# Runtime

FormaK provides runtime tools that are designed to work with the filters
generated by FormaK to create a performant, easy to use, reference runtime.

The FormaK runtime can also be used by non-FormaK filters and FormaK filters do
not require the runtime.

## ManagedFilter

Features available in the runtime via ManagedFilter:
- Easier to use interface
- Automated handling of sensor readings that arrive at any time in any order

Setting up a `ManagedFilter`:

```cpp
formak::runtime::ManagedFilter<featuretest::ExtendedKalmanFilter> mf(
    initial_time,
    {
        .state = state,
        .covariance = {},
    }
);
```

Updating the filter with new data:

```cpp
mf.tick(
    output_time,
    control,
    {
        mf.wrap<Readingtype>(reading_time, reading),
    }
);
```

Note: The call to `wrap` is used to provide a uniform interface for all reading
types. Multiple reading types can be wrapped into the same vector of inputs to
the ManagedFilter.

### Requirements for Filters used with ManagedFilter

- The type passed to the ManagedFilter must be default constructable

A type can be checked for compatibility at compile time against these rules and
any additional features as the library evolved with the following static
function:

```cpp
static_assert(formak::runtime::ManagedFilter<featuretest::ExtendedKalmanFilter>::compatible);
```

Required runtime properties for instance of the filter type can be checked with
the following static function:

```cpp
ASSERT_TRUE(formak::runtime::ManagedFilter<featuretest::ExtendedKalmanFilter>::runtime_compatible());
```

### Python Interface

FormaK also provides a Python implementation of a ManagedFilter.

Setting up a `ManagedFilter`:

```python
mf = ManagedFilter(ekf=ekf, start_time=3.0, state=state, covariance=covariance)
```

Note: The Python `ManagedFilter` takes the EKF as a constructor argument
instead of default constructing the EKF.

Updating the filter with new data:

```python
state0p1 = mf.tick(3.1, control=control, readings=[
    StampedReading(3.05, "simple", np.zeros((1, 1))),
])
```