# Variable initialisation¶

Variables in BOUT++ are not initialised automatically, but must be
explicitly given a value. For example the following code declares a
`Field3D`

variable then attempts to access a particular element:

```
Field3D f; // Declare a variable
f(0,0,0) = 1.0; // Error!
```

This results in an error because the data array to store values in `f`

has not been allocated. Allocating data can be done in several ways:

Initialise with a value:

Field3D f = 0.0; // Allocates memory, fills with zeros f(0,0,0) = 1.0; // ok

This cannot be done at a global scope, since it requires the mesh to already exist and have a defined size.

Set to a scalar value:

Field3D f; f = 0.0; // Allocates memory, fills with zeros f(0,0,0) = 1.0; // ok

Note that setting a field equal to another field has the effect of making both fields share the same underlying data. This behaviour is similar to how NumPy arrays behave in Python.

Field3D g = 0.0; // Allocates memory, fills with zeros Field3D f = g; // f now shares memory with g f(0,0,0) = 1.0; // g also modified

To ensure that a field has a unique underlying memory array call the

`Field3D::allocate()`

method before writing to individual indices.Use

`Field3D::allocate()`

to allocate memory:Field3D f; f.allocate(); // Allocates memory, values undefined f(0,0,0) = 1.0; // ok

In a BOUT++ simulation some variables are typically evolved in time. The initialisation of these variables is handled by the time integration solver.

## Initialisation of time evolved variables¶

Each variable being evolved has its own section, with the same name as
the output data. For example, the high-\(\beta\) model has
variables “P”, “jpar”, and “U”, and so has sections `[P]`

, `[jpar]`

,
`[U]`

(not case sensitive).

### Expressions¶

The recommended way to initialise a variable is to use the `function`

option for each variable:

```
[p]
function = 1 + gauss(x-0.5)*gauss(y)*sin(z)
```

This evaluates an analytic expression to initialise the \(P\)
variable. Expressions can include the usual operators
(`+`

,`-`

,`*`

,`/`

), including `^`

for exponents. The
following values are also already defined:

Name | Description |
---|---|

x | \(x\) position between \(0\) and \(1\) |

y | \(y\) position between \(0\) and \(2\pi\) (excluding the last point) |

z | \(z\) position between \(0\) and \(2\pi\) (excluding the last point) |

pi π | \(3.1415\ldots\) |

Table: Initialisation expression values

By default, \(x\) is defined as `i / (nx - 2*MXG)`

, where `MXG`

is the width of the boundary region, by default 2. Hence \(x\)
actually goes from 0 on the leftmost point to `(nx-1)/(nx-4)`

on the
rightmost point. This is not a particularly good definition, but for
most cases its sufficient to create some initial profiles. For some
problems like island reconnection simulations, it’s useful to define
\(x\) in a particular way which is more symmetric than the default.
To do this, set in BOUT.inp

```
[mesh]
symmetricGlobalX = true
```

This will change the definition of \(x\) to `i / (nx - 1)`

, so
\(x\) is then between \(0\) and \(1\) everywhere.

By default the expressions are evaluated in a field-aligned coordinate system,
i.e. if you are using the `[mesh]`

option `paralleltransform = shifted`

,
the input `f`

will have `f = fromFieldAligned(f)`

applied before being
returned. To switch off this behaviour and evaluate the input expressions in
coordinates with orthogonal x-z (i.e. toroidal \(\{\psi,\theta,\phi\}\)
coordinates when using `paralleltransform = shifted`

), set in BOUT.inp

```
[input]
transform_from_field_aligned = false
```

The functions in Table 1 are also available in expressions.

Name | Description |
---|---|

`abs(x)` |
Absolute value \(|x|\) |

`asin(x)` , `acos(x)` , `atan(x)` ,
`atan(y,x)` |
Inverse trigonometric functions |

