EOS/src/akkudoktoreos/utils/visualize.py

import os
from collections.abc import Sequence
from typing import Callable, Optional, Union

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages

from akkudoktoreos.core.coreabc import ConfigMixin
from akkudoktoreos.optimization.genetic import OptimizationParameters


class VisualizationReport(ConfigMixin):
    def __init__(self, filename: str = "visualization_results.pdf") -> None:
        # Initialize the report with a given filename and empty groups
        self.filename = filename
        self.groups: list[list[Callable[[], None]]] = []  # Store groups of charts
        self.current_group: list[
            Callable[[], None]
        ] = []  # Store current group of charts being created
        self.pdf_pages = PdfPages(filename, metadata={})  # Initialize PdfPages without metadata

    def add_chart_to_group(self, chart_func: Callable[[], None]) -> None:
        """Add a chart function to the current group."""
        self.current_group.append(chart_func)

    def finalize_group(self) -> None:
        """Finalize the current group and prepare for a new group."""
        if self.current_group:  # Check if current group has charts
            self.groups.append(self.current_group)  # Add current group to groups
        else:
            print("Finalizing an empty group!")  # Warn if group is empty
        self.current_group = []  # Reset current group for new charts

    def _initialize_pdf(self) -> None:
        """Create the output directory if it doesn't exist and initialize the PDF."""
        output_dir = self.config.data_output_path

        # If self.filename is already a valid path, use it; otherwise, combine it with output_dir
        if os.path.isabs(self.filename):
            output_file = self.filename
        else:
            output_dir.mkdir(parents=True, exist_ok=True)
            output_file = os.path.join(output_dir, self.filename)

        self.pdf_pages = PdfPages(
            output_file, metadata={}
        )  # Re-initialize PdfPages without metadata

    def _save_group_to_pdf(self, group: list[Callable[[], None]]) -> None:
        """Save a group of charts to the PDF."""
        fig_count = len(group)  # Number of charts in the group
        if fig_count == 0:
            print("Attempted to save an empty group to PDF!")  # Warn if group is empty
            return  # Prevent saving an empty group

        # Create a figure layout based on the number of charts
        if fig_count == 3:
            # Layout for three charts: 1 full-width on top, 2 below
            fig = plt.figure(figsize=(14, 10))  # Set a larger figure size
            ax1 = fig.add_subplot(2, 1, 1)  # Full-width subplot
            ax2 = fig.add_subplot(2, 2, 3)  # Bottom left subplot
            ax3 = fig.add_subplot(2, 2, 4)  # Bottom right subplot

            # Store axes in a list for easy access
            axs = [ax1, ax2, ax3]
        else:
            # Dynamic layout for any other number of charts
            cols = 2 if fig_count > 1 else 1  # Determine number of columns
            rows = (fig_count // 2) + (fig_count % 2)  # Calculate required rows
            fig, axs = plt.subplots(rows, cols, figsize=(14, 7 * rows))  # Create subplots
            # If axs is a 2D array of axes, flatten it into a 1D list
            # if isinstance(axs, np.ndarray):
            axs = list(np.array(axs).reshape(-1))

        # Draw each chart in the corresponding axes
        for idx, chart_func in enumerate(group):
            plt.sca(axs[idx])  # Set current axes
            chart_func()  # Call the chart function to draw

        # Hide any unused axes
        for idx in range(fig_count, len(axs)):
            axs[idx].set_visible(False)  # Hide unused axes
        self.pdf_pages.savefig(fig)  # Save the figure to the PDF

        plt.close(fig)  # Close the figure to free up memory

    def create_line_chart(
        self,
        start_hour: Optional[int],
        y_list: list[Union[np.ndarray, list[float]]],
        title: str,
        xlabel: str,
        ylabel: str,
        labels: Optional[list[str]] = None,
        markers: Optional[list[str]] = None,
        line_styles: Optional[list[str]] = None,
    ) -> None:
        """Create a line chart and add it to the current group."""

