partial_deriv_plot.py#
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.
- Parameters:
- ofstr
Variable whose derivatives will be computed.
- wrtstr
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.
- titlestr (Optional)
Title for the plot If None, use the values of the arguments “of” and “wrt”.
- jac_methodstr (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.
- Returns:
- matplotlib.figure.Figure
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.
- Raises:
- KeyError
If one of the Jacobians is not available.