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47 lines
1.5 KiB
47 lines
1.5 KiB
# Colored scatter plots of port quadrature fields
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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# Load CSV
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csv_path = Path("./port_quadrature_export.csv")
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df = pd.read_csv(csv_path)
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# Compute magnitudes
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df["Et_mag"] = np.sqrt(df["Et_x"]**2 + df["Et_y"]**2 + df["Et_z"]**2)
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df["Ht_mag"] = np.sqrt(df["Ht_x"]**2 + df["Ht_y"]**2 + df["Ht_z"]**2)
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# Example: scatter color-coded by |Et| for all quadrature points
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plt.figure(figsize=(7,6))
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sc = plt.scatter(df["x"], df["y"], c=df["Et_mag"], cmap="viridis", s=50)
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plt.colorbar(sc, label="|Et| magnitude")
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plt.xlabel("x")
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plt.ylabel("y")
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plt.title("Tangential E-field magnitude (all faces)")
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plt.axis("equal")
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plt.show()
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# Example: scatter color-coded by |Ht|
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plt.figure(figsize=(7,6))
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sc = plt.scatter(df["x"], df["y"], c=df["Ht_mag"], cmap="plasma", s=50)
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plt.colorbar(sc, label="|Ht| magnitude")
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plt.xlabel("x")
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plt.ylabel("y")
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plt.title("Tangential H-field magnitude (all faces)")
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plt.axis("equal")
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plt.show()
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# Example: per-face plotting (loop through faces)
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for (face_id, local_idx), face_df in df.groupby(["exc_face_id", "local_face_idx"]):
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plt.figure(figsize=(6,5))
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sc = plt.scatter(face_df["x"], face_df["y"], c=face_df["Et_mag"], cmap="viridis", s=60)
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plt.colorbar(sc, label="|Et|")
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plt.xlabel("x")
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plt.ylabel("y")
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plt.title(f"Face {face_id}-{local_idx} |Et|")
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plt.axis("equal")
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plt.show()
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# break after one or two faces to avoid too many plots
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break
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