"""Plot efficiency vs input voltage vs current from bench CSV logs. Auto-detects the three CSV formats produced by the tooling: - GUI data log (data_*.csv: instrument columns + merged stm_* snapshot) - GUI telemetry log (*_telem.csv: full-rate 100 Hz board broadcast) - CLI sweep (sweep_vi_*.csv: voltage_set/load_setpoint grid) Left panel: operating-point scatter (x = Vin, y = current, color = efficiency). Right panel: efficiency vs current, one curve per Vin bin. Usage: bench-plot data_20260703_120000.csv bench-plot run_telem.csv another_telem.csv --current iin --save eff.png """ from __future__ import annotations import argparse import csv import math import sys import matplotlib.pyplot as plt import numpy as np MIN_P_IN_W = 0.1 # same gate as the firmware/GUI efficiency calc def _f(row: dict, key: str) -> float: """Float cell value; blank/missing/garbage -> NaN.""" v = row.get(key, "") if v is None or v == "": return math.nan try: return float(v) except ValueError: return math.nan def _detect_format(header: list[str]) -> str: cols = set(header) if "stm_eff_net_pct" in cols: return "datalog" if "iout_slow" in cols and "p_in_W" in cols: return "telem" if "voltage_set" in cols and "efficiency" in cols: return "sweep" raise ValueError(f"unrecognized CSV header: {header[:6]}...") def _extract(path: str, source: str) -> tuple[dict, str]: """Read one CSV -> dict of float lists (vin_V, iin_A, iout_A, eff_pct, p_out_W) plus the efficiency-source label actually used.""" with open(path, newline="") as fh: reader = csv.DictReader(fh) header = reader.fieldnames or [] rows = list(reader) fmt = _detect_format(header) out: dict[str, list] = {k: [] for k in ("vin_V", "iin_A", "iout_A", "eff_pct", "p_out_W")} if fmt == "sweep": for r in rows: out["vin_V"].append(_f(r, "supply_V")) out["iin_A"].append(_f(r, "supply_I")) out["iout_A"].append(_f(r, "load_I")) out["eff_pct"].append(_f(r, "efficiency")) out["p_out_W"].append(_f(r, "output_power")) return out, "sweep efficiency (HIOKI)" if fmt == "telem": # Wire units: vin/vout in mV, currents in mA (iin negative into the # converter); eff net = (P_out - P_sys) / P_in, same as the GUI panel. for r in rows: p_in = _f(r, "p_in_W") p_out = _f(r, "p_out_W") p_sys = _f(r, "vout") * _f(r, "sys_current_ma") / 1e6 eff = (p_out - p_sys) / p_in * 100.0 if p_in > MIN_P_IN_W else math.nan out["vin_V"].append(_f(r, "vin") / 1000.0) out["iin_A"].append(-_f(r, "iin") / 1000.0) out["iout_A"].append(_f(r, "iout_slow") / 1000.0) out["eff_pct"].append(eff) out["p_out_W"].append(p_out) return out, "board eff net (iout_slow, -P_sys)" # datalog: three consistent (vin, current, eff) triples to choose from if source == "auto": med_eff1 = np.nanmedian([_f(r, "meter_EFF1") for r in rows]) if rows else math.nan med_psup = np.nanmedian([_f(r, "supply_P") for r in rows]) if rows else math.nan if med_eff1 > 1.0: source = "hioki" elif med_psup > MIN_P_IN_W: source = "instr" else: source = "stm" for r in rows: if source == "hioki": out["vin_V"].append(_f(r, "meter_U5")) out["iin_A"].append(_f(r, "meter_I5")) out["iout_A"].append(_f(r, "meter_I6")) out["eff_pct"].append(_f(r, "meter_EFF1")) out["p_out_W"].append(_f(r, "meter_P6")) elif source == "instr": p_sup = _f(r, "supply_P") eff = _f(r, "load_P") / p_sup * 100.0 if p_sup > MIN_P_IN_W else math.nan out["vin_V"].append(_f(r, "supply_V")) out["iin_A"].append(_f(r, "supply_I")) out["iout_A"].append(_f(r, "load_I")) out["eff_pct"].append(eff) out["p_out_W"].append(_f(r, "load_P")) else: # stm out["vin_V"].append(_f(r, "stm_vin_mV") / 1000.0) out["iin_A"].append(-_f(r, "stm_iin_mA") / 1000.0) out["iout_A"].append(_f(r, "stm_iout_slow_mA") / 1000.0) out["eff_pct"].append(_f(r, "stm_eff_net_pct")) out["p_out_W"].append(_f(r, "stm_p_out_W")) labels = {"hioki": "HIOKI EFF1", "instr": "supply/load power", "stm": "board eff net"} return out, labels[source] def main() -> None: ap = argparse.ArgumentParser( description="Plot