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Update visualize.py
initial clean up, translations
This commit is contained in:
NormannK
drbacke
parent
a2770e8410
commit
c2254dc5ed
@ -1,276 +1,193 @@
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import numpy as np
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from modules.class_sommerzeit import *
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from modules.class_load_container import Gesamtlast # Stellen Sie sicher, dass dies dem tatsächlichen Importpfad entspricht
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import matplotlib
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matplotlib.use('Agg') # Setzt das Backend auf Agg
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import matplotlib.pyplot as plt
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from matplotlib.backends.backend_pdf import PdfPages
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from datetime import datetime
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from modules.class_sommerzeit import * # Ensure this matches the actual import path
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from modules.class_load_container import Gesamtlast # Ensure this matches the actual import path
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# Set the backend for matplotlib to Agg
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import matplotlib
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matplotlib.use('Agg')
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def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, discharge_hours, laden_moeglich, temperature, start_hour, prediction_hours,einspeiseverguetung_euro_pro_wh, filename="visualisierungsergebnisse.pdf", extra_data=None):
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def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, discharge_hours, laden_moeglich, temperature, start_hour, prediction_hours, einspeiseverguetung_euro_pro_wh, filename="visualization_results.pdf", extra_data=None):
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#####################
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# 24h
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# 24-hour visualization
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#####################
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with PdfPages(filename) as pdf:
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# Last und PV-Erzeugung
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# Load and PV generation
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plt.figure(figsize=(14, 14))
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plt.subplot(3, 3, 1)
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stunden = np.arange(0, prediction_hours)
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hours = np.arange(0, prediction_hours)
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gesamtlast_array = np.array(gesamtlast)
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# Einzellasten plotten
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#for name, last_array in gesamtlast.lasten.items():
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plt.plot(stunden, gesamtlast_array, label=f'Last (Wh)', marker='o')
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# Plot individual loads
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plt.plot(hours, gesamtlast_array, label='Load (Wh)', marker='o')
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# Gesamtlast berechnen und plotten
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gesamtlast_array = np.array(gesamtlast)
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plt.plot(stunden, gesamtlast_array, label='Gesamtlast (Wh)', marker='o', linewidth=2, linestyle='--')
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plt.xlabel('Stunde')
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plt.ylabel('Last (Wh)')
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plt.title('Lastprofile')
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# Calculate and plot total load
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plt.plot(hours, gesamtlast_array, label='Total Load (Wh)', marker='o', linewidth=2, linestyle='--')
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plt.xlabel('Hour')
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plt.ylabel('Load (Wh)')
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plt.title('Load Profiles')
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plt.grid(True)
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plt.legend()
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# Strompreise
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stundenp = np.arange(0, len(strompreise))
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# Electricity prices
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hours_p = np.arange(0, len(strompreise))
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plt.subplot(3, 2, 2)
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plt.plot(stundenp, strompreise, label='Strompreis (€/Wh)', color='purple', marker='s')
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plt.title('Strompreise')
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plt.xlabel('Stunde des Tages')
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plt.ylabel('Preis (€/Wh)')
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plt.plot(hours_p, strompreise, label='Electricity Price (€/Wh)', color='purple', marker='s')
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plt.title('Electricity Prices')
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plt.xlabel('Hour of the Day')
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plt.ylabel('Price (€/Wh)')
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plt.legend()
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plt.grid(True)
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# Strompreise
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stundenp = np.arange(1, len(strompreise)+1)
