with equations >> Functions >> Built-in functions

hypot function

hypot(X,Y)

Returns length of hypotenuse of right-angle triangle with base X and height Y

Inputs: numeric, numeric

Result: numeric

Examples:

hypot(3,4) --> 5 (a 3:4:5 triangle)

hypot(x1-x2,y1-y2) --> the distance between two points with co-ordinates (x1,y1) and (x2,y2) respectively.

hypot(x-[xs],y-[ys]) --> [distances] I.e. an array containing the distance from one point with co-ordinates (x,y) to a set of points, with co-ordinates held in the arrays [xs] and [ys]. See comments.

Spatial modelling frequently requires that one object knows the distance to another. This requires that each has x,y co-ordinates. It is then simple to use the hypot function to work out the straight-line distance between them, as shown in the second example above.

The same principle applies when you use a multiple-instance submodel to represent a set of spatially-located objects. In this case, each object may want to know how far it is to all the other objects - for example, in working out the competition between trees in an individual-based tree model. The following model diagram fragment shows a typical model configuration for doing this:

Each tree has x,y co-ordinates. These are exported to two array variables, xs and ys, whose equations are simply:

xs = [x]

and

ys = [ys]

These arrays are then brought back into the submodel, and used to generate an array containing the distance for each tree to all the other trees, using the equation given in the third example above.

In: Contents >> Working with equations >> Functions >> Built-in functions

fmod(X,Y)

Returns remainder after dividing X by Y

Inputs: numeric, numeric

Result: numeric

Examples:

fmod(7,3) --> 0.333 (7/2 = 2.333, i.e. the remainder is 0.333)

fmod(time(1),1) --> a result that climbs from 0 to 1 repeatedly (i.e. a sawtooth pattern) as the simulation proceeds. See comments below.

fmod((index(1)-1),5)+1 --> 1,2,3,4,5,1,2,3,4,5,1,2,3... for successive values of index(1). See comments below.

This apparently obscure function in fact has (at least) two very valuable uses.

First, it can be used to generate regular cycles, in particular annual or daily cycles. Consider the case or a model with the time unit being one year, and a time step of less than a year. You want various exogenous variables (such as temperature or rainfall) to vary in a prescribed fashion during the course of each year, with the annual pattern repeating itself from one year to the next. The following diagram is typical of the model fragment you could use for representing this:

The variable time is simply set equal to current simulation time, using the function time(1). The variable season is set to rise from 0 to 1 every year. If your model used a time unit of one week, then the equation would be changed to

season = fmod(time,52)

and the value for season would then correspond to week number. The equations for rainfall and temperature are for illustration purposes only: you would need to replace them by something appropriate.

Second, the fmod function can be used to generate a regular spatial arrangement (rows and columns) for a 2D grid. Let's say that you are modelling an area of 10x10 grid squares. In Simile, you would set up a submodel, called perhaps Patch, with 25 instances. In order to give each patch location on a grid, each one needs to have a row and column attributes, with each patch having a unique combination of the numbers 1..5 for row and column. The only thing we know about each patch is that it has an index number (given by the built-in function index(1)), a value ranging between 1 and 25. The trick is to get row number to be, in sequence,

1,1,1,1,1,2,2,2,2,2,3,3,3...

and column number to be, in sequence,

1,2,3,4,5,1,2,3,4,5,1,2,3...

thus giving each of the 25 instances a unique row-and-column pair.

This is readily done using the following two equations:

row = floor((index(1)-1)/5)+1

column = fmod((index(1)-1),5)+1

See the floor function to understand why the row numbers should be in the first sequence above. For column, we divide the index number for each instance by 5, taking the remainder: the '-1' and +1' are there to ensure that we get the results in the blocks of five that we require. See a grid-based spatial model example to see this in action.

In: Contents >> Working with equations >> Functions >> Built-in functions

product function

product([X])

product({X})

Result is the product of all elements of the array [X] or the list {X}

Input: numeric array or list

Result: numeric

Example:

product([2,3,4]) --> 24

In: Contents >> Working with equations >> Functions >> Built-in functions

place_in function

place_in(I)

When making an array with the makearray function, place_in() returns the current position in the array. If makearray() functions are nested one inside another, the argument to the place_in() function will determine which position is returned. An argument of 1 refers to the innermost makearray().

