neural/linear/stats.py [238:284]:
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    r_average_epochs = R_score_v2(Y_true, Y_true.mean(0, keepdims=True), avg_out="epochs")

    mse_dynamic_epochs = R_score_v2(Y_true, Y_pred, score="relativemse", avg_out="epochs")

    mse_average_epochs = R_score_v2(
        Y_true, Y_true.mean(0, keepdims=True), score="relativemse", avg_out="epochs")
    # r_scalar = R_score(Y_true,
    #                    Y_pred,
    #                    avg_out="times").mean()

    r_average_times = R_score_v2(Y_true[:, start:, :], Y_pred[:, start:, :], avg_out="times")

    r_average_times_evoked = R_score_v2(
        Y_true[:, start:, :].mean(0, keepdims=True),
        Y_pred[:, start:, :].mean(0, keepdims=True),
        avg_out="times")

    mse_average_times = R_score_v2(
        Y_true[:, start:, :], Y_pred[:, start:, :], score="relativemse", avg_out="times")

    mse_average_times_evoked = R_score_v2(
        Y_true[:, start:, :].mean(0, keepdims=True),
        Y_pred[:, start:, :].mean(0, keepdims=True),
        score="relativemse",
        avg_out="times")

    # print(r_scalar)

    fig, axes = plt.subplots(2, 4, figsize=(15, 5))

    # Mean response
    axes[0, 0].plot(Y_pred.mean(0))
    axes[0, 0].set_title("Predicted Response (Evoked)")
    axes[0, 0].axvline(x=start, ls="--")
    axes[0, 0].text(x=start, y=0, s="init")

    axes[1, 0].plot(Y_true.mean(0))
    axes[1, 0].set_title("True Response (Evoked)")

    # Reponse to one stimulus
    axes[0, 1].plot(Y_pred[0])
    axes[0, 1].set_title("Predicted Response (Epoch 0)")
    axes[0, 1].axvline(start, ls="--")
    axes[0, 1].text(x=start, y=0, s="init")

    axes[1, 1].plot(Y_true[0])
    axes[1, 1].set_title("True Response (Epoch 0)")
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neural/visuals.py [66:112]:
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        r_average_epochs = R_score_v2(Y_true, Y_true.mean(0, keepdims=True), avg_out="epochs")

    mse_dynamic_epochs = R_score_v2(Y_true, Y_pred, score="relativemse", avg_out="epochs")

    mse_average_epochs = R_score_v2(
        Y_true, Y_true.mean(0, keepdims=True), score="relativemse", avg_out="epochs")
    # r_scalar = R_score(Y_true,
    #                    Y_pred,
    #                    avg_out="times").mean()

    r_average_times = R_score_v2(Y_true[:, start:, :], Y_pred[:, start:, :], avg_out="times")

    r_average_times_evoked = R_score_v2(
        Y_true[:, start:, :].mean(0, keepdims=True),
        Y_pred[:, start:, :].mean(0, keepdims=True),
        avg_out="times")

    mse_average_times = R_score_v2(
        Y_true[:, start:, :], Y_pred[:, start:, :], score="relativemse", avg_out="times")

    mse_average_times_evoked = R_score_v2(
        Y_true[:, start:, :].mean(0, keepdims=True),
        Y_pred[:, start:, :].mean(0, keepdims=True),
        score="relativemse",
        avg_out="times")

    # print(r_scalar)

    fig, axes = plt.subplots(2, 4, figsize=(15, 5))

    # Mean response
    axes[0, 0].plot(Y_pred.mean(0))
    axes[0, 0].set_title("Predicted Response (Evoked)")
    axes[0, 0].axvline(x=start, ls="--")
    axes[0, 0].text(x=start, y=0, s="init")

    axes[1, 0].plot(Y_true.mean(0))
    axes[1, 0].set_title("True Response (Evoked)")

    # Reponse to one stimulus
    axes[0, 1].plot(Y_pred[0])
    axes[0, 1].set_title("Predicted Response (Epoch 0)")
    axes[0, 1].axvline(start, ls="--")
    axes[0, 1].text(x=start, y=0, s="init")

    axes[1, 1].plot(Y_true[0])
    axes[1, 1].set_title("True Response (Epoch 0)")
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