`ballooning(x)` |
Ballooning transform ((1), Fig. 3) |

`ballooning(x,n)` |
Ballooning transform, using \(n\) terms (default 3) |

`cos(x)` |
Cosine |

`cosh(x)` |
Hyperbolic cosine |

`exp(x)` |
Exponential |

`tanh(x)` |
Hyperbolic tangent |

`gauss(x)` |
Gaussian \(\exp(-x^2/2) / \sqrt{2\pi}\) |

`gauss(x, w)` |
Gaussian \(\exp[-x^2/(2w^2)] / (w\sqrt{2\pi})\) |

`H(x)` |
Heaviside function: \(1\) if \(x > 0\) otherwise \(0\) |

`log(x)` |
Natural logarithm |

`max(x,y,...)` |
Maximum (variable arguments) |

`min(x,y,...)` |
Minimum (variable arguments) |

`mixmode(x)` |
A mixture of Fourier modes |

`mixmode(x, seed)` |
seed determines random phase (default 0.5) |

`power(x,y)` |
Exponent \(x^y\) |

`sin(x)` |
Sine |

`sinh(x)` |
Hyperbolic sine |

`sqrt(x)` |
\(\sqrt{x}\) |

`tan(x)` |
Tangent |

`erf(x)` |
The error function |

`TanhHat(x, width, centre, steepness)` |
The hat function \(\frac{1}{2}(\tanh[s (x-[c-\frac{w}{2}])]\) \(- \tanh[s (x-[c+\frac{w}{2}])] )\) |

`fmod(x)` |
The modulo operator, returns floating point remainder |

For field-aligned tokamak simulations, the Y direction is along the
field and in the core this will have a discontinuity at the twist-shift
location where field-lines are matched onto each other. To handle this,
the `ballooning`

function applies a truncated Ballooning
transformation to construct a smooth initial perturbation:

There is an example code `test-ballooning`

which compares methods of
setting initial conditions with the ballooning transform.

The `mixmode(x)`

function is a mixture of Fourier modes of the form:

where \(\phi\) is a random phase between \(-\pi\) and \(+\pi\), which depends on the seed. The factor in front of each term is chosen so that the 4th harmonic (\(i=4\)) has the highest amplitude. This is useful mainly for initialising turbulence simulations, where a mixture of mode numbers is desired.

## Initalising variables with the `FieldFactory`

class¶

This class provides a way to generate a field with a specified form. For
example to create a variable `var`

from options we could write

```
FieldFactory f(mesh);
Field2D var = f.create2D("var");
```

This will look for an option called “var”, and use that expression to
initialise the variable `var`

. This could then be set in the BOUT.inp
file or on the command line.

```
var = gauss(x-0.5,0.2)*gauss(y)*sin(3*z)
```

To do this, `FieldFactory`

implements a recursive descent
parser to turn a string containing something like
`"gauss(x-0.5,0.2)*gauss(y)*sin(3*z)"`

into values in a
`Field3D`

or `Field2D`

object. Examples are
given in the `test-fieldfactory`

example:

```
FieldFactory f(mesh);
Field2D b = f.create2D("1 - x");
Field3D d = f.create3D("gauss(x-0.5,0.2)*gauss(y)*sin(z)");
```

This is done by creating a tree of `FieldGenerator`

objects
which then generate the field values:

```
class FieldGenerator {
public:
virtual ~FieldGenerator() { }
virtual FieldGenerator* clone(const list<FieldGenerator*> args) {return NULL;}
virtual BoutReal generate(int x, int y, int z) = 0;
};
```

All classes inheriting from `FieldGenerator`

must implement
a `FieldGenerator::generate()`

function, which returns the
value at the given `(x,y,z)`

position. Classes should also implement
a `FieldGenerator::clone()`

function, which takes a list of
arguments and creates a new instance of its class. This takes as input
a list of other `FieldGenerator`

objects, allowing a
variable number of arguments.

The simplest generator is a fixed numerical value, which is
represented by a `FieldValue`

object:

```
class FieldValue : public FieldGenerator {
public:
FieldValue(BoutReal val) : value(val) {}
BoutReal generate(int x, int y, int z) { return value; }
private:
BoutReal value;
};
```

## Adding a new function¶

To add a new function to the FieldFactory, a new
`FieldGenerator`

class must be defined. Here we will use
the example of the `sinh`

function, implemented using a class
`FieldSinh`

. This takes a single argument as input, but
`FieldPI`

takes no arguments, and
`FieldGaussian`

takes either one or two. Study these after
reading this to see how these are handled.

First, edit `src/field/fieldgenerators.hxx`

and add a class
definition:

```
class FieldSinh : public FieldGenerator {
public:
FieldSinh(FieldGenerator* g) : gen(g) {}
~FieldSinh() {if(gen) delete gen;}
FieldGenerator* clone(const list<FieldGenerator*> args);
BoutReal generate(int x, int y, int z);
private:
FieldGenerator *gen;
};
```

The `gen`

member is used to store the input argument, and to make
sure it’s deleted properly we add some code to the destructor. The
constructor takes a single input, the `FieldGenerator`

argument to the `sinh`

function, which is stored in the member
`gen`

.

