(calibration_circe_results)=
# Looking at the results

Once the computation is complete, several methods are available to extract or display the results.

The bias vector and the correlation matrix can be retrieved with the `getBVector` and `getCMatrix` 
methods, respectively:

````{only} cpp
```cpp
TVectorD getBVector();
TMatrixD getCMatrix();
```
````

````{only} py
```python
getBVector()
getCMatrix()
```
````

These methods take no arguments, as they are directly applied to the `TCirce` object after the process 
has been run. They return a `TVectorD` and a `TMatrixD` corresponding to the bias vector and the 
correlation matrix.

It is also possible to obtain the likelihood value for the optimal parameters with the `getLikelihood` method:

````{only} cpp
```cpp
Double_t getLikelihood();
```
````

````{only} py
```python
getLikelihood()
```
````

This method also takes no arguments. It returns a double corresponding to the likelihood value of the optimal 
parameters.

The methods `getMatrixVarianceMu` and `getMatrixVarianceSigma` return, respectively, the variance matrices of mu and sigma, as computed via the Fisher Matrix confidence interval:

````{only} cpp
```cpp
TMatrixD getMatrixVarianceMu();
TMatrixD getMatrixVarianceSigma();
```
````

````{only} py
```python
getMatrixVarianceMu()
getMatrixVarianceSigma()
```
````

Finally, all of this information can be displayed on the console using the `printResults` method:

````{only} cpp
```cpp
void printResults(Option_t * option = "");
```
````

````{only} py
```python
printResults(option = "")
```
````

This method accepts a single optional argument, which can specify options for running the process. At present, 
no options are implemented for this class, so the argument should not be used.