Reconcile overview¶

Loads every reactor.yaml under reactors/, reconciles each one, and renders a single variable-by-reactor table plus the plasma cross-sections.

Reactor header is red when reconciliation failed, normal otherwise.

Variable cells (a variable is used when it enters the reactor's active relation graph):

• green — input, used, value unchanged after reconcile (value).
• yellow — input, used, moved within tolerance (input (value)).
• red — input, used, moved beyond tolerance (input → value, the culprit input is bold).
• transparent + grey value — variable not used.
• transparent + normal value — value computed by reconcile.

Diagnostics (warnings, errors, failed relations) are printed inline while each reactor is solved.

In [1]:

from pathlib import Path
import sys
import time
import warnings

import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display, HTML

# Locate the project root and import the package.
root = Path.cwd().resolve()
while root != root.parent and not (root / "src" / "fusdb").is_dir():
    root = root.parent
if str(root / "src") not in sys.path:
    sys.path.insert(0, str(root / "src"))

from fusdb import Reactor
from fusdb.relations.geometry.plasma_geometry import sauter_cross_section_points


def fmt(x):
    """Compact number / profile formatting for table and legend cells."""
    if x is None:
        return ""
    a = np.asarray(x, dtype=float)
    if a.ndim == 0 or a.size == 1:
        v = float(a.ravel()[0])
        if v == 0:
            return "0"
        if abs(v) >= 1e4 or abs(v) < 1e-3:
            return f"{v:.3e}"
        return f"{v:.4g}"
    return f"prof[{a.size}] mean={np.nanmean(a):.3g}"


def pick(system, name):
    """Return a variable's reconciled value, falling back to its input."""
    var = system.variables_by_name.get(name)
    if var is None:
        return None
    return var.value if var.value is not None else var.input_value


reactor_files = sorted((root / "reactors").rglob("reactor.yaml"))
print(f"Found {len(reactor_files)} reactor(s) under {root / 'reactors'}")

from pathlib import Path import sys import time import warnings import numpy as np import matplotlib.pyplot as plt from IPython.display import display, HTML # Locate the project root and import the package. root = Path.cwd().resolve() while root != root.parent and not (root / "src" / "fusdb").is_dir(): root = root.parent if str(root / "src") not in sys.path: sys.path.insert(0, str(root / "src")) from fusdb import Reactor from fusdb.relations.geometry.plasma_geometry import sauter_cross_section_points def fmt(x): """Compact number / profile formatting for table and legend cells.""" if x is None: return "" a = np.asarray(x, dtype=float) if a.ndim == 0 or a.size == 1: v = float(a.ravel()[0]) if v == 0: return "0" if abs(v) >= 1e4 or abs(v) < 1e-3: return f"{v:.3e}" return f"{v:.4g}" return f"prof[{a.size}] mean={np.nanmean(a):.3g}" def pick(system, name): """Return a variable's reconciled value, falling back to its input.""" var = system.variables_by_name.get(name) if var is None: return None return var.value if var.value is not None else var.input_value reactor_files = sorted((root / "reactors").rglob("reactor.yaml")) print(f"Found {len(reactor_files)} reactor(s) under {root / 'reactors'}")

Found 7 reactor(s) under /home/alessmor/Scrivania/fusdb/reactors

In [2]:

# Reconcile every reactor, printing warnings and errors as each one is solved.
records = []
for path in reactor_files:
    reactor = Reactor.from_yaml(path)
    system = reactor.relation_system()
    started = time.perf_counter()
    with warnings.catch_warnings(record=True) as caught:
        warnings.simplefilter("always")
        try:
            result = system.run("reconcile")
        except Exception as exc:
            result = {"success": False, "termination": f"crashed: {exc!r}", "errors": [repr(exc)], "warnings": []}
    elapsed = time.perf_counter() - started

    success = bool(result.get("success", False))
    relation_status = result.get("relation_status", {})
    failed = [n for n, s in relation_status.items() if isinstance(s, dict) and s.get("enforced", True) and not s.get("verified", False)]

    records.append({
        "name": reactor.name,
        "system": system,
        "active": set(system.active_variable_names),
        "success": success,
    })

    print(f"{'OK  ' if success else 'FAIL'} {reactor.name} in {elapsed:.1f}s | termination={result.get('termination', '?')}")
    for err in result.get("errors", []):
        print(f"      error: {err}")
    for msg in result.get("warnings", []):
        print(f"      warning: {msg}")
    for w in caught:
        print(f"      {w.category.__name__}: {w.message}")
    if failed:
        print(f"      failed relations: {', '.join(failed)}")

