report-temperatura/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project purpose

Thermal analysis of boxes passing through a production oven (Cevolani Italia s.r.l.), measured with IR sensors. The pipeline accepts any data.csv that respects the column specifications in README.md and produces:

• A Newton's cooling fit for each inductor zone (induttoreX/appX.py)
• A professional PDF report (report_completo.pdf) aggregating all results

The user directs the analysis (defines time windows, chooses exclusion zones); Claude implements the requested models and pipeline changes in Python.

Setup

python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

Project architecture

main.py                  ← interactive menu, auto-discovers induttore*/ folders
start/
  calcola_t_amb.py       ← computes T∞ (weighted mean), writes start/report.md
  plot_temperatura.py    ← plots raw temp_obj and temp_amb over [T0, T_END]
induttoreX/
  appX.py                ← nonlinear fit for one inductor zone; params hardcoded by user
report_completo.py       ← reads all induttore*/report*.md, generates .tex + .pdf
algoritmo.md             ← detailed explanation of the TRF algorithm
clear.sh                 ← deletes all generated files (*.md, *.png, report_completo.*)

Data

data.csv required columns (exact names):

• time since start [ms] — converted to seconds (/1000) in every script
• temp_obj IR [C] — object/box temperature, the fitting target
• temp_amb IR [C] — ambient temperature, used to compute T∞

T∞ is computed once by calcola_t_amb.py (weighted mean over the full acquisition) and stored in start/report.md. All other scripts read T∞ from there — it is not hardcoded.

Fitting architecture

All fits use scipy.optimize.curve_fit(..., method="trf") (Trust Region Reflective, nonlinear least squares). Newton's cooling model:

T(t) = T∞ + A · exp(-(t - t0) / τ)

Standard pattern in every appX.py:

T0, T_END = ...          # time window [s], set by user
W0_START, W0_END = ...   # optional exclusion zone [s], set by user

df = pd.read_csv(ROOT / "data.csv")
df["time_s"] = df["time since start [ms]"] / 1000.0
mask = (df["time_s"] >= T0) & (df["time_s"] <= T_END)
t_fit = df.loc[mask, "time_s"].values
T_fit = df.loc[mask, "temp_obj IR [C]"].values

sigma = np.where((t_fit >= W0_START) & (t_fit <= W0_END), 1e10, 1.0)
mask_clean = sigma < 1e10

popt, pcov = curve_fit(model, t_fit, T_fit, p0=[...], bounds=([...], [...]),
                       sigma=sigma, absolute_sigma=True, method="trf")
residuals = T_fit[mask_clean] - model(t_fit[mask_clean], *popt)
ss_res = np.sum(residuals**2)
ss_tot = np.sum((T_fit[mask_clean] - np.mean(T_fit[mask_clean]))**2)
R2 = 1 - ss_res / ss_tot
perr = np.sqrt(np.diag(pcov))

If no exclusion zone is needed, sigma = np.ones(len(t_fit)) and mask_clean = slice(None).

Adding a new inductor

1. Copy an existing induttoreX/appX.py into a new folder induttoreY/appY.py.
2. Set T0, T_END to the desired time window.
3. Set W0_START, W0_END if a sub-interval must be excluded (or remove the exclusion logic).
4. Run via main.py — the menu discovers it automatically.
5. The script produces induttoreY/induttoreY.png and induttoreY/reportY.md.
6. Running report_completo.py includes it automatically in the PDF.

Report generation

report_completo.py is fully dynamic:

• Discovers all induttore*/report*.md via glob
• Reads T∞ from start/report.md
• Renders equations and math symbols as PNG via matplotlib mathtext (no system LaTeX required)
• Outputs report_completo.tex and report_completo.pdf

Corporate identity constants (editable at the top of report_completo.py):

SOCIETA, DIPARTIMENTO, DOC_NUM, REVISIONE, STATO, CLASSIFICAZIONE.