Input: integer

Result: integer

Examples:

makearray(if place_in(1)==1 then 10 else 5, 4) --> [10, 5, 5, 5]

makearray(if place_in(1)==2 then 10 else 5, 4) --> [5, 10, 5, 5]

makearray(makearray(if place_in(2)==1 then 10 else 5, 4), 2) --> [[10, 10, 10, 10], [5, 5, 5, 5]]

makearray(makearray(if place_in(1)==1 then 10 else 5, 4), 2) --> [[10, 5, 5, 5], [10, 5, 5, 5]]

makearray(4*place_in(1),12) --> [4 8 12 16 20 24 28 32 36 40 44 48]

makearray(makearray(place_in(1)*place_in(2),12),12) -->

[[1 2 3 4 5 6 7 8 9 10 11 12],

[2 4 6 8 10 12 14 16 18 20 22 24],

[3 6 9 12 15 18 21 24 27 30 33 36],

[4 8 12 16 20 24 28 32 36 40 44 48],

[5 10 15 20 25 30 35 40 45 50 55 60],

[6 12 18 24 30 36 42 48 54 60 66 72],

[7 14 21 28 35 42 49 56 63 70 77 84],

[8 16 24 32 40 48 56 64 72 80 88 96],

[9 18 27 36 45 54 63 72 81 90 99 108],

[10 20 30 40 50 60 70 80 90 100 110 120,

[11 22 33 44 55 66 77 88 99 110 121 132],

[12 24 36 48 60 72 84 96 108 120 132 144]]

Comments:

This function is only meaningful inside makearray() function. If used elsewhere, the equation parser will signal an error.

In: Contents >> Working with equations >> Functions >> Built-in functions

sum function

sum([X])

sum({X})

Result is the sum of all elements of the array [X] or the list {X}

Input: numeric array/list

Result: numeric

Example:

sum([2,3,4]) --> 9

sum([[1,2],[3,4]]) --> [4,6]

Comment:

Note the behaviour with nested arrays. A new array results, consisting of the sum of the first value of each array, the sum of the second value of each array, etc.

sum function

sum([X])

sum({X})

Result is the sum of all elements of the array [X] or the list {X}

Input: numeric array/list

Result: numeric

Example:

sum([2,3,4]) --> 9

sum([[1,2],[3,4]]) --> [4,6]

Comment:

Note the behaviour with nested arrays. A new array results, consisting of the sum of the first value of each array, the sum of the second value of each array, etc. The reason it is implemented this way is that the converse (generating an array of the sums of each sub-array in the original, i.e., [3,7] in the above example) is easier to generate explicitly if required. In the case where the 2-D array is a value coming out of nested 1-D submodels, it can be produced by putting the sum() function inside the outer submodel. If the array is only available as a 2-D value, the same effect can be produced using the element() and makearray() functions as follows:

makearray(sum(element([[1,2],[3,4]], place_in(1))),2) --> [3,7]

All other cumulative functions behave the same way regarding selection of elements from multidimensional arrays.

In: Contents >> Working with equations >> Functions >> Built-in functions

element function

element([X],I)

Picks the I^th value from the array [X]

Inputs: an array [X] of any type

integer I

Result: type

Examples:

element([10,20,30,40],3) --> 30 (since 30 is the value of the 3rd element of the array.

element([[1,2], [3,4], [5,6]],2) --> [3,4] (since the array [3,4] is the 2nd element of the input array)

element([10,20,30,40],index(1)) --> 10 for the first instance of a four-instance submodel, 20 for the second instance, etc, since index(1) has the value 1 for the first instance, 2 for the second instance, etc.

Comments:

This is an essential function for use with multiple-instance submodels, in which case it is almost always used in combination with the function index(1) in the second argument. One common use is to provide each instance of a multiple-instance submodel with a unique value for some parameter or other value. The third example (above) illustrates this: that could, for example, be the expression in a compartment element inside a four-instance submodel, initialising the compartment for each of the four instances to 10, 20, 30 and 40 respectively.

For advanced users:

The element function has rather more power than suggested above. The second argument can act as a sort of template to say how values (or sub-arrays) from the first argument are to be picked up. This is illustrated by the following example:

element([3,2,7,4,9,34,1,5], [[5,2], [1,5]]) --> [[9,2], [3,9]]

If the first argument is multidimensional and the second argument is a one-dimensional array, the elements of the second argument will be used to pick elements from the innermost arrays of the first argument from whuch to build the result, e.g.,

element([[3,5,11], [1,2,8]], [2,1,2]) --> [1,5,8].