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# PV forecast
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plt.subplot(3, 2, 3)
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plt.plot(stunden, pv_forecast, label='PV-Erzeugung (Wh)', marker='x')
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plt.plot(hours, pv_forecast, label='PV Generation (Wh)', marker='x')
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plt.title('PV Forecast')
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plt.xlabel('Stunde des Tages')
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plt.xlabel('Hour of the Day')
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plt.ylabel('Wh')
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plt.legend()
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plt.grid(True)
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# Vergütung
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stundenp = np.arange(0, len(strompreise))
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# Feed-in remuneration
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plt.subplot(3, 2, 4)
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plt.plot(stunden, einspeiseverguetung_euro_pro_wh, label='Vergütung €/Wh', marker='x')
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plt.title('Vergütung')
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plt.xlabel('Stunde des Tages')
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plt.plot(hours, einspeiseverguetung_euro_pro_wh, label='Remuneration (€/Wh)', marker='x')
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plt.title('Remuneration')
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plt.xlabel('Hour of the Day')
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plt.ylabel('€/Wh')
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plt.legend()
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plt.grid(True)
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# Temperatur Forecast
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# Temperature forecast
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plt.subplot(3, 2, 5)
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plt.title('Temperatur Forecast °C')
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plt.plot(stunden, temperature, label='Temperatur °C', marker='x')
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plt.xlabel('Stunde des Tages')
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plt.title('Temperature Forecast (°C)')
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plt.plot(hours, temperature, label='Temperature (°C)', marker='x')
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plt.xlabel('Hour of the Day')
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plt.ylabel('°C')
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plt.legend()
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plt.grid(True)
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pdf.savefig() # Speichert den aktuellen Figure-State im PDF
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plt.close() # Schließt die aktuelle Figure, um Speicher freizugeben
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pdf.savefig() # Save the current figure state to the PDF
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plt.close() # Close the current figure to free up memory
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#####################
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# Start_Hour
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# Start hour visualization
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#####################
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plt.figure(figsize=(14, 10))
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if ist_dst_wechsel(datetime.now()):
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stunden = np.arange(start_hour, prediction_hours-1)
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if ist_dst_wechsel(datetime.datetime.now()):
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hours = np.arange(start_hour, prediction_hours - 1)
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else:
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stunden = np.arange(start_hour, prediction_hours)
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hours = np.arange(start_hour, prediction_hours)
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# print(ist_dst_wechsel(datetime.now())," ",datetime.now())
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# print(start_hour," ",prediction_hours," ",stunden)
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# print(ergebnisse['Eigenverbrauch_Wh_pro_Stunde'])
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# Eigenverbrauch, Netzeinspeisung und Netzbezug
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# Energy flow, grid feed-in, and grid consumption
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plt.subplot(3, 2, 1)
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plt.plot(stunden, ergebnisse['Last_Wh_pro_Stunde'], label='Last (Wh)', marker='o')
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plt.plot(stunden, ergebnisse['Haushaltsgeraet_wh_pro_stunde'], label='Haushaltsgerät (Wh)', marker='o')
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plt.plot(stunden, ergebnisse['Netzeinspeisung_Wh_pro_Stunde'], label='Netzeinspeisung (Wh)', marker='x')
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plt.plot(stunden, ergebnisse['Netzbezug_Wh_pro_Stunde'], label='Netzbezug (Wh)', marker='^')
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plt.plot(stunden, ergebnisse['Verluste_Pro_Stunde'], label='Verluste (Wh)', marker='^')
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plt.title('Energiefluss pro Stunde')
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plt.xlabel('Stunde')
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plt.ylabel('Energie (Wh)')
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plt.plot(hours, ergebnisse['Last_Wh_pro_Stunde'], label='Load (Wh)', marker='o')
