Functions
	set_plot_config (cfg)

	_set_title (ax, text, **kw)

	_apply_axis_config (ax, group)

	_apply_axis_config_to_fig (fig, group)

	_legend_fs ()

	_legend_below (fig, axes, ncol=8, fontsize=None, bottom=0.28)

	_check_mpl ()

	_dose_xlim (dose)

	_align_dose_to_y (results)

	plot_point_defects (results, out_path=None, title='')

	plot_totals (results, out_path=None, title='')

	plot_swelling (results, out_path=None, title='')

	plot_network_density (results, out_path=None, title='')

	_has_conversion (results)

	plot_mean_sizes (results, out_path=None, title='')

	plot_he_content (results, out_path=None, title='')

	plot_size_distribution (results, input_data, t_idx=-1, out_path=None, title='')

	_extract_cluster_arrays (results, input_data)

	plot_sia_clusters (results, input_data, out_path=None, title='')

	plot_vac_clusters (results, input_data, out_path=None, title='')

	_log_snapshot_indices (dose, n_times=10)

	_b_100 (results, input_data, b_111)

	_loop_dist_evolution (c_src, b, Omega, dose, n_times, char, title)

	_save_pair (figs, out_path)

	plot_sia_distribution_evolution (results, input_data, n_times=10, out_path=None, title='')

	plot_100_distribution_evolution (results, input_data, n_times=10, out_path=None, title='')

	plot_void_distribution_evolution (results, input_data, n_times=10, out_path=None, title='')

	plot_number_densities (results, out_path=None, title='')

	plot_loop_fraction (results, out_path=None, title='')

	_tem_filter (c_arr, n_min=_N_MIN_TEM)

	plot_number_densities_tem (results, input_data, rate_eq_obj, out_path=None, title='')

	plot_mean_sizes_tem (results, input_data, rate_eq_obj, out_path=None, title='')

	_visualization_bins (rate_eq_obj, max_size, kind)

	_aggregate_to_bins (c_per_size, bins_full)

	_plot_tem_density_panel (bins_full, mu0_arr, dose, *n_times, xform, n_min, xlabel, ylabel, plot_title, cmap_name, use_logx, out_path, ymin=None)

	plot_sia_distribution_tem_size (results, input_data, rate_eq_obj, n_times=8, out_path=None, title='')

	plot_sia_distribution_tem_diameter (results, input_data, rate_eq_obj, n_times=8, out_path=None, title='')

	plot_vac_distribution_tem_size (results, input_data, rate_eq_obj, n_times=8, out_path=None, title='')

	plot_vac_distribution_tem_diameter (results, input_data, rate_eq_obj, n_times=8, out_path=None, title='')

	_interp_bins_at_doses (mu0_arr, dose, targets)

	_plot_smooth_diameter_panel (bins_full, mu0_arr, dose, *xform, n_min, xlabel, ylabel, plot_title, cmap_name, out_path, ymin, xmax=None, doses=_SMOOTH_DOSES)

	plot_vac_distribution_diameter_smooth (results, input_data, rate_eq_obj, out_path=None, title='')

	plot_sia_distribution_diameter_smooth (results, input_data, rate_eq_obj, out_path=None, title='')

	_reconstruct_distributions (results, input_data, rate_eq_obj)

	plot_sia_conc_vs_dose (results, input_data, rate_eq_obj, out_path=None, title='')

	_plot_vac_moment_vs_dose (results, input_data, rate_eq_obj, *weight, ylabel, plot_title, out_path, ymin=None)

	plot_vac_conc_vs_dose (results, input_data, rate_eq_obj, out_path=None, title='')

	plot_vac_content_vs_dose (results, input_data, rate_eq_obj, out_path=None, title='')

	_quarter_indices (dose)

	_logspaced_indices (dose, n_snap=8)

	plot_sia_fraction_breakdown (results, out_path=None, title='')

	plot_vac_fraction_breakdown (results, out_path=None, title='')

	_atomic_pickle_dump (payload, out_path)

	save_plot_data (results, input_data, out_path, *rate_eq_obj=None, label='')

	load_plot_data (path)

	_safe_save (artifact, fn, args, *kwargs)

	save_all_plots (results, input_data, out_dir, label='', rate_eq_obj=None)