The reason it works this way is that the converse behaviour, i.e., each element of the second argument selecting a value from the corresponding element of the first argument, is easy to get in the case where the first argument is a value from a nested submodel by putting the element() function inside the outer submodel, and can be obtained in general by building up from simple cases, e.g.,

makearray(element(element([[3,5,11], [1,2,8]],place_in(1)), element([3,1], place_in(1))), 2) --> [11,1].

makearray(element([[5,7],[1,4],[8,5]], element([3,3,2,2], place_in(1))), 4) --> [[8,5], [8,5], [1,4], [1,4]]

Use of element() on lists

Starting with Simile version 6.1, it is possible to have a list-valued expression as the first argument of element(), in which case the result is a sublist of that list, i.e., a list containing some, all or none of the members of the original list in the same order. The second argument can be a single value, in which case the resulting list has one element if the list includes a value with that index, and none otherwise. So applying sum() to it gives either a value from the original list or zero.

If the second argument is an array or list, the result is a sublist of the first argument containing all the values whose indices appear as values in the second argument. There are a few points to note about all these uses:

• It is not computationally efficient. The values from the list are found by searching through it sequentially rather than by lookup as can be done on arrays.
• If the second argument is an array or list, its values must be in ascending order, or in the order in which they appear in the definition if they are of an enumerated type. If a value occurs more than once in the second argument, the value with that index will still only appear once in the result. This is because rather than searching the list from the start for each value, the generated code merely starts from where the last one was found, since the indices in the original list should always be in ascending order. (This does not apply when selecting a sublist from a list of neighbour values in a special-purpose submodel; the direction identifiers can be in any order, and if one occurs more than once, its value from the original list will also be repeated).
• The resulting list cannot itself be used as the first argument of element(), or arithmetically combined with other list-valued expressions.

element() with multiple indices

Starting with Simile version 6.1, if you have a 2-D (or higher) array, you can look up a single member by using element() with 3 (or more) arguments, e.g.,

element([[arr]], x, y)

Formerly you would have had to do this by nesting element() calls, but the new format is neater and allows the indices to be matching arrays or lists themselves to get multiple values.

Examples:

element([[6,1,8],[7,5,3],[2,9,4]], 2, 2) --> 5

element([[6,1,8],[7,5,3],[2,9,4]], [2,1,3], [3,1,2]) --> [3,6,9]

In: Contents >> Working with equations >> Functions >> Built-in functions

any function

any(([X])

any({X})

Result is true if any of the elements of the array [X] or the list {X} are true.

Input: boolean array or list

Result: boolean

Examples:

any([false,false,false,true,false]) --> true

any([false,false,false]) --> false

In: Contents >> Working with equations >> Functions >> Built-in functions

all function

all([X])

all({X})

Result is true if all the elements of the array [X] or the list {X} are true

Input: boolean array/list

Result: boolean

Examples:

all([true,true,true,false]) --> false

all([true,true,true,true]) --> true

In: Contents >> Working with equations >> Functions >> Built-in functions

greatest function

greatest([X])

greatest({X})

Returns the largest value from an array [X] or the list {X}

Inputs: numeric array or list

Result: numeric

Example:

greatest([2,5,7,3]) --> 7

In: Contents >> Working with equations >> Functions >> Built-in functions

last function

last(X)

Recalls value of X, another element, from previous time step. X must influence this element in order to be used in the equation. See prev, for a function that returns a previous value from this element itself.

This function has been replaced with the const_delay( ) and var_delay( ) functions, which are more general in allowing the value of a variable to be returned from an arbitrary number of time steps before.

Input: numeric

Result: numeric

In: Contents >> Working with equations >> Functions >> Built-in functions

prev function

prev(N)

Returns the value of this element itself, N time steps ago. See last, for a function that returns a previous value of any element other than this one itself.

Input: numeric

Result: numeric

Example:

Consider a variable that flips from state 1 to state 2 when some triggering condition is satisfied (the Boolean variable, trigger, is true, for example) then stays in state 2. The equation for the variable could make use of prev, as follows:

if time()==0 then 1 elseif trigger then 2 else prev(1)

In: Contents >> Working with equations >> Functions >> Built-in functions

parent function

parent(1)

Returns the id (instance number) of the individual whose reproduction gave rise to this one, or 0 if the individual being considered was created at the start of the simulation or by immigration.

Input: numeric (but a dummy value: use the value 1).

Result: integer (in fact, a negative integer number, being the instance number of a member of a population submodel. These are all numbered from -1 downwards.)