# Reconcile every reactor, printing warnings and errors as each one is solved. records = [] for path in reactor_files: reactor = Reactor.from_yaml(path) system = reactor.relation_system() started = time.perf_counter() with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") try: result = system.run("reconcile") except Exception as exc: result = {"success": False, "termination": f"crashed: {exc!r}", "errors": [repr(exc)], "warnings": []} elapsed = time.perf_counter() - started success = bool(result.get("success", False)) relation_status = result.get("relation_status", {}) failed = [n for n, s in relation_status.items() if isinstance(s, dict) and s.get("enforced", True) and not s.get("verified", False)] records.append({ "name": reactor.name, "system": system, "active": set(system.active_variable_names), "success": success, }) print(f"{'OK ' if success else 'FAIL'} {reactor.name} in {elapsed:.1f}s | termination={result.get('termination', '?')}") for err in result.get("errors", []): print(f" error: {err}") for msg in result.get("warnings", []): print(f" warning: {msg}") for w in caught: print(f" {w.category.__name__}: {w.message}") if failed: print(f" failed relations: {', '.join(failed)}")

FAIL ARC 2015 in 9.2s | termination=The maximum number of function evaluations is exceeded.
      failed relations: Total fusion power, Toroidal beta, Normalized beta, Default electron density profile from average density, Default electron temperature profile from average temperature, Default ion temperature profile from average temperature
OK   ARC V3A in 8.3s | termination=Both `ftol` and `xtol` termination conditions are satisfied.
FAIL EU-DEMO 2022 in 14.5s | termination=The maximum number of function evaluations is exceeded.
      failed relations: Elongation 95%, L-H transition threshold power, Total fusion power, Greenwald density fraction, Normalized beta, P_sep metric
OK   Hammir 2024 in 0.1s | termination=already verified; no reconcile solve
OK   INFINITY TWO 2025 in 3.0s | termination=`gtol` termination condition is satisfied.
FAIL Stellaris 2025 - Point A in 2.6s | termination=`ftol` termination condition is satisfied.
      failed relations: Total fusion power
FAIL STEP 2024 EB-CC in 13.2s | termination=The maximum number of function evaluations is exceeded.
      failed relations: Major radius, Aspect ratio, Elongation 95%, ST elongation vs aspect ratio, Energy confinement balance, Total fusion power, Toroidal beta, Normalized beta

In [3]:

# Variable-by-reactor table: variables as rows, reactors as columns.
# Rows are every variable that is an input or is used in at least one reactor.
var_names = set()
for rec in records:
    var_names |= rec["active"]
    for name, var in rec["system"].variables_by_name.items():
        if var.input_value is not None:
            var_names.add(name)
var_names = sorted(var_names)

html = ["<table style='border-collapse:collapse;font-size:0.8em'>"]
html.append("<tr><th style='text-align:left;padding:2px 8px'>variable</th>")
for rec in records:
    color = "#c00000" if not rec["success"] else "#1EFF00"
    html.append(f"<th style='padding:2px 8px;color:{color}'>{rec['name']}</th>")
html.append("</tr>")

for name in var_names:
    html.append(f"<tr><td style='text-align:left;padding:2px 8px;font-weight:bold'>{name}</td>")
    for rec in records:
        var = rec["system"].variables_by_name.get(name)
        background = ""
        color = "#000000"
        text = ""
        if var is not None:
            has_input = var.input_value is not None
            used = name in rec["active"]
            inp = var.input_value
            val = var.value
            if has_input and used and val is not None:
                ai = np.asarray(inp, dtype=float)
                av = np.asarray(val, dtype=float)
                try:
                    exact = bool(np.array_equal(ai, av))
                    scale = max(float(np.max(np.abs(ai))), float(np.max(np.abs(av))), 1e-300)
                    tol = max(float(var.abs_tol or 0.0), float(var.rel_tol or 0.0) * scale)
                    within = bool(np.all(np.abs(av - ai) <= tol))
                except Exception:
                    exact, within = False, False
                if exact:
                    background, color, text = "#c6efce", "#006100", fmt(val)
                elif within:
                    background, color, text = "#ffeb9c", "#9c6500", f"{fmt(inp)} ({fmt(val)})"
                else:
                    background, color, text = "#ffc7ce", "#9c0006", f"<b>{fmt(inp)}</b> &rarr; {fmt(val)}"
            elif has_input and not used:
                color, text = "#6E6E6E", fmt(inp)
            elif (not has_input) and used and val is not None:
                color, text = "#FFFFFF", fmt(val)
            elif has_input and used and val is None:
                color, text = "#606060", fmt(inp)
        style = f"padding:2px 8px;color:{color}"
        if background:
            style += f";background-color:{background}"
        html.append(f"<td style='{style}'>{text}</td>")
    html.append("</tr>")
html.append("</table>")
display(HTML("".join(html)))

