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Features:
1. Simulation Comparison - Compare multiple simulation runs side-by-side
2. Performance Trends - Analyze metrics over time
3. Fairness Analysis - Evaluate equity and distribution
4. Report Generation - Export comprehensive analysis
"""
from __future__ import annotations
from datetime import datetime
from pathlib import Path
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
# Page configuration
st.set_page_config(
page_title="Analytics & Reports",
page_icon="chart",
layout="wide",
)
st.title("Analytics & Reports")
st.markdown("Compare simulation runs and analyze system performance")
st.markdown("---")
# Main tabs
tab1, tab2, tab3, tab4 = st.tabs(
[
"Simulation Comparison",
"Performance Trends",
"Fairness Analysis",
"Report Generation",
]
)
# TAB 1: Simulation Comparison
with tab1:
st.markdown("### Simulation Comparison")
st.markdown(
"Compare multiple simulation runs to evaluate different policies and parameters."
)
# Check for available simulation runs (centralized base)
from src.config.paths import get_runs_base
runs_dir = get_runs_base()
if not runs_dir.exists():
st.warning(
"No simulation outputs found. Run simulations first to generate data."
)
else:
# Collect all run directories that actually contain a metrics.csv file.
# Some runs may be nested (version folder inside timestamp). We treat every
# directory that has metrics.csv as a runnable result.
metric_files = list(runs_dir.rglob("metrics.csv"))
run_paths = sorted({p.parent for p in metric_files})
# Build label -> path map; label is relative path inside simulation_runs
run_map = {str(p.relative_to(runs_dir)): p for p in run_paths}
if len(run_map) < 2:
st.info(
"At least 2 simulation runs needed for comparison. Run more simulations to enable comparison."
)
else:
st.markdown(f"**{len(run_map)} simulation run(s) available**")
# Select runs to compare
col1, col2 = st.columns(2)
labels = sorted(run_map.keys())
with col1:
run1_label = st.selectbox(
"First simulation run", options=labels, key="compare_run1"
)
with col2:
run2_options = [lbl for lbl in labels if lbl != run1_label]
run2_label = st.selectbox(
"Second simulation run",
options=run2_options,
key="compare_run2",
)
if st.button("Compare Runs", type="primary"):
# Load metrics from both runs
run1_metrics_path = run_map[run1_label] / "metrics.csv"
run2_metrics_path = run_map[run2_label] / "metrics.csv"
if not run1_metrics_path.exists() or not run2_metrics_path.exists():
st.error("Metrics files not found for one or both runs.")
else:
try:
df1 = pd.read_csv(run1_metrics_path)
df2 = pd.read_csv(run2_metrics_path)
st.success("Loaded metrics successfully")
# Show Key Insights from report.txt for both runs
st.markdown("#### Key Insights (from report.txt)")
col_ins_1, col_ins_2 = st.columns(2)
report1_path = run_map[run1_label] / "report.txt"
report2_path = run_map[run2_label] / "report.txt"
with col_ins_1:
st.markdown(f"**{run1_label}**")
if report1_path.exists():
st.code(
report1_path.read_text(encoding="utf-8"),
language="text",
)
else:
st.info("No report.txt found for this run.")
with col_ins_2:
st.markdown(f"**{run2_label}**")
if report2_path.exists():
st.code(
report2_path.read_text(encoding="utf-8"),
language="text",
)
else:
st.info("No report.txt found for this run.")
