---
title: "Data Science Methods for Forecasting in Energy and Economics"
date: 2025-07-10
format:
    revealjs:
        embed-resources: true
        footer: ""
execute:
  daemon: false
highlight-style: github
---

#### B-spline Basis Functions

```{ojs}
bsplineData = FileAttachment("basis_functions.csv").csv({ typed: true })
```

```{ojs}
knotValues = Array.from(new Set(bsplineData.map(d => d.knots))).sort((a, b) => a - b)
minKnots = Math.min(...knotValues)
maxKnots = Math.max(...knotValues)

muValues = Array.from(new Set(bsplineData.map(d => d.mu))).sort((a, b) => a - b)
minMu = Math.min(...muValues)
maxMu = Math.max(...muValues)
```

```{ojs}
// Create a more compact layout for controls
viewof controls = Inputs.form({
  knots: Inputs.range([minKnots, maxKnots], {value: minKnots, step: 1, label: "Knots:", width: 200}),
  mu: Inputs.range([minMu, maxMu], {value: 0.5, step: 0.1, label: "μ:", width: 200})
}, {
  submit: false,
  layout: 'horizontal',
  style: 'display: flex; gap: 20px; align-items: center; margin-bottom: 10px; font-size: 0.9em;'
})

selectedKnots = controls.knots
selectedMu = controls.mu
```

```{ojs}
filteredBspline = bsplineData.filter(function(row) {
  return selectedKnots == row.knots && Math.abs(selectedMu - row.mu) < 0.001;
})
```

```{ojs}
Plot.plot({
  grid: true,
  y: {domain: [0, 0.7]},
  x: {label: "x", domain: [0, 1]},
  marks: [
    Plot.line(filteredBspline, {
      x: "x", 
      y: "y", 
      stroke: "b", 
      strokeWidth: 5,
    }),
    Plot.ruleY([0])
  ],
  color: {
    legend: false,
    label: "Basis Function"
  },
  marginRight: 80,
  width: 800,
  height: 400,
  // title: `B-spline Basis Functions (${selectedKnots} knots, μ = ${selectedMu})`
})
```

::: {.callout-note}
TODO
:::