Visualization of data functions.

openmdao.visualization.partial_deriv_plot.partial_deriv_plot(of, wrt, check_partials_data, title=None, jac_method='J_fwd', tol=1e-10, binary=True)[source]

Visually examine the computed and finite differenced Jacobians.


Variable whose derivatives will be computed.


Variable with respect to which the derivatives will be computed.

check_partials_datadict of dicts of dicts
First key:

is the component name;

Second key:

is the (output, input) tuple of strings;

Third key:

is one of [‘rel error’, ‘abs error’, ‘magnitude’, ‘J_fd’, ‘J_fwd’, ‘J_rev’];

For ‘rel error’, ‘abs error’, ‘magnitude’ the value is: A tuple containing norms for

forward - fd, adjoint - fd, forward - adjoint.

For ‘J_fd’, ‘J_fwd’, ‘J_rev’ the value is: A numpy array representing the computed

Jacobian for the three different methods of computation.

titlestring (Optional)

Title for the plot If None, use the values of the arguments “of” and “wrt”

jac_methodstring (Optional)

Method of computating Jacobian Is one of [‘J_fwd’, ‘J_rev’]. Optional, default is ‘J_fwd’.

tolfloat (Optional)

The tolerance, below which the two numbers are considered the same for plotting purposes.

binarybool (Optional)

If true, the plot will only show the presence of a non-zero derivative, not the value. Otherwise, plot the value. Default is true.


The top level container for all the plot elements in the plot.

array of matplotlib.axes.Axes objects

An array of Axes objects, one for each of the three subplots created.


If one of the Jacobians is not available.