Comment:

This function is vital for the simulation of any form of biological inheritance from one generation to the next. You have to know who the parent is before you can allow the newly-created individual to inherit (possibly with modification) one or more of the characteristics of the parent.

In: Contents >> Working with equations >> Functions >> Built-in functions

time function

time()

Returns the current simulation time.

Input: none

Result: numeric (units = day)

Comment:

Any model which has exogenous variables (variables that change as a function of time, independently of the behaviour of the model, such as air temperature or rainfall) needs some way of knowing what the current clock time is: i.e. how far the simulation has proceeded. This function provides that information.

This function is not strictly-speaking necessary: you could get exactly the same behaviour by having a single compartment, initialised to zero, with a single flow in, with a constant value of 1. So, after 1 time unit the value of the compartment would be 1, after 12.5 it would be 12.5, and so on. However, the function is provided to avoid cluttering up the model with an extra compartment and flow.

In: Contents >> Working with equations >> Functions >> Built-in functions

stop function

stop(n)

Input: is a number (real or integer) of your choice

Result: None (see text)

When executed, this function halts execution of the model, and produces the following error message:

Simile ran into a problem trying to run this model.

While it was trying to calculate the value of variable

var (node x) during execution of the model at

time t, there was a user-defined interruption: n.

It is useful to define error conditions where you (the model designer) know that the model should not be used or is not applicable for some reason. Trivially, it can be used to guard against mathematical errors, for example:

if (time()-50) != 0 then 1/(time()-50) else stop(5)

This form has some merit when running in C++, but generally, to track down mathematical errors, it is easier to debug in Tcl. If execution in Tcl would take too long, then this is a useful alternative. Its primary use however, is to enable you to catch out-of-range conditions in the specific circumstances of your model.

The use of an error code in the user-defined interruption (e.g. stop(13) ) enables you to see which stop( ) function caused the model to stop running, if there is more than one in your model.

Result is undefined, because simulation stops at the point at which the function is called, but has integer type (this is important because if it is called in a conditional, the other branch of the conditional must also have a numerical type).

Examples:

if population>50 then stop(1) else 0

if (time()-50) != 0 then 1/(time()-50) else stop(5)

In: Contents >> Working with equations >> Functions >> Built-in functions

sofar function

sofar([X])

sofar({X})

Result is…

Input: numeric array/list

Result: numeric

Example:

Comment:

In: Contents >> Working with equations >> Functions >> Built-in functions

size function

size(S)

size(S,I)

The first form takes the name of a fixed-membership submodel and returns the number of instances that it has. The second form takes the name of a fixed-membership submodel and returns the size of the Ith level of nesting of this submodel.

Input: submodel name

Result: integer

In: Contents >> Working with equations >> Functions >> Built-in functions

dt function

dt(I)

Returns the duration of the level I time step.

Input: numeric. This is the time step level.

Result: numeric

Examples:

dt(1) --> 0.1 (for a model whose top-level time step was set to 0.1 in the Run Control panel)

dt(2) --> 0.001 (for a model whose 2nd-level time step was set to 0.001 in the Run Control panel)

If you are starting off with Simile, it is unlikely you will need to understand the concept of "time step index". You will probably just be making models with a single time step, hence one level, so don't worry about anything except the use of dt(1).

The main use of the dt function is to engineer the addition or removal of a specified amount of a substance into or out of a compartment. The only handle we have for causing changes to the amount in a compartment are flows, and flows are expressed as a rate per unit of time (whatever time unit is used for the model, e.g. year). This creates a problem if we want to add or remove a specified amount of substance at some instant in time. For example, consider a model with a time unit of year, a time step of 0.1, and with a compartment X from which we want to remove 5 units at the instant that some condition, which only lasts for 1 time step (0.1 years), is met. If we simply had a flow out that was zero when the condition was not met, and was 5 when the condition was met, then for one time step the flow would be 5 (units per year): hence, only 0.5 units would be removed in the 1/10th of a year, not the 5 we intended. What we need to do is to artificially inflate the flow rate by a factor of 10 (in this case, with a time step of 0.1) for this one time step. We do this by dividing the flow rate (in our flow equation) by 0.1 (in this case), or by dt(1) in general. The flow rate then appears to be 50 units per year for that one time step, giving a loss of 5 units in the one time step. Bingo!

The actual flow expression for the case considered above would be:

flowout = 5/dt(1)

Special case

Using the argument zero in the function, i.e. dt(0), is equivalent to saying dt(n), where n is the time step index of the submodel in which the function is used. This is useful, because when a submodel time step index is changed, it is then not necessary to edit the dt() functions within it to preserve the meaning.

