at.plot.response_matrix#

Functions

plot_norm(resp[, ax])

Plot the norm of the lines and columns of the weighted response matrix

plot_obs_analysis(resp, lattice[, ax, logscale])

Plot the decomposition of an error vector on the basis of singular vectors

plot_singular_values(resp[, ax, logscale])

Plot the singular values of a response matrix

plot_var_analysis(resp, lattice[, ax, logscale])

Plot the decomposition of a correction vector on the basis of singular vectors
plot_norm(resp, ax=None)[source]#

Plot the norm of the lines and columns of the weighted response matrix

For a stable solution, the norms should have the same order of magnitude. If not, the weights of observables and variables should be adjusted.

Parameters:
plot_obs_analysis(resp, lattice, ax=None, logscale=True)[source]#

Plot the decomposition of an error vector on the basis of singular vectors

Parameters:

  • resp (ResponseMatrix) – Response matrix object

  • lattice (Lattice) – Lattice description. The response matrix observables will be evaluated for this Lattice and the deviation from target will be decomposed on the basis of singular vectors,

  • logscale (bool) – If True, use log scale

  • ax (Axes) – If given, plots will be drawn in these axes.

plot_singular_values(resp, ax=None, logscale=True)[source]#

Plot the singular values of a response matrix

Parameters:

  • resp (ResponseMatrix) – Response matrix object

  • logscale (bool) – If True, use log scale

  • ax (Axes) – If given, plots will be drawn in these axes.

plot_var_analysis(resp, lattice, ax=None, logscale=False)[source]#

Plot the decomposition of a correction vector on the basis of singular vectors

Parameters:

  • resp (ResponseMatrix) – Response matrix object

  • lattice (Lattice) – Lattice description. The variables will be evaluated for this Lattice and will be decomposed on the basis of singular vectors,

  • logscale (bool) – If True, use log scale

  • ax (Axes) – If given, plots will be drawn in these axes.