Source code for openmdao.visualization.partial_deriv_plot

"""
Visualization of data functions.
"""
import numpy as np


[docs]def partial_deriv_plot(of, wrt, check_partials_data, title=None, jac_method='J_fwd', tol=1e-10, binary=True): """ Visually examine the computed and finite differenced Jacobians. Parameters ---------- of : string Variable whose derivatives will be computed. wrt : string Variable with respect to which the derivatives will be computed. check_partials_data : dict 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. title : string (Optional) Title for the plot If None, use the values of the arguments "of" and "wrt" jac_method : string (Optional) Method of computating Jacobian Is one of ['J_fwd', 'J_rev']. Optional, default is 'J_fwd'. tol : float (Optional) The tolerance, below which the two numbers are considered the same for plotting purposes. binary : bool (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. Raises ------ KeyError If one of the Jacobians is not available. 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. """ # Get the first item in the dict, which will be the model model_name = list(check_partials_data)[0] model_jacs = check_partials_data[model_name] key = (of, wrt) model_jac = model_jacs[key] # get finite difference arrays if 'J_fd' not in model_jac: msg = 'Jacobian "{}" not found.' raise KeyError(msg.format('J_fd')) fd_full = model_jac['J_fd'] fd_full_flat = fd_full.flatten() # for getting max min later if binary: fd_binary = fd_full.copy() fd_binary[np.nonzero(fd_binary)] = 1.0 # get computed arrays if jac_method not in model_jac: msg = 'Jacobian "{}" not found.' raise KeyError(msg.format(jac_method)) computed_full = model_jac[jac_method] computed_full_flat = computed_full.flatten() # for getting max min later if binary: computed_binary = computed_full.copy() computed_binary[np.nonzero(computed_binary)] = 1.0 # get plotting scales stacked = np.hstack((fd_full_flat, computed_full_flat)) vmin = np.amin(stacked) vmax = np.amax(stacked) # basics of plot # Cannot do this sooner because of import and matplotlib # backend issues when testing import matplotlib.pyplot as plt BINARY_CMP = plt.cm.gray NON_BINARY_CMP = plt.cm.RdBu fig, ax = plt.subplots(ncols=3, figsize=(12, 6)) if title is None: title = str(key) plt.suptitle(title) # plot Jacobians if binary: ax[0].imshow(fd_binary.real, interpolation='none', cmap=BINARY_CMP) im_computed = ax[1].imshow(computed_binary.real, interpolation='none', cmap=BINARY_CMP) else: ax[0].imshow(fd_full.real, interpolation='none', vmin=vmin, vmax=vmax, cmap=NON_BINARY_CMP) im_computed = ax[1].imshow(computed_full.real, interpolation='none', vmin=vmin, vmax=vmax, cmap=NON_BINARY_CMP) ax[0].set_title('Approximated Jacobian') ax[1].set_title('User-Defined Jacobian') # Legend fig.colorbar(im_computed, orientation='horizontal', ax=ax[0:2].ravel().tolist()) # plot difference between computed and finite differenced diff = computed_full.real - fd_full.real diff_flat = diff.flatten() vmin = np.amin(diff_flat) vmax = np.amax(diff_flat) if vmax - vmin < tol: # Do not want range to be too small vmin = -1 * tol vmax = tol im_diff = ax[2].imshow(diff, interpolation='none', vmin=vmin, vmax=vmax, cmap=NON_BINARY_CMP) fig.colorbar(im_diff, orientation='horizontal', ax=ax[2], aspect=10) ax[2].set_title('Difference') plt.show() return fig, ax