efficiency vs input voltage vs current from bench CSVs.") ap.add_argument("csv", nargs="*", help="logged CSV file(s); a file dialog opens if omitted") ap.add_argument("--current", choices=("iout", "iin"), default="iout", help="current axis: output (default) or input current") ap.add_argument("--source", choices=("auto", "hioki", "instr", "stm"), default="auto", help="efficiency source for GUI data logs (default auto: " "HIOKI if present, else supply/load, else board)") ap.add_argument("--vin-bin", type=float, default=1.0, metavar="V", help="Vin bin width for the per-voltage curves (default 1.0)") ap.add_argument("--min-pout", type=float, default=5.0, metavar="W", help="drop points below this output power (default 5.0)") ap.add_argument("--save", metavar="PNG", help="write the figure instead of showing it") ap.add_argument("--title", default=None, help="figure title override") args = ap.parse_args() if not args.csv: import tkinter as tk from tkinter import filedialog root = tk.Tk() root.withdraw() args.csv = list(filedialog.askopenfilenames( title="Select logged CSV(s) to plot", filetypes=[("CSV files", "*.csv"), ("All files", "*.*")])) root.destroy() if not args.csv: sys.exit("no file selected") data: dict[str, list] = {k: [] for k in ("vin_V", "iin_A", "iout_A", "eff_pct", "p_out_W")} labels = set() for path in args.csv: part, label = _extract(path, args.source) for k in data: data[k].extend(part[k]) labels.add(label) vin = np.asarray(data["vin_V"]) cur = np.asarray(data["iin_A" if args.current == "iin" else "iout_A"]) eff = np.asarray(data["eff_pct"]) pout = np.asarray(data["p_out_W"]) keep = (np.isfinite(vin) & np.isfinite(cur) & np.isfinite(eff) & (eff > 0.0) & (eff <= 105.0) & (pout >= args.min_pout)) n_total = len(vin) vin, cur, eff = vin[keep], cur[keep], eff[keep] if len(vin) == 0: sys.exit(f"no usable points ({n_total} rows read; all filtered — " f"check --min-pout / --source)") ipk = int(np.argmax(eff)) print(f"{len(vin)} points ({n_total - len(vin)} filtered) | " f"Vin {vin.min():.1f}..{vin.max():.1f} V | " f"I {cur.min():.2f}..{cur.max():.2f} A | " f"peak eff {eff[ipk]:.2f} % @ {vin[ipk]:.1f} V, {cur[ipk]:.2f} A") cur_name = "Input current (A)" if args.current == "iin" else "Output current (A)" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13.5, 5.8)) fig.suptitle(args.title or f"Efficiency map — {', '.join(sorted(labels))}") # Left: operating-point scatter, color = efficiency vmin = np.percentile(eff, 5) sc = ax1.scatter(vin, cur, c=eff, s=14, cmap="viridis", vmin=vmin, vmax=eff.max(), rasterized=True) fig.colorbar(sc, ax=ax1, label="Efficiency (%)") ax1.plot(vin[ipk], cur[ipk], "r*", ms=14, mec="k", label=f"peak {eff[ipk]:.2f} %") ax1.set_xlabel("Input voltage (V)") ax1.set_ylabel(cur_name) ax1.legend(loc="best", fontsize=8) ax1.grid(alpha=0.3) # Right: efficiency vs current, one mean curve per Vin bin w = args.vin_bin centers = np.unique(np.round(vin / w) * w) cmap = plt.cm.plasma(np.linspace(0.0, 0.9, len(centers))) for color, c0 in zip(cmap, centers): m = np.abs(vin - c0) <= w / 2 if m.sum() < 2: ax2.plot(cur[m], eff[m], "o", color=color, ms=4, label=f"{c0:g} V") continue edges = np.linspace(cur[m].min(), cur[m].max() + 1e-9, 41) idx = np.digitize(cur[m], edges) xs, ys = [], [] for b in np.unique(idx): bm = idx == b xs.append(cur[m][bm].mean()) ys.append(eff[m][bm].mean()) ax2.plot(xs, ys, "-o", color=color, ms=3, lw=1.2, label=f"{c0:g} V") ax2.set_xlabel(cur_name) ax2.set_ylabel("Efficiency (%)") ax2.grid(alpha=0.3) if len(centers) <= 14: ax2.legend(title="Vin bin", fontsize=8, ncols=1 + len(centers) // 8) else: norm = plt.Normalize(centers.min(), centers.max()) fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap="plasma"), ax=ax2, label="Vin bin (V)") fig.tight_layout() if args.save: fig.savefig(args.save, dpi=140) print(f"saved: {args.save}") else: plt.show() if __name__ == "__main__": main()