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Visualize reworked v2 (#267)
* initial commit * Delete duplicate openapi.json * revert temp cahnges * test fixed * mypy fixes * mypy fixed * Financial Overview included * test data save path * almost done * mypy fix * Update visualize.py * ruff fix * config, label * improved start_hour vis * fix * fix2
This commit is contained in:
Normann
GitHub
@@ -21,7 +21,6 @@ from akkudoktoreos.devices.battery import (
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from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
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from akkudoktoreos.devices.inverter import Inverter, InverterParameters
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from akkudoktoreos.utils.utils import NumpyEncoder
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from akkudoktoreos.visualize import visualisiere_ergebnisse
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class OptimizationParameters(BaseModel):
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@@ -520,19 +519,20 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
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ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin)
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# Visualize the results
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visualisiere_ergebnisse(
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parameters.ems.gesamtlast,
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parameters.ems.pv_prognose_wh,
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parameters.ems.strompreis_euro_pro_wh,
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o,
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ac_charge,
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dc_charge,
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discharge,
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parameters.temperature_forecast,
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start_hour,
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einspeiseverguetung_euro_pro_wh,
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extra_data=extra_data,
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)
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visualize = {
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"ac_charge": ac_charge.tolist(),
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"dc_charge": dc_charge.tolist(),
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"discharge_allowed": discharge.tolist(),
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"eautocharge_hours_float": eautocharge_hours_float,
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"result": o,
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"eauto_obj": self.ems.eauto.to_dict(),
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"start_solution": start_solution,
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"spuelstart": washingstart_int,
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"extra_data": extra_data,
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}
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from akkudoktoreos.utils.visualize import prepare_visualize
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prepare_visualize(parameters, visualize, start_hour=start_hour)
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return OptimizeResponse(
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**{
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504
src/akkudoktoreos/utils/visualize.py
Normal file
504
src/akkudoktoreos/utils/visualize.py
Normal file
@@ -0,0 +1,504 @@
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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[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_new.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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|
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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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|
