.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/VedEf_optim/3_analysis_power_constraint.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_VedEf_optim_3_analysis_power_constraint.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_VedEf_optim_3_analysis_power_constraint.py:

Analyze power constraint violations to find near-feasible operating points.

.. GENERATED FROM PYTHON SOURCE LINES 2-102

.. code-block:: Python


    import numpy as np

    # Load results
    results = np.load("../../sweep_output/operating_window_results.npz")

    # Extract data
    V = results["V"]
    e = results["e"]
    d_t = results["d_t"]
    f = results["f"]
    E_p = results["E_p"]
    G_PW_OUT = results["G_PW_OUT"]  # Total power output (ARC002 naming)
    power_error = results["power_error"]
    converged = results["converged"]

    print("=" * 70)
    print("  Power Constraint Analysis")
    print("=" * 70)

    # Basic statistics
    print(f"\nTotal power range: {G_PW_OUT.min() / 1e3:.1f} - {G_PW_OUT.max() / 1e3:.1f} kW")
    print("Target power: 25.0 kW")
    print(f"Power error range: {power_error.min():.3f} - {power_error.max():.3f}")

    # Find points closest to target power (25 kW)
    power_diff = np.abs(G_PW_OUT - 25000.0)
    closest_indices = np.argsort(power_diff)[:10]

    print("\n" + "-" * 70)
    print("  Top 10 closest to 25 kW target:")
    print("-" * 70)
    header = (
        f"{'Rank':<6}{'V (kV)':<10}{'e (mm)':<10}{'d_t (mm)':<12}"
        f"{'f (kHz)':<12}{'E_p (mJ)':<12}{'Power (kW)':<12}"
    )
    print(header)
    print("-" * 70)

    for rank, idx in enumerate(closest_indices, 1):
        print(
            f"{rank:<6}{V[idx] / 1e3:<10.1f}{e[idx] * 1e3:<10.1f}{d_t[idx] * 1e3:<12.1f}"
            f"{f[idx] / 1e3:<12.1f}{E_p[idx] * 1e3:<12.1f}{G_PW_OUT[idx] / 1e3:<12.2f}"
        )

    # Find minimum and maximum power points
    min_idx = np.argmin(G_PW_OUT)
    max_idx = np.argmax(G_PW_OUT)

    print("\n" + "-" * 70)
    print("  Minimum Power Point:")
    print("-" * 70)
    print(f"  V = {V[min_idx] / 1e3:.1f} kV")
    print(f"  e = {e[min_idx] * 1e3:.1f} mm")
    print(f"  d_t = {d_t[min_idx] * 1e3:.1f} mm")
    print(f"  f = {f[min_idx] / 1e3:.1f} kHz")
    print(f"  E_p = {E_p[min_idx] * 1e3:.1f} mJ")
    print(f"  Total Power = {G_PW_OUT[min_idx] / 1e3:.2f} kW")

    print("\n" + "-" * 70)
    print("  Maximum Power Point:")
    print("-" * 70)
    print(f"  V = {V[max_idx] / 1e3:.1f} kV")
    print(f"  e = {e[max_idx] * 1e3:.1f} mm")
    print(f"  d_t = {d_t[max_idx] * 1e3:.1f} mm")
    print(f"  f = {f[max_idx] / 1e3:.1f} kHz")
    print(f"  E_p = {E_p[max_idx] * 1e3:.1f} mJ")
    print(f"  Total Power = {G_PW_OUT[max_idx] / 1e3:.2f} kW")

    # Analyze other constraints for best power matches
    print("\n" + "-" * 70)
    print("  Constraint Status for Top 3 Closest Points:")
    print("-" * 70)

    for rank, idx in enumerate(closest_indices[:3], 1):
        print(f"\nRank {rank}: Power = {G_PW_OUT[idx] / 1e3:.2f} kW")
        print(f"  Breakdown margin: {results['breakdown_margin'][idx]:.1f} V")
        print(f"  Coverage margin: {results['coverage_margin'][idx]:.3f}")
        print(f"  Density margin: {results['density_margin'][idx] / 1e6:.1f} MJ/m³")
        print(
            "  All other constraints: OK"
            if results["breakdown_margin"][idx] > 0
            and results["coverage_margin"][idx] > 0
            and results["density_margin"][idx] > 0
            else "  Some constraints violated"
        )

    print("\n" + "=" * 70)
    print("  Recommendation:")
    print("=" * 70)
    best_idx = closest_indices[0]
    print("\nClosest operating point to 25 kW target:")
    print(f"  V = {V[best_idx] / 1e3:.1f} kV")
    print(f"  e = {e[best_idx] * 1e3:.1f} mm")
    print(f"  d_t = {d_t[best_idx] * 1e3:.1f} mm")
    print(f"  f = {f[best_idx] / 1e3:.1f} kHz")
    print(f"  E_p = {E_p[best_idx] * 1e3:.1f} mJ")
    print(f"  Actual Power = {G_PW_OUT[best_idx] / 1e3:.2f} kW")
    print(f"  Power Error = {abs(G_PW_OUT[best_idx] - 25000) / 25000 * 100:.1f}%")
    print()


.. _sphx_glr_download_auto_examples_VedEf_optim_3_analysis_power_constraint.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 3_analysis_power_constraint.ipynb <3_analysis_power_constraint.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 3_analysis_power_constraint.py <3_analysis_power_constraint.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 3_analysis_power_constraint.zip <3_analysis_power_constraint.zip>`