        def chart() -> None:
            nonlocal start_hour  # Allow modifying `x` within the nested function
            if start_hour is None:
                start_hour = 0
            first_element = y_list[0]
            x: np.ndarray
            # Case 1: y_list contains np.ndarray elements
            if isinstance(first_element, np.ndarray):
                x = np.arange(
                    start_hour, start_hour + len(first_element)
                )  # Start at x and extend by ndarray length
            # Case 2: y_list contains float elements (1D list)
            elif isinstance(first_element, float):
                x = np.arange(
                    start_hour, start_hour + len(y_list)
                )  # Start at x and extend by list length
            # Case 3: y_list is a nested list of floats
            elif isinstance(first_element, list) and all(
                isinstance(i, float) for i in first_element
            ):
                max_len = max(len(sublist) for sublist in y_list)
                x = np.arange(
                    start_hour, start_hour + max_len
                )  # Start at x and extend by max sublist length
            else:
                print(f"Unsupported y_list structure: {type(y_list)}, {y_list}")
                raise TypeError(
                    "y_list elements must be np.ndarray, float, or a nested list of floats"
                )

            for idx, y_data in enumerate(y_list):
                label = labels[idx] if labels else None  # Chart label
                marker = markers[idx] if markers and idx < len(markers) else "o"  # Marker style
                line_style = (
                    line_styles[idx] if line_styles and idx < len(line_styles) else "-"
                )  # Line style
                plt.plot(x, y_data, label=label, marker=marker, linestyle=line_style)  # Plot line
            plt.title(title)  # Set title
            plt.xlabel(xlabel)  # Set x-axis label
            plt.ylabel(ylabel)  # Set y-axis label
            if labels:
                plt.legend()  # Show legend if labels are provided
            plt.grid(True)  # Show grid
            plt.xlim(x[0] - 0.5, x[-1] + 0.5)  # Adjust x-limits

        self.add_chart_to_group(chart)  # Add chart function to current group

    def create_scatter_plot(
        self,
        x: np.ndarray,
        y: np.ndarray,
        title: str,
        xlabel: str,
        ylabel: str,
        c: Optional[np.ndarray] = None,
    ) -> None:
        """Create a scatter plot and add it to the current group."""

        def chart() -> None:
            scatter = plt.scatter(x, y, c=c, cmap="viridis")  # Create scatter plot
            plt.title(title)  # Set title
            plt.xlabel(xlabel)  # Set x-axis label
            plt.ylabel(ylabel)  # Set y-axis label
            if c is not None:
                plt.colorbar(scatter, label="Constraint")  # Add colorbar if color data is provided
            plt.grid(True)  # Show grid

        self.add_chart_to_group(chart)  # Add chart function to current group

    def create_bar_chart(
        self,
        labels: list[str],
        values_list: Sequence[Union[int, float, list[Union[int, float]]]],
        title: str,
        ylabel: str,
        xlabels: Optional[list[str]] = None,
        label_names: Optional[list[str]] = None,
        colors: Optional[list[str]] = None,
        bar_width: float = 0.35,
        bottom: Optional[int] = None,
    ) -> None:
        """Create a bar chart and add it to the current group."""

        def chart() -> None:
            num_groups = len(values_list)  # Number of data groups
            num_bars = len(labels)  # Number of bars (categories)
            # Calculate the positions for each bar group on the x-axis
            x = np.arange(num_bars)  # x positions for bars
            offset = np.linspace(
                -bar_width * (num_groups - 1) / 2, bar_width * (num_groups - 1) / 2, num_groups
            )  # Bar offsets
            for i, values in enumerate(values_list):
                bottom_use = None
                if bottom == i + 1:  # Set bottom if specified
                    bottom_use = 1
                color = colors[i] if colors and i < len(colors) else None  # Bar color
                label_name = label_names[i] if label_names else None  # Bar label
                plt.bar(
                    x + offset[i],
                    values,
                    bar_width,
                    label=label_name,
                    color=color,
                    zorder=2,
                    alpha=0.6,
                    bottom=bottom_use,
                )  # Create bar
            if xlabels:
                plt.xticks(x, labels)  # Add custom labels to the x-axis
            plt.title(title)  # Set title
            plt.ylabel(ylabel)  # Set y-axis label

            if colors and label_names:
                plt.legend()  # Show legend if colors are provided
            plt.grid(True, zorder=0)  # Show grid in the background
            plt.xlim(-0.5, len(labels) - 0.5)  # Set x-axis limits

        self.add_chart_to_group(chart)  # Add chart function to current group

    def create_violin_plot(
        self, data_list: list[np.ndarray], labels: list[str], title: str, xlabel: str, ylabel: str
    ) -> None:
        """Create a violin plot and add it to the current group."""

