Using the Electron Unload Models

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from dotenv import load_dotenv
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import os
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from conductorquantum import ConductorQuantum
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# Load API key from .env file
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load_dotenv()
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TOKEN = os.getenv("CONDUCTOR_API_KEY")
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# Initialize client
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client = ConductorQuantum(token=TOKEN)

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from conductorquantum import ConductorQuantum
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# Initialize client with API key
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client = ConductorQuantum(token="YOUR_API_KEY")

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import os
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from dotenv import load_dotenv
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import numpy as np
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import matplotlib.pyplot as plt
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from conductorquantum import ConductorQuantum
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# Load API key from .env file
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load_dotenv()
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TOKEN = os.getenv("CONDUCTOR_API_KEY")
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client = ConductorQuantum(token=TOKEN)
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# Load your resonator measurement (shape: [n_bias, n_voltage])
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data = np.load("path/to/your/data.npy")
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# Run inference with the Electron Unload Detector
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results = client.control.models.run(
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    model="electron-unload-detector-v0",
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    data=data,
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)
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# Access peak locations (column indices)
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unload_indices = results.output["unload_indices"]
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print(f"Detected electron unloading at: {unload_indices}")
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# Visualize the measurement and overlay detections
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plt.imshow(data, aspect="auto")
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for unload_index in unload_indices:
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    plt.axvline(x=unload_index, color="red", linewidth=3, alpha=0.8)
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plt.title("Electron Unload Detector v0")
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plt.xlabel("Unload voltage (a.u.)")
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plt.ylabel("Resonator bias (a.u.)")
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plt.colorbar(label="Frequency (a.u.)")
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plt.tight_layout()
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plt.show()

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Detected electron unloading at: [10, 23, 32, 36, 39, 44, 49]

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# Your physical axes
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unload_voltages = np.linspace(-0.5, 0.5, data.shape[1])  # (n_voltage,)
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resonator_bias = np.linspace(0, 1, data.shape[0])        # (n_bias,)
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.pcolormesh(
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    unload_voltages,
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    resonator_bias,
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    data / 1e9,
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    cmap="RdYlBu_r",
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    shading="auto",
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)
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# Convert indices to physical coordinates
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for unload_index in unload_indices:
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    if unload_index < len(unload_voltages):
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        voltage = unload_voltages[unload_index]
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        ax.axvline(x=voltage, color="black", linewidth=4, alpha=0.8)
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ax.set_xlabel("Unload Voltage (V)")
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ax.set_ylabel("Resonator Bias (V)")
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plt.colorbar(im, label="$f_0$ (GHz)")
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plt.title("Electron Unload Detector v0")
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plt.tight_layout()
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plt.show()

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import os
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from dotenv import load_dotenv
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import numpy as np
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import matplotlib.pyplot as plt
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from conductorquantum import ConductorQuantum
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# Load API key from .env file
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load_dotenv()
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TOKEN = os.getenv("CONDUCTOR_API_KEY")
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client = ConductorQuantum(token=TOKEN)
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# Load your resonator measurement (shape: [n_bias, n_voltage])
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data = np.load("path/to/your/data.npy")
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results = client.control.models.run(
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    model="electron-unload-texture-detector-v0",
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    data=data,
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)
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print("Raw results:", results)
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texture_clusters = results.output["texture_clusters"]
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plt.imshow(data, aspect="auto")
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for cluster in texture_clusters:
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    indices = np.array(cluster["indices"])
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    x_coords = indices[:, 1]  # column = voltage index (x-axis)
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    y_coords = indices[:, 0]  # row = bias index (y-axis)
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    plt.scatter(x_coords, y_coords, s=50, alpha=0.8)
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plt.title("Electron Unload Texture Detector v0")
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plt.xlabel("Unload voltage (a.u.)")
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plt.ylabel("Resonator bias (a.u.)")
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plt.colorbar(label="Frequency (a.u.)")
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plt.tight_layout()
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plt.savefig("electron-unload-texture-detector-v0.png")
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plt.show()

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# 10 texture clusters detected
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texture_clusters = [
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    {"indices": [[2, 1], [3, 3], [4, 4], [7, 4]]},
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    {"indices": [[57, 6], [57, 7], [57, 8], [57, 9], [59, 10], [57, 11]]},
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    {"indices": [[23, 7], [23, 8], [23, 9]]},
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    ...
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]

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# Your physical axes
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unload_voltages = np.linspace(-0.5, 0.5, data.shape[1])  # (n_voltage,)
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resonator_bias = np.linspace(0, 1, data.shape[0])        # (n_bias,)
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.pcolormesh(
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    unload_voltages,
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    resonator_bias,
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    data / 1e9,
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    cmap="magma",
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    shading="auto",
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)
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# Convert indices to physical coordinates
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for cluster in texture_clusters:
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    indices = np.array(cluster["indices"])
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    # indices[:, 0] = row = bias index (y-axis)
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    # indices[:, 1] = col = voltage index (x-axis)
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    voltage_coords = unload_voltages[indices[:, 1].astype(int)]
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    bias_coords = resonator_bias[indices[:, 0].astype(int)]
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    ax.scatter(voltage_coords, bias_coords, s=50, alpha=0.8)
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ax.set_xlabel("Unload Voltage (V)")
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ax.set_ylabel("Resonator Bias (V)")
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plt.colorbar(im, label="$f_0$ (GHz)")
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plt.title("Electron Unload Texture Detector v0")
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plt.tight_layout()
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plt.show()