Variables
bool	_HAS_MPL = True

str	_CONC_LABEL = r'Concentration (m$^{-3}$)'

int	_PLOT_FONTSIZE = 16

int	_LABEL_FONTSIZE = 22

int	_TICK_FONTSIZE = 20

int	_INTERIOR_LEGEND_FONTSIZE = 12

	_LW

	_LW_THIN

dict	_PLOT_CONFIG

str	_LBL_111 = r'$\frac{1}{2}\langle 111\rangle$ loops'

str	_LBL_100 = r'$\langle 100\rangle$ loops'

str	_C_111 = 'steelblue'

str	_C_100 = 'darkviolet'

	_DIST_N_MAX

	_DIST_D_MAX_NM

int	_N_MIN_TEM = 10

float	_OMEGA = 1.18e-29

float	_B_111 = 2.482e-10

str	_DENSITY_LABEL_SIZE = r'$dc/dn$ (m$^{-3}$)'

str	_DENSITY_LABEL_DIAM = r'$dc/dD$ (m$^{-3}$ nm$^{-1}$)'

tuple	_SMOOTH_DOSES = (1.0, 5.0, 10.0, 20.0, 40.0)

str	PLOT_DATA_FILENAME = 'plot_data.pkl'

Detailed Description

visualization.py — Plotting routines for RadCluster_2_1.

Generates standardized figures from the ODE post-processing results.
All concentration quantities are in m^-3 (converted in post_process.py).

Function Documentation

◆ _aggregate_to_bins()

py_utils.visualization._aggregate_to_bins	(	c_per_size,
		bins_full
	)

protected

Sum per-size concentrations over each visualization bin.

Returns mu0 of shape [K, n_t] where K = len(bins_full).  For width-1
bins this is just c_n; for grouped bins it is the bin's zeroth
moment (the lognormal/linear closure preserves this exactly).

Definition at line 975 of file visualization.py.

◆ _align_dose_to_y()

py_utils.visualization._align_dose_to_y ( results )

protected

Return (y, dose) with matching time dimensions.

After adaptive domain-doubling, ``results['y']`` may contain only the
last segment (its row count changed), while ``results['dose']`` spans
the full run.  When the two lengths differ, take the *tail* of dose
to match ``y``.

Definition at line 158 of file visualization.py.

◆ _apply_axis_config()

py_utils.visualization._apply_axis_config	(	ax,
		group
	)

protected

Apply the named group's scale and limits to a single axis.

Definition at line 88 of file visualization.py.

◆ _apply_axis_config_to_fig()

py_utils.visualization._apply_axis_config_to_fig	(	fig,
		group
	)

protected

Definition at line 106 of file visualization.py.

◆ _atomic_pickle_dump()

py_utils.visualization._atomic_pickle_dump	(	payload,
		out_path
	)

protected

Pickle to a sibling .tmp file, fsync, then atomic-rename to out_path.

A crash, Ctrl+C, or OOM during the dump leaves the .tmp behind (cleaned
up here on its way out) and never touches an existing good file at
out_path.  Callers can therefore re-load the previous run's pickle even
if the latest save was interrupted.

Definition at line 1673 of file visualization.py.

◆ _b_100()

py_utils.visualization._b_100	(	results,
		input_data,
		b_111
	)

protected

⟨100⟩ Burgers vector (= lattice parameter a) [m].

Definition at line 546 of file visualization.py.

◆ _check_mpl()

py_utils.visualization._check_mpl ( )

protected

Definition at line 142 of file visualization.py.

◆ _dose_xlim()

py_utils.visualization._dose_xlim ( dose )

protected

Return the left x-limit for a dose axis.

Uses the minimum positive dose value in the data so the plot is never
reversed when the run ends at a dose below any hard-coded threshold.

Definition at line 147 of file visualization.py.

◆ _extract_cluster_arrays()

py_utils.visualization._extract_cluster_arrays	(	results,
		input_data
	)

protected

Return (c_n, c_v, dose) arrays [size x n_t] in m^-3.

Works only for full_CD modes (y has per-cluster rows).
Returns None for bin-moment runs where y rows are moments.

Definition at line 425 of file visualization.py.

