Using the Anticrossing Detector

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import os
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import numpy as np
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from dotenv import load_dotenv
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from conductorquantum import ConductorQuantum
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load_dotenv()
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client = ConductorQuantum(token=os.getenv("CONDUCTOR_API_KEY"))
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# Load your measurement data: shape (2, n_freqs, n_sweeps)
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data = np.load("path/to/your/data.npy")
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magnitudes = data[0]  # shape: (n_freqs, n_sweeps)
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phases = data[1]
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results = client.control.models.run(
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    model="anticrossing-detector-v0",
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    data=data,
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)
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print(f"Results output: {results.output}")
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output = results.output
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frequency_fit_indices = np.asarray(output["frequency_fit_indices"])
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anticrossing_indices = np.asarray(output["anticrossing_indices"])
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peak_indices = np.asarray(output["peak_indices"])
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valley_indices = np.asarray(output["valley_indices"])

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Results output: {'frequency_fit_indices': [[392, 0], [392, 1], [393, 2], [391, 3], [394, 4], [390, 5], [391, 6], [391, 7], [393, 8], [392, 9], [391, 10], [393, 11], [392, 12], [390, 13], [392, 14], [390, 15], [391, 16], [389, 17], [390, 18], [387, 19], [389, 20], [390, 21], [389, 22], [389, 23], [385, 24], [385, 25], [385, 26], [383, 27], [381, 28], [379, 29], [375, 30], [364, 31], [359, 32], [326, 33], [489, 34], [425, 35], [420, 36], [412, 37], [415, 38], [412, 39], [408, 40], [408, 41], [409, 42], [409, 43], [409, 44], [410, 45], [411, 46], [409, 47], [411, 48], [409, 49], [416, 50], [414, 51], [414, 52], [415, 53], [416, 54], [415, 55], [417, 56], [418, 57], [417, 58], [417, 59], [422, 60], [419, 61], [420, 62], [421, 63], [424, 64], [423, 65], [427, 66], [430, 67], [432, 68], [430, 69], [435, 70], [442, 71], [444, 72], [450, 73], [459, 74], [465, 75], [471, 76], [481, 77], [493, 78], [525, 79], [572, 80], [647, 81], [767, 82], [800, 83], [176, 84], [231, 85], [267, 86], [298, 87], [306, 88], [322, 89], [332, 90], [340, 91], [344, 92], [349, 93], [354, 94], [361, 95], [364, 96], [365, 97], [368, 98], [369, 99], [370, 100], [374, 101], [374, 102], [373, 103], [376, 104], [376, 105], [377, 106], [380, 107], [379, 108], [380, 109], [380, 110], [382, 111], [382, 112], [381, 113], [383, 114], [384, 115], [384, 116], [386, 117], [384, 118], [385, 119], [386, 120], [385, 121], [386, 122], [388, 123], [388, 124], [387, 125], [388, 126]], 'anticrossing_indices': [[489, 34], [800, 83]], 'peak_indices': [[489, 34], [800, 83]], 'valley_indices': [[326, 33], [176, 84]], 'score': 0.6434958298396819}

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import matplotlib.pyplot as plt
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freq_axis = np.arange(magnitudes.shape[0])
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sweep_axis = np.arange(magnitudes.shape[1])
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fig, ax = plt.subplots(figsize=(10, 6))
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im = ax.imshow(
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    magnitudes,
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    origin="lower",
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    aspect="auto",
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    extent=[sweep_axis.min(), sweep_axis.max(), freq_axis.min(), freq_axis.max()],
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    cmap="viridis",
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)
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fig.colorbar(im, ax=ax, label="Magnitude (a.u.)")
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def scatter_indices(indices_array, color, label, marker="o"):
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    if indices_array.size == 0:
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        return
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    rows = indices_array[:, 0]
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    cols = indices_array[:, 1]
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    ax.scatter(
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        cols,
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        rows,
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        s=80,
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        c=color,
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        marker=marker,
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        edgecolor="white",
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        linewidth=2,
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        label=label,
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    )
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scatter_indices(frequency_fit_indices, color="red", label="Frequency fit", marker=".")
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scatter_indices(peak_indices, color="cyan", label="Peak", marker="^")
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scatter_indices(valley_indices, color="magenta", label="Valley", marker="v")
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if anticrossing_indices.size:
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    unique_columns = np.unique(anticrossing_indices[:, 1])
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    for idx, column in enumerate(unique_columns):
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        ax.axvline(
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            x=sweep_axis[column],
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            color="black",
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            linestyle="--",
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            linewidth=2,
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            alpha=0.9,
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            label="Anticrossing" if idx == 0 else "_nolegend_",
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        )
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ax.set_xlabel("Sweep voltage (a.u.)")
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ax.set_ylabel("Frequency (a.u.)")
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ax.set_title(f"Anticrossing Detector v0\nScore = {output['score']:.3f}")
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ax.legend(loc="upper right", frameon=False)
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plt.tight_layout()
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plt.show()

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# Your physical axes
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sweep_voltages = np.linspace(-3.5, -3.3, magnitudes.shape[1])  # (n_sweeps,)
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frequencies_ghz = np.linspace(7.1, 7.2, magnitudes.shape[0])   # (n_freqs,)
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fig, ax = plt.subplots(figsize=(10, 6))
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im = ax.imshow(
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    magnitudes,
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    origin="lower",
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    aspect="auto",
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    extent=[sweep_voltages.min(), sweep_voltages.max(),
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            frequencies_ghz.min(), frequencies_ghz.max()],
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    cmap="viridis",
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)
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# Convert indices to physical coordinates
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if peak_indices.size:
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    peak_freqs = frequencies_ghz[peak_indices[:, 0]]
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    peak_volts = sweep_voltages[peak_indices[:, 1]]
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    ax.scatter(peak_volts, peak_freqs, c="cyan", marker="^", s=150,
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               edgecolor="black", label="Peak")
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ax.set_xlabel("Sweep Voltage (V)")
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ax.set_ylabel("Frequency (GHz)")