# Covariance calculation¶

This is a brief document describing where we learn how to access how we can calculate and use the covariance and weight properties of the quadtree. More, detailed information how `kite`

calculates the covariance matrix can be found at the module docmentation `kite.Covariance`

.

## Accessing the Covariance¶

In this example we will access the covariance attributes of a pre-configured scene.

```
from kite import Scene
import matplotlib.pyplot as plt
# Assume we have a existing kite.Scene with defined quadtree parametrized
scene = Scene.load('acquila_2016.yml')
ax = plt.gca()
# Inspect the noise data which is used to calculate the covariance
ax.imshow(scene.covariance.noise_data)
plt.show()
# Inspect the focal-point (quick mode) covariance matrix
ax.imshow(scene.covariance.covariance_matrix_focal)
# Inspect the full covariance matrix
ax.imshow(scene.covariance.covariance_matrix)
# Get the full weight matrix
ax.imshow(scene.covariance.weight_matrix)
# Get the covariance and weight between two leafes
leaf_1 = scene.quadtree.leaves[0]
leaf_2 = scene.quadtree.leaves[0]
scene.covariance.getLeafCovariance(leaf_1, leaf_2)
scene.covariance.getLeafWeight(leaf_1, leaf_2)
```

## Graphical Inspection and Tuning of the Covariance¶

The covariance can be manipulated through `spool`

. More information here Covariance parametrisation.

## Manual Covariance Tuning¶

The covariance can be manipulated by editing the `.yml`

file. Or by changing the covariance parameters during runtime:

```
scene.covariance.a = 1.412e-4
scene.covariance.b = 1.2521
# Will show the updated covariance
plt.imshow(scene.covariance.covariance_focal)
# Will calculate the covariance matrix
scene.covariance
scene.save('filename')
```