◆ _has_conversion()

py_utils.visualization._has_conversion ( results )

protected

True if the run carries a populated ⟨100⟩ loop population.

Definition at line 276 of file visualization.py.

◆ _interp_bins_at_doses()

py_utils.visualization._interp_bins_at_doses	(	mu0_arr,
		dose,
		targets
	)

protected

Interpolate every bin's mu0 onto `targets`, linearly in log-log.

The solution is written out on a coarse dose grid -- in the reference run
only 37 points, with a single stored point covering 6.2-15.7 dpa -- so
snapping to the nearest stored dose would hand two different requested
doses the same curve.  Interpolating in log(mu0) vs log(dose) is exact at
the stored points and smooth between them.  THESE CURVES ARE THEREFORE A
DISPLAY INTERPOLATION, not solver output at those doses; write the run out
on a finer dose grid if the intermediate curves need to be exact.

Definition at line 1159 of file visualization.py.

◆ _legend_below()

py_utils.visualization._legend_below	(	fig,
		axes,
		ncol = `8`,
		fontsize = `None`,
		bottom = `0.28`
	)

protected

Place a single shared legend underneath the plot area.

Removes any existing per-axes legends, then adds a figure-level legend
using the handles from the first axis (cluster-concentration plots
duplicate the same labels across panels).

Definition at line 115 of file visualization.py.

◆ _legend_fs()

py_utils.visualization._legend_fs ( )

protected

Definition at line 111 of file visualization.py.

◆ _log_snapshot_indices()

py_utils.visualization._log_snapshot_indices	(	dose,
		n_times = `10`
	)

protected

Return n_times indices log-spaced across the dose array.

Definition at line 534 of file visualization.py.

◆ _logspaced_indices()

py_utils.visualization._logspaced_indices	(	dose,
		n_snap = `8`
	)

protected

Return *n_snap* time indices log-spaced over the dose range.

Definition at line 1557 of file visualization.py.

◆ _loop_dist_evolution()

py_utils.visualization._loop_dist_evolution	(	c_src,
		b,
		Omega,
		dose,
		n_times,
		char,
		title
	)

protected

Shared SIA-loop size-distribution-evolution figures.

c_src : [I, n_t] loop densities in at.frac; b : Burgers vector [m];
char  : population label for titles/axes (e.g. '1/2<111>', '<100>').

Returns TWO figures -- (vs cluster size n, vs loop diameter nm).  They were
one stacked 8x9 canvas until 2026-09-06, which left both panels squeezed and
their titles colliding; separate figures let each carry a real axis range.

Definition at line 558 of file visualization.py.

◆ _plot_smooth_diameter_panel()

py_utils.visualization._plot_smooth_diameter_panel	(		bins_full,
			mu0_arr,
			dose,
		*	xform,
			n_min,
			xlabel,
			ylabel,
			plot_title,
			cmap_name,
			out_path,
			ymin,
			xmax = `None`,
			doses = `_SMOOTH_DOSES`
	)

protected

Per-bin density vs diameter as smooth curves through the bin midpoints.

The stair rendering of `_plot_tem_density_panel` is faithful to the
coarse-grained representation but hard to read when several doses overlap.
Here each visualization bin contributes one point -- the midpoint of its
diameter interval, at height rho_k = mu0_k / (d_hi - d_lo) -- and the points
are joined.  Same quantity, same normalisation: the area under the curve
still approximates the concentration in the displayed range.

Definition at line 1182 of file visualization.py.

◆ _plot_tem_density_panel()

py_utils.visualization._plot_tem_density_panel	(		bins_full,
			mu0_arr,
			dose,
		*	n_times,
			xform,
			n_min,
			xlabel,
			ylabel,
			plot_title,
			cmap_name,
			use_logx,
			out_path,
			ymin = `None`
	)

protected

TEM-visible per-bin density plot.

Each visualization bin is rendered as a stair segment of height
rho_k = mu_0(k) / (x_hi - x_lo) where the x-edges come from
`xform` (identity for size axis, diameter formula for diameter
axes).  This is the standard microstructure-distribution
representation: integral under the stairs equals total
concentration in the displayed range.  Per-bin density is well
defined regardless of the intra-bin shape function, so the comb
artefact of integer-resolved lognormal reconstruction is avoided
by construction.