# Summary comparison
st.markdown("#### Summary Comparison")
col1, col2, col3 = st.columns(3)
with col1:
st.markdown(f"**{run1_label}**")
if "disposal_rate" in df1.columns:
avg_disposal1 = df1["disposal_rate"].mean()
st.metric("Avg. Disposal Rate", f"{avg_disposal1:.2%}")
if "utilization" in df1.columns:
avg_util1 = df1["utilization"].mean()
st.metric("Avg. Utilization", f"{avg_util1:.2%}")
with col2:
st.markdown(f"**{run2_label}**")
if "disposal_rate" in df2.columns:
avg_disposal2 = df2["disposal_rate"].mean()
st.metric("Avg. Disposal Rate", f"{avg_disposal2:.2%}")
if "utilization" in df2.columns:
avg_util2 = df2["utilization"].mean()
st.metric("Avg. Utilization", f"{avg_util2:.2%}")
with col3:
st.markdown("**Difference**")
if (
"disposal_rate" in df1.columns
and "disposal_rate" in df2.columns
):
diff_disposal = avg_disposal2 - avg_disposal1
st.metric("Disposal Rate Δ", f"{diff_disposal:+.2%}")
if (
"utilization" in df1.columns
and "utilization" in df2.columns
):
diff_util = avg_util2 - avg_util1
st.metric("Utilization Δ", f"{diff_util:+.2%}")
st.markdown("---")
# Time series comparison
st.markdown("#### Performance Over Time")
if (
"disposal_rate" in df1.columns
and "disposal_rate" in df2.columns
):
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=df1.index,
y=df1["disposal_rate"],
mode="lines",
name=run1_label,
line=dict(color="blue"),
)
)
fig.add_trace(
go.Scatter(
x=df2.index,
y=df2["disposal_rate"],
mode="lines",
name=run2_label,
line=dict(color="red"),
)
)
fig.update_layout(
title="Disposal Rate Comparison",
xaxis_title="Day",
yaxis_title="Disposal Rate",
height=400,
)
st.plotly_chart(fig, use_container_width=True)
if (
"utilization" in df1.columns
and "utilization" in df2.columns
):
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=df1.index,
y=df1["utilization"],
mode="lines",
name=run1_label,
line=dict(color="blue"),
)
)
fig.add_trace(
go.Scatter(
x=df2.index,
y=df2["utilization"],
mode="lines",
name=run2_label,
line=dict(color="red"),
)
)
fig.update_layout(
title="Utilization Comparison",
xaxis_title="Day",
yaxis_title="Utilization",
height=400,
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.error(f"Error comparing runs: {e}")
# TAB 2: Performance Trends
with tab2:
st.markdown("### Performance Trends")
st.markdown("Analyze performance metrics across all simulation runs.")
# Use centralized runs directory recursively
from src.config.paths import get_runs_base
runs_dir = get_runs_base()
if not runs_dir.exists():
st.warning("No simulation outputs found.")
else:
metric_files = list(runs_dir.rglob("metrics.csv"))
run_paths = sorted({p.parent for p in metric_files})
if not run_paths:
st.info("No simulation runs found.")
else:
# Aggregate metrics from all runs
all_metrics = []
for run_dir in run_paths:
metrics_path = run_dir / "metrics.csv"
try:
df = pd.read_csv(metrics_path)
# Use relative label for clarity across nested structures
try:
df["run"] = str(run_dir.relative_to(runs_dir))
except ValueError:
# Fallback to folder name if not under base (shouldn't happen)
df["run"] = run_dir.name
all_metrics.append(df)
except Exception:
pass # Skip invalid metrics files
if not all_metrics:
st.warning("No valid metrics files found.")
else:
combined_df = pd.concat(all_metrics, ignore_index=True)
st.markdown(f"**Loaded metrics from {len(all_metrics)} run(s)**")
# Aggregate statistics
st.markdown("#### Aggregate Statistics")
col1, col2, col3 = st.columns(3)
with col1:
if "disposal_rate" in combined_df.columns:
overall_avg = combined_df["disposal_rate"].mean()
st.metric("Overall Avg. Disposal Rate", f"{overall_avg:.2%}")
with col2:
if "utilization" in combined_df.columns:
overall_util = combined_df["utilization"].mean()
st.metric("Overall Avg. Utilization", f"{overall_util:.2%}")
with col3:
st.metric("Total Simulation Days", len(combined_df))
st.markdown("---")
# Distribution plots
st.markdown("#### Metric Distributions")
if "disposal_rate" in combined_df.columns:
fig = px.box(
combined_df,
x="run",
y="disposal_rate",
title="Disposal Rate Distribution by Run",
labels={
"disposal_rate": "Disposal Rate",
"run": "Simulation Run",
},
)
fig.update_layout(height=400)
st.plotly_chart(fig, use_container_width=True)
if "utilization" in combined_df.columns:
fig = px.box(
combined_df,
x="run",
y="utilization",
title="Utilization Distribution by Run",
labels={"utilization": "Utilization", "run": "Simulation Run"},
)
fig.update_layout(height=400)
st.plotly_chart(fig, use_container_width=True)
# TAB 3: Fairness Analysis
with tab3:
st.markdown("### Fairness Analysis")
st.markdown("Evaluate equity and distribution of case handling across the system.")
st.markdown("""
Fairness metrics evaluate whether the scheduling system treats all cases equitably:
- **Gini Coefficient**: Measures inequality in disposal times (0 = perfect equality, 1 = maximum inequality)
- **Age Distribution**: Shows how long cases wait before disposal
- **Case Type Balance**: Ensures no case type is systematically disadvantaged
""")
from src.config.paths import get_runs_base
runs_dir = get_runs_base()
if not runs_dir.exists():
st.warning("No simulation outputs found.")
else:
event_files = list(runs_dir.rglob("events.csv"))
run_event_paths = sorted({p.parent for p in event_files})
if not run_event_paths:
st.info("No simulation runs found.")
else:
# Select run for fairness analysis
labels = [str(p.relative_to(runs_dir)) for p in run_event_paths]
label_to_path = {str(p.relative_to(runs_dir)): p for p in run_event_paths}
selected_run = st.selectbox(
"Select simulation run for fairness analysis",
options=labels,
key="fairness_run",
)
# Look for events file (contains case-level data)
events_path = label_to_path[selected_run] / "events.csv"
if not events_path.exists():
st.warning(
"Events file not found. Fairness analysis requires detailed event logs."
