Py.Cafe

# imports
import os
import streamlit as st
import plotly.express as px
import pandas as pd

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

def calc_metrics(df: pd.DataFrame, runway: pd.DataFrame):
    """Calculates metrics to be used for plots"""
    tech_aircraft = df['tech_aircraft'].unique().tolist() # Populate all combinations
    # Calculate metrics for plots
    # Determine mission feasibility
    df.loc[df['departure_airport_code'].isin(runway['AIRPORT_IATA']), 'Dep RWY Length [m]']= runway.set_index('AIRPORT_IATA').loc[df['departure_airport_code'],'RUNWAY_TAKE_OFF_LENGTH_m'].values
    df.loc[df['arrival_airport_code'].isin(runway['AIRPORT_IATA']), 'Arr RWY Length [m]']= runway.set_index('AIRPORT_IATA').loc[df['arrival_airport_code'],'RUNWAY_TAKE_OFF_LENGTH_m'].values
    df['TOFL feasible for mission']= (df['Dep RWY Length [m]'] > df['TechAircraft.TOFL.TOFL'])&(df['Arr RWY Length [m]'] > df['TechAircraft.TOFL.TOFL'])

    # Column headers

    headers = ['Max. MTOW [kg]', 'Max. MLW [kg]', 'Avg. nPAX', 'Sum. of COC RUNWAY', 'Avg. of TOFL [m]', 'Avg. of Vapp [kts]',
               'Sum. of DOC_BlockFuel', 'Avg. of COC', 'Sum. of COC Fuel', 'Sum. of COC MAINTENANCE'
               , 'Sum. of COC NAVIGATION', 'Sum. of COC RAMP', 'Avg. of Equivalent_Thrust [lbf]', 'TechAircraft.Results.Missions.DOC.DOC_Range',
                'TechAircraft.TOFL.TOFL', 'TechAircraft.Results.Missions.DOC.DOC_BlockFuel', 'TOFL feasible for mission']
    for spec in tech_aircraft:
        df.loc[df['tech_aircraft']== spec, 'Max. MTOW [kg]'] = df.loc[df['tech_aircraft']== spec, 'TechAircraft.Mission Data.MTOW'].max()
        df.loc[df['tech_aircraft']== spec, 'Max. MLW [kg]'] = df.loc[df['tech_aircraft']== spec, 'TechAircraft.Mission Data.MLW'].max()
        df.loc[df['tech_aircraft']== spec, 'Avg. nPAX'] = df.loc[df['tech_aircraft']== spec, 'TechAircraft.Mass Derivation inputs.nPAX'].mean()
        df.loc[df['tech_aircraft']== spec, 'Sum. of COC RUNWAY'] = df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.RUNWAY'].sum()
        # df.loc[df['tech_aircraft']== spec, 'Sum. of TOFL infeasible for mission'] = df['TechAircraft.TOFL.TOFL infeasible for this mission'].sum()["INFEASIBLE"] 
        df.loc[df['tech_aircraft']== spec, 'Avg. of TOFL [m]']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.TOFL.TOFL'].mean()
        df.loc[df['tech_aircraft']== spec, 'Avg. of Vapp [kts]']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.TOFL.Vapp'].mean()
        df.loc[df['tech_aircraft']== spec, 'Sum. of DOC_BlockFuel']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.Results.Missions.DOC.DOC_BlockFuel'].sum()
        df.loc[df['tech_aircraft']== spec, 'Avg. of COC']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.COMPLETE_COC'].mean()
        df.loc[df['tech_aircraft']== spec, 'Sum. of COC Fuel']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.FUEL'].sum()
        df.loc[df['tech_aircraft']== spec, 'Sum. of COC MAINTENANCE']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.MAINTENANCE'].sum()
        df.loc[df['tech_aircraft']== spec, 'Sum. of COC NAVIGATION']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.NAVIGATION'].sum()
        df.loc[df['tech_aircraft']== spec, 'Sum. of COC RAMP']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.COC outputs.RAMP_HANDLING'].sum()
        df.loc[df['tech_aircraft']== spec, 'Avg. of Equivalent_Thrust [lbf]']= df.loc[df['tech_aircraft']== spec, 'TechAircraft.TOFL.Airbus_equivalent_thrust'].mean()