Definition at line 991 of file visualization.py.

◆ _plot_vac_moment_vs_dose()

py_utils.visualization._plot_vac_moment_vs_dose	(		results,
			input_data,
			rate_eq_obj,
		*	weight,
			ylabel,
			plot_title,
			out_path,
			ymin = `None`
	)

protected

Cavity moment vs. dose — discrete sizes + bin mu0, weighted by `weight`.

weight='mu0'    → plot mu0 (number density per bin).
weight='content'→ plot mu0 * (m or midpoint) (vacancy content per bin).
ymin            → fixed y floor; None leaves it to the data (the two
                  weightings do not share a scale).

Definition at line 1454 of file visualization.py.

◆ _quarter_indices()

py_utils.visualization._quarter_indices ( dose )

protected

Return 4 time indices at ~25%, 50%, 75%, 100% of the log-dose range.

Definition at line 1552 of file visualization.py.

◆ _reconstruct_distributions()

py_utils.visualization._reconstruct_distributions	(	results,
		input_data,
		rate_eq_obj
	)

protected

Reconstruct per-size c_n [N, n_t] and c_v [M, n_t] in m^-3 from
ODE state, handling both full_CD and bin-moment modes.

Returns (c_n, c_v, dose) or (None, None, None) on failure.

Definition at line 1287 of file visualization.py.

◆ _safe_save()

py_utils.visualization._safe_save	(		artifact,
			fn,
		*	args,
		**	kwargs
	)

protected

Run a save call, catching/printing any failure with a traceback.

KeyboardInterrupt is NOT caught here — it propagates so the outer
interrupt shield (see simulation._InterruptDeferred) can decide whether
to defer it through the rest of the save or escalate.

Definition at line 1725 of file visualization.py.

◆ _save_pair()

py_utils.visualization._save_pair	(	figs,
		out_path
	)

protected

Write the (size, diameter) figure pair as <stem>_size / _diameter PNGs.

Definition at line 602 of file visualization.py.

◆ _set_title()

py_utils.visualization._set_title	(		ax,
			text,
		**	kw
	)

protected

Axes (or panel) title, drawn only when `show_titles` is on.

Definition at line 82 of file visualization.py.

◆ _tem_filter()

py_utils.visualization._tem_filter	(	c_arr,
		n_min = `_N_MIN_TEM`
	)

protected

Return mask for TEM-visible clusters (size index >= n_min - 1).

Definition at line 785 of file visualization.py.

◆ _visualization_bins()

py_utils.visualization._visualization_bins	(	rate_eq_obj,
		max_size,
		kind
	)

protected

Build the visualization bin list covering sizes 1..max_size.

Each entry is (n_lo, n_hi) with n_hi exclusive (matching the
bin_moment_rates convention).  For full_CD modes every integer is
its own width-1 bin.  For bin_moment modes the discrete portion is
width-1 integers and the grouped portion is taken from
rate_eq_obj.bins (kind='sia') or rate_eq_obj.vac_bins (kind='vac').

Definition at line 951 of file visualization.py.

◆ load_plot_data()

py_utils.visualization.load_plot_data ( path )

Inverse of save_plot_data — returns the dict that was pickled.

Definition at line 1718 of file visualization.py.

◆ plot_100_distribution_evolution()

py_utils.visualization.plot_100_distribution_evolution	(	results,
		input_data,
		n_times = `10`,
		out_path = `None`,
		title = `''`
	)

⟨100⟩ loop size distribution c_n(n) at n_times log-spaced snapshots
(loop conversion).  Mirrors plot_sia_distribution_evolution for the sessile
⟨100⟩ population; returns None when conversion is off.

Definition at line 636 of file visualization.py.

◆ plot_he_content()

py_utils.visualization.plot_he_content	(	results,
		out_path = `None`,
		title = `''`
	)

Free and total He vs. dose.

Definition at line 314 of file visualization.py.

◆ plot_loop_fraction()

py_utils.visualization.plot_loop_fraction	(	results,
		out_path = `None`,
		title = `''`
	)

½⟨111⟩ loop fraction f₁₁₁ vs. dose (loop conversion).

f₁₁₁ = Σ n c_n^{111} / Σ n (c_n^{111}+c_n^{100}) — the content-weighted
fraction of SIA-loop atoms still in ½⟨111⟩ loops, directly comparable to the
experimental Burgers-vector fraction.  Returns None if conversion is off.

