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

.. only:: html

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

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

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

.. _sphx_glr_gen_examples_plot_comparison.py:


Comparison plot
==========================

This example demonstrates how to load simulations_data from a pickle and calculate values on the fly.
The newly calculated values are then used to create a comparison plot.

.. GENERATED FROM PYTHON SOURCE LINES 8-103



.. image-sg:: /gen_examples/images/sphx_glr_plot_comparison_001.png
   :alt: plot comparison
   :srcset: /gen_examples/images/sphx_glr_plot_comparison_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import pathlib as _pl
    import matplotlib.pyplot as _plt

    from pytrnsys_process import api


    def main():
        ###############################################
        # Step 1: Load Data
        # -----------------
        # Load pre-processed simulation data from pickle
        ###############################################

        simulations_data = api.load_simulations_data_from_pickle(
            _pl.Path("../example_data/small/simulations_data_reduced.pkl")
        )

        for sim_name, sim in simulations_data.simulations.items():
            ################################################
            # Step 2: Extract Ice Storage Performance Metrics
            # --------------------------------------------
            # Calculate maximum ice ratio from hourly data
            ################################################

            hourly_data = sim.hourly
            max_ice_ratio = hourly_data["VIceRatio"].max()
            simulations_data.scalar.loc[sim_name, "VIceRatioMax"] = max_ice_ratio

            ##############################################
            # Step 3: Process Energy Demand Data
            # --------------------------------
            # Sum up demands from multiple heat sinks and
            # convert to yearly totals in GWh
            ##############################################

            demand_columns = [
                "qSysOut_QSnk131Demand",
                "qSysOut_QSnk183Demand",
                "qSysOut_QSnk191Demand",
                "qSysOut_QSnk225Demand",
                "qSysOut_QSnk243Demand",
                "qSysOut_QSnk266Demand",
                "qSysOut_QSnk322Demand",
                "qSysOut_QSnk335Demand",
                "qSysOut_QSnk358Demand",
                "qSysOut_QSnk417Demand",
                "qSysOut_QSnk448Demand",
                "qSysOut_QSnk469Demand",
                "qSysOut_QSnk488Demand",
                "qSysOut_QSnk524Demand",
                "qSysOut_QSnk539Demand",
                "qSysOut_QSnk558Demand",
                "qSysOut_QSnk579Demand",
                "qSysOut_QSnk60Demand",
                "qSysOut_QSnk85Demand",
            ]

            # Unit conversion factor: kWh to GWh
            kwh_to_gwh = 1e-6

            # Process monthly data
            monthly_df = sim.monthly
            monthly_df["total_demand_GWh"] = (
                monthly_df[demand_columns].sum(axis=1) * kwh_to_gwh
            )

            # Calculate yearly total (excluding first 2 months)
            yearly_total = int(monthly_df["total_demand_GWh"].iloc[2::].sum())
            simulations_data.scalar.loc[sim_name, "yearly_demand_GWh"] = (
                yearly_total
            )

        ################################################
        # Step 4: Create Comparison Plot
        # ---------------------------------------
        # Scatter plot showing:
        # - X-axis: Scaled ice volume (VIceSscaled)
        # - Y-axis: Maximum ice ratio (VIceRatioMax)
        # - Color: Yearly demand in GWh
        # - Markers: DHW to space heating ratio
        ################################################

        api.scatter_plot(
            simulations_data.scalar,
            "VIceSscaled",
            "VIceRatioMax",
            group_by_color="yearly_demand_GWh",
            group_by_marker="ratioDHWtoSH_allSinks",
        )
        _plt.show()


    if __name__ == "__main__":
        main()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.170 seconds)


.. _sphx_glr_download_gen_examples_plot_comparison.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_comparison.ipynb <plot_comparison.ipynb>`

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

      :download:`Download Python source code: plot_comparison.py <plot_comparison.py>`

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

      :download:`Download zipped: plot_comparison.zip <plot_comparison.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_