costs_workflow/costs/printing_results.py

import plotly.graph_objects as go
import matplotlib.pyplot as plt
import plotly.express as px


def printing_results(investmentcosts, life_cycle_results,total_floor_area):

  labels = investmentcosts.index
  values = investmentcosts['retrofitting_scenario_1']
  values2 = investmentcosts['retrofitting_scenario_2']
  values3 = investmentcosts['retrofitting_scenario_3']

  fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
  fig2 = go.Figure(data=[go.Pie(labels=labels, values=values2)])
  fig3 = go.Figure(data=[go.Pie(labels=labels, values=values3)])
  # Set the layout properties
  fig.update_layout(
    title='Retrofitting scenario 1',
    showlegend=True
  )
  fig2.update_layout(
    title='Retrofitting scenario 2',
    showlegend=True
  )
  fig3.update_layout(
    title='Retrofitting scenario 3',
    showlegend=True
  )

  # Display the chart
  fig.show()
  fig2.show()
  fig3.show()

  df = life_cycle_results / total_floor_area

  # Transpose the DataFrame (swap columns and rows)
  df_swapped = df.transpose()

  # Reset the index to make the current index a regular column
  df_swapped = df_swapped.reset_index()

  # Assign new column names
  df_swapped.columns = ['Scenarios', 'total_capital_costs_skin',
                        'total_capital_costs_systems',
                        'end_of_life_costs',
                        'total_operational_costs',
                        'total_maintenance_costs',
                        'operational_incomes',
                        'capital_incomes']

  df_swapped.index = df_swapped['Scenarios']
  df_swapped = df_swapped.drop('Scenarios', axis=1)
  print(df_swapped)
  fig = px.bar(df_swapped, title='Life Cycle Costs for buildings')
  fig.show()
  # Display the chart
  plt.show()