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plt.plot(hours, ergebnisse['Haushaltsgeraet_wh_pro_stunde'], label='Household Device (Wh)', marker='o')
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plt.plot(hours, ergebnisse['Netzeinspeisung_Wh_pro_Stunde'], label='Grid Feed-in (Wh)', marker='x')
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plt.plot(hours, ergebnisse['Netzbezug_Wh_pro_Stunde'], label='Grid Consumption (Wh)', marker='^')
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plt.plot(hours, ergebnisse['Verluste_Pro_Stunde'], label='Losses (Wh)', marker='^')
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plt.title('Energy Flow per Hour')
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plt.xlabel('Hour')
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plt.ylabel('Energy (Wh)')
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plt.legend()
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# State of charge for batteries
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plt.subplot(3, 2, 2)
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plt.plot(stunden, ergebnisse['akku_soc_pro_stunde'], label='PV Akku (%)', marker='x')
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plt.plot(stunden, ergebnisse['E-Auto_SoC_pro_Stunde'], label='E-Auto Akku (%)', marker='x')
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plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) # Legende außerhalb des Plots platzieren
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plt.grid(True, which='both', axis='x') # Grid für jede Stunde
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plt.plot(hours, ergebnisse['akku_soc_pro_stunde'], label='PV Battery (%)', marker='x')
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plt.plot(hours, ergebnisse['E-Auto_SoC_pro_Stunde'], label='E-Car Battery (%)', marker='x')
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plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) # Place legend outside the plot
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plt.grid(True, which='both', axis='x') # Grid for every hour
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ax1 = plt.subplot(3, 2, 3)
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for hour, value in enumerate(discharge_hours):
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#if value == 1:
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print(hour)
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ax1.axvspan(hour, hour+1, color='red',ymax=value, alpha=0.3, label='Entlademöglichkeit' if hour == 0 else "")
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ax1.axvspan(hour, hour + 1, color='red', ymax=value, alpha=0.3, label='Discharge Possibility' if hour == 0 else "")
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for hour, value in enumerate(laden_moeglich):
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#if value == 1:
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ax1.axvspan(hour, hour+1, color='green',ymax=value, alpha=0.3, label='Lademöglichkeit' if hour == 0 else "")
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ax1.axvspan(hour, hour + 1, color='green', ymax=value, alpha=0.3, label='Charging Possibility' if hour == 0 else "")
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ax1.legend(loc='upper left')
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ax1.set_xlim(0, prediction_hours)
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pdf.savefig() # Speichert den aktuellen Figure-State im PDF
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plt.close() # Schließt die aktuelle Figure, um Speicher freizugeben
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pdf.savefig() # Save the current figure state to the PDF
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plt.close() # Close the current figure to free up memory
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# Financial overview
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fig, axs = plt.subplots(1, 2, figsize=(14, 10)) # Create a 1x2 grid of subplots
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total_costs = ergebnisse['Gesamtkosten_Euro']
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total_revenue = ergebnisse['Gesamteinnahmen_Euro']
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total_balance = ergebnisse['Gesamtbilanz_Euro']
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losses = ergebnisse['Gesamt_Verluste']
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plt.grid(True)
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fig, axs = plt.subplots(1, 2, figsize=(14, 10)) # Erstellt 1x2 Raster von Subplots
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gesamtkosten = ergebnisse['Gesamtkosten_Euro']
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gesamteinnahmen = ergebnisse['Gesamteinnahmen_Euro']
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gesamtbilanz = ergebnisse['Gesamtbilanz_Euro']
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verluste = ergebnisse['Gesamt_Verluste']
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# Kosten und Einnahmen pro Stunde auf der ersten Achse (axs[0])
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axs[0].plot(stunden, ergebnisse['Kosten_Euro_pro_Stunde'], label='Kosten (Euro)', marker='o', color='red')
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axs[0].plot(stunden, ergebnisse['Einnahmen_Euro_pro_Stunde'], label='Einnahmen (Euro)', marker='x', color='green')
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axs[0].set_title('Finanzielle Bilanz pro Stunde')
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axs[0].set_xlabel('Stunde')
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# Costs and revenues per hour on the first axis (axs[0])
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axs[0].plot(hours, ergebnisse['Kosten_Euro_pro_Stunde'], label='Costs (Euro)', marker='o', color='red')
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axs[0].plot(hours, ergebnisse['Einnahmen_Euro_pro_Stunde'], label='Revenue (Euro)', marker='x', color='green')
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axs[0].set_title('Financial Balance per Hour')