Next edit `src/field/fieldgenerators.cxx`

and add the implementation
of the `clone`

and `generate`

functions:

```
FieldGenerator* FieldSinh::clone(const list<FieldGenerator*> args) {
if(args.size() != 1) {
throw ParseException("Incorrect number of arguments to sinh function. Expecting 1, got %d", args.size());
}
return new FieldSinh(args.front());
}
BoutReal FieldSinh::generate(double x, double y, double z, double t) {
return sinh(gen->generate(x,y,z,t));
}
```

The `clone`

function first checks the number of arguments using
`args.size()`

. This is used in `FieldGaussian`

to handle
different numbers of input, but in this case we throw a
`ParseException`

if the number of inputs isn’t
one. `clone`

then creates a new `FieldSinh`

object,
passing the first argument ( `args.front()`

) to the constructor
(which then gets stored in the `gen`

member variable).

The `generate`

function for `sinh`

just gets the value of the input
by calling `gen->generate(x,y,z)`

, calculates `sinh`

of it and
returns the result.

The `clone`

function means that the parsing code can make copies of
any `FieldGenerator`

class if it’s given a single instance
to start with. The final step is therefore to give the
`FieldFactory`

class an instance of this new
generator. Edit the `FieldFactory`

constructor
`FieldFactory::FieldFactory()`

in
`src/field/field_factory.cxx`

and add the line:

```
addGenerator("sinh", new FieldSinh(NULL));
```

That’s it! This line associates the string `"sinh"`

with a
`FieldGenerator`

. Even though `FieldFactory`

doesn’t know what type of `FieldGenerator`

it is, it can
make more copies by calling the `clone`

member function. This is a
useful technique for polymorphic objects in C++ called the “Virtual
Constructor” idiom.

## Parser internals¶

When a `FieldGenerator`

is added using the `addGenerator`

function, it is entered into a `std::map`

which maps strings to
`FieldGenerator`

objects (`include/field_factory.hxx`

):

```
map<string, FieldGenerator*> gen;
```

Parsing a string into a tree of `FieldGenerator`

objects is
done by first splitting the string up into separate tokens like
operators like ’*’, brackets ’(’, names like ’sinh’ and so on, then
recognising patterns in the stream of tokens. Recognising tokens is
done in `src/field/field_factory.cxx`

:

```
char FieldFactory::nextToken() {
...
```

This returns the next token, and setting the variable `char curtok`

to
the same value. This can be one of:

- -1 if the next token is a number. The variable
`BoutReal curval`

is set to the value of the token - -2 for a string (e.g. “sinh”, “x” or “pi”). This includes anything
which starts with a letter, and contains only letters, numbers, and
underscores. The string is stored in the variable
`string curident`

. - 0 to mean end of input
- The character if none of the above. Since letters and numbers are taken care of (see above), this includes brackets and operators like ’+’ and ’-’.

The parsing stage turns these tokens into a tree of
`FieldGenerator`

objects, starting with the `parse()`

function:

```
FieldGenerator* FieldFactory::parse(const string &input) {
...
```

which puts the input string into a stream so that `nextToken()`

can
use it, then calls the `parseExpression()`

function to do the actual
parsing:

```
FieldGenerator* FieldFactory::parseExpression() {
...
```

This breaks down expressions in stages, starting with writing every expression as:

```
expression := primary [ op primary ]
```

i.e. a primary expression, and optionally an operator and another
primary expression. Primary expressions are handled by the
`parsePrimary()`

function, so first `parsePrimary()`

is called, and
then `parseBinOpRHS`

which checks if there is an operator, and if so
calls `parsePrimary()`

to parse it. This code also takes care of
operator precedence by keeping track of the precedence of the current
operator. Primary expressions are then further broken down and can
consist of either a number, a name (identifier), a minus sign and a
primary expression, or brackets around an expression:

```
primary := number
:= identifier
:= '-' primary
:= '(' expression ')'
:= '[' expression ']'
```

The minus sign case is needed to handle the unary minus e.g. `"-x"`

.
Identifiers are handled in `parseIdentifierExpr()`

which handles
either variable names, or functions

```
identifier := name
:= name '(' expression [ ',' expression [ ',' ... ] ] ')'
```

i.e. a name, optionally followed by brackets containing one or more
expressions separated by commas. names without brackets are treated the
same as those with empty brackets, so `"x"`

is the same as `"x()"`

.
A list of inputs (`list<FieldGenerator*> args;`

) is created, the
`gen`

map is searched to find the `FieldGenerator`

object
corresponding to the name, and the list of inputs is passed to the
object’s `clone`

function.