Functions
	run_one (T_C, t_end, n_points, I=1000, V=1000, i_mobile=20, v_mobile=5, rtol=1e-5, network_loss=True, conversion=True)

	run_conv (T_C, t_end=3e6, n_points=60, I=200, i_mobile=10, v_mobile=3, rtol=3e-3, timeout_s=4500, progress_path=None)

	summarize (T_C, r)

Variables
	ROOT = Path(__file__).resolve().parents[2]

	encoding

float	T_STAR_CONV = 250.0

float	E_A0_CONV = 1.6

float	GAMMA_A_CONV = 0.02

	DH2_CONV = None

	PHI_MAX_JUNC = None

float	RHO_D0 = 5.0e13

float	RHO_MAX = 5.0e14

float	LOOP_NET_WC = 3.72e-8

float	LOOP_NET_KREC = 0.0

float	LOOP_NET_CHI = 50.0

int	LOOP_NET_NINC = 10

	ap = argparse.ArgumentParser()

	action

	help

	type

	float

	default

	str

	args = ap.parse_args()

tuple	temps

	flush

list	rows = []

	t0 = time.perf_counter()

dict	_kw = {}

	r = run_conv(T_C, progress_path=args.progress, **_kw)

	dt = time.perf_counter() - t0

	f111 = float(np.asarray(r["f_111_loop"])[-1])

	dose = float(np.asarray(r["dose"])[-1])

	t_end

	n_points

	I

	row = summarize(T_C, r)

Detailed Description

sweep_loop100_and_network.py — exercise the two newest RadCluster_2_1 features
against experiment:

  (1) ½⟨111⟩ → ⟨100⟩ loop conversion — content-weighted f₁₁₁(T) crossover.
      Sweeps T ∈ {450, 500, 550} °C with the design-note-calibrated crossover
      T_star_conv_C = 340 °C, so the Dudarev unary driving force turns on and
      the large loops convert as T rises (⟨100⟩ becomes dominant at high T —
      the experimental high-T regime).

  (2) Loop → network-dislocation loss — dynamic ρ_net(dose).
      Enables LOOP_NETWORK_LOSS and drives ρ_net through run_adaptive's
      operator-split so it rises ~1 order of magnitude and saturates near the
      experimental EUROFER network density (~5×10¹⁴ m⁻²).

Run (from module root, needs a built solver.exe):
  python codes/Python_Testing/sweep_loop100_and_network.py [--quick] [--T 500]

Function Documentation

◆ run_conv()

sweep_loop100_and_network.run_conv	(	T_C,
		t_end = `3e6`,
		n_points = `60`,
		I = `200`,
		i_mobile = `10`,
		v_mobile = `3`,
		rtol = `3e-3`,
		timeout_s = `4500`,
		progress_path = `None`
	)

f_111(T) CROSSOVER point: loop conversion ON, network loss OFF, on a
reduced active_window/discrete grid so the (stiff) conversion runs complete.
f_100 is a growth-vs-conversion competition gated by the dF cutoff (T_star):
large loops convert fast at high T (gamma_a tuned) -> f_100->1, but stay
partial at low T where the dF cutoff caps the convertible size.

If ``progress_path`` is given, per-SEGMENT f_100 / loop concentrations are
appended live (via a wrapper on sim._merge_results) so an in-flight run's
progress is visible without waiting for the point to finish.

Definition at line 123 of file sweep_loop100_and_network.py.

◆ run_one()

sweep_loop100_and_network.run_one	(	T_C,
		t_end,
		n_points,
		I = `1000`,
		V = `1000`,
		i_mobile = `20`,
		v_mobile = `5`,
		rtol = `1e-5`,
		network_loss = `True`,
		conversion = `True`
	)

One temperature: conversion ON + (optionally) network loss, via
run_adaptive so ρ_net advances between segments. Returns the results dict.

Uses the efficient full_system + bin_moment + Woodbury path (the original
16-min config) so loops reach n≈200 — the size the network-loss channel
needs to activate (the capture switch needs χ·d_loop ≳ network spacing).

Definition at line 73 of file sweep_loop100_and_network.py.