docs: aggiorna README e CLAUDE.md

README: aggiunge algoritmo.md e clear.sh alla struttura e alle voci menu.
CLAUDE.md: riscrittura completa — rimuove script obsoleti e valori
hardcoded, documenta architettura attuale (induttoreX/, report dinamico,
T∞ da start/report.md, corporate identity in report_completo.py).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

CLAUDE.md

@@ -4,7 +4,12 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project purpose
 
-Thermal analysis of a box passing through a production oven, measured with IR sensors. The goal is to find the mathematical law that best interpolates the temperature data in `data.csv`. The user directs the analysis and Claude implements the requested models in Python.
+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
 
@@ -14,53 +19,80 @@ venv\Scripts\activate
 pip install -r requirements.txt
 ```
 
-## Running scripts
+## Project architecture
 
-```bat
-python <script_name>.py
+```text
+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.*)
 ```
 
-Each script is standalone and produces a PNG plot. No test suite exists.
-
 ## Data
 
-`data.csv` columns used for fitting:
-- `time since start [ms]` — converted to seconds in every script (`/1000`)
-- `temp_obj IR [C]` — box temperature, the fitting target
+`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∞
 
-All fits start from t = 105 s. The transition zone [115.9–117.2 s] is excluded by setting `sigma = 1e10` for those points (weighted least squares, not deletion). T∞ = 22.99°C is a fixed constant computed once in `calcola_t_amb.py` and hardcoded in the other scripts.
+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). The base pattern shared across every fit script:
+All fits use `scipy.optimize.curve_fit(..., method="trf")` (Trust Region Reflective, nonlinear least squares). Newton's cooling model:
 
-```python
-popt, pcov = curve_fit(model, t_fit, T_fit, p0=[...], bounds=([...], [...]),
-                       sigma=sigma, absolute_sigma=True, method="trf")
-residuals = T_fit - model(t_fit, *popt)
-ss_res = np.sum((residuals / sigma_clean)**2)   # ignores excluded zone
-ss_tot = np.sum((T_fit[mask] - np.mean(T_fit[mask]))**2)
-R2 = 1 - ss_res / ss_tot
-perr = np.sqrt(np.diag(pcov))  # parameter uncertainties (1σ)
+```text
+T(t) = T∞ + A · exp(-(t - t0) / τ)
 ```
 
-### Models implemented
+Standard pattern in every `appX.py`:
 
-| Script | Model | Fixed params |
-|--------|-------|-------------|
-| `fit_raffreddamento_intero.py` | `T∞ + A·exp(-(t-t0)/τ)` — full range, weighted | T∞, t0=115.0s |
-| `fit_raffreddamento_1tratto.py` | same — segment [115.0–115.9s] | T∞, t0=115.0s |
-| `fit_raffreddamento_2tratto.py` | same — segment [117.5s–end] | T∞, t0=117.5s |
-| `fit_doppio_esponenziale.py` | `T∞ + A1·exp(-(t-t1)/τ1) + A2·exp(-(t-t2)/τ2)` | T∞, t1=115.0s, t2=117.5s |
+```python
+T0, T_END = ...          # time window [s], set by user
+W0_START, W0_END = ...   # optional exclusion zone [s], set by user
 
-The double exponential (`fit_doppio_esponenziale.py`) is the best model: R² = 0.9991, decomposing cooling into a fast surface component (τ₁ ≈ 1.8 s) and a slow core component (τ₂ ≈ 17.6 s).
+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
 
-## Adding a new fit
+sigma = np.where((t_fit >= W0_START) & (t_fit <= W0_END), 1e10, 1.0)
+mask_clean = sigma < 1e10
 
-1. Copy an existing fit script as a template.
-2. Define the model function with T∞ baked in or passed as a fixed parameter.
-3. Set `p0` from physical intuition or from a preliminary simpler fit.
-4. Use `sigma = np.where(mask_exclude, 1e10, 1.0)` to down-weight bad zones.
-5. Compute R² only over the unmasked points (`mask_clean`).
-6. Save output as a new PNG; do not overwrite existing plots.
+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`.

README.md

@@ -30,6 +30,8 @@ temperatura/
 ├── data.csv                  # dati acquisiti (input)
 ├── main.py                   # menu interattivo
 ├── report_completo.py        # generatore report PDF + LaTeX
+├── algoritmo.md              # descrizione dell'algoritmo di regressione
+├── clear.sh                  # elimina tutti i file generati
 ├── requirements.txt
 ├── start/
 │   ├── calcola_t_amb.py      # calcola T∞ media ponderata
@@ -75,6 +77,14 @@ Per aggiungere un nuovo induttore: copiare uno script esistente, aggiornare `T0`
 
 ---
 
+### Algoritmo di regressione
+
+**File:** `algoritmo.md`
+
+Spiega l'algoritmo usato per tutti i fit: minimi quadrati non lineari, metodo Trust Region Reflective (TRF), gestione dei pesi, stima iniziale dei parametri e metriche di qualità (R², incertezze dalla matrice di covarianza).
+
+---
+
 ### Report completo (PDF + LaTeX)
 
 **Script:** `report_completo.py`
@@ -88,6 +98,18 @@ Il PDF viene generato direttamente da Python tramite `reportlab` (nessuna dipend
 
 ---
 
+### Pulizia file generati
+
+**Script:** `clear.sh`
+
+Elimina tutti i file prodotti dagli script (`*.md` e `*.png` in `start/` e `induttore*/`, più `report_completo.tex` e `report_completo.pdf`). I sorgenti e `data.csv` non vengono toccati.
+
+```bash
+bash clear.sh
+```
+
+---
+
 ## Requisiti di data.csv
 
 ### Formato