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https://github.com/Akkudoktor-EOS/EOS.git
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267a9bf427
* temperature, relativehumidity_2m, windspeed_10m can be null * prettify pv_forecast_input_1.json (and add null value)
509 lines
18 KiB
Python
509 lines
18 KiB
Python
import os
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from collections.abc import Sequence
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from typing import Callable, Optional, Union
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.backends.backend_pdf import PdfPages
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from akkudoktoreos.core.coreabc import ConfigMixin
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from akkudoktoreos.optimization.genetic import OptimizationParameters
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class VisualizationReport(ConfigMixin):
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def __init__(self, filename: str = "visualization_results.pdf") -> None:
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# Initialize the report with a given filename and empty groups
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self.filename = filename
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self.groups: list[list[Callable[[], None]]] = [] # Store groups of charts
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self.current_group: list[
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Callable[[], None]
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] = [] # Store current group of charts being created
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self.pdf_pages = PdfPages(filename, metadata={}) # Initialize PdfPages without metadata
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def add_chart_to_group(self, chart_func: Callable[[], None]) -> None:
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"""Add a chart function to the current group."""
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self.current_group.append(chart_func)
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def finalize_group(self) -> None:
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"""Finalize the current group and prepare for a new group."""
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if self.current_group: # Check if current group has charts
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self.groups.append(self.current_group) # Add current group to groups
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else:
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print("Finalizing an empty group!") # Warn if group is empty
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self.current_group = [] # Reset current group for new charts
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def _initialize_pdf(self) -> None:
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"""Create the output directory if it doesn't exist and initialize the PDF."""
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output_dir = self.config.data_output_path
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# If self.filename is already a valid path, use it; otherwise, combine it with output_dir
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if os.path.isabs(self.filename):
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output_file = self.filename
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else:
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output_dir.mkdir(parents=True, exist_ok=True)
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output_file = os.path.join(output_dir, self.filename)
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self.pdf_pages = PdfPages(
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output_file, metadata={}
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) # Re-initialize PdfPages without metadata
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def _save_group_to_pdf(self, group: list[Callable[[], None]]) -> None:
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"""Save a group of charts to the PDF."""
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fig_count = len(group) # Number of charts in the group
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if fig_count == 0:
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print("Attempted to save an empty group to PDF!") # Warn if group is empty
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return # Prevent saving an empty group
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# Create a figure layout based on the number of charts
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if fig_count == 3:
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# Layout for three charts: 1 full-width on top, 2 below
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fig = plt.figure(figsize=(14, 10)) # Set a larger figure size
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ax1 = fig.add_subplot(2, 1, 1) # Full-width subplot
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ax2 = fig.add_subplot(2, 2, 3) # Bottom left subplot
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ax3 = fig.add_subplot(2, 2, 4) # Bottom right subplot
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# Store axes in a list for easy access
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axs = [ax1, ax2, ax3]
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else:
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# Dynamic layout for any other number of charts
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cols = 2 if fig_count > 1 else 1 # Determine number of columns
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rows = (fig_count // 2) + (fig_count % 2) # Calculate required rows
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fig, axs = plt.subplots(rows, cols, figsize=(14, 7 * rows)) # Create subplots
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# If axs is a 2D array of axes, flatten it into a 1D list
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# if isinstance(axs, np.ndarray):
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axs = list(np.array(axs).reshape(-1))
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# Draw each chart in the corresponding axes
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for idx, chart_func in enumerate(group):
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plt.sca(axs[idx]) # Set current axes
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chart_func() # Call the chart function to draw
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# Hide any unused axes
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for idx in range(fig_count, len(axs)):
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axs[idx].set_visible(False) # Hide unused axes
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self.pdf_pages.savefig(fig) # Save the figure to the PDF
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plt.close(fig) # Close the figure to free up memory
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def create_line_chart(
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self,
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start_hour: Optional[int],
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y_list: list[Union[np.ndarray, list[Optional[float]], list[float]]],
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title: str,
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xlabel: str,
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ylabel: str,
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labels: Optional[list[str]] = None,
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markers: Optional[list[str]] = None,
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line_styles: Optional[list[str]] = None,
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) -> None:
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"""Create a line chart and add it to the current group."""
