"""Plotting helpers for the Analyzer compatibility facade."""
from __future__ import annotations
import datashader as ds
import datashader.transfer_functions as tf
import matplotlib.pyplot as plt
import seaborn as sns
[docs]
def build_closest_design_hspace_plot(df, target_params, closest_df_entry, selected_resonator_type):
"""Build the legacy closest-design H-space plot and return the figure and axes."""
sns.set_style("whitegrid")
sns.set_context("paper", font_scale=1.4)
viridis_cmap = plt.get_cmap("viridis")
viridis_cmap(0.2)
color_presim = viridis_cmap(0.9)
color_database = viridis_cmap(0.6)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
if selected_resonator_type == "quarter":
ax1.scatter(
x=df["cavity_frequency_GHz"],
y=df["kappa_kHz"],
color=color_presim,
marker=".",
s=50,
label="Pre-Simulated",
)
ax1.scatter(
x=target_params["cavity_frequency_GHz"],
y=target_params["kappa_kHz"],
color="red",
s=100,
marker="x",
label="Target",
)
closest_fres = closest_df_entry["cavity_frequency_GHz"]
closest_kappa_kHz = closest_df_entry["kappa_kHz"]
ax1.scatter(
closest_fres, closest_kappa_kHz, color=[color_database], s=100, marker="s", alpha=0.7, label="Closest"
)
ax1.set_xlabel(r"$f_{res}$ (GHz)", fontweight="bold", fontsize=24)
ax1.set_ylabel(r"$\kappa / 2 \pi$ (kHz)", fontweight="bold", fontsize=24)
ax1.tick_params(axis="both", which="major", labelsize=20)
ax2.scatter(
x=df["anharmonicity_MHz"],
y=df["g_MHz"],
color=color_presim,
marker=".",
s=50,
label="Pre-Simulated",
)
ax2.scatter(
x=target_params["anharmonicity_MHz"],
y=target_params["g_MHz"],
color="red",
s=100,
marker="x",
label="Target",
)
closest_alpha = [closest_df_entry["anharmonicity_MHz"]]
closest_g = [closest_df_entry["g_MHz"]]
ax2.scatter(closest_alpha, closest_g, color=[color_database], s=100, marker="s", alpha=0.7, label="Closest")
ax2.set_xlabel(r"$\alpha / 2 \pi$ (MHz)", fontweight="bold", fontsize=24)
ax2.set_ylabel(r"$g / 2 \pi$ (MHz)", fontweight="bold", fontsize=24)
ax2.tick_params(axis="both", which="major", labelsize=20)
elif selected_resonator_type == "half":
x1_range = (df["cavity_frequency_GHz"].min(), df["cavity_frequency_GHz"].max())
y1_range = (df["kappa_kHz"].min(), df["kappa_kHz"].max())
x2_range = (df["anharmonicity_MHz"].min(), df["anharmonicity_MHz"].max())
y2_range = (df["g_MHz"].min(), df["g_MHz"].max())
canvas1 = ds.Canvas(plot_width=800, plot_height=600, x_range=x1_range, y_range=y1_range)
canvas2 = ds.Canvas(plot_width=800, plot_height=600, x_range=x2_range, y_range=y2_range)
agg1 = canvas1.points(df, "cavity_frequency_GHz", "kappa_kHz")
agg2 = canvas2.points(df, "anharmonicity_MHz", "g_MHz")
cmap = plt.get_cmap("Blues")
colors = [cmap(i) for i in range(cmap.N)]
hex_colors = [f"#{int(r * 255):02x}{int(g * 255):02x}{int(b * 255):02x}" for r, g, b, _ in colors]
img1 = tf.shade(agg1, cmap=hex_colors)
img2 = tf.shade(agg2, cmap=hex_colors)
ax1.imshow(img1.to_pil(), aspect="auto", extent=[*x1_range, *y1_range])
ax1.set_xlabel(r"$f_{res}$ (GHz)", fontweight="bold", fontsize=24)
ax1.set_ylabel(r"$\kappa / 2 \pi$ (Hz)", fontweight="bold", fontsize=24)
ax1.tick_params(axis="both", which="major", labelsize=20)
ax2.imshow(img2.to_pil(), aspect="auto", extent=[*x2_range, *y2_range])
ax2.set_xlabel(r"$\alpha / 2 \pi$ (MHz)", fontweight="bold", fontsize=24)
ax2.set_ylabel(r"$g / 2 \pi$ (MHz)", fontweight="bold", fontsize=24)
ax2.tick_params(axis="both", which="major", labelsize=20)
ax1.plot(target_params["cavity_frequency_GHz"], target_params["kappa_kHz"] * 1e3, "rx", label="Target")
ax2.plot(target_params["anharmonicity_MHz"], target_params["g_MHz"], "ro", label="Target")
ax1.plot(closest_df_entry["cavity_frequency_GHz"], closest_df_entry["kappa"], "bs", alpha=1, label="Closest")
ax2.plot(
closest_df_entry["anharmonicity_MHz"],
closest_df_entry["g_MHz"],
"bs",
alpha=0.7,
label="Closest",
)
else:
raise ValueError(
f'Your chosen resonator type - {selected_resonator_type} - is not supported. Please use "quarter" or "half"'
)
legend1 = ax1.legend(loc="upper left", fontsize=16)
for text in legend1.get_texts():
text.set_fontweight("bold")
legend2 = ax2.legend(loc="lower left", fontsize=16)
for text in legend2.get_texts():
text.set_fontweight("bold")
plt.tight_layout()
return fig, (ax1, ax2)
