import { useMemo } from 'react';
import Plot from 'react-plotly.js';
import { TrendingUp, TrendingDown, Minus } from 'lucide-react';
interface Run {
run_id: number;
name: string;
source: 'FEA' | 'NN';
trial_count: number;
best_value: number | null;
avg_value: number | null;
first_trial: string | null;
last_trial: string | null;
}
interface PlotlyRunComparisonProps {
runs: Run[];
height?: number;
}
export function PlotlyRunComparison({ runs, height = 400 }: PlotlyRunComparisonProps) {
const chartData = useMemo(() => {
if (runs.length === 0) return null;
// Separate FEA and NN runs
const feaRuns = runs.filter(r => r.source === 'FEA');
const nnRuns = runs.filter(r => r.source === 'NN');
// Create bar chart for trial counts
const trialCountData = {
x: runs.map(r => r.name),
y: runs.map(r => r.trial_count),
type: 'bar' as const,
name: 'Trial Count',
marker: {
color: runs.map(r => r.source === 'NN' ? 'rgba(147, 51, 234, 0.8)' : 'rgba(59, 130, 246, 0.8)'),
line: { color: runs.map(r => r.source === 'NN' ? 'rgb(147, 51, 234)' : 'rgb(59, 130, 246)'), width: 1 }
},
hovertemplate: '%{x}
Trials: %{y}'
};
// Create line chart for best values
const bestValueData = {
x: runs.map(r => r.name),
y: runs.map(r => r.best_value),
type: 'scatter' as const,
mode: 'lines+markers' as const,
name: 'Best Value',
yaxis: 'y2',
line: { color: 'rgba(16, 185, 129, 1)', width: 2 },
marker: { size: 8, color: 'rgba(16, 185, 129, 1)' },
hovertemplate: '%{x}
Best: %{y:.4e}'
};
return { trialCountData, bestValueData, feaRuns, nnRuns };
}, [runs]);
// Calculate statistics
const stats = useMemo(() => {
if (runs.length === 0) return null;
const totalTrials = runs.reduce((sum, r) => sum + r.trial_count, 0);
const feaTrials = runs.filter(r => r.source === 'FEA').reduce((sum, r) => sum + r.trial_count, 0);
const nnTrials = runs.filter(r => r.source === 'NN').reduce((sum, r) => sum + r.trial_count, 0);
const bestValues = runs.map(r => r.best_value).filter((v): v is number => v !== null);
const overallBest = bestValues.length > 0 ? Math.min(...bestValues) : null;
// Calculate improvement from first FEA run to overall best
const feaRuns = runs.filter(r => r.source === 'FEA');
const firstFEA = feaRuns.length > 0 ? feaRuns[0].best_value : null;
const improvement = firstFEA && overallBest ? ((firstFEA - overallBest) / Math.abs(firstFEA)) * 100 : null;
return {
totalTrials,
feaTrials,
nnTrials,
overallBest,
improvement,
totalRuns: runs.length,
feaRuns: runs.filter(r => r.source === 'FEA').length,
nnRuns: runs.filter(r => r.source === 'NN').length
};
}, [runs]);
if (!chartData || !stats) {
return (
No run data available
);
}
return (
{/* Stats Summary */}
Total Runs
{stats.totalRuns}
Total Trials
{stats.totalTrials}
FEA Trials
{stats.feaTrials}
NN Trials
{stats.nnTrials}
Best Value
{stats.overallBest !== null ? stats.overallBest.toExponential(3) : 'N/A'}
Improvement
{stats.improvement !== null ? (
<>
{stats.improvement > 0 ? :
stats.improvement < 0 ? :
}
{Math.abs(stats.improvement).toFixed(1)}%
>
) : 'N/A'}
{/* Chart */}
{/* Runs Table */}
| Run Name |
Source |
Trials |
Best Value |
Avg Value |
Duration |
{runs.map((run) => {
// Calculate duration if times available
let duration = '-';
if (run.first_trial && run.last_trial) {
const start = new Date(run.first_trial);
const end = new Date(run.last_trial);
const diffMs = end.getTime() - start.getTime();
const diffMins = Math.round(diffMs / 60000);
if (diffMins < 60) {
duration = `${diffMins}m`;
} else {
const hours = Math.floor(diffMins / 60);
const mins = diffMins % 60;
duration = `${hours}h ${mins}m`;
}
}
return (
| {run.name} |
{run.source}
|
{run.trial_count} |
{run.best_value !== null ? run.best_value.toExponential(4) : '-'}
|
{run.avg_value !== null ? run.avg_value.toExponential(4) : '-'}
|
{duration} |
);
})}
);
}
export default PlotlyRunComparison;