Init simple web-app

app.qmd

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+---
+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
+:::

test.R

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+# %%
+library(profoc)
+library(ggplot2)
+library(tidyr)
+library(dplyr)
+library(readr)
+
+# Creating faceted plots for different knot values and mu values
+# Create a function to generate the data for a given number of knots and mu value
+generate_basis_data <- function(num_knots, mu_value) {
+  B <- profoc:::make_basis_matrix(1:99 / 100,
+    profoc::make_knots(
+      n = num_knots,
+      mu = mu_value,
+      sig = 1,
+      nonc = 0,
+      deg = 1
+    ),
+    deg = 3
+  )
+  df <- as.data.frame(as.matrix(B))
+  df$x <- 1:99 / 100
+  df_long <- pivot_longer(df,
+    cols = -x,
+    names_to = "b",
+    values_to = "y"
+  )
+  df_long$knots <- num_knots # Store knot count as numeric
+  df_long$mu <- mu_value # Add mu parameter information
+  return(df_long)
+}
+
+# Generate data for each combination of knot value and mu value
+knot_values <- 5:10
+mu_values <- seq(0.1, 0.9, by = 0.1)
+
+# Create an empty list to store all combinations
+all_data <- list()
+counter <- 1
+
+# Generate data for all combinations
+for (k in knot_values) {
+  for (m in mu_values) {
+    all_data[[counter]] <- generate_basis_data(k, m)
+    counter <- counter + 1
+  }
+}
+
+# Combine all data frames
+all_data <- bind_rows(all_data)
+
+write_csv(all_data, "basis_functions.csv")