## Evaluation¶

To make it so that your models can accept parameters with units and be evaluated using inputs with units, you need to make sure that the evaluate() method works correctly with input values and parameters with units. For simple arithmetic, this may work out of the box since Quantity objects are understood by a number of Numpy functions.

If users of your models provide input during evaluation that is not compatible with the parameter units, they may get cryptic errors such as:

UnitsError : Can only apply 'subtract' function to dimensionless quantities
when other argument is not a quantity (unless the latter is all
zero/infinity/nan)


There are several attributes or properties that can be set on models that adjust the behavior of models with units. These attributes can be changed from the defaults in the class definition, e.g.:

class MyModel(Model):
input_units = {'x': u.deg}
...


Note that these are all optional.

### input_units¶

You can easily add checking of the input units by adding an input_units property or attribute on your model class. This should return either None (to indicate no constraints) or a dictionary where the keys are the input names (e.g. x for many 1D models) and the values are the units expected, which can be a function of the parameter units:

@property
def input_units(self):
if self.mean.unit is None:
return None
else:
return {'x': self.mean.unit}


If the user then gives values with incorrect input units, a clear error will be displayed:

UnitsError: Units of input 'x', (dimensionless), could not be converted to
required input units of m (length)


Note that the input units don’t have to match exactly those returned by input_units, but be convertible to them. In addition, input_units can also be specified as an attribute rather than a property in simple cases:

input_units = {'x': u.deg}


### return_units¶

Similarly to input_units, this should be dictionary that maps the return values of a model to units. If evaluate() was called with quantities but returns unitless values, the units are added to the output. If the return values are quantities in different units, they are converted to return_units.

### input_units_strict¶

If set to True, values that are passed in compatible units will be converted to the exact units specified in input_units.

This attribute can also be a dictionary that maps input names to a Boolean to enable converting of that input to the specified unit.

### input_units_equivalencies¶

This can be set to a dictionary that maps the input names to a list of equivalencies, for example:

input_units_equivalencies = {'nu': u.spectral()}


### _input_units_allow_dimensionless¶

If set to True, values that are plain scalars or Numpy arrays can be passed to evaluate even if input_units specifies that the input should have units. It is up to the evaluate() to then decide how to handle these dimensionless values. This attribute can also be a dictionary that maps input names to a Boolean to enable passing dimensionless values to evaluate() for that input.

## Fitting¶

To allow models with parameters that have units to be fitted to data with units, you will need to add a method called _parameter_units_for_data_units to your model class. This should take two arguments input_units and output_units - input_units will be set to a dictionary with the units of the independent variables in the data, while output_units will be set to a dictionary with the units the dependent variables in the data (for example, for a simple 1D model, input_units will have one key, x, and output_units will have one key, y). This method should then return a dictionary giving for each parameter the units the parameter should be converted to so that the model could be used on the data if units were removed from both the models and the data. The following example shows the implementation for the 1D Gaussian:

def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'mean': inputs_unit['x'],
'stddev': inputs_unit['x'],
'amplitude': outputs_unit['y']}


With this method in place, the model can then be fit to data that has units.