Definition at line 742 of file visualization.py.

◆ plot_mean_sizes()

py_utils.visualization.plot_mean_sizes	(	results,
		out_path = `None`,
		title = `''`
	)

Mean SIA and vacancy cluster sizes vs. dose.

With loop conversion, the SIA loops are split into ½⟨111⟩ and ⟨100⟩.

Definition at line 282 of file visualization.py.

◆ plot_mean_sizes_tem()

py_utils.visualization.plot_mean_sizes_tem	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

Mean cluster sizes vs dose — TEM-visible only (n,m ≥ 10).
Shows both atoms (left axis) and physical diameter in nm (right axis).

Definition at line 856 of file visualization.py.

◆ plot_network_density()

py_utils.visualization.plot_network_density	(	results,
		out_path = `None`,
		title = `''`
	)

Network dislocation density ρ_net vs. dose (loop_network_loss.tex).

With the LOOP_NETWORK_LOSS channel active, sessile loops are swept into the
pre-existing network, so ρ_net rises with dose and (with a calibrated
recovery sink) saturates — the feedback that bounds the loop density.  When
the channel is off the curve is flat at the static ρ_d, which is still a
valid reading of the network density.

Definition at line 238 of file visualization.py.

◆ plot_number_densities()

py_utils.visualization.plot_number_densities	(	results,
		out_path = `None`,
		title = `''`
	)

Loop and void number densities (clusters n,m ≥ 2) vs. dose.

Definition at line 710 of file visualization.py.

◆ plot_number_densities_tem()

py_utils.visualization.plot_number_densities_tem	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

Loop and void number densities vs dose — TEM-visible only (n,m ≥ 10).
Plotted alongside the total (all sizes) for comparison.

Definition at line 791 of file visualization.py.

◆ plot_point_defects()

py_utils.visualization.plot_point_defects	(	results,
		out_path = `None`,
		title = `''`
	)

Free SIA (Ci1) and vacancy (Cv1) monomers vs. dose.

Definition at line 175 of file visualization.py.

◆ plot_sia_clusters()

py_utils.visualization.plot_sia_clusters	(	results,
		input_data,
		out_path = `None`,
		title = `''`
	)

SIA cluster concentrations vs. dose, split into small / mid / large.

Definition at line 447 of file visualization.py.

◆ plot_sia_conc_vs_dose()

py_utils.visualization.plot_sia_conc_vs_dose	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

(1) SIA concentrations vs. dose — discrete sizes + bin mu0.

Discrete sizes plotted individually; each bin as one curve.

Definition at line 1388 of file visualization.py.

◆ plot_sia_distribution_diameter_smooth()

py_utils.visualization.plot_sia_distribution_diameter_smooth	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

SIA loop diameter distribution as smooth curves at fixed doses.

Definition at line 1260 of file visualization.py.

◆ plot_sia_distribution_evolution()

py_utils.visualization.plot_sia_distribution_evolution	(	results,
		input_data,
		n_times = `10`,
		out_path = `None`,
		title = `''`
	)

½⟨111⟩ SIA cluster size distribution c_n(n) at n_times log-spaced snapshots.

Top panel: log-scale concentration vs cluster size n.
Bottom panel: linear-scale concentration vs loop diameter (nm).

Definition at line 614 of file visualization.py.

◆ plot_sia_distribution_tem_diameter()

py_utils.visualization.plot_sia_distribution_tem_diameter	(	results,
		input_data,
		rate_eq_obj,
		n_times = `8`,
		out_path = `None`,
		title = `''`
	)

SIA distribution density vs loop diameter (TEM-visible, n ≥ N_MIN_TEM).

Definition at line 1075 of file visualization.py.

◆ plot_sia_distribution_tem_size()

py_utils.visualization.plot_sia_distribution_tem_size	(	results,
		input_data,
		rate_eq_obj,
		n_times = `8`,
		out_path = `None`,
		title = `''`
	)

SIA distribution density vs cluster size (TEM-visible, n ≥ N_MIN_TEM).

Definition at line 1050 of file visualization.py.

