davideandClaude Sonnet 4.6
a30a9410c7
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>
99 lines
3.8 KiB
Markdown
99 lines
3.8 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project purpose
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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:
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- A Newton's cooling fit for each inductor zone (`induttoreX/appX.py`)
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- A professional PDF report (`report_completo.pdf`) aggregating all results
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The user directs the analysis (defines time windows, chooses exclusion zones); Claude implements the requested models and pipeline changes in Python.
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## Setup
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```bat
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python -m venv venv
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venv\Scripts\activate
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pip install -r requirements.txt
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```
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## Project architecture
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```text
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main.py ← interactive menu, auto-discovers induttore*/ folders
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start/
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calcola_t_amb.py ← computes T∞ (weighted mean), writes start/report.md
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plot_temperatura.py ← plots raw temp_obj and temp_amb over [T0, T_END]
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induttoreX/
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appX.py ← nonlinear fit for one inductor zone; params hardcoded by user
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report_completo.py ← reads all induttore*/report*.md, generates .tex + .pdf
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algoritmo.md ← detailed explanation of the TRF algorithm
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clear.sh ← deletes all generated files (*.md, *.png, report_completo.*)
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```
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## Data
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`data.csv` required columns (exact names):
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- `time since start [ms]` — converted to seconds (`/1000`) in every script
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- `temp_obj IR [C]` — object/box temperature, the fitting target
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- `temp_amb IR [C]` — ambient temperature, used to compute T∞
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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**.
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## Fitting architecture
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All fits use `scipy.optimize.curve_fit(..., method="trf")` (Trust Region Reflective, nonlinear least squares). Newton's cooling model:
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```text
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T(t) = T∞ + A · exp(-(t - t0) / τ)
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```
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Standard pattern in every `appX.py`:
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```python
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T0, T_END = ... # time window [s], set by user
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W0_START, W0_END = ... # optional exclusion zone [s], set by user
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df = pd.read_csv(ROOT / "data.csv")
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df["time_s"] = df["time since start [ms]"] / 1000.0
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mask = (df["time_s"] >= T0) & (df["time_s"] <= T_END)
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t_fit = df.loc[mask, "time_s"].values
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T_fit = df.loc[mask, "temp_obj IR [C]"].values
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sigma = np.where((t_fit >= W0_START) & (t_fit <= W0_END), 1e10, 1.0)
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mask_clean = sigma < 1e10
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popt, pcov = curve_fit(model, t_fit, T_fit, p0=[...], bounds=([...], [...]),
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sigma=sigma, absolute_sigma=True, method="trf")
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residuals = T_fit[mask_clean] - model(t_fit[mask_clean], *popt)
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ss_res = np.sum(residuals**2)
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ss_tot = np.sum((T_fit[mask_clean] - np.mean(T_fit[mask_clean]))**2)
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R2 = 1 - ss_res / ss_tot
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perr = np.sqrt(np.diag(pcov))
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```
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If no exclusion zone is needed, `sigma = np.ones(len(t_fit))` and `mask_clean = slice(None)`.
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## Adding a new inductor
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1. Copy an existing `induttoreX/appX.py` into a new folder `induttoreY/appY.py`.
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2. Set `T0`, `T_END` to the desired time window.
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3. Set `W0_START`, `W0_END` if a sub-interval must be excluded (or remove the exclusion logic).
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4. Run via `main.py` — the menu discovers it automatically.
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5. The script produces `induttoreY/induttoreY.png` and `induttoreY/reportY.md`.
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6. Running `report_completo.py` includes it automatically in the PDF.
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## Report generation
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`report_completo.py` is fully dynamic:
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- Discovers all `induttore*/report*.md` via glob
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- Reads T∞ from `start/report.md`
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- Renders equations and math symbols as PNG via matplotlib mathtext (no system LaTeX required)
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- Outputs `report_completo.tex` and `report_completo.pdf`
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Corporate identity constants (editable at the top of `report_completo.py`):
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`SOCIETA`, `DIPARTIMENTO`, `DOC_NUM`, `REVISIONE`, `STATO`, `CLASSIFICAZIONE`.
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