        def chart() -> None:
            plt.violinplot(data_list, showmeans=True, showmedians=True)  # Create violin plot
            plt.xticks(np.arange(1, len(labels) + 1), labels)  # Set x-ticks and labels
            plt.title(title)  # Set title
            plt.xlabel(xlabel)  # Set x-axis label
            plt.ylabel(ylabel)  # Set y-axis label
            plt.grid(True)  # Show grid

        self.add_chart_to_group(chart)  # Add chart function to current group

    def generate_pdf(self) -> None:
        """Generate the PDF report with all the added chart groups."""
        self._initialize_pdf()  # Initialize the PDF

        for group in self.groups:
            self._save_group_to_pdf(group)  # Save each group to the PDF

        self.pdf_pages.close()  # Close the PDF to finalize the report


def prepare_visualize(
    parameters: OptimizationParameters,
    results: dict,
    filename: str = "visualization_results.pdf",
    start_hour: Optional[int] = 0,
) -> None:
    report = VisualizationReport(filename)
    # Group 1:
    report.create_line_chart(
        None,
        [parameters.ems.gesamtlast],
        title="Load Profile",
        xlabel="Hours",
        ylabel="Load (Wh)",
        labels=["Total Load (Wh)"],
        markers=["s"],
        line_styles=["-"],
    )
    report.create_line_chart(
        None,
        [parameters.ems.pv_prognose_wh],
        title="PV Forecast",
        xlabel="Hours",
        ylabel="PV Generation (Wh)",
    )

    report.create_line_chart(
        None,
        [np.full(len(parameters.ems.gesamtlast), parameters.ems.einspeiseverguetung_euro_pro_wh)],
        title="Remuneration",
        xlabel="Hours",
        ylabel="€/Wh",
    )
    if parameters.temperature_forecast:
        report.create_line_chart(
            None,
            [parameters.temperature_forecast],
            title="Temperature Forecast",
            xlabel="Hours",
            ylabel="°C",
        )
    report.finalize_group()

    # Group 2:
    report.create_line_chart(
        start_hour,
        [
            results["result"]["Last_Wh_pro_Stunde"],
            results["result"]["Home_appliance_wh_per_hour"],
            results["result"]["Netzeinspeisung_Wh_pro_Stunde"],
            results["result"]["Netzbezug_Wh_pro_Stunde"],
            results["result"]["Verluste_Pro_Stunde"],
        ],
        title="Energy Flow per Hour",
        xlabel="Hours",
        ylabel="Energy (Wh)",
        labels=[
            "Load (Wh)",
            "Household Device (Wh)",
            "Grid Feed-in (Wh)",
            "Grid Consumption (Wh)",
            "Losses (Wh)",
        ],
        markers=["o", "o", "x", "^", "^"],
        line_styles=["-", "--", ":", "-.", "-"],
    )
    report.finalize_group()

    # Group 3:
    report.create_line_chart(
        start_hour,
        [results["result"]["akku_soc_pro_stunde"], results["result"]["EAuto_SoC_pro_Stunde"]],
        title="Battery SOC",
        xlabel="Hours",
        ylabel="%",
        labels=[
            "Battery SOC (%)",
            "Electric Vehicle SOC (%)",
        ],
        markers=["o", "x"],
    )
    report.create_line_chart(
        None,
        [parameters.ems.strompreis_euro_pro_wh],
        title="Electricity Price",
        xlabel="Hours",
        ylabel="Price (€/Wh)",
    )

    report.create_bar_chart(
        list(str(i) for i in range(len(results["ac_charge"]))),
        [results["ac_charge"], results["dc_charge"], results["discharge_allowed"]],
        title="AC/DC Charging and Discharge Overview",
        ylabel="Relative Power (0-1) / Discharge (0 or 1)",
        label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
        colors=["blue", "green", "red"],
        bottom=3,
    )
    report.finalize_group()

    # Group 4:

    report.create_line_chart(
        start_hour,
        [
            results["result"]["Kosten_Euro_pro_Stunde"],
            results["result"]["Einnahmen_Euro_pro_Stunde"],
        ],
        title="Financial Balance per Hour",
        xlabel="Hours",
        ylabel="Euro",
        labels=["Costs", "Revenue"],
    )

    extra_data = results["extra_data"]
    report.create_scatter_plot(
        extra_data["verluste"],
        extra_data["bilanz"],
        title="",
        xlabel="losses",
        ylabel="balance",
        c=extra_data["nebenbedingung"],
    )