◆ plot_sia_fraction_breakdown()

py_utils.visualization.plot_sia_fraction_breakdown	(	results,
		out_path = `None`,
		title = `''`
	)

SIA content fraction breakdown vs. dose.

Three curves that should sum to 1.0 at every dose:
  - In clusters (content in system / total produced)
  - At fixed sinks (cumulative fixed-sink loss / total produced)
  - Recombined + cavity (mutual annihilation / total produced)

Definition at line 1569 of file visualization.py.

◆ plot_size_distribution()

py_utils.visualization.plot_size_distribution	(	results,
		input_data,
		t_idx = `-1`,
		out_path = `None`,
		title = `''`
	)

SIA and vacancy cluster size distributions at a given time index.

Definition at line 341 of file visualization.py.

◆ plot_swelling()

py_utils.visualization.plot_swelling	(	results,
		out_path = `None`,
		title = `''`
	)

Void swelling S(t) vs. dose.

Definition at line 221 of file visualization.py.

◆ plot_totals()

py_utils.visualization.plot_totals	(	results,
		out_path = `None`,
		title = `''`
	)

Total SIA, vacancy, and He contents vs. dose.

Definition at line 199 of file visualization.py.

◆ plot_vac_clusters()

py_utils.visualization.plot_vac_clusters	(	results,
		input_data,
		out_path = `None`,
		title = `''`
	)

Vacancy cluster concentrations vs. dose, split into small / mid / large.

Definition at line 491 of file visualization.py.

◆ plot_vac_conc_vs_dose()

py_utils.visualization.plot_vac_conc_vs_dose	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

Cavity zeroth moment μ_k^(0) (number density per bin) vs. dose.

Definition at line 1530 of file visualization.py.

◆ plot_vac_content_vs_dose()

py_utils.visualization.plot_vac_content_vs_dose	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

Cavity content per bin (μ_k^(0) × m̄_k) vs. dose.

Definition at line 1541 of file visualization.py.

◆ plot_vac_distribution_diameter_smooth()

py_utils.visualization.plot_vac_distribution_diameter_smooth	(	results,
		input_data,
		rate_eq_obj,
		out_path = `None`,
		title = `''`
	)

Cavity diameter distribution as smooth curves at fixed doses.

Definition at line 1234 of file visualization.py.

◆ plot_vac_distribution_tem_diameter()

py_utils.visualization.plot_vac_distribution_tem_diameter	(	results,
		input_data,
		rate_eq_obj,
		n_times = `8`,
		out_path = `None`,
		title = `''`
	)

Cavity distribution density vs void diameter (TEM-visible, m ≥ N_MIN_TEM).

Definition at line 1128 of file visualization.py.

◆ plot_vac_distribution_tem_size()

py_utils.visualization.plot_vac_distribution_tem_size	(	results,
		input_data,
		rate_eq_obj,
		n_times = `8`,
		out_path = `None`,
		title = `''`
	)

Cavity distribution density vs cluster size (TEM-visible, m ≥ N_MIN_TEM).

Definition at line 1103 of file visualization.py.

◆ plot_vac_fraction_breakdown()

py_utils.visualization.plot_vac_fraction_breakdown	(	results,
		out_path = `None`,
		title = `''`
	)

Vacancy content fraction breakdown vs. dose.

Three curves that should sum to 1.0:
  - In clusters (swelling content / total produced)
  - At fixed sinks (cumulative fixed-sink loss / total produced)
  - Annihilated by SIA (SIA-cavity + recombination / total produced)

Definition at line 1620 of file visualization.py.

◆ plot_void_distribution_evolution()

py_utils.visualization.plot_void_distribution_evolution	(	results,
		input_data,
		n_times = `10`,
		out_path = `None`,
		title = `''`
	)

Void/vacancy cluster size distribution c_m(m) at n_times log-spaced dose snapshots.

Top panel: log-scale concentration vs vacancy cluster size m.
Bottom panel: linear-scale concentration vs void diameter (nm).

Definition at line 657 of file visualization.py.

◆ save_all_plots()

py_utils.visualization.save_all_plots	(	results,
		input_data,
		out_dir,
		label = `''`,
		rate_eq_obj = `None`
	)

Save all standard plots to out_dir/.

