import pandas as pd import streamlit as st import numpy as np from pre import preprocess_uploaded_file from difflib import SequenceMatcher import time def similar(a, b, threshold=0.9): return SequenceMatcher(None, a, b).ratio() > threshold def find_different_scenarios(grouped_data, area): # Filter data for the specific functional area area_data = grouped_data[grouped_data['Functional area'] == area] # Get scenarios for each environment scenarios_by_env = {env: set(area_data[area_data['Environment'] == env]['Scenario Name']) for env in area_data['Environment'].unique()} # Find scenarios that are in one environment but not the other diff_scenarios = [] envs = list(scenarios_by_env.keys()) for i in range(len(envs)): for j in range(i+1, len(envs)): env1, env2 = envs[i], envs[j] diff = scenarios_by_env[env1] ^ scenarios_by_env[env2] # symmetric difference for scenario in diff: if scenario in scenarios_by_env[env1]: diff_scenarios.append((scenario, env1, 'Present', env2, 'Missing')) else: diff_scenarios.append((scenario, env2, 'Present', env1, 'Missing')) return diff_scenarios def perform_multi_env_analysis(uploaded_dataframes): # Concatenate all dataframes into a single dataframe combined_data = pd.concat(uploaded_dataframes, ignore_index=True) # Get unique environments and functional areas unique_environments = combined_data['Environment'].unique() unique_areas = np.append(combined_data['Functional area'].unique(), "All") # Select environments to display selected_environments = st.multiselect("Select environments to display", unique_environments, default=unique_environments) # Initialize session state for selected functional areas if it doesn't exist if 'selected_functional_areas' not in st.session_state: st.session_state.selected_functional_areas = ["All"] # Make sure functional_areas_multiselect is also initialized if 'functional_areas_multiselect' not in st.session_state: st.session_state.functional_areas_multiselect = st.session_state.selected_functional_areas # Select functional areas to display, using session state selected_functional_areas = st.multiselect( "Select functional areas", unique_areas, default=st.session_state.selected_functional_areas, key="functional_areas_multiselect" ) # Add a button to confirm the selection if st.button("Confirm Functional Area Selection"): # Update session state with the new selection st.session_state.selected_functional_areas = selected_functional_areas st.success("Functional area selection updated!") time.sleep(0.5) # Add a small delay for better user experience st.rerun() # Rerun the app to reflect the changes if "All" in selected_functional_areas: selected_functional_areas = combined_data['Functional area'].unique() # Filter data based on selected environments and functional areas filtered_data = combined_data[ (combined_data['Environment'].isin(selected_environments)) & (combined_data['Functional area'].isin(selected_functional_areas)) ] # Group data by Environment, Functional area, Scenario Name, and Status grouped_data = filtered_data.groupby(['Environment', 'Functional area', 'Scenario Name', 'Status']).size().unstack(fill_value=0) # Ensure 'PASSED' and 'FAILED' columns exist if 'PASSED' not in grouped_data.columns: grouped_data['PASSED'] = 0 if 'FAILED' not in grouped_data.columns: grouped_data['FAILED'] = 0 # Calculate total scenarios grouped_data['Total'] = grouped_data['PASSED'] + grouped_data['FAILED'] # Reset index to make Environment, Functional area, and Scenario Name as columns grouped_data = grouped_data.reset_index() # Reorder columns grouped_data = grouped_data[['Environment', 'Functional area', 'Scenario Name', 'Total', 'PASSED', 'FAILED']] # Display summary statistics st.write("### Summary Statistics") summary = grouped_data.groupby('Environment').agg({ 'Total': 'sum', 'PASSED': 'sum', 'FAILED': 'sum' }).reset_index() # Add column names as the first row summary_with_headers = pd.concat([pd.DataFrame([summary.columns], columns=summary.columns), summary], ignore_index=True) # Display the DataFrame st.dataframe(summary_with_headers) # Define scenarios_by_env here scenarios_by_env = {env: set(grouped_data[grouped_data['Environment'] == env]['Scenario Name']) for env in selected_environments} missing_scenarios = [] mismatched_scenarios = [] st.write("### Inconsistent Scenario Count Analysis by Functional Area") if len(selected_environments) > 1: # Group data by Environment and Functional area, count scenarios scenario_counts = filtered_data.groupby(['Environment', 'Functional area'])['Scenario Name'].nunique().unstack(fill_value=0) # Calculate the difference between max and min counts for each functional area count_diff = scenario_counts.max() - scenario_counts.min() # Sort functional areas by count difference, descending inconsistent_areas = count_diff.sort_values(ascending=False) st.write("Functional areas with inconsistent scenario counts across environments:") for area, diff in inconsistent_areas.items(): if diff > 0: st.write(f"- {area}: Difference of {diff} scenarios") st.write(scenario_counts[area]) st.write("\n") # Option to show detailed breakdown with a unique key if st.checkbox("Show detailed scenario count breakdown", key="show_detailed_breakdown"): st.write(scenario_counts) # Add a selectbox for choosing the functional area to analyze selected_area = st.selectbox("Select a functional area to analyze:", options=[area for area, diff in inconsistent_areas.items() if diff > 0]) if selected_area: st.write(f"### Detailed Analysis of Different Scenarios for '{selected_area}'") # Get scenarios for each environment scenarios_by_env = {env: set(filtered_data[(filtered_data['Environment'] == env) & (filtered_data['Functional area'] == selected_area)]['Scenario Name']) for env in selected_environments} # Find scenarios that are different between environments all_scenarios = set.union(*scenarios_by_env.values()) diff_scenarios = [scenario for scenario in all_scenarios if any(scenario not in env_scenarios for env_scenarios in scenarios_by_env.values())] # Create a DataFrame to show presence/absence of scenarios diff_df = pd.DataFrame(index=diff_scenarios, columns=selected_environments) for scenario in diff_scenarios: for env in selected_environments: diff_df.at[scenario, env] = 'Present' if scenario in scenarios_by_env[env] else 'Missing' diff_df.reset_index(inplace=True) diff_df.rename(columns={'index': 'Scenario'}, inplace=True) # Sort the DataFrame to show scenarios with differences first diff_df['has_diff'] = diff_df.apply(lambda row: len(set(row[1:])) > 1, axis=1) diff_df = diff_df.sort_values('has_diff', ascending=False).drop('has_diff', axis=1) st.write(f"Number of scenarios that differ between environments: {len(diff_scenarios)}") # Display the DataFrame st.dataframe(diff_df) # Provide a download button for the DataFrame csv = diff_df.to_csv(index=False) st.download_button( label="Download CSV", data=csv, file_name=f"{selected_area}_scenario_comparison.csv", mime="text/csv", ) else: st.write("Please select at least two environments for comparison.") def multi_env_compare_main(): st.title("Multi-Environment Comparison") # Get the number of environments from the user num_environments = st.number_input("Enter the number of environments", min_value=1, value=1, step=1) # Initialize list to store uploaded dataframes uploaded_dataframes = [] # Loop through the number of environments and create file uploaders for i in range(num_environments): uploaded_files = st.file_uploader(f"Upload CSV or XLSX files for Environment {i + 1}", type=["csv", "xlsx"], accept_multiple_files=True) for uploaded_file in uploaded_files: # Preprocess the uploaded file data = preprocess_uploaded_file(uploaded_file) # Append the dataframe to the list uploaded_dataframes.append(data) # Check if any files were uploaded if uploaded_dataframes: # Perform analysis for uploaded data perform_multi_env_analysis(uploaded_dataframes) else: st.write("Please upload at least one file.") if __name__ == "__main__": multi_env_compare_main()