67 lines
2.8 KiB
Markdown
67 lines
2.8 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 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.
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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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## Running scripts
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```bat
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python <script_name>.py
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```
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Each script is standalone and produces a PNG plot. No test suite exists.
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## Data
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`data.csv` columns used for fitting:
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- `time since start [ms]` — converted to seconds in every script (`/1000`)
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- `temp_obj IR [C]` — box temperature, the fitting target
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- `temp_amb IR [C]` — ambient temperature, used to compute T∞
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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.
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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). The base pattern shared across every fit script:
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```python
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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 - model(t_fit, *popt)
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ss_res = np.sum((residuals / sigma_clean)**2) # ignores excluded zone
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ss_tot = np.sum((T_fit[mask] - np.mean(T_fit[mask]))**2)
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R2 = 1 - ss_res / ss_tot
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perr = np.sqrt(np.diag(pcov)) # parameter uncertainties (1σ)
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```
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### Models implemented
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| Script | Model | Fixed params |
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|--------|-------|-------------|
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| `fit_raffreddamento_intero.py` | `T∞ + A·exp(-(t-t0)/τ)` — full range, weighted | T∞, t0=115.0s |
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| `fit_raffreddamento_1tratto.py` | same — segment [115.0–115.9s] | T∞, t0=115.0s |
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| `fit_raffreddamento_2tratto.py` | same — segment [117.5s–end] | T∞, t0=117.5s |
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| `fit_doppio_esponenziale.py` | `T∞ + A1·exp(-(t-t1)/τ1) + A2·exp(-(t-t2)/τ2)` | T∞, t1=115.0s, t2=117.5s |
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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).
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## Adding a new fit
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1. Copy an existing fit script as a template.
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2. Define the model function with T∞ baked in or passed as a fixed parameter.
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3. Set `p0` from physical intuition or from a preliminary simpler fit.
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4. Use `sigma = np.where(mask_exclude, 1e10, 1.0)` to down-weight bad zones.
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5. Compute R² only over the unmasked points (`mask_clean`).
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6. Save output as a new PNG; do not overwrite existing plots.
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