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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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|
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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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]
|
||||
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",
|
||||
ylabel="Euro",
|
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xlabels=["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"],
|
||||
)
|
||||
|
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report.finalize_group()
|
||||
|
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# Group 1: Scatter plot of losses vs balance with color-coded constraints
|
||||
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
|
||||
|
||||
# Filter data where 'nebenbedingung' < 0.01
|
||||
filtered_indices = n1 < 0.01
|
||||
filtered_losses = f1[filtered_indices]
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filtered_balance = f2[filtered_indices]
|
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|
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# Group 2: Violin plot for filtered losses
|
||||
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
|
||||
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()
|
||||
@@ -1,350 +0,0 @@
|
||||
# Set the backend for matplotlib to Agg
|
||||
from typing import Any, Optional
|
||||
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from matplotlib.backends.backend_pdf import PdfPages
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from akkudoktoreos.config.config import get_config
|
||||
|
||||
matplotlib.use("Agg")
|
||||
|
||||
|
||||
def visualisiere_ergebnisse(
|
||||
gesamtlast: list[float],
|
||||
pv_forecast: list[float],
|
||||
strompreise: list[float],
|
||||
ergebnisse: dict[str, Any],
|
||||
ac: np.ndarray, # AC charging allowed
|
||||
dc: np.ndarray, # DC charging allowed
|
||||
discharge: np.ndarray, # Discharge allowed
|
||||
temperature: Optional[list[float]],
|
||||
start_hour: int,
|
||||
einspeiseverguetung_euro_pro_wh: np.ndarray,
|
||||
filename: str = "visualization_results.pdf",
|
||||
extra_data: Optional[dict[str, Any]] = None,
|
||||
) -> None:
|
||||
#####################
|
||||
# 24-hour visualization
|
||||
#####################
|
||||
config = get_config()
|
||||
output_dir = config.data_output_path
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
output_file = output_dir.joinpath(filename)
|
||||
with PdfPages(output_file) as pdf:
|
||||
# Load and PV generation
|
||||
plt.figure(figsize=(14, 14))
|
||||
plt.subplot(3, 3, 1)
|
||||
hours: NDArray[np.int_] = np.arange(0, config.prediction_hours, dtype=np.int_)
|
||||
|
||||
gesamtlast_array = np.array(gesamtlast)
|
||||
# Plot individual loads
|
||||
plt.plot(hours, gesamtlast_array, label="Load (Wh)", marker="o")
|
||||
|
||||
# Calculate and plot total load
|
||||
plt.plot(
|
||||
hours,
|
||||
gesamtlast_array,
|
||||
label="Total Load (Wh)",
|
||||
marker="o",
|
||||
linewidth=2,
|
||||
linestyle="--",
|
||||
)
|
||||
plt.xlabel("Hour")
|
||||
plt.ylabel("Load (Wh)")
|
||||
plt.title("Load Profiles")
|
||||
plt.grid(True)
|
||||
plt.legend()
|
||||
|
||||
# PV forecast
|
||||
plt.subplot(3, 2, 3)
|
||||
plt.plot(hours, pv_forecast, label="PV Generation (Wh)", marker="x")
|
||||
plt.title("PV Forecast")
|
||||
plt.xlabel("Hour of the Day")
|
||||
plt.ylabel("Wh")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
# Feed-in remuneration
|
||||
plt.subplot(3, 2, 4)
|
||||
plt.plot(
|
||||
hours,
|
||||
einspeiseverguetung_euro_pro_wh,
|
||||
label="Remuneration (€/Wh)",
|
||||
marker="x",
|
||||
)
|
||||
plt.title("Remuneration")
|
||||
plt.xlabel("Hour of the Day")
|
||||
plt.ylabel("€/Wh")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
# Temperature forecast
|
||||
if temperature is not None:
|
||||
plt.subplot(3, 2, 5)
|
||||
plt.title("Temperature Forecast (°C)")
|
||||
plt.plot(hours, temperature, label="Temperature (°C)", marker="x")