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axs[0].set_xlabel('Hour')
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axs[0].set_ylabel('Euro')
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axs[0].legend()
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axs[0].grid(True)
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# Zusammenfassende Finanzen auf der zweiten Achse (axs[1])
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labels = ['GesamtKosten [€]', 'GesamtEinnahmen [€]', 'GesamtBilanz [€]']
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werte = [gesamtkosten, gesamteinnahmen, gesamtbilanz]
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colors = ['red' if wert > 0 else 'green' for wert in werte]
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axs[1].bar(labels, werte, color=colors)
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axs[1].set_title('Finanzübersicht')
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# Summary of finances on the second axis (axs[1])
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labels = ['Total Costs [€]', 'Total Revenue [€]', 'Total Balance [€]']
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values = [total_costs, total_revenue, total_balance]
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colors = ['red' if value > 0 else 'green' for value in values]
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axs[1].bar(labels, values, color=colors)
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axs[1].set_title('Financial Overview')
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axs[1].set_ylabel('Euro')
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# Zweite Achse (ax2) für die Verluste, geteilt mit axs[1]
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# Second axis (ax2) for losses, shared with axs[1]
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ax2 = axs[1].twinx()
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ax2.bar('GesamtVerluste', verluste, color='blue')
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ax2.set_ylabel('Verluste [Wh]', color='blue')
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ax2.bar('Total Losses', losses, color='blue')
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ax2.set_ylabel('Losses [Wh]', color='blue')
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ax2.tick_params(axis='y', labelcolor='blue')
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pdf.savefig() # Speichert die komplette Figure im PDF
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plt.close() # Schließt die Figure
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pdf.savefig() # Save the complete figure to the PDF
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plt.close() # Close the figure
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# Additional data visualization if provided
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if extra_data is not None:
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plt.figure(figsize=(14, 10))
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plt.subplot(1, 2, 1)
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f1 = np.array(extra_data["verluste"])
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f2 = np.array(extra_data["bilanz"])
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n1 = np.array(extra_data["nebenbedingung"])
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scatter = plt.scatter(f1, f2, c=n1, cmap='viridis')
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# Add color legend
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plt.colorbar(scatter, label='Constraint')
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if extra_data != None:
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plt.figure(figsize=(14, 10))
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plt.subplot(1, 2, 1)
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f1 = np.array(extra_data["verluste"])
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f2 = np.array(extra_data["bilanz"])
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n1 = np.array(extra_data["nebenbedingung"])
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scatter = plt.scatter(f1, f2, c=n1, cmap='viridis')
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pdf.savefig() # Save the complete figure to the PDF
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plt.close() # Close the figure
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# Farblegende hinzufügen
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plt.colorbar(scatter, label='Nebenbedingung')
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plt.figure(figsize=(14, 10))
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filtered_losses = np.array([v for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"]) if n < 0.01])
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filtered_balance = np.array([b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n < 0.01])
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pdf.savefig() # Speichert die komplette Figure im PDF
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plt.close() # Schließt die Figure
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best_loss = min(filtered_losses)
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worst_loss = max(filtered_losses)
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best_balance = min(filtered_balance)
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worst_balance = max(filtered_balance)
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data = [filtered_losses, filtered_balance]
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labels = ['Losses', 'Balance']
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# Create plots
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fig, axs = plt.subplots(1, 2, figsize=(10, 6), sharey=False) # Two subplots, separate y-axes
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plt.figure(figsize=(14, 10))
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filtered_verluste = np.array([v for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"]) if n < 0.01])
