"""
.. _tutorial_individual_sims:
=================================
Processing Individual Simulations
=================================

This is an introduction to the processing API.
For a more in-depth view, please refer to the :ref:`how_tos` and the API docs :ref:`api-reference-api`

"""

# %%
# Introduction to processing individual simulations
# =================================================
# First, you will need to download the
# `example data. <https://raw.githubusercontent.com/SPF-OST/pytrnsys_process/main/example_data/example_data.zip>`_
#
# Extract the data into a project folder you will use for this tutorial.
# You should end up with a folder structure looking like this.
#
# | root
# | ├─ example_data
# | ├─ tutorials
# |     ├─ tutorial_individual_sims.py
#
# For the rest of this tutorial, you should work in the last file: "tutorial_individual_sims.py"


# %%
# Required Imports
# ================
# First, let's add the required imports to the top of the script.

import pathlib as _pl

from pytrnsys_process import api


# %%
# Defining a processing step
# --------------------------
# This step will use the specified columns to plot a bar chart using the monthly data.
# The Simulation object will be provided by the processing function which we will call later.


def plot_monthly_bar_chart(simulation: api.Simulation):
    columns_to_plot = ["QSnk60P", "QSnk60PauxCondSwitch_kW"]
    fig, _ = api.bar_chart(
        simulation.monthly,
        columns_to_plot,
    )
    fig.show()


# %%
# Creating multiple processing steps
# ----------------------------------
# It makes sense to split your plots into multiple steps.
# If one step fails, the others will continue to run.
# Let's define another step that creates a line plot using hourly data.
# We can customize the plot further using the `figure <https://matplotlib.org/stable/api/figure_api.html>`_
# and `axes <https://matplotlib.org/stable/api/axes_api.html>`_ objects returned by the plot function.


def plot_hourly_line_plot(simulation: api.Simulation):
    columns_to_plot = ["QSrc1TIn", "QSrc1TOut"]
    fig, ax = api.line_plot(simulation.hourly, columns_to_plot, cmap="Paired")
    ax.set_ylabel("In/Out")
    ax.set_xlabel("Timeline")
    fig.show()


# %%
# Note
# ____
# By setting up processing steps in this way, we enable you to take full advantage of both Pandas and
# MatPlotLib functionality. If you have not already, check out their respective tutorials and guides.
# For Pandas: `Tutorials <https://pandas.pydata.org/docs/getting_started/index.html>`_ and `User's Guide
# <https://pandas.pydata.org/docs/user_guide/index.html>`_.
# For MatPlotLib: `Quick start guide <https://matplotlib.org/stable/users/explain/quick_start.html>`_ and `User's
# Guide <https://matplotlib.org/stable/users/index.html>`_.

# %%
# Running the processing steps
# ----------------------------
# At the end of the script, we define our entry point
# and specify which processing steps we would like to run.
# We need to pass the path to the simulation files
# and the processing steps we defined above
# to the processing function.


if __name__ == "__main__":
    path_to_sim = _pl.Path("../example_data/small/sim-1")
    sim = api.process_single_simulation(
        path_to_sim, [plot_monthly_bar_chart, plot_hourly_line_plot]
    )
    # sphinx_gallery_start_ignore
    plot_monthly_bar_chart(sim)
    plot_hourly_line_plot(sim)
# sphinx_gallery_end_ignore