# Variable-by-reactor table: variables as rows, reactors as columns. # Rows are every variable that is an input or is used in at least one reactor. var_names = set() for rec in records: var_names |= rec["active"] for name, var in rec["system"].variables_by_name.items(): if var.input_value is not None: var_names.add(name) var_names = sorted(var_names) html = [""] html.append("") for rec in records: color = "#c00000" if not rec["success"] else "#1EFF00" html.append(f"") html.append("") for name in var_names: html.append(f"") for rec in records: var = rec["system"].variables_by_name.get(name) background = "" color = "#000000" text = "" if var is not None: has_input = var.input_value is not None used = name in rec["active"] inp = var.input_value val = var.value if has_input and used and val is not None: ai = np.asarray(inp, dtype=float) av = np.asarray(val, dtype=float) try: exact = bool(np.array_equal(ai, av)) scale = max(float(np.max(np.abs(ai))), float(np.max(np.abs(av))), 1e-300) tol = max(float(var.abs_tol or 0.0), float(var.rel_tol or 0.0) * scale) within = bool(np.all(np.abs(av - ai) <= tol)) except Exception: exact, within = False, False if exact: background, color, text = "#c6efce", "#006100", fmt(val) elif within: background, color, text = "#ffeb9c", "#9c6500", f"{fmt(inp)} ({fmt(val)})" else: background, color, text = "#ffc7ce", "#9c0006", f"{fmt(inp)} → {fmt(val)}" elif has_input and not used: color, text = "#6E6E6E", fmt(inp) elif (not has_input) and used and val is not None: color, text = "#FFFFFF", fmt(val) elif has_input and used and val is None: color, text = "#606060", fmt(inp) style = f"padding:2px 8px;color:{color}" if background: style += f";background-color:{background}" html.append(f"") html.append("") html.append("

variable	{rec['name']}
{name}	{text}

") display(HTML("".join(html)))