    final_columns = ['tech_aircraft'] + ['Fuel', 'TOFL', 'Engine', 'Range', 'Year'] + headers
    df = df[final_columns].drop_duplicates()
    df['Range'] = df['Range'].str.extract('(\d+)')
    df = df.sort_values(["Year"], ascending=[True])
    # df = df.sort_values(["Year", "Range"], ascending=[True, True])
    return df
    # Remove unnecessary columns

def plot_SliderScatter(df: pd.DataFrame, xlabel: str, ylabel: str):
    fig = px.scatter(df, x=xlabel, y=ylabel, 
                     animation_frame="Year", 
                     animation_group="tech_aircraft",
                     color="TOFL",
                     symbol = "Fuel",
                     hover_name="tech_aircraft",
                    #  text = "Engine",
                     title=(xlabel + " vs " + ylabel + " overtime") 
                     )
    fig.update_traces(textposition='top center')
    fig["layout"].pop("updatemenus") # optional, drop animation buttons
    return fig

def plot_3D(df: pd.DataFrame, xlabel: str, ylabel: str, zlabel: str):
    fig = go.Figure()

    colors = px.colors.qualitative.Plotly  # Default qualitative color scheme
    symbols = ['circle', 'square', 'diamond', 'cross', 'x']  # Default symbols    
    fig = px.scatter_3d(df, x=xlabel, y=ylabel, z=zlabel,
                     animation_frame="Year",
                     animation_group="tech_aircraft",
                     symbol = "Fuel",
                     color="TOFL",
                     hover_name="tech_aircraft",
                    #  text = "Engine",
                     title=(xlabel + " vs " + ylabel + " overtime") 
                     )
    fig.update_traces(textposition='top center')
    fig.update_layout(scene_camera=dict(eye=dict(x=2, y=2, z=0.5)))

    # fig.update_layout(scene=dict(
    #     yaxis=dict(range=[df[ylabel].min(), df[ylabel].max()]), 
    #     zaxis=dict(range=[df[zlabel].min(), df[zlabel].max()]) 
    # ))
    # fig["layout"].pop("updatemenus") # optional, drop animation buttons
    return fig

# File reading

# Get script directory
# script_dir = os.path.dirname(os.path.abspath(__file__))
# os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz-12.1.0-win64/bin/' # Solve graphviz path problem

st.set_page_config(page_title="Hackathon Results Visualisation", layout="centered")
def parameters_study_page():
    upload_file = st.file_uploader(label="Design point output files in .csv format.", type="csv", accept_multiple_files=True)
    file_flag = [0,0]
    if len(upload_file) != 0:
        dfs =[pd.read_csv(f) for f in upload_file]
        df = pd.concat(dfs, ignore_index=True)
        df[['Fuel', 'TOFL', 'Engine', 'Range', 'Year']] = df['tech_aircraft'].str.extract(r'([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^_]+)')
        file_flag[0] = 1
    runway_file = st.file_uploader(label="Runway data csv", type="csv")
    if runway_file is not None:
        df_runway = pd.read_csv(runway_file)
        file_flag[1] = 1
    if file_flag[0]==1 & file_flag[1]==1:
        st.write("Files uploaded.")
        df = calc_metrics(df, df_runway)
        with st.form(key="Select parameters"):
                heads = list(df.columns.values)
                params = st.multiselect("Select parameters to compare against: (Default: 'Range', 'TechAircraft.Results.Missions.DOC.DOC_Range', 'TechAircraft.TOFL.TOFL')", heads, ['Range', 'TechAircraft.Results.Missions.DOC.DOC_Range', 'TechAircraft.TOFL.TOFL'], max_selections=3)
                plot_button = st.form_submit_button("Generate 3D plot")
        if st.button("Plot"):
            st.plotly_chart(plot_SliderScatter(df, 'Avg. of TOFL [m]', 'Avg. of Equivalent_Thrust [lbf]'), use_container_width=True)
            st.plotly_chart(plot_SliderScatter(df, 'Avg. of TOFL [m]', 'Max. MTOW [kg]'), use_container_width=True)
            st.plotly_chart(plot_3D(df, params[0], params[1], params[2]), use_container_width=True)
            st.toast("Plots completed!")
    else:
        st.write("Input files incomplete.")

