davide
b37f30c174
Combina potenziale normalizzato e ultima colonna di piu' CSV in un unico file e li plotta su un grafico sovrapposto con legenda, per confrontare visivamente i cicli a scan rate diversi. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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from pathlib import Path
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folder = Path(__file__).parent
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files = sorted(folder.glob("*_ultimo_ciclo.csv"))
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# categorical palette (light mode), fixed order
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colors = ["#2a78d6", "#1baf7a", "#eda100", "#e34948"]
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combined = {}
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series = []
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for f in files:
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df = pd.read_csv(f, sep=";", decimal=",")
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label = f.stem.replace(" soluzione (-1.6_1.2", "").replace(")_ultimo_ciclo", "").replace("_ultimo_ciclo", "")
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label = label.replace("AL7 10 mgml", "").strip()
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if not label.lower().startswith("scan"):
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label = "scan " + label.split("scan")[-1].strip() if "scan" in label else label
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c = df.iloc[:, 2] # column C: Potential normalized/V
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e = df.iloc[:, 4] # column E: last column
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combined[f"{label} - Potential normalized/V"] = c.reset_index(drop=True)
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combined[f"{label} - {df.columns[4]}"] = e.reset_index(drop=True)
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series.append((label, c, e))
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out_df = pd.concat(combined, axis=1)
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out_csv = folder / "combined_C_E.csv"
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out_df.to_csv(out_csv, sep=";", decimal=",", index=False)
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print(f"Saved {out_csv}")
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fig, ax = plt.subplots(figsize=(9, 6), dpi=150)
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fig.patch.set_facecolor("#fcfcfb")
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ax.set_facecolor("#fcfcfb")
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for i, (label, c, e) in enumerate(series):
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ax.plot(c, e, label=label, color=colors[i % len(colors)], linewidth=2)
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ax.set_xlabel("Potential normalized / V", color="#0b0b0b")
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ax.set_ylabel("Current / sqrt(scan rate) / scan rate", color="#0b0b0b")
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ax.set_title("Confronto cicli - Potenziale normalizzato vs ultima colonna", color="#0b0b0b")
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ax.tick_params(colors="#52514e")
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ax.grid(True, color="#e1e0d9", linewidth=0.8)
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for spine in ax.spines.values():
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spine.set_color("#c3c2b7")
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legend = ax.legend(frameon=False, labelcolor="#0b0b0b")
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out_png = folder / "combined_plot.png"
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fig.savefig(out_png, facecolor=fig.get_facecolor())
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print(f"Saved {out_png}")
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