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filtered_bilanz = np.array([b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n< 0.01])
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beste_verluste = min(filtered_verluste)
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schlechteste_verluste = max(filtered_verluste)
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beste_bilanz = min(filtered_bilanz)
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schlechteste_bilanz = max(filtered_bilanz)
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data = [filtered_verluste, filtered_bilanz]
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labels = ['Verluste', 'Bilanz']
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# Plot-Erstellung
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fig, axs = plt.subplots(1, 2, figsize=(10, 6), sharey=False) # Zwei Subplots, getrennte y-Achsen
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# Erster Boxplot für Verluste
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#axs[0].boxplot(data[0])
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axs[0].violinplot(data[0],
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showmeans=True,
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showmedians=True)
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axs[0].set_title('Verluste')
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axs[0].set_xticklabels(['Verluste'])
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# Zweiter Boxplot für Bilanz
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axs[1].violinplot(data[1],
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showmeans=True,
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showmedians=True)
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axs[1].set_title('Bilanz')
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axs[1].set_xticklabels(['Bilanz'])
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# Feinabstimmung
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plt.tight_layout()
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pdf.savefig() # Speichert den aktuellen Figure-State im PDF
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plt.close()
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# plt.figure(figsize=(14, 10))
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# # Kosten und Einnahmen pro Stunde
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# plt.subplot(1, 2, 1)
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# plt.plot(stunden, ergebnisse['Kosten_Euro_pro_Stunde'], label='Kosten (Euro)', marker='o', color='red')
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# plt.plot(stunden, ergebnisse['Einnahmen_Euro_pro_Stunde'], label='Einnahmen (Euro)', marker='x', color='green')
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# plt.title('Finanzielle Bilanz pro Stunde')
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# plt.xlabel('Stunde')
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# plt.ylabel('Euro')
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# plt.legend()
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# plt.grid(True)
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# #plt.figure(figsize=(14, 10))
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# # Zusammenfassende Finanzen
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# #fig, ax1 = plt.subplot(1, 2, 2)
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# fig, ax1 = plt.subplots()
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# gesamtkosten = ergebnisse['Gesamtkosten_Euro']
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# gesamteinnahmen = ergebnisse['Gesamteinnahmen_Euro']
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# gesamtbilanz = ergebnisse['Gesamtbilanz_Euro']
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# labels = ['GesamtKosten [€]', 'GesamtEinnahmen [€]', 'GesamtBilanz [€]']
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# werte = [gesamtkosten, gesamteinnahmen, gesamtbilanz]
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# colors = ['red' if wert > 0 else 'green' for wert in werte]
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# ax1.bar(labels, werte, color=colors)
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# ax1.set_ylabel('Euro')
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# ax1.set_title('Finanzübersicht')
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# # Zweite Achse (ax2) für die Verluste, geteilt mit ax1
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# ax2 = ax1.twinx()
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# verluste = ergebnisse['Gesamt_Verluste']
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# ax2.bar('GesamtVerluste', verluste, color='blue')
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# ax2.set_ylabel('Verluste [Wh]', color='blue')
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||||
|
||||
# # Stellt sicher, dass die Achsenbeschriftungen der zweiten Achse in der gleichen Farbe angezeigt werden
|
||||
# ax2.tick_params(axis='y', labelcolor='blue')
|
||||
|
||||
# pdf.savefig() # Speichert den aktuellen Figure-State im PDF
|
||||
# plt.close() # Schließt die aktuelle Figure, um Speicher freizugeben
|
||||
|
||||
|
||||
# plt.title('Gesamtkosten')
|
||||
# plt.ylabel('Euro')
|
||||
|
||||
|
||||
# plt.legend()
|
||||
# plt.grid(True)
|
||||
|
||||
# plt.tight_layout()
|
||||
#plt.show()
|
||||
# First violin plot for losses
|
||||
axs[0].violinplot(data[0], showmeans=True, showmedians=True)
|
||||
axs[0].set_title('Losses')
|
||||
axs[0].set_xticklabels(['Losses'])
|
||||
|
||||
# Second violin plot for balance
|
||||
axs[1].violinplot(data[1], showmeans=True, showmedians=True)
|
||||
axs[1].set_title('Balance')
|
||||
axs[1].set_xticklabels(['Balance'])
|
||||
|
||||
# Fine-tuning
|
||||
plt.tight_layout()
|
||||
|
||||
pdf.savefig() # Save the current figure state to the PDF
|
||||
plt.close() # Close the figure
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user