60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
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import numpy as np
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import plotly.graph_objects as go
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import plotly.offline as offline
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import matplotlib.pyplot as plt
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import plotly.express as px
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def printing_results(investmentcosts, life_cycle_results,total_floor_area):
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labels = investmentcosts.index
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values = investmentcosts['retrofitting_scenario 1']
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values2 = investmentcosts['retrofitting_scenario 2']
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values3 = investmentcosts['retrofitting_scenario 3']
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fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
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fig2 = go.Figure(data=[go.Pie(labels=labels, values=values2)])
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fig3 = go.Figure(data=[go.Pie(labels=labels, values=values3)])
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# Set the layout properties
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fig.update_layout(
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title='Retrofitting scenario 1',
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showlegend=True
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)
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fig2.update_layout(
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title='Retrofitting scenario 1',
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showlegend=True
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)
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fig3.update_layout(
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title='Retrofitting scenario 1',
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showlegend=True
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)
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# Display the chart
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fig.show()
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fig2.show()
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fig3.show()
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df = life_cycle_results / total_floor_area
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# Transpose the DataFrame (swap columns and rows)
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df_swapped = df.transpose()
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# Reset the index to make the current index a regular column
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df_swapped = df_swapped.reset_index()
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# Assign new column names
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df_swapped.columns = ['Scenarios', 'total_capital_costs_skin',
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'total_capital_costs_systems',
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'end_of_life_costs',
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'total_operational_costs',
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'total_maintenance_costs',
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'operational_incomes',
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'capital_incomes']
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df_swapped.index = df_swapped['Scenarios']
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df_swapped = df_swapped.drop('Scenarios', axis=1)
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print(df_swapped)
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fig = px.bar(df_swapped, title='Life Cycle Costs for buildings')
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fig.show()
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# Display the chart
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plt.show()
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