def Fuel_Fleet_study_page():
    colour_group = px.colors.qualitative.Plotly # Change this if we have more fuel/TOFL/engine options
    def update (change):
        # Example of dynamically downselection using sliders.
        if change == 'Kero':
            st.session_state.LH2 = st.session_state.Kero / 2
            st.session_state.SAF = st.session_state.Kero / 2
        else:
            st.session_state.Kero = st.session_state.LH2 + st.session_state.SAF
        
    def calcStep(df, Col:str) -> int:
        df_ans = (df[Col].max()-df[Col].min())/(df[Col].unique().size-1)
        return df_ans.astype('int64')

    upload_file = st.file_uploader(label="CCC Fuel price files in .csv format.", type="csv", accept_multiple_files=True)

    if len(upload_file) != 0:
        dfs =[pd.read_csv(f) for f in upload_file]
        for df in dfs:
            df['Year'] = df.columns.values[0]
            df.drop(df.columns[0], axis=1, inplace=True)
        df = pd.concat(dfs, ignore_index=True)

        with st.form(key="Fleet mixing"):
            KeroInput = st.slider('Price of Kerosene [$/USG]', df['Kerosene price [$/USG]'].min(), df['Kerosene price [$/USG]'].max(), value=2, step=calcStep(df, 'Kerosene price [$/USG]'), key='Kero')
            LH2Input = st.slider('Price of LH2 [$/USG]', df['LH2 price [$/USG]'].min(), df['LH2 price [$/USG]'].max(), value=2, step=calcStep(df, 'LH2 price [$/USG]'), key='LH2')
            SAFInput = st.slider('Price of SAF [$/USG]', df['SAF price [$/USG]'].min(), df['SAF price [$/USG]'].max(), value=2, step=calcStep(df, 'SAF price [$/USG]'), key='SAF')
            NH3Input = st.slider('Price of NH3 [$/USG]', df['NH3 price [$/USG]'].min(), df['NH3 price [$/USG]'].max(), value=2, step=calcStep(df, 'NH3 price [$/USG]'), key='NH3')
            MethanolInput = st.slider('Price of Methanol [$/USG]', df['CH3OH price [$/USG]'].min(), df['CH3OH price [$/USG]'].max(), value=2, step=calcStep(df, 'CH3OH price [$/USG]'), key='CH3OH')

            df_ans = df[(df['Kerosene price [$/USG]'] == KeroInput) & (df['LH2 price [$/USG]']==LH2Input) & (df['SAF price [$/USG]'] == SAFInput) & (df['NH3 price [$/USG]'] == NH3Input) & (df['CH3OH price [$/USG]'] == MethanolInput)]
            df_ans.reset_index(drop=True, inplace=True)
            st.form_submit_button("Find current scenario")
            if df_ans.empty:
                st.warning("Current scenario does not exist, please try again 🙁")
            else:
                st.success("Congrats for finding a valid scenario, current chance: " + str(round(len(df[df['Year'] == df['Year'].unique()[0]])/(df['Kerosene price [$/USG]'].unique().size**5), 2)), icon="🍀")
        
        if not df_ans.empty:
            st.write(df_ans)
            fig = make_subplots(
                rows=2, cols=3,
                column_widths=[0.6, 0.2, 0.2],
                row_heights=[1, 1],
                specs=[[{"type": "bar"}, {"type": "pie"}, {"type": "pie"}],
                    [{"type": "bar"}, {"type": "pie"}, {"type": "pie"}]],
                    subplot_titles=("Scheduled no. of Flights", "Fleet % by Fuel", "", "Fuel Consumption [kg]", "Fleet % by TOFL", "Fleet % by Engine"))

