ptyrad.plotting

Visualization functions for summary figures

ptyrad.plotting.plot_affine_transformation(scale, asymmetry, rotation, shear)

ptyrad.plotting.plot_convergence_dashboard(loss_iters, lr_iters, dz_iters, avg_tilt_iters, convergence_iters, iter_offset=None, show_fig=True, pass_fig=False)

Unified dashboard of all scalar time-series in a fixed 2x4 grid.

Layout:

Row 0: Loss | obja | objp | Object tilts
Row 1: LR   | Probe amplitude | Probe position shifts | Slice thickness

Panels with no data show a blank placeholder so the layout stays fixed across save cycles. iter_offset sets the starting iteration for all panels; if None, each panel determines its own start via the smart Kneedle router. Because panels have different logging strides (e.g. loss every iter, convergence metrics every 50 iters), each panel independently converts the iteration number to its own array index.

ptyrad.plotting.plot_loss_curves(loss_iters, last_n_iters=10, show_fig=True, pass_fig=False)

ptyrad.plotting.plot_obj_tilts(pos, tilts, figsize=(16, 16), show_fig=True, pass_fig=False)

Plot the obj tilts given the probe position and pos-dependent tilts

ptyrad.plotting.plot_obj_tilts_avg(avg_tilt_iters, last_n_iters=2, show_fig=True, pass_fig=False)

ptyrad.plotting.plot_pos_grouping(pos, batches, circle_diameter=False, diameter_type='90%', figsize=(16, 8), dot_scale=1, show_fig=True, pass_fig=False)

ptyrad.plotting.plot_probe_modes(init_probe=None, opt_probe=None, amp_or_phase='amplitude', real_or_fourier='real', phase_cmap=None, amplitude_cmap=None, dpi=200, show_fig=True, pass_fig=False)

ptyrad.plotting.plot_scan_positions(pos, init_pos=None, img=None, offset=None, figsize=(16, 16), dot_scale=0.001, show_arrow=True, show_fig=True, pass_fig=False)

Plot the scan positions given an array of (N,2)

ptyrad.plotting.plot_sigmoid_mask(Npix, relative_radius, relative_width, img=None, show_circles=False)

Plot a sigmoid mask overlay on img with a line profile

ptyrad.plotting.plot_slice_thickness(dz_iters, last_n_iters=10, show_fig=True, pass_fig=False)

ptyrad.plotting.plot_learning_rates_schedule(lr_iters, log=True, show_fig=True, pass_fig=False)

Plots the learning rate schedule for each optimizable parameter group over iterations.

Modules

basic	Plotting functions for basic outputs including probe modes, loss curves, positions, etc.
model	Plotting functions related to PyTorch models