variable	ARC 2015	ARC V3A	EU-DEMO 2022	Hammir 2024	INFINITY TWO 2025	Stellaris 2025 - Point A	STEP 2024 EB-CC
A	2.92	3.915	3.1		10	9.8	1.8
A_p	232.4	257 → 292.8	1591			327	369.8
B0	9.2 → 9.695	11.4	5.86 → 4.118			9 (9)	3.2 → 2.073
B_max	23						12
G89	0.14
H89	2.8
H98_y2	1.8		0.98				1.03
I_p	7.800e+06 (7.799e+06)	1.200e+07	1.775e+07 → 1.972e+07			1.603e+09	2.270e+07 → 2.934e+06
L_p	11.21	10.66	29.64				17.6
P_LH			1.208e+08 (1.210e+08)				1.673e+07
P_aux		2.194e+07	2.000e+08		2.000e+07	5.000e+07	1.300e+08
P_brem			7.254e+07			1.032e+08	3.977e+06
P_charged	1.480e+08	2.228e+08	2.567e+08		1.593e+08	1.050e+09	4.914e+06
P_elec_tot		4.000e+08
P_fus	5.250e+08 (5.252e+08)	1.130e+09 (1.119e+09)	2.000e+09 (1.984e+09)		8.000e+08	2.700e+09 (2.700e+09)	1.560e+09 (1.560e+09)
P_fus_DD	9.338e+05	1.197e+06	1.410e+06		9.602e+05	5.602e+06	3.083e+04
P_fus_DDn	3.987e+05	5.191e+05	6.189e+05		4.246e+05	2.439e+06	1.366e+04
P_fus_DDn_He3	9.998e+04	1.302e+05	1.552e+05		1.065e+05	6.117e+05	3427
P_fus_DDn_n	2.987e+05	3.889e+05	4.637e+05		3.181e+05	1.828e+06	1.024e+04
P_fus_DDp	5.351e+05	6.777e+05	7.913e+05		5.356e+05	3.163e+06	1.717e+04
P_fus_DDp_T	1.341e+05	1.698e+05	1.983e+05		1.342e+05	7.927e+05	4303
P_fus_DDp_p	4.010e+05	5.079e+05	5.930e+05		4.014e+05	2.370e+06	1.287e+04
P_fus_DHe3	0.001497	7.988e-04	4.223e-04		1.452e-04	0.002952	3.675e-06
P_fus_DHe3_alpha	2.944e-04	1.571e-04	8.308e-05		2.857e-05	5.807e-04	7.230e-07
P_fus_DHe3_p	0.001202	6.417e-04	3.392e-04		1.167e-04	0.002371	2.952e-06
P_fus_DT	7.411e+08	1.116e+09	1.286e+09		7.979e+08	5.261e+09	2.461e+07
P_fus_DT_alpha	1.474e+08	2.219e+08	2.557e+08		1.587e+08	1.046e+09	4.893e+06
P_fus_DT_n	5.937e+08	8.941e+08	1.030e+09		6.393e+08	4.215e+09	1.971e+07
P_fus_TT	1.296e+06	1.624e+06	1.775e+06		1.109e+06	7.461e+06	3.442e+04
P_loss	2.438e+08		1.974e+08		3.030e+08	5.322e+08	4.581e+08
P_neutron	5.940e+08	8.945e+08	1.031e+09		6.396e+08	4.217e+09	1.972e+07
P_rad							2.970e+08
P_sep			1.704e+08 (1.705e+08)
P_sep_B_over_q95AR			9.200e+06 (9.191e+06)
P_sep_over_R			1.890e+07 (1.890e+07)
P_wall						4.050e+06
Q_sci		51 (51)	9.918		40	54	12 (12)
R	3.3	4.62	9		12.5	12.74	3.6
R_max	4.43	5.8	11.9		13.75	14.04	2.682
R_min	2.17	3.44	6.1		11.25	11.44	0.2177
Rr_DDn	7.610e+17	9.908e+17	1.181e+18		8.104e+17	4.656e+18	2.608e+16
Rr_DDp	8.287e+17	1.050e+18	1.226e+18		8.295e+17	4.899e+18	2.659e+16
Rr_DHe3	5.104e+08	2.725e+08	1.440e+08		4.953e+07	1.007e+09	1.253e+06
Rr_DT	2.628e+20	3.958e+20	4.560e+20		2.830e+20	1.866e+21	8.726e+18
Rr_He3He3	9.801e-06	1.400e-06	2.299e-07		2.774e-08	3.689e-06	4.831e-10
Rr_THe3	2.463e+07	1.412e+07	7.402e+06		2.443e+06	5.233e+07	6.060e+04
Rr_THe3_D	1.012e+07	5.815e+06	3.053e+06		1.008e+06	2.156e+07	2.503e+04
Rr_THe3_np	1.452e+07	8.305e+06	4.349e+06		1.434e+06	3.077e+07	3.557e+04
Rr_TT	7.161e+17	8.970e+17	9.804e+17		6.123e+17	4.121e+18	1.901e+16
S_phi	7.381	6.725	48.93				14.01
T0	27
TBR	1.1				1.3
T_avg	14 → 14.86	13.54	12.49 → 9.177	150	6.8	12 (12)	9.69 → 6.161
T_e	prof[46] mean=15	prof[46] mean=13.5	prof[46] mean=9.18		prof[46] mean=6.8	prof[46] mean=12	prof[46] mean=6.16
T_i	prof[46] mean=15	prof[46] mean=13.5	prof[46] mean=9.18		prof[46] mean=6.8	prof[46] mean=12	prof[46] mean=6.16
T_i_avg	15.01	100	9.177		6.8	12	6.161
V_p	153 (152.8)	189 → 187	2656	46	838.4	425 (425)	299.4
W_th	1.653e+08	2.310e+08 → 2.354e+08	1.018e+09		5.152e+08	7.771e+08	5.735e+07
Z_eff			2.12			1.2	2.31
a	1.13	1.18	2.9		1.25	1.3	1.232
afuel	2.5	2.5	2.5 (2.491)		2.5	2.5	2.5
beta_N	2.59 (2.59)	1.819	2.5 (2.5)			0.0276	0.0393 (0.03928)
beta_T	0.019 (0.01897)	0.01622	0.03786			0.03782	0.139 (0.139)
beta_limit	0.01993	0.02498	0.04623				0.03216
delta	0.003397	0.65 (0.6547)	0.495				0.4001
delta_95	0.002265	0.4365	0.33				0.2667
eps	0.3425	0.26 → 0.2554	0.3226		0.1	0.102	0.5556
f_BS	0.63
f_CD			0.05
f_D	0.5 (0.5007)	0.5 (0.5025)	0.5		0.5	0.5	0.5