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def chart() -> None:
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nonlocal start_hour # Allow modifying `x` within the nested function
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if start_hour is None:
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start_hour = 0
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first_element = y_list[0]
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x: np.ndarray
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# Case 1: y_list contains np.ndarray elements
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if isinstance(first_element, np.ndarray):
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x = np.arange(
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start_hour, start_hour + len(first_element)
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) # Start at x and extend by ndarray length
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# Case 2: y_list contains float elements (1D list)
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elif isinstance(first_element, float):
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x = np.arange(
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start_hour, start_hour + len(y_list)
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) # Start at x and extend by list length
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# Case 3: y_list is a nested list of floats
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elif isinstance(first_element, list) and all(
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isinstance(i, float) for i in first_element
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):
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max_len = max(len(sublist) for sublist in y_list)
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x = np.arange(
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start_hour, start_hour + max_len
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) # Start at x and extend by max sublist length
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else:
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print(f"Unsupported y_list structure: {type(y_list)}, {y_list}")
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raise TypeError(
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"y_list elements must be np.ndarray, float, or a nested list of floats"
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)
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for idx, y_data in enumerate(y_list):
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label = labels[idx] if labels else None # Chart label
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marker = markers[idx] if markers and idx < len(markers) else "o" # Marker style
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line_style = (
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line_styles[idx] if line_styles and idx < len(line_styles) else "-"
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) # Line style
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plt.plot(x, y_data, label=label, marker=marker, linestyle=line_style) # Plot line
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plt.title(title) # Set title
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plt.xlabel(xlabel) # Set x-axis label
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plt.ylabel(ylabel) # Set y-axis label
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if labels:
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plt.legend() # Show legend if labels are provided
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plt.grid(True) # Show grid
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plt.xlim(x[0] - 0.5, x[-1] + 0.5) # Adjust x-limits
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self.add_chart_to_group(chart) # Add chart function to current group
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def create_scatter_plot(
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self,
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x: np.ndarray,
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y: np.ndarray,
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title: str,
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xlabel: str,
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ylabel: str,
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c: Optional[np.ndarray] = None,
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) -> None:
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"""Create a scatter plot and add it to the current group."""
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def chart() -> None:
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scatter = plt.scatter(x, y, c=c, cmap="viridis") # Create scatter plot
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plt.title(title) # Set title
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plt.xlabel(xlabel) # Set x-axis label
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plt.ylabel(ylabel) # Set y-axis label
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if c is not None:
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plt.colorbar(scatter, label="Constraint") # Add colorbar if color data is provided
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plt.grid(True) # Show grid
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self.add_chart_to_group(chart) # Add chart function to current group
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def create_bar_chart(
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self,
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labels: list[str],
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values_list: Sequence[Union[int, float, list[Union[int, float]]]],
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title: str,
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ylabel: str,
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xlabels: Optional[list[str]] = None,
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label_names: Optional[list[str]] = None,
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colors: Optional[list[str]] = None,
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bar_width: float = 0.35,
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bottom: Optional[int] = None,
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) -> None:
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"""Create a bar chart and add it to the current group."""
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def chart() -> None:
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num_groups = len(values_list) # Number of data groups
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num_bars = len(labels) # Number of bars (categories)
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# Calculate the positions for each bar group on the x-axis
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x = np.arange(num_bars) # x positions for bars
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offset = np.linspace(
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-bar_width * (num_groups - 1) / 2, bar_width * (num_groups - 1) / 2, num_groups
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) # Bar offsets
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for i, values in enumerate(values_list):
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bottom_use = None
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if bottom == i + 1: # Set bottom if specified
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bottom_use = 1
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color = colors[i] if colors and i < len(colors) else None # Bar color
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label_name = label_names[i] if label_names else None # Bar label
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plt.bar(
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x + offset[i],
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values,
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bar_width,
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label=label_name,
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color=color,
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zorder=2,
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alpha=0.6,
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bottom=bottom_use,
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) # Create bar
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if xlabels:
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plt.xticks(x, labels) # Add custom labels to the x-axis
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plt.title(title) # Set title
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plt.ylabel(ylabel) # Set y-axis label
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if colors and label_names:
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plt.legend() # Show legend if colors are provided
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plt.grid(True, zorder=0) # Show grid in the background
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plt.xlim(-0.5, len(labels) - 0.5) # Set x-axis limits
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self.add_chart_to_group(chart) # Add chart function to current group
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def create_violin_plot(
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self, data_list: list[np.ndarray], labels: list[str], title: str, xlabel: str, ylabel: str
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) -> None:
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"""Create a violin plot and add it to the current group."""
