# The observation model¶

To define the relationship between the Lorenz-63 simulation model and observations of this system, we need to define an **observation model**.
When the observation model can be described using a standard probability distribution, we only need to create a `Univariate`

subclass that returns a SciPy distribution for the given state vectors \(\mathbf{x_t}\).

For simplicity, we assume that \(x(t)\), \(y(t)\), and \(z(t)\) can be directly observed (define the observed values as \(X_t\), \(Y_t\), and \(Z_t\), respectively) and that the observation error is distributed normally with zero mean and standard deviation \(\sigma = 1.5\):

The implementation of these observation models is straightforward, and comprises two steps. First, extract the expected value for each particle from the state vectors, then construct a corresponding normal distribution for each particle:

```
class ObsLorenz63(Univariate):
def distribution(self, ctx, snapshot):
expect = snapshot.state_vec[self.unit]
return scipy.stats.norm(loc=expect, scale=1.5)
```

Note

pypfilt support multiple observation models.
Each observation model is associated with a unique identifier (an **“observation unit”**) that is used to identify the observations related to this model.
Here, we use the observation unit (`self.unit`

) to identify the field in the state vector that is being observed (see the highlighted line, above).
This allows us to use three instances of the `ObsLorenz63`

class to observe \(x(t)\), \(y(t)\), and \(z(t)\).

We could define an observation model specifically for \(x(t)\) (which is named `'x'`

in the state vector) by replacing the highlighted line above with:

```
expect = snapshot.state_vec['x']
```