|
||||
plt.xlabel("Hour of the Day")
|
||||
plt.ylabel("°C")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
pdf.savefig() # Save the current figure state to the PDF
|
||||
plt.close() # Close the current figure to free up memory
|
||||
|
||||
#####################
|
||||
# Start hour visualization
|
||||
#####################
|
||||
|
||||
plt.figure(figsize=(14, 10))
|
||||
hours = np.arange(start_hour, config.prediction_hours)
|
||||
|
||||
# Energy flow, grid feed-in, and grid consumption
|
||||
plt.subplot(3, 2, 1)
|
||||
# Plot with transparency (alpha) and different linestyles
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["Last_Wh_pro_Stunde"],
|
||||
label="Load (Wh)",
|
||||
marker="o",
|
||||
linestyle="-",
|
||||
alpha=0.8,
|
||||
)
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["Home_appliance_wh_per_hour"],
|
||||
label="Household Device (Wh)",
|
||||
marker="o",
|
||||
linestyle="--",
|
||||
alpha=0.8,
|
||||
)
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["Netzeinspeisung_Wh_pro_Stunde"],
|
||||
label="Grid Feed-in (Wh)",
|
||||
marker="x",
|
||||
linestyle=":",
|
||||
alpha=0.8,
|
||||
)
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["Netzbezug_Wh_pro_Stunde"],
|
||||
label="Grid Consumption (Wh)",
|
||||
marker="^",
|
||||
linestyle="-.",
|
||||
alpha=0.8,
|
||||
)
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["Verluste_Pro_Stunde"],
|
||||
label="Losses (Wh)",
|
||||
marker="^",
|
||||
linestyle="-",
|
||||
alpha=0.8,
|
||||
)
|
||||
|
||||
# Title and labels
|
||||
plt.title("Energy Flow per Hour")
|
||||
plt.xlabel("Hour")
|
||||
plt.ylabel("Energy (Wh)")
|
||||
|
||||
# Show legend with a higher number of columns to avoid overlap
|
||||
plt.legend(ncol=2)
|
||||
|
||||
# Electricity prices
|
||||
hours_p = np.arange(0, len(strompreise))
|
||||
plt.subplot(3, 2, 3)
|
||||
plt.plot(
|
||||
hours_p,
|
||||
strompreise,
|
||||
label="Electricity Price (€/Wh)",
|
||||
color="purple",
|
||||
marker="s",
|
||||
)
|
||||
plt.title("Electricity Prices")
|
||||
plt.xlabel("Hour of the Day")
|
||||
plt.ylabel("Price (€/Wh)")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
|
||||
# State of charge for batteries
|
||||
plt.subplot(3, 2, 2)
|
||||
plt.plot(hours, ergebnisse["akku_soc_pro_stunde"], label="PV Battery (%)", marker="x")
|
||||
plt.plot(
|
||||
hours,
|
||||
ergebnisse["EAuto_SoC_pro_Stunde"],
|
||||
label="E-Car Battery (%)",
|
||||
marker="x",
|
||||
)
|
||||
plt.legend(loc="upper left", bbox_to_anchor=(1, 1)) # Place legend outside the plot
|
||||
plt.grid(True, which="both", axis="x") # Grid for every hour
|
||||
|
||||
# Plot for AC, DC charging, and Discharge status using bar charts
|
||||
ax1 = plt.subplot(3, 2, 5)
|
||||
hours = np.arange(0, config.prediction_hours)
|
||||
# Plot AC charging as bars (relative values between 0 and 1)
|
||||
plt.bar(hours, ac, width=0.4, label="AC Charging (relative)", color="blue", alpha=0.6)
|
||||
|
||||
# Plot DC charging as bars (relative values between 0 and 1)
|
||||
plt.bar(
|
||||
hours + 0.4, dc, width=0.4, label="DC Charging (relative)", color="green", alpha=0.6
|
||||
)
|
||||
|
||||
# Plot Discharge as bars (0 or 1, binary values)
|
||||
plt.bar(
|
||||
hours,
|
||||
discharge,
|
||||
width=0.4,
|
||||
label="Discharge Allowed",
|
||||
color="red",
|
||||
alpha=0.6,
|
||||
bottom=np.maximum(ac, dc),
|
||||
)
|
||||
|
||||
# Configure the plot
|
||||
ax1.legend(loc="upper left")
|
||||
ax1.set_xlim(0, config.prediction_hours)
|
||||
ax1.set_xlabel("Hour")
|
||||
ax1.set_ylabel("Relative Power (0-1) / Discharge (0 or 1)")
|
||||
ax1.set_title("AC/DC Charging and Discharge Overview")
|
||||
ax1.grid(True)
|
||||
|
||||
hours = np.arange(start_hour, config.prediction_hours)
|
||||
|
||||
pdf.savefig() # Save the current figure state to the PDF
|
||||
plt.close() # Close the current figure to free up memory
|
||||
|
||||
# Financial overview
|
||||
fig, axs = plt.subplots(1, 2, figsize=(14, 10)) # Create a 1x2 grid of subplots
|
||||
total_costs = ergebnisse["Gesamtkosten_Euro"]
|
||||
