# Deriving a variable#

The variable derivation preprocessor module allows to derive variables which are not in the CMIP standard data request using standard variables as input. This is a special type of preprocessor function. All derivation scripts are located in esmvalcore/preprocessor/_derive/. A typical example looks like this:

```
"""Derivation of variable `dummy`."""
from ._baseclass import DerivedVariableBase
class DerivedVariable(DerivedVariableBase):
"""Derivation of variable `dummy`."""
@staticmethod
def required(project):
"""Declare the variables needed for derivation."""
mip = 'fx'
if project == 'CMIP6':
mip = 'Ofx'
required = [
{'short_name': 'var_a'},
{'short_name': 'var_b', 'mip': mip, 'optional': True},
]
return required
@staticmethod
def calculate(cubes):
"""Compute `dummy`."""
# `cubes` is a CubeList containing all required variables.
cube = do_something_with(cubes)
# Return single cube at the end
return cube
```

The static function `required(project)`

returns a `list`

of `dict`

containing all required variables for deriving the derived variable. Its only
argument is the `project`

of the specific dataset. In this particular
example script, the derived variable `dummy`

is derived from `var_a`

and
`var_b`

. It is possible to specify arbitrary attributes for each required
variable, e.g. `var_b`

uses the mip `fx`

(or `Ofx`

in the case of
CMIP6) instead of the original one of `dummy`

. Note that you can also declare
a required variable as `optional=True`

, which allows the skipping of this
particular variable during data extraction. For example, this is useful for
fx variables which are often not available for observational datasets.
Otherwise, the tool will fail if not all required variables are available for
all datasets.

The actual derivation takes place in the static function `calculate(cubes)`

which returns a single `cube`

containing the derived variable. Its only
argument `cubes`

is a `CubeList`

containing all required variables.