f_GW	0.67	0.7067	1.2 → 1.183				1.052
f_He3	1.000e-10	1.000e-10	1.000e-10		1.000e-10	1.000e-10	1.000e-10
f_He4	1.000e-10	1.000e-10	1.000e-10		1.000e-10	1.000e-10	1.000e-10
f_Imp	1.000e-10	1.000e-10	1.000e-10		1.000e-10	1.000e-10	1.000e-10
f_NI			0.39
f_T	0.5 (0.5004)	0.5 (0.5025)	0.5		0.5	0.5	0.5
greenwald_margin	-6.517e+19	-8.047e+19	1.224e+19				3.177e+18
kappa	1.84	1.8 → 1.587	1.91				3
kappa_95	1.643	1.417	1.65				2.8
kappa_ipb	1.837	1.473	1.778		2.175	1	2.776
li	0.67
n0	1.800e+20					5.060e+20
n_D	prof[46] mean=6.99e+19	prof[46] mean=9.69e+19	prof[46] mean=4.34e+19		prof[46] mean=9.4e+19	prof[46] mean=1.58e+20	prof[46] mean=3.23e+19
n_GW	1.944e+20	2.743e+20	7.463e+19				6.152e+19
n_He3	prof[46] mean=1.4e+10	prof[46] mean=1.93e+10	prof[46] mean=8.69e+09		prof[46] mean=1.88e+10	prof[46] mean=3.17e+10	prof[46] mean=6.47e+09
n_He4	prof[46] mean=1.4e+10	prof[46] mean=1.93e+10	prof[46] mean=8.69e+09		prof[46] mean=1.88e+10	prof[46] mean=3.17e+10	prof[46] mean=6.47e+09
n_SUDO						5.562e+21
n_T	prof[46] mean=6.99e+19	prof[46] mean=9.69e+19	prof[46] mean=4.34e+19		prof[46] mean=9.4e+19	prof[46] mean=1.58e+20	prof[46] mean=3.23e+19
n_avg	1.300e+20 (1.292e+20)	1.939e+20	8.687e+19	7.500e+19	1.880e+20 (1.880e+20)	3.170e+20 (3.170e+20)	1.640e+20 → 6.470e+19
n_e	prof[46] mean=1.4e+20	prof[46] mean=1.94e+20	prof[46] mean=8.69e+19		prof[46] mean=1.88e+20	prof[46] mean=3.17e+20	prof[46] mean=6.47e+19
n_i	prof[46] mean=1.4e+20	prof[46] mean=1.93e+20	prof[46] mean=8.69e+19		prof[46] mean=1.88e+20	prof[46] mean=3.17e+20	prof[46] mean=6.47e+19
n_i_avg	1.396e+20		8.687e+19		1.880e+20	3.170e+20	6.470e+19
n_imp	prof[46] mean=1.4e+10	prof[46] mean=1.93e+10	prof[46] mean=8.69e+09		prof[46] mean=1.88e+10	prof[46] mean=3.17e+10	prof[46] mean=6.47e+09
n_la	1.292e+20	1.939e+20	8.687e+19		1.880e+20	3.170e+20	6.470e+19
p_th	7.212e+05	8.390e+05	2.555e+05		4.096e+05	1.219e+06	1.277e+05
q95	7.2		3.89 (3.885)				8.03
q_a	4.7
q_min	3.5
rho	prof[46] mean=0.5	prof[46] mean=0.5	prof[46] mean=0.5	prof[46] mean=0.5	prof[46] mean=0.5	prof[46] mean=0.5	prof[46] mean=0.5
sigmav_DD	prof[46] mean=3.4e-24	prof[46] mean=2.32e-24	prof[46] mean=9.61e-25		prof[46] mean=4.43e-25	prof[46] mean=1.79e-24	prof[46] mean=3.36e-25
sigmav_DDn	prof[46] mean=1.63e-24	prof[46] mean=1.13e-24	prof[46] mean=4.71e-25		prof[46] mean=2.19e-25	prof[46] mean=8.72e-25	prof[46] mean=1.67e-25
sigmav_DDp	prof[46] mean=1.77e-24	prof[46] mean=1.19e-24	prof[46] mean=4.89e-25		prof[46] mean=2.24e-25	prof[46] mean=9.18e-25	prof[46] mean=1.7e-25
sigmav_DHe3	prof[46] mean=2.59e-24	prof[46] mean=7.79e-25	prof[46] mean=1.44e-25		prof[46] mean=3.34e-26	prof[46] mean=4.71e-25	prof[46] mean=2e-26
sigmav_DT	prof[46] mean=2.84e-22	prof[46] mean=2.25e-22	prof[46] mean=9.1e-23		prof[46] mean=3.82e-23	prof[46] mean=1.75e-22	prof[46] mean=2.79e-23
sigmav_He3He3	prof[46] mean=4.86e-28	prof[46] mean=4.02e-29	prof[46] mean=2.29e-30		prof[46] mean=1.87e-31	prof[46] mean=1.73e-29	prof[46] mean=7.71e-32
sigmav_THe3	prof[46] mean=1.26e-25	prof[46] mean=4.04e-26	prof[46] mean=7.38e-27		prof[46] mean=1.65e-27	prof[46] mean=2.45e-26	prof[46] mean=9.67e-28
sigmav_THe3_D	prof[46] mean=5.16e-26	prof[46] mean=1.66e-26	prof[46] mean=3.05e-27		prof[46] mean=6.81e-28	prof[46] mean=1.01e-26	prof[46] mean=3.99e-28
sigmav_THe3_np	prof[46] mean=7.4e-26	prof[46] mean=2.37e-26	prof[46] mean=4.34e-27		prof[46] mean=9.68e-28	prof[46] mean=1.44e-26	prof[46] mean=5.68e-28
sigmav_TT	prof[46] mean=1.53e-24	prof[46] mean=1.02e-24	prof[46] mean=3.91e-25		prof[46] mean=1.65e-25	prof[46] mean=7.72e-25	prof[46] mean=1.21e-25
squareness	0	0.02616	0 (6.745e-04)		0	0	0
sudo_margin						-5.245e+21
tau_E	0.64 → 0.678		5.158		1.7	1.46 (1.46)	0.1252
tau_p	1					11
tau_pulse			7200
troyon_margin	-9.664e-04	-0.008753	-0.008367				0.1068