)
else:
try:
events_df = pd.read_csv(events_path)
st.success("Loaded event data")
# Case age analysis
if "case_id" in events_df.columns and "date" in events_df.columns:
st.markdown("#### Case Age Distribution")
# Calculate case ages (simplified - would need filed_date for accurate calculation)
case_dates = events_df.groupby("case_id")["date"].agg(
["min", "max"]
)
case_dates["age_days"] = (
pd.to_datetime(case_dates["max"])
- pd.to_datetime(case_dates["min"])
).dt.days
fig = px.histogram(
case_dates,
x="age_days",
nbins=30,
title="Distribution of Case Ages",
labels={
"age_days": "Age (days)",
"count": "Number of Cases",
},
)
fig.update_layout(height=400)
st.plotly_chart(fig, use_container_width=True)
# Summary statistics
col1, col2, col3 = st.columns(3)
with col1:
st.metric(
"Median Age",
f"{case_dates['age_days'].median():.0f} days",
)
with col2:
st.metric(
"Mean Age", f"{case_dates['age_days'].mean():.0f} days"
)
with col3:
st.metric(
"Max Age", f"{case_dates['age_days'].max():.0f} days"
)
# Additional Fairness Metrics: Gini and Lorenz Curve
st.markdown("#### Inequality Metrics (Fairness)")
def _gini(values: np.ndarray) -> float:
v = np.asarray(values, dtype=float)
v = v[np.isfinite(v)]
v = v[v >= 0]
if v.size == 0:
return float("nan")
if np.all(v == 0):
return 0.0
v_sorted = np.sort(v)
n = v_sorted.size
cumulative = np.cumsum(v_sorted)
# Gini based on cumulative shares
gini = (n + 1 - 2 * np.sum(cumulative) / cumulative[-1]) / n
return float(gini)
ages = case_dates["age_days"].to_numpy()
gini_age = _gini(ages)
col_a, col_b = st.columns(2)
with col_a:
if np.isfinite(gini_age):
st.metric("Gini (Age Inequality)", f"{gini_age:.3f}")
else:
st.info("Gini (Age) not available")
# Lorenz curve for ages
with col_b:
try:
ages_clean = ages[np.isfinite(ages)]
ages_clean = ages_clean[ages_clean >= 0]
if ages_clean.size > 0:
ages_sorted = np.sort(ages_clean)
cum_ages = np.cumsum(ages_sorted)
cum_ages = np.insert(cum_ages, 0, 0)
cum_pop = np.linspace(0, 1, num=cum_ages.size)
lorenz = cum_ages / cum_ages[-1]
fig_lorenz = go.Figure()
fig_lorenz.add_trace(
go.Scatter(
x=cum_pop,
y=lorenz,
mode="lines",
name="Lorenz",
)
)
fig_lorenz.add_trace(
go.Scatter(
x=[0, 1],
y=[0, 1],
mode="lines",
name="Equality",
line=dict(dash="dash"),
)
)
fig_lorenz.update_layout(
title="Lorenz Curve of Case Ages",
xaxis_title="Cumulative share of cases",
yaxis_title="Cumulative share of total age",
height=350,
)
st.plotly_chart(
fig_lorenz, use_container_width=True
)
else:
st.info("Not enough data to plot Lorenz curve")
except Exception:
st.info(
"Unable to compute Lorenz curve for current data"
)
# Case type fairness
if "case_type" in events_df.columns:
st.markdown("---")
st.markdown("#### Case Type Balance")
case_type_counts = (
events_df["case_type"].value_counts().reset_index()
)
case_type_counts.columns = ["case_type", "count"]
fig = px.bar(
case_type_counts.head(10),
x="case_type",
y="count",
title="Top 10 Case Types by Hearing Count",
labels={
"case_type": "Case Type",
"count": "Number of Hearings",
},
)
fig.update_layout(height=400, xaxis_tickangle=-45)
st.plotly_chart(fig, use_container_width=True)
# Age distribution by case type (top N by cases)
st.markdown("#### Age Distribution by Case Type (Top 8)")
try:
# Map each case_id to a case_type (take the first occurrence)
cid_to_type = (
events_df.sort_values("date")
.groupby("case_id")["case_type"]
.first()
)
age_with_type = (
case_dates[["age_days"]]
.join(cid_to_type, how="left")
.dropna(
subset=["case_type"]
) # keep only cases with type
)
top_types = (
age_with_type["case_type"]
.value_counts()
.head(8)
.index.tolist()
)
filt = age_with_type["case_type"].isin(top_types)
fig_box = px.box(
age_with_type[filt],
x="case_type",
y="age_days",
points="outliers",
title="Case Age by Case Type (Top 8)",
labels={
"case_type": "Case Type",
"age_days": "Age (days)",
},
)
fig_box.update_layout(height=420, xaxis_tickangle=-45)
st.plotly_chart(fig_box, use_container_width=True)
# Gini by case type (Top 8)
st.markdown("#### Inequality by Case Type (Gini)")