In: Contents >> Working with equations >> Functions >> Built-in functions

count function

count([X])

count({X})

Number of values in the array [X] or the list {X}

Input: array or list of values (numeric or boolean)

Result: integer

Examples:

count([4,5,6]) --> 3

In: Contents >> Working with equations >> Functions >> Built-in functions

channel function

channel_is(X)

X is an immigration, reproduction or creation channel. Returns true if this individual appeared through that channel.

Input: numeric

Result: Boolean

Examples:

channel_is(cr1) --> true, for each instance of the population that was initially created through channel cr1.

Comments:

This function can only be used inside population submodels. Its argument is the name of a channel (i.e. a population control symbol, one of creation, immigration or reproduction). Note that the value of the channel itself is not used, just the name. The result can be used in calculations inside the population submodel. For example, the expression

land_owned = if channel_is(im1) then 0 elseif channel_is(cr1) then 10

would allocate 10 acres of land to each member of the original population, but none to immigrants.

In: Contents >> Working with equations >> Functions >> Built-in functions

init_time function

init_time(1)

Returns the time at which this model component first came into existence. This really only has any use for:

• population submodels, so that the model knows when a new member of the population was created; and
• conditional submodels, so that the model knows when the submodel, or one instance of it, came into existence because the condition controlling it became true.

Input: numeric. In fact, the argument is not used, so simply insert the number 1. The only reason for having the brackets and an argument enclosed between them is that this is the only way that Simile can recognise that this is a function.

Result: numeric

Example:

Let's assume you have a population submodel, and some property of each individual is related to its age (e.g. its growth rate, or its probability of dying). Simply create a variable called age (inside the submodel), and insert the following equation:

age = time(1) - init_time(1)

The result is the difference between the current simulation time (given by the function time(1), and the time when the instance was created, given by init_time(1).

In: Contents >> Working with equations >> Functions >> Built-in functions

rand_const function

rand_const(X,Y)

Returns a random number between X and Y at the start of the simulation or when the submodel instance is created. The random-number generator is not called again, and so the value stays the same until the simulation is reset.

Input: numeric, numeric

Result: numeric

Comment:

The main use of this function is to assign values to a set of instances of a multiple-instance submodel (fixed-membership or population). For example, to randomly assign initial sizes to a set of trees in a multiple-instance tree submodel, we could use the equation:

size = rand_const(12,20)

or to assign random locations to the trees, we could use the equations:

x = rand_const(0,50)

y = rand_const(0,100)

which would randomly place the trees in the left-hand half of a one-hectare plot, assuming that the values are in metres.

The use of rand_const() is deprecated because it cannot be made to behave in the same way as rand_var when implicitly replicating over an array. It is implemented by internal conversion to at_init(rand_var(x,y)) and this form should be used in full to make the replicatio behaviour clear.

Historical note: You may come across some models that use a rand(X,Y) function. This behaves like rand_const if Simile deduces that the model element will only be called at initialisation time, and like rand_var if the equation contains some variable that changes over time. The use of this function is now also deprecated because the semantics of the two uses are so very different. Also, there are some situations when you need to be able to over-ride this behind-the-scenes decision about how the function should behave.

In: Contents >> Working with equations >> Functions >> Built-in functions

rand_var function

rand_var(X,Y)

Returns a random number between X and Y, with a new value every time step.

Input: numeric, numeric

Result: numeric

Comment:

This function is used for doing stochastic modelling and Monte-Carlo simulations, i.e. one or more processes in the model (like giving birth or dying) have a random element to them.

rand_var gives a new result for every call, and if it is used in an expression that is replicated to make an array, each element's random value will be different.

rand_var uses the pseudo-random sequence generator built into the c++ compiler which Simile is using to create executable models. The sequence is initialized with a value generated from the process ID and clock time when Simile starts up, so no two runs will produce the same results. However, if it is required that a model has exactly the same behaviour each time it runs, despite including calls to rand_var, this can be achieved by means of a tool that sets the seed to a given value; see Initializing pseudo-random sequence.

Historical note: You may come across some models that use a rand(X,Y) function. This behaves like rand_const if Simile deduces that the model element will only be called at initialisation time, and like rand_var if the equation contains some variable that changes over time. The use of this function is now deprecated because the semantics of the two uses are so very different. Also, there are some situations when you need to be able to over-ride this behind-the-scenes decision about how the function should behave.

In: Contents >> Working with equations >> Functions >> Built-in functions