diff --git "a/pages/4_\360\237\217\245_The_Full_Model.py" "b/pages/4_\360\237\217\245_The_Full_Model.py" index 49d2d46..5c40f65 100644 --- "a/pages/4_\360\237\217\245_The_Full_Model.py" +++ "b/pages/4_\360\237\217\245_The_Full_Model.py" @@ -11,7 +11,7 @@ import plotly.express as px import plotly.graph_objects as go import streamlit as st - +import numpy as np from helper_functions import add_logo, mermaid, center_running from model_classes import Scenario, multiple_replications from output_animation_functions import reshape_for_animations, generate_animation_df, generate_animation @@ -754,7 +754,7 @@ class T ZZ5a all_run_util_bar.update_xaxes(title_text='Resource Utilisation (%)') all_run_util_bar.update_layout(xaxis_tickformat = '.0%', - legend_title_text='Model Run') + legend_title_text='Scenario') st.plotly_chart( all_run_util_bar, @@ -784,7 +784,7 @@ class T ZZ5a all_run_wait_bar.update_xaxes(title_text='Wait for Stage (minutes)') - all_run_wait_bar.update_layout(legend_title_text='Model Run') + all_run_wait_bar.update_layout(legend_title_text='Scenario') all_run_wait_bar.add_vrect(x0=0, x1=60*2, fillcolor="#5DFDA0", opacity=0.3, line_width=0) @@ -836,7 +836,7 @@ class T ZZ5a all_run_util_box.update_xaxes(title_text='Resource Utilisation (%)') all_run_util_box.update_layout(xaxis_tickformat = '.0%', - legend_title_text='Model Run') + legend_title_text='Scenario') st.plotly_chart(all_run_util_box, use_container_width=True @@ -872,7 +872,7 @@ class T ZZ5a all_run_wait_box.add_vrect(x0=0, x1=60*2, fillcolor="#5DFDA0", opacity=0.3, line_width=0) - all_run_wait_box.update_layout(legend_title_text='Model Run') + all_run_wait_box.update_layout(legend_title_text='Scenario') # Add in a box plot showing waits st.plotly_chart(all_run_wait_box, @@ -903,7 +903,7 @@ class T ZZ5a height=800) all_results_throughput_box.update_layout(xaxis_tickformat = '.0%', - legend_title_text='Model Run') + legend_title_text='Scenario') all_run_util_bar.update_yaxes(title_text='', labelalias={ @@ -929,7 +929,7 @@ class T ZZ5a st.markdown("This displays the median value for each metric across all model runs per scenario.") - import numpy as np + output_scenario_df = all_run_results.groupby('Model Run').median().T output_scenario_df = output_scenario_df.reset_index(drop=False).melt(id_vars="index") # st.dataframe(output_scenario_df)