    # Example usage
    values_list = [
        [
            results["result"]["Gesamtkosten_Euro"],
            results["result"]["Gesamteinnahmen_Euro"],
            results["result"]["Gesamtbilanz_Euro"],
        ]
    ]
    labels = ["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"]

    report.create_bar_chart(
        labels=labels,
        values_list=values_list,
        title="Financial Overview",
        ylabel="Euro",
        xlabels=["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"],
    )

    report.finalize_group()

    # Group 1: Scatter plot of losses vs balance with color-coded constraints
    f1 = np.array(extra_data["verluste"])  # Losses
    f2 = np.array(extra_data["bilanz"])  # Balance
    n1 = np.array(extra_data["nebenbedingung"])  # Constraints

    # Filter data where 'nebenbedingung' < 0.01
    filtered_indices = n1 < 0.01
    filtered_losses = f1[filtered_indices]
    filtered_balance = f2[filtered_indices]

    # Group 2: Violin plot for filtered losses
    if filtered_losses.size > 0:
        report.create_violin_plot(
            data_list=[filtered_losses],  # Data for filtered losses
            labels=["Filtered Losses"],  # Label for the violin plot
            title="Violin Plot for Filtered Losses (Constraint < 0.01)",
            xlabel="Losses",
            ylabel="Values",
        )
    else:
        print("No data available for filtered losses violin plot (Constraint < 0.01)")

    # Group 3: Violin plot for filtered balance
    if filtered_balance.size > 0:
        report.create_violin_plot(
            data_list=[filtered_balance],  # Data for filtered balance
            labels=["Filtered Balance"],  # Label for the violin plot
            title="Violin Plot for Filtered Balance (Constraint < 0.01)",
            xlabel="Balance",
            ylabel="Values",
        )
    else:
        print("No data available for filtered balance violin plot (Constraint < 0.01)")

    if filtered_balance.size > 0 or filtered_losses.size > 0:
        report.finalize_group()

    # Generate the PDF report
    report.generate_pdf()


if __name__ == "__main__":
    # Example usage
    report = VisualizationReport("example_report.pdf")
    x_hours = 0  # Define x-axis start values (e.g., hours)

    # Group 1: Adding charts to be displayed on the same page
    report.create_line_chart(
        x_hours,
        [np.array([10, 20, 30, 40])],
        title="Load Profile",
        xlabel="Hours",
        ylabel="Load (Wh)",
    )
    report.create_line_chart(
        x_hours,
        [np.array([5, 15, 25, 35])],
        title="PV Forecast",
        xlabel="Hours",
        ylabel="PV Generation (Wh)",
    )
    report.create_line_chart(
        x_hours,
        [np.array([5, 15, 25, 35])],
        title="PV Forecast",
        xlabel="Hours",
        ylabel="PV Generation (Wh)",
    )
    # Note: If there are only 3 charts per page, the first is as wide as the page

    report.finalize_group()  # Finalize the first group of charts

    # Group 2: Adding more charts to be displayed on another page
    report.create_line_chart(
        x_hours,
        [np.array([0.2, 0.25, 0.3, 0.35])],
        title="Electricity Price",
        xlabel="Hours",
        ylabel="Price (€/Wh)",
    )
    report.create_bar_chart(
        ["Costs", "Revenue", "Balance"],
        [[500.0], [600.0], [100.0]],
        title="Financial Overview",
        ylabel="Euro",
        label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
        colors=["red", "green", "blue"],
    )
    report.create_scatter_plot(
        np.array([5, 6, 7, 8]),
        np.array([100, 200, 150, 250]),
        title="Scatter Plot",
        xlabel="Losses",
        ylabel="Balance",
        c=np.array([0.1, 0.2, 0.3, 0.4]),
    )
    report.finalize_group()  # Finalize the second group of charts

    # Group 3: Adding a violin plot
    data = [np.random.normal(0, std, 100) for std in range(1, 5)]  # Example data for violin plot
    report.create_violin_plot(
        data,
        labels=["Group 1", "Group 2", "Group 3", "Group 4"],
        title="Violin Plot",
        xlabel="Groups",
        ylabel="Values",
    )
    data = [np.random.normal(0, 1, 100)]  # Example data for violin plot
    report.create_violin_plot(
        data, labels=["Group 1"], title="Violin Plot", xlabel="Group", ylabel="Values"
    )

    report.finalize_group()  # Finalize the third group of charts

    # Generate the PDF report
    report.generate_pdf()