Each artifact (the pickle and every PNG) is wrapped in its own
try/except so a single failure cannot abort the rest.  The pickle uses
atomic writes; if it fails, any pre-existing good pickle is preserved.

Definition at line 1744 of file visualization.py.

◆ save_plot_data()

py_utils.visualization.save_plot_data	(		results,
			input_data,
			out_path,
		*	rate_eq_obj = `None`,
			label = `''`
	)

Pickle everything needed to regenerate the plots later.

The dump contains the full `results` dict, the `InputData` object, the
rate-equation object (if provided), and the title `label` so a notebook
can call any plot function later without re-running the simulation.

Writes are atomic (temp file + os.replace) so an interrupt mid-dump
never leaves a truncated pickle at `out_path`.

Definition at line 1698 of file visualization.py.

◆ set_plot_config()

py_utils.visualization.set_plot_config ( cfg )

Update the module-level plot config; partial overrides are merged.

Definition at line 72 of file visualization.py.

Variable Documentation

◆ _B_111

float py_utils.visualization._B_111 = 2.482e-10

protected

Definition at line 782 of file visualization.py.

◆ _C_100

str py_utils.visualization._C_100 = 'darkviolet'

protected

Definition at line 273 of file visualization.py.

◆ _C_111

str py_utils.visualization._C_111 = 'steelblue'

protected

Definition at line 272 of file visualization.py.

◆ _CONC_LABEL

str py_utils.visualization._CONC_LABEL = r'Concentration (m$^{-3}$)'

protected

Definition at line 16 of file visualization.py.

◆ _DENSITY_LABEL_DIAM

str py_utils.visualization._DENSITY_LABEL_DIAM = r'$dc/dD$ (m$^{-3}$ nm$^{-1}$)'

protected

Definition at line 1047 of file visualization.py.

◆ _DENSITY_LABEL_SIZE

str py_utils.visualization._DENSITY_LABEL_SIZE = r'$dc/dn$ (m$^{-3}$)'

protected

Definition at line 1046 of file visualization.py.

◆ _DIST_D_MAX_NM

py_utils.visualization._DIST_D_MAX_NM

protected

Definition at line 555 of file visualization.py.

◆ _DIST_N_MAX

py_utils.visualization._DIST_N_MAX

protected

Definition at line 555 of file visualization.py.

◆ _HAS_MPL

bool py_utils.visualization._HAS_MPL = True

protected

Definition at line 12 of file visualization.py.

◆ _INTERIOR_LEGEND_FONTSIZE

int py_utils.visualization._INTERIOR_LEGEND_FONTSIZE = 12

protected

Definition at line 21 of file visualization.py.

◆ _LABEL_FONTSIZE

int py_utils.visualization._LABEL_FONTSIZE = 22

protected

Definition at line 19 of file visualization.py.

◆ _LBL_100

str py_utils.visualization._LBL_100 = r'$\langle 100\rangle$ loops'

protected

Definition at line 271 of file visualization.py.

◆ _LBL_111

str py_utils.visualization._LBL_111 = r'$\frac{1}{2}\langle 111\rangle$ loops'

protected

Definition at line 270 of file visualization.py.

◆ _LW

py_utils.visualization._LW

protected

Definition at line 24 of file visualization.py.

◆ _LW_THIN

py_utils.visualization._LW_THIN

protected

Definition at line 24 of file visualization.py.

◆ _N_MIN_TEM

int py_utils.visualization._N_MIN_TEM = 10

protected

Definition at line 780 of file visualization.py.

◆ _OMEGA

float py_utils.visualization._OMEGA = 1.18e-29

protected

Definition at line 781 of file visualization.py.

