Initial commit

fit_sequence.py

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+#!/usr/bin/env python
+"""Fit a temperature to each spectrum in a sequence and plot T versus time.
+
+For a cooling or heating object: grab a burst of frames, then fit each and watch
+the temperature track. Reuses the Planck fit from fit_temperature.py.
+
+Inputs are spectrum CSVs in time order. With --response (and --dark plus
+--integration) each is response-corrected first, exactly as grab_spectrum.py
+--response would; otherwise they are assumed already corrected. Time comes from
+the file modification times (first file = 0), or pass --interval for even spacing.
+
+Note the printed +/- is the formal fit error and understates the truth; the
+real scatter is frame-to-frame noise and the gray-body and reference systematics.
+
+Usage: python fit_sequence.py f1.csv f2.csv ... [--response R.csv --dark D.csv
+                              --integration US] [--band LO HI] [--interval S]
+                              [--out T.csv] [--png fig.png]
+"""
+import argparse
+import os
+
+import numpy as np
+
+from response import add_calibration_args, apply_response, dark_at, load_csv
+from fit_temperature import fit_planck
+
+p = argparse.ArgumentParser(description=__doc__,
+                            formatter_class=argparse.RawDescriptionHelpFormatter)
+p.add_argument("spectra", nargs="+", help="spectrum CSVs, in time order")
+p.add_argument("--integration", type=int, metavar="US",
+               help="integration time (us) the spectra were captured at; required with --dark")
+p.add_argument("--band", nargs=2, type=float, default=[420.0, 800.0],
+               metavar=("LO", "HI"), help="fit band in nm (default: 420 800)")
+p.add_argument("--interval", type=float, default=None,
+               help="seconds between frames (default: use file modification times)")
+p.add_argument("--out", default=None, help="write a time,temperature_K,dT_K CSV")
+p.add_argument("--png", default="temperature_sequence.png")
+add_calibration_args(p)
+args = p.parse_args()
+
+cal = load_csv(args.response) if args.response else None
+dark = None
+if args.dark:
+    if args.integration is None:
+        p.error("--integration US is required with --dark")
+    dark = dark_at(args.dark, args.integration)
+
+if args.interval is not None:
+    times = np.arange(len(args.spectra)) * args.interval
+else:
+    t0 = os.path.getmtime(args.spectra[0])
+    times = np.array([os.path.getmtime(f) - t0 for f in args.spectra])
+
+rows = []
+for t, f in zip(times, args.spectra):
+    wl, y = load_csv(f)
+    if cal is not None:
+        y = apply_response(wl, y, cal, dark)
+    result = fit_planck(wl, y, tuple(args.band))
+    if result is None:
+        print(f"{f}: no fit (too few usable points in band)")
+        continue
+    temperature, dT, _ = result
+    rows.append((t, temperature, dT))
+    print(f"{f}: t = {t:7.2f} s   T = {temperature:.2f} +/- {dT:.2f} K")
+
+if not rows:
+    p.error("no spectra could be fit; check --band and inputs")
+rows = np.array(rows)
+
+if args.out:
+    np.savetxt(args.out, rows, delimiter=",",
+               header="time_s,temperature_K,dT_K", comments="")
+    print(f"wrote {args.out}")
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(9, 5))
+ax.errorbar(rows[:, 0], rows[:, 1], yerr=rows[:, 2], fmt="o-", capsize=3)
+ax.set_xlabel("time (s)")
+ax.set_ylabel("temperature (K)")
+ax.set_title(f"Temperature sequence, fit {args.band[0]:.0f}-{args.band[1]:.0f} nm")
+ax.grid(alpha=0.3)
+fig.tight_layout()
+fig.savefig(args.png, dpi=110)
+print(f"wrote {args.png}")