# Colored scatter plots of port quadrature fields
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path

# Load CSV
csv_path = Path("./port_quadrature_export.csv")
df = pd.read_csv(csv_path)

# Compute magnitudes
df["Et_mag"] = np.sqrt(df["Et_x"]**2 + df["Et_y"]**2 + df["Et_z"]**2)
df["Ht_mag"] = np.sqrt(df["Ht_x"]**2 + df["Ht_y"]**2 + df["Ht_z"]**2)

# Example: scatter color-coded by |Et| for all quadrature points
plt.figure(figsize=(7,6))
sc = plt.scatter(df["x"], df["y"], c=df["Et_mag"], cmap="viridis", s=50)
plt.colorbar(sc, label="|Et| magnitude")
plt.xlabel("x")
plt.ylabel("y")
plt.title("Tangential E-field magnitude (all faces)")
plt.axis("equal")
plt.show()

# Example: scatter color-coded by |Ht|
plt.figure(figsize=(7,6))
sc = plt.scatter(df["x"], df["y"], c=df["Ht_mag"], cmap="plasma", s=50)
plt.colorbar(sc, label="|Ht| magnitude")
plt.xlabel("x")
plt.ylabel("y")
plt.title("Tangential H-field magnitude (all faces)")
plt.axis("equal")
plt.show()

# Example: per-face plotting (loop through faces)
for (face_id, local_idx), face_df in df.groupby(["exc_face_id", "local_face_idx"]):
    plt.figure(figsize=(6,5))
    sc = plt.scatter(face_df["x"], face_df["y"], c=face_df["Et_mag"], cmap="viridis", s=60)
    plt.colorbar(sc, label="|Et|")
    plt.xlabel("x")
    plt.ylabel("y")
    plt.title(f"Face {face_id}-{local_idx} |Et|")
    plt.axis("equal")
    plt.show()
    # break after one or two faces to avoid too many plots
    break