gini_rows = []
for ctype in top_types:
vals = age_with_type.loc[
age_with_type["case_type"] == ctype, "age_days"
].to_numpy()
g = _gini(vals)
gini_rows.append({"case_type": ctype, "gini": g})
gini_df = pd.DataFrame(gini_rows).dropna()
if not gini_df.empty:
fig_gini = px.bar(
gini_df,
x="case_type",
y="gini",
title="Gini Coefficient by Case Type (Top 8)",
labels={"case_type": "Case Type", "gini": "Gini"},
)
fig_gini.update_layout(
height=380, xaxis_tickangle=-45, yaxis_range=[0, 1]
)
st.plotly_chart(fig_gini, use_container_width=True)
else:
st.info("Insufficient data to compute per-type Gini")
except Exception as _:
st.info(
"Unable to compute per-type age distributions for current data"
)
except Exception as e:
st.error(f"Error loading events data: {e}")
# TAB 4: Report Generation
with tab4:
st.markdown("### Report Generation")
st.markdown(
"Generate comprehensive reports summarizing system performance and analysis."
)
outputs_dir = Path("outputs")
runs_dir = outputs_dir / "simulation_runs"
if not runs_dir.exists():
st.warning("No simulation outputs found.")
else:
metric_files = list(runs_dir.rglob("metrics.csv"))
run_paths = sorted({p.parent for p in metric_files})
if not run_paths:
st.info("No simulation runs found.")
else:
st.markdown("#### Select Data for Report")
# Multi-select runs
labels = [str(p.relative_to(runs_dir)) for p in run_paths]
label_to_path = {str(p.relative_to(runs_dir)): p for p in run_paths}
selected_runs = st.multiselect(
"Include simulation runs",
options=labels,
default=[labels[0]] if labels else [],
key="report_runs",
)
# Report options
include_metrics = st.checkbox("Include performance metrics", value=True)
include_fairness = st.checkbox("Include fairness analysis", value=True)
include_comparison = st.checkbox(
"Include run comparisons", value=len(selected_runs) > 1
)
if st.button("Generate Report", type="primary", use_container_width=True):
if not selected_runs:
st.error("Select at least one simulation run")
else:
with st.spinner("Generating report..."):
# Create report content
report_sections = []
# Header
report_sections.append(
"# Court Scheduling System - Performance Report"
)
report_sections.append(
f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
report_sections.append(
f"Runs included: {', '.join(selected_runs)}"
)
report_sections.append("")
# Performance metrics
if include_metrics:
report_sections.append("## Performance Metrics")
for run_name in selected_runs:
metrics_path = label_to_path[run_name] / "metrics.csv"
if metrics_path.exists():
df = pd.read_csv(metrics_path)
report_sections.append(f"### {run_name}")
if "disposal_rate" in df.columns:
avg_disposal = df["disposal_rate"].mean()
report_sections.append(
f"- Average Disposal Rate: {avg_disposal:.2%}"
)
if "utilization" in df.columns:
avg_util = df["utilization"].mean()
report_sections.append(
f"- Average Utilization: {avg_util:.2%}"
)
report_sections.append(
f"- Simulation Days: {len(df)}"
)
report_sections.append("")
# Comparison
if include_comparison and len(selected_runs) > 1:
report_sections.append("## Comparison Analysis")
report_sections.append(
f"Comparing: {selected_runs[0]} vs {selected_runs[1]}"
)
report_sections.append("")
# Fairness
if include_fairness:
report_sections.append("## Fairness Analysis")
report_sections.append(
"Fairness metrics evaluate equitable treatment of all cases."
)
report_sections.append("")
# Footer
report_sections.append("---")
report_sections.append(
"Report generated by Court Scheduling System Analytics"
)
report_content = "\n".join(report_sections)
# Display report
st.markdown("#### Report Preview")
st.markdown(report_content)
# Download button
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
st.download_button(
label="Download Report (Markdown)",
data=report_content,
file_name=f"scheduling_report_{timestamp}.md",
mime="text/markdown",
)
# Footer
st.markdown("---")
st.caption("Analytics & Reports - Performance analysis and comparative evaluation")
|