◆ _PLOT_CONFIG

dict py_utils.visualization._PLOT_CONFIG

protected

Initial value:

=  {
    # Concentration vs dose: point defects, totals, He content, number
    # densities (incl. TEM), SIA/cavity cluster concentrations.
    'concentration': {'xlim': (None, None), 'ylim': (None, None),
                      'xscale': None,       'yscale': None},
    # Scalar metrics vs dose: swelling, mean sizes, fraction balances,
    # conservation diagnostics.
    'scalar':        {'xlim': (None, None), 'ylim': (None, None),
                      'xscale': None,       'yscale': None},
    # Size-distribution snapshots: concentration vs cluster size n/m or
    # vs loop/void diameter (nm).
    'size_dist':     {'xlim': (None, None), 'ylim': (None, None),
                      'xscale': None,       'yscale': None},
    'legend_fontsize': _PLOT_FONTSIZE,
    # Rendered titles are suppressed suite-wide: these figures are placed in
    # documents and notebooks that caption them, and a baked-in title then
    # duplicates the caption.  The `title=` kwargs are still plumbed through, so
    # turning this back on -- set_plot_config({'show_titles': True}) -- restores
    # every title, including the per-panel labels on the multi-panel figures.
    'show_titles': False,
}

Definition at line 49 of file visualization.py.

◆ _PLOT_FONTSIZE

int py_utils.visualization._PLOT_FONTSIZE = 16

protected

Definition at line 18 of file visualization.py.

◆ _SMOOTH_DOSES

tuple py_utils.visualization._SMOOTH_DOSES = (1.0, 5.0, 10.0, 20.0, 40.0)

protected

Definition at line 1156 of file visualization.py.

◆ _TICK_FONTSIZE

int py_utils.visualization._TICK_FONTSIZE = 20

protected

Definition at line 20 of file visualization.py.

◆ PLOT_DATA_FILENAME

str py_utils.visualization.PLOT_DATA_FILENAME = 'plot_data.pkl'

Definition at line 1670 of file visualization.py.

Functions

Variables

Detailed Description

Function Documentation

◆ _aggregate_to_bins()

◆ _align_dose_to_y()

◆ _apply_axis_config()

◆ _apply_axis_config_to_fig()

◆ _atomic_pickle_dump()

◆ _b_100()

◆ _check_mpl()

◆ _dose_xlim()

◆ _extract_cluster_arrays()

◆ _has_conversion()

◆ _interp_bins_at_doses()

◆ _legend_below()

◆ _legend_fs()

◆ _log_snapshot_indices()

◆ _logspaced_indices()

◆ _loop_dist_evolution()

◆ _plot_smooth_diameter_panel()

◆ _plot_tem_density_panel()

◆ _plot_vac_moment_vs_dose()

◆ _quarter_indices()

◆ _reconstruct_distributions()

◆ _safe_save()

◆ _save_pair()

◆ _set_title()

◆ _tem_filter()

◆ _visualization_bins()

◆ load_plot_data()

◆ plot_100_distribution_evolution()

◆ plot_he_content()

◆ plot_loop_fraction()

◆ plot_mean_sizes()

◆ plot_mean_sizes_tem()

◆ plot_network_density()

◆ plot_number_densities()

◆ plot_number_densities_tem()

◆ plot_point_defects()

◆ plot_sia_clusters()

◆ plot_sia_conc_vs_dose()

◆ plot_sia_distribution_diameter_smooth()

◆ plot_sia_distribution_evolution()

◆ plot_sia_distribution_tem_diameter()

◆ plot_sia_distribution_tem_size()

◆ plot_sia_fraction_breakdown()

◆ plot_size_distribution()

◆ plot_swelling()

◆ plot_totals()

◆ plot_vac_clusters()

◆ plot_vac_conc_vs_dose()

◆ plot_vac_content_vs_dose()

◆ plot_vac_distribution_diameter_smooth()

◆ plot_vac_distribution_tem_diameter()

◆ plot_vac_distribution_tem_size()

◆ plot_vac_fraction_breakdown()

◆ plot_void_distribution_evolution()

◆ save_all_plots()

◆ save_plot_data()

◆ set_plot_config()

Variable Documentation

◆ _B_111

◆ _C_100

◆ _C_111

◆ _CONC_LABEL

◆ _DENSITY_LABEL_DIAM

◆ _DENSITY_LABEL_SIZE

◆ _DIST_D_MAX_NM

◆ _DIST_N_MAX

◆ _HAS_MPL

◆ _INTERIOR_LEGEND_FONTSIZE

◆ _LABEL_FONTSIZE

◆ _LBL_100

◆ _LBL_111

◆ _LW

◆ _LW_THIN

◆ _N_MIN_TEM

◆ _OMEGA

◆ _PLOT_CONFIG