Aggiunge la generazione del grafico del voltammogramma

Traccia Potential normalized/V (colonna C) contro
Current/sqrt(scan rate)/scan rate (colonna E) e salva un PNG ad alta
risoluzione per ciascun *_ultimo_ciclo.csv, con etichette degli assi in
notazione scientifica (E - E_ref, i/v^3/2). Aggiunge requirements.txt
(matplotlib) e ignora i .png generati.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

.gitignore

@@ -6,3 +6,5 @@ venv/
 
 *.txt
 *.csv
+*.png
+!requirements.txt

cv_plot.py

@@ -0,0 +1,91 @@
+"""Genera un grafico Potenziale normalizzato vs Current/sqrt(v)/v per ogni ciclo.
+
+Legge la colonna C (Potential normalized/V) e la colonna E
+(Current/sqrt(scan rate)/scan rate) dai file *_ultimo_ciclo.csv e salva,
+per ciascuno, un PNG ad alta risoluzione con lo stesso nome del CSV.
+"""
+
+import csv
+import sys
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+
+INPUT_SUFFIX = "_ultimo_ciclo"
+X_COLUMN = "Potential normalized/V"
+Y_COLUMN = "Current/sqrt(scan rate)/scan rate"
+
+X_LABEL = "$E - E_{ref}$ (V)"
+Y_LABEL = r"$i \, / \, v^{3/2}$ (A$\cdot$V$^{-3/2}\cdot$s$^{3/2}$)"
+
+
+def read_columns(path: Path) -> tuple[list[float], list[float]]:
+    with path.open("r", encoding="utf-8", newline="") as f:
+        reader = csv.reader(f, delimiter=";")
+        header = next(reader)
+        try:
+            x_idx = header.index(X_COLUMN)
+            y_idx = header.index(Y_COLUMN)
+        except ValueError as e:
+            raise ValueError(
+                f"colonna mancante ({e}); esegui prima cv_normalize_potential.py e cv_current_over_sqrt_scanrate.py"
+            )
+
+        x_vals, y_vals = [], []
+        for row in reader:
+            x_vals.append(float(row[x_idx].replace(",", ".")))
+            y_vals.append(float(row[y_idx].replace(",", ".")))
+
+    return x_vals, y_vals
+
+
+def plot_file(csv_path: Path, output_path: Path) -> None:
+    x_vals, y_vals = read_columns(csv_path)
+
+    fig, ax = plt.subplots(figsize=(8, 6), dpi=300)
+
+    ax.plot(x_vals, y_vals, color="#1f4e79", linewidth=1.6)
+
+    ax.set_title(csv_path.stem.replace(INPUT_SUFFIX, ""), fontsize=13, fontweight="bold", pad=12)
+    ax.set_xlabel(X_LABEL, fontsize=11)
+    ax.set_ylabel(Y_LABEL, fontsize=11)
+
+    ax.axhline(0, color="black", linewidth=0.8, alpha=0.5)
+    ax.axvline(0, color="black", linewidth=0.8, alpha=0.5)
+
+    ax.yaxis.set_major_formatter(mticker.ScalarFormatter(useMathText=True))
+    ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
+
+    ax.grid(True, linestyle="--", linewidth=0.5, alpha=0.6)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=300)
+    plt.close(fig)
+
+
+def main(argv: list[str]) -> None:
+    folder = Path(argv[1]) if len(argv) > 1 else Path(__file__).parent
+    csv_files = sorted(
+        p for p in folder.glob("*.csv") if p.stem.endswith(INPUT_SUFFIX)
+    )
+
+    if not csv_files:
+        print(f"Nessun file *{INPUT_SUFFIX}.csv trovato in {folder}")
+        return
+
+    for csv_file in csv_files:
+        output_path = csv_file.with_suffix(".png")
+        try:
+            plot_file(csv_file, output_path)
+            print(f"OK  {csv_file.name} -> {output_path.name}")
+        except ValueError as e:
+            print(f"SKIP {csv_file.name}: {e}")
+
+
+if __name__ == "__main__":
+    main(sys.argv)

requirements.txt

@@ -0,0 +1 @@
+matplotlib