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def chart() -> None:
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plt.violinplot(data_list, showmeans=True, showmedians=True) # Create violin plot
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plt.xticks(np.arange(1, len(labels) + 1), labels) # Set x-ticks and labels
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plt.title(title) # Set title
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plt.xlabel(xlabel) # Set x-axis label
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plt.ylabel(ylabel) # Set y-axis label
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plt.grid(True) # Show grid
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self.add_chart_to_group(chart) # Add chart function to current group
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def generate_pdf(self) -> None:
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"""Generate the PDF report with all the added chart groups."""
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self._initialize_pdf() # Initialize the PDF
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for group in self.groups:
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self._save_group_to_pdf(group) # Save each group to the PDF
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self.pdf_pages.close() # Close the PDF to finalize the report
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def prepare_visualize(
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parameters: OptimizationParameters,
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results: dict,
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filename: str = "visualization_results.pdf",
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start_hour: Optional[int] = 0,
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) -> None:
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report = VisualizationReport(filename)
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# Group 1:
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report.create_line_chart(
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None,
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[parameters.ems.gesamtlast],
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title="Load Profile",
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xlabel="Hours",
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ylabel="Load (Wh)",
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labels=["Total Load (Wh)"],
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markers=["s"],
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line_styles=["-"],
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)
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report.create_line_chart(
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None,
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[parameters.ems.pv_prognose_wh],
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title="PV Forecast",
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xlabel="Hours",
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ylabel="PV Generation (Wh)",
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)
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report.create_line_chart(
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None,
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[np.full(len(parameters.ems.gesamtlast), parameters.ems.einspeiseverguetung_euro_pro_wh)],
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title="Remuneration",
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xlabel="Hours",
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ylabel="€/Wh",
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)
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if parameters.temperature_forecast:
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report.create_line_chart(
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None,
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[parameters.temperature_forecast],
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title="Temperature Forecast",
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xlabel="Hours",
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ylabel="°C",
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)
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report.finalize_group()
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# Group 2:
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report.create_line_chart(
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start_hour,
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[
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results["result"]["Last_Wh_pro_Stunde"],
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results["result"]["Home_appliance_wh_per_hour"],
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results["result"]["Netzeinspeisung_Wh_pro_Stunde"],
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results["result"]["Netzbezug_Wh_pro_Stunde"],
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results["result"]["Verluste_Pro_Stunde"],
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],
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title="Energy Flow per Hour",
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xlabel="Hours",
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ylabel="Energy (Wh)",
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labels=[
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"Load (Wh)",
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"Household Device (Wh)",
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"Grid Feed-in (Wh)",
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"Grid Consumption (Wh)",
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"Losses (Wh)",
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],
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markers=["o", "o", "x", "^", "^"],
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line_styles=["-", "--", ":", "-.", "-"],
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)
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report.finalize_group()
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# Group 3:
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report.create_line_chart(
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start_hour,
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[results["result"]["akku_soc_pro_stunde"], results["result"]["EAuto_SoC_pro_Stunde"]],
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title="Battery SOC",
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xlabel="Hours",
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ylabel="%",
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labels=[
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"Battery SOC (%)",
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"Electric Vehicle SOC (%)",
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],
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markers=["o", "x"],
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)
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report.create_line_chart(
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None,
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[parameters.ems.strompreis_euro_pro_wh],
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title="Electricity Price",
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xlabel="Hours",
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ylabel="Price (€/Wh)",
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)
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report.create_bar_chart(
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list(str(i) for i in range(len(results["ac_charge"]))),
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[results["ac_charge"], results["dc_charge"], results["discharge_allowed"]],
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title="AC/DC Charging and Discharge Overview",
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ylabel="Relative Power (0-1) / Discharge (0 or 1)",
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label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
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colors=["blue", "green", "red"],
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bottom=3,
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)
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report.finalize_group()
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# Group 4:
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report.create_line_chart(
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start_hour,
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[
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results["result"]["Kosten_Euro_pro_Stunde"],
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results["result"]["Einnahmen_Euro_pro_Stunde"],
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],
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title="Financial Balance per Hour",
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xlabel="Hours",
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ylabel="Euro",
