LuasKernel.visualise_covariance_in_data#
- LuasKernel.visualise_covariance_in_data(hp: Any, x_l: Array, x_t: Array, i: int, j: int, corr: bool | None = False, wn: bool | None = True, x_l_plot: Array | None = None, x_t_plot: Array | None = None, **kwargs) Figure [source]#
Creates a plot to aid in visualising how the kernel function is defining the covariance between different points in the observed data. Calculates the covariance of each point in the observed data with a point located at
(i, j)
in the observed data. The plot then displays this covariance usingplt.pcolormesh
with every other point in the observed data.If
corr = True
this will display the correlation instead of the covariance. Also ifwn = False
then white noise will be excluded from the calculation of the covariance/correlation between each point. This can be helpful if the white noise has a much larger amplitude than correlated noise which can make it difficult to visualise how points are correlated.- Parameters:
hp (Pytree) – Hyperparameters needed to build the covariance matrices
Kl
,Kt
,Sl
,St
. Will be unaffected if additional mean function parameters are also included.x_l (JAXArray) – Array containing wavelength/vertical dimension regression variable(s) for the observed locations. May be of shape
(N_l,)
or(d_l,N_l)
ford_l
different wavelength/vertical regression variables.x_t (JAXArray) – Array containing time/horizontal dimension regression variable(s) for the observed locations. May be of shape
(N_t,)
or(d_t,N_t)
ford_t
different time/horizontal regression variables.i (int) – The wavelength/vertical location of the point to visualise covariance with.
j (int) – The time/horizontal location of the point to visualise covariance with.
corr (bool, optional) – If
True
will plot the correlation between points instead of the covariance. Defaults toFalse
.wn (bool, optional) – Whether to include white noise in the calculation of covariance. Defaults to
True
.x_l_plot (JAXArray, optional) – The values on the y-axis used by
plt.pcolormesh
for the plot. If not included will default tox_l
ifx_l
is of shape(N_l,)
or tox_l[0, :]
ifx_l
is of shape(d_l, N_l)
.x_t_plot (JAXArray, optional) – The values on the x-axis used by
plt.pcolormesh
for the plot. If not included will default tox_t
ifx_t
is of shape(N_t,)
or tox_t[0, :]
ifx_t
is of shape(d_t, N_t)
.
- Returns:
A figure displaying the covariance of each point in the observed data with the selected point located at
(i, j)
in the observed dataY
.- Return type:
plt.Figure