            columns_BarPlot = ["Scheduled number of Kerosene flights", "Scheduled number of CH3OH flights", "Scheduled number of LH2 flights","Scheduled number of NH3 flights","Scheduled number of SAF flights"]
            columns_2nd_BarPlot = ["Kerosene fuel consumption across all bases [kg]","CH3OH fuel consumption across all bases [kg]","LH2 fuel consumption across all bases [kg]","NH3 fuel consumption across all bases [kg]","SAF fuel consumption across all bases [kg]"]
            columns_fleet_PieChart = ["Fleet size (Kerosene) after scheduling","Fleet size (CH3OH) after scheduling","Fleet size (LH2) after scheduling","Fleet size (NH3) before after scheduling","Fleet size (SAF) after scheduling"]
            TOFL_PieChart = ["Fleet size (TOFL1) after scheduling","Fleet size (TOFL2) after scheduling","Fleet size (TOFL3) after scheduling"]
            Engine_PieChart = ["Fleet size (Engine1) after scheduling","Fleet size (Engine2) after scheduling"]
            fuelTypes = ["Kerosene", "CH3OH", "LH2", "NH3", "SAF"]
            years = df_ans["Year"].unique().tolist()
            
            j = 0
            for i, year in enumerate(years):
                for i in range(len(fuelTypes)):
                    fig.add_trace(
                        go.Bar(x = [fuelTypes[i]], y=[df_ans.loc[j, columns_BarPlot[i]]], name=fuelTypes[i] + " at " + year, marker=dict(color=colour_group[i]) ),
                        row=1, col=1,
                    )
                
                for i in range(len(fuelTypes)):
                    fig.add_trace(
                        go.Bar(x = [fuelTypes[i]], y=[df_ans.loc[j, columns_2nd_BarPlot[i]]], name=fuelTypes[i] + " at " + year, marker=dict(color=colour_group[i]), showlegend=False),
                        row=2, col=1,
                    )

                fig.add_trace(
                    go.Pie( labels=fuelTypes, values=df_ans.loc[j, columns_fleet_PieChart], marker=dict(colors=colour_group), showlegend=False),
                    row=1, col=2,
                )

                fig.add_trace(
                    go.Pie( labels=TOFL_PieChart, values=df_ans.loc[j, TOFL_PieChart], marker=dict(colors=colour_group[5:8])), # Change this if we have more fuel/TOFL/engine options
                    row=2, col=2,
                )

                fig.add_trace(
                    go.Pie( labels=Engine_PieChart, values=df_ans.loc[j, Engine_PieChart], marker=dict(colors=colour_group[8:])), # Change this if we have more fuel/TOFL/engine options
                    row=2, col=3,
                )
                j = j + 1
            
            steps = []
            for i in range(0,len(fig.data),len(fuelTypes)*2+3):
                step = dict(
                    method="update",
                    args=[{"visible": [False] * len(fig.data)},
                        {"title": "Current Year: " + years[int(i/(len(fuelTypes)*2+3))]},
                          {"label": years[int(i/(len(fuelTypes)*2+3))]}],  # layout attribute
                    label= years[int(i/(len(fuelTypes)*2+3))]
                )
                for k in range(i, i+len(fuelTypes)*2+3):
                    step["args"][0]["visible"][k] = True  # Toggle i to i+k'th traces to "visible"
                steps.append(step)

            sliders = [dict(
                active=0,
                currentvalue={"prefix": "Current Year: " },
                pad={"t": 50},
                steps=steps,
            )]

            fig.update_layout(
                sliders=sliders
            )

            st.plotly_chart(fig)

pages = {
    "Page 1": parameters_study_page,
    "Fleet Mixing study": Fuel_Fleet_study_page,
}

selection = st.sidebar.radio("GoTo", list(pages.keys()))

# Page rendering
page = pages[selection]
page()