total_revenue = ergebnisse["Gesamteinnahmen_Euro"]
|
||||
total_balance = ergebnisse["Gesamtbilanz_Euro"]
|
||||
losses = ergebnisse["Gesamt_Verluste"]
|
||||
|
||||
# Costs and revenues per hour on the first axis (axs[0])
|
||||
costs = ergebnisse["Kosten_Euro_pro_Stunde"]
|
||||
revenues = ergebnisse["Einnahmen_Euro_pro_Stunde"]
|
||||
|
||||
# Plot costs
|
||||
axs[0].plot(
|
||||
hours,
|
||||
costs,
|
||||
label="Costs (Euro)",
|
||||
marker="o",
|
||||
color="red",
|
||||
)
|
||||
# Annotate costs
|
||||
for hour, value in enumerate(costs):
|
||||
if value is None or np.isnan(value):
|
||||
value = 0
|
||||
axs[0].annotate(
|
||||
f"{value:.2f}",
|
||||
(hour, value),
|
||||
textcoords="offset points",
|
||||
xytext=(0, 5),
|
||||
ha="center",
|
||||
fontsize=8,
|
||||
color="red",
|
||||
)
|
||||
|
||||
# Plot revenues
|
||||
axs[0].plot(
|
||||
hours,
|
||||
revenues,
|
||||
label="Revenue (Euro)",
|
||||
marker="x",
|
||||
color="green",
|
||||
)
|
||||
# Annotate revenues
|
||||
for hour, value in enumerate(revenues):
|
||||
if value is None or np.isnan(value):
|
||||
value = 0
|
||||
axs[0].annotate(
|
||||
f"{value:.2f}",
|
||||
(hour, value),
|
||||
textcoords="offset points",
|
||||
xytext=(0, 5),
|
||||
ha="center",
|
||||
fontsize=8,
|
||||
color="green",
|
||||
)
|
||||
|
||||
# Title and labels
|
||||
axs[0].set_title("Financial Balance per Hour")
|
||||
axs[0].set_xlabel("Hour")
|
||||
axs[0].set_ylabel("Euro")
|
||||
axs[0].legend()
|
||||
axs[0].grid(True)
|
||||
|
||||
# Summary of finances on the second axis (axs[1])
|
||||
labels = ["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"]
|
||||
values = [total_costs, total_revenue, total_balance]
|
||||
colors = ["red" if value > 0 else "green" for value in values]
|
||||
axs[1].bar(labels, values, color=colors)
|
||||
axs[1].set_title("Financial Overview")
|
||||
axs[1].set_ylabel("Euro")
|
||||
|
||||
# Second axis (ax2) for losses, shared with axs[1]
|
||||
ax2 = axs[1].twinx()
|
||||
ax2.bar("Total Losses", losses, color="blue")
|
||||
ax2.set_ylabel("Losses [Wh]", color="blue")
|
||||
ax2.tick_params(axis="y", labelcolor="blue")
|
||||
|
||||
pdf.savefig() # Save the complete figure to the PDF
|
||||
plt.close() # Close the figure
|
||||
|
||||
# Additional data visualization if provided
|
||||
if extra_data is not None:
|
||||
plt.figure(figsize=(14, 10))
|
||||
plt.subplot(1, 2, 1)
|
||||
f1 = np.array(extra_data["verluste"])
|
||||
f2 = np.array(extra_data["bilanz"])
|
||||
n1 = np.array(extra_data["nebenbedingung"])
|
||||
scatter = plt.scatter(f1, f2, c=n1, cmap="viridis")
|
||||
|
||||
# Add color legend
|
||||
plt.colorbar(scatter, label="Constraint")
|
||||
|
||||
pdf.savefig() # Save the complete figure to the PDF
|
||||
plt.close() # Close the figure
|
||||
|
||||
plt.figure(figsize=(14, 10))
|
||||
filtered_losses = np.array(
|
||||
[
|
||||
v
|
||||
for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"])
|
||||
if n < 0.01
|
||||
]
|
||||
)
|
||||
filtered_balance = np.array(
|
||||
[b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n < 0.01]
|
||||
)
|
||||
if filtered_losses.size != 0:
|
||||
best_loss = min(filtered_losses)
|
||||
worst_loss = max(filtered_losses)
|
||||
best_balance = min(filtered_balance)
|
||||
worst_balance = max(filtered_balance)
|
||||
|
||||
data = [filtered_losses, filtered_balance]
|
||||
labels = ["Losses", "Balance"]
|
||||
# Create plots
|
||||
fig, axs = plt.subplots(
|
||||
1, 2, figsize=(10, 6), sharey=False
|
||||
) # Two subplots, separate y-axes
|
||||
|
||||
# First violin plot for losses
|
||||
axs[0].violinplot(data[0], positions=[1], showmeans=True, showmedians=True)
|
||||
axs[0].set(xticks=[1], xticklabels=["Losses"])
|
||||
|
||||
# Second violin plot for balance
|
||||
axs[1].violinplot(data[1], positions=[1], showmeans=True, showmedians=True)
|
||||
axs[1].set(xticks=[1], xticklabels=["Balance"])
|
||||
|
||||
# Fine-tuning
|
||||
plt.tight_layout()
|
||||
|
||||
pdf.savefig() # Save the current figure state to the PDF
|
||||
plt.close() # Close the figure
|
||||
Reference in New Issue
Block a user