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labels=["Costs", "Revenue"],
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)
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extra_data = results["extra_data"]
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report.create_scatter_plot(
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extra_data["verluste"],
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extra_data["bilanz"],
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title="",
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xlabel="losses",
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ylabel="balance",
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c=extra_data["nebenbedingung"],
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)
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# Example usage
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values_list = [
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[
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results["result"]["Gesamtkosten_Euro"],
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results["result"]["Gesamteinnahmen_Euro"],
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results["result"]["Gesamtbilanz_Euro"],
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]
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]
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labels = ["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"]
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report.create_bar_chart(
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labels=labels,
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values_list=values_list,
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title="Financial Overview",
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ylabel="Euro",
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xlabels=["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"],
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)
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report.finalize_group()
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# Group 1: Scatter plot of losses vs balance with color-coded constraints
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f1 = np.array(extra_data["verluste"]) # Losses
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f2 = np.array(extra_data["bilanz"]) # Balance
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n1 = np.array(extra_data["nebenbedingung"]) # Constraints
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# Filter data where 'nebenbedingung' < 0.01
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filtered_indices = n1 < 0.01
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filtered_losses = f1[filtered_indices]
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filtered_balance = f2[filtered_indices]
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# Group 2: Violin plot for filtered losses
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if filtered_losses.size > 0:
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report.create_violin_plot(
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data_list=[filtered_losses], # Data for filtered losses
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labels=["Filtered Losses"], # Label for the violin plot
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title="Violin Plot for Filtered Losses (Constraint < 0.01)",
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xlabel="Losses",
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ylabel="Values",
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)
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else:
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print("No data available for filtered losses violin plot (Constraint < 0.01)")
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# Group 3: Violin plot for filtered balance
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if filtered_balance.size > 0:
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report.create_violin_plot(
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data_list=[filtered_balance], # Data for filtered balance
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labels=["Filtered Balance"], # Label for the violin plot
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title="Violin Plot for Filtered Balance (Constraint < 0.01)",
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xlabel="Balance",
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ylabel="Values",
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)
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else:
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print("No data available for filtered balance violin plot (Constraint < 0.01)")
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if filtered_balance.size > 0 or filtered_losses.size > 0:
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report.finalize_group()
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# Generate the PDF report
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report.generate_pdf()
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def generate_example_report(filename: str = "example_report.pdf") -> None:
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"""Generate example visualization report."""
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report = VisualizationReport(filename)
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x_hours = 0 # Define x-axis start values (e.g., hours)
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# Group 1: Adding charts to be displayed on the same page
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report.create_line_chart(
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x_hours,
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[np.array([10, 20, 30, 40])],
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title="Load Profile",
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xlabel="Hours",
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ylabel="Load (Wh)",
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)
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report.create_line_chart(
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x_hours,
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[np.array([5, 15, 25, 35])],
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title="PV Forecast",
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xlabel="Hours",
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ylabel="PV Generation (Wh)",
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)
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report.create_line_chart(
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x_hours,
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[np.array([5, 15, 25, 35])],
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title="PV Forecast",
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xlabel="Hours",
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ylabel="PV Generation (Wh)",
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)
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# Note: If there are only 3 charts per page, the first is as wide as the page
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report.finalize_group() # Finalize the first group of charts
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# Group 2: Adding more charts to be displayed on another page
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report.create_line_chart(
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x_hours,
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[np.array([0.2, 0.25, 0.3, 0.35])],
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title="Electricity Price",
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xlabel="Hours",
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ylabel="Price (€/Wh)",
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)
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report.create_bar_chart(
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["Costs", "Revenue", "Balance"],
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[[500.0], [600.0], [100.0]],
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title="Financial Overview",
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ylabel="Euro",
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label_names=["AC Charging (relative)", "DC Charging (relative)", "Discharge Allowed"],
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colors=["red", "green", "blue"],
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)
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report.create_scatter_plot(
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np.array([5, 6, 7, 8]),
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np.array([100, 200, 150, 250]),
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title="Scatter Plot",
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xlabel="Losses",
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ylabel="Balance",
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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()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
generate_example_report()
|