In [4]:

# Plasma cross-sections, with reactor geometry summarised in the legend below.
fig, ax = plt.subplots(figsize=(7, 8))
for rec in records:
    system = rec["system"]
    R = pick(system, "R")
    a = pick(system, "a")
    kappa = pick(system, "kappa")
    delta = pick(system, "delta")
    xi = pick(system, "squareness") or 0.0
    V_p = pick(system, "V_p")
    if None in (R, a, kappa, delta):
        continue
    r_pts, z_pts = sauter_cross_section_points(float(R), float(a), kappa=float(kappa), delta=float(delta), squareness=float(xi), n=256)
    vp_text = fmt(V_p) if V_p is not None else "—"
    label = f"{rec['name']}   R: {fmt(R)}  a: {fmt(a)}  k: {fmt(kappa)}  δ: {fmt(delta)}  ξ: {fmt(xi)}  V_p: {vp_text}"
    ax.plot(r_pts, z_pts, label=label)

ax.set_aspect("equal")
ax.set_xlabel("R [m]")
ax.set_ylabel("Z [m]")
ax.set_title("Plasma cross-sections")
ax.grid(True, alpha=0.3)
ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.10), fontsize=8, frameon=False)
fig.tight_layout()
plt.show()

# Plasma cross-sections, with reactor geometry summarised in the legend below. fig, ax = plt.subplots(figsize=(7, 8)) for rec in records: system = rec["system"] R = pick(system, "R") a = pick(system, "a") kappa = pick(system, "kappa") delta = pick(system, "delta") xi = pick(system, "squareness") or 0.0 V_p = pick(system, "V_p") if None in (R, a, kappa, delta): continue r_pts, z_pts = sauter_cross_section_points(float(R), float(a), kappa=float(kappa), delta=float(delta), squareness=float(xi), n=256) vp_text = fmt(V_p) if V_p is not None else "—" label = f"{rec['name']} R: {fmt(R)} a: {fmt(a)} k: {fmt(kappa)} δ: {fmt(delta)} ξ: {fmt(xi)} V_p: {vp_text}" ax.plot(r_pts, z_pts, label=label) ax.set_aspect("equal") ax.set_xlabel("R [m]") ax.set_ylabel("Z [m]") ax.set_title("Plasma cross-sections") ax.grid(True, alpha=0.3) ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.10), fontsize=8, frameon=False) fig.tight_layout() plt.show()