revue 2.71.1

A Vue-style TUI framework for Rust with CSS styling
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143

//! Chart statistics tests extracted from src/widget/data/chart/chart_stats.rs
//!
//! This file contains tests for statistical functions used by charts:
//! - filter_finite() - Filter out NaN and infinite values
//! - percentile() - Calculate percentile from sorted data
//! - mean() - Calculate arithmetic mean
//! - median() - Calculate median of data
//! - quartiles() - Calculate Q1, median, Q3
//! - min_max() - Get minimum and maximum values
//! - iqr() - Calculate interquartile range
//! - outliers_iqr() - Detect outliers using IQR method
//! - compute_bins() - Compute histogram bins from data
//! - HistogramBin struct and BinConfig enum

use revue::widget::data::chart::chart_stats::{
    BinConfig, compute_bins, filter_finite, iqr, mean, median, min_max, outliers_iqr,
    percentile, HistogramBin,
};

#[test]
fn test_filter_finite() {
    let data = vec![1.0, f64::NAN, 2.0, f64::INFINITY, 3.0];
    let filtered = filter_finite(&data);
    assert_eq!(filtered, vec![1.0, 2.0, 3.0]);
}

#[test]
fn test_percentile() {
    let sorted = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    assert_eq!(percentile(&sorted, 0.0), 1.0);
    assert_eq!(percentile(&sorted, 50.0), 3.0);
    assert_eq!(percentile(&sorted, 100.0), 5.0);
}

#[test]
fn test_percentile_empty() {
    let sorted: Vec<f64> = vec![];
    assert_eq!(percentile(&sorted, 50.0), 0.0);
}

#[test]
fn test_mean() {
    assert_eq!(mean(&[1.0, 2.0, 3.0, 4.0, 5.0]), Some(3.0));
    assert_eq!(mean(&[]), None);
    assert_eq!(mean(&[f64::NAN]), None);
}

#[test]
fn test_median_odd() {
    assert_eq!(median(&[1.0, 2.0, 3.0, 4.0, 5.0]), Some(3.0));
}

#[test]
fn test_median_even() {
    assert_eq!(median(&[1.0, 2.0, 3.0, 4.0]), Some(2.5));
}

#[test]
fn test_median_empty() {
    assert_eq!(median(&[]), None);
}

#[test]
fn test_quartiles() {
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
    let (q1, med, q3) = quartiles(&data).unwrap();
    // Linear interpolation: k = p/100 * (n-1), where n=9
    // Q1: k = 0.25 * 8 = 2.0 -> sorted[2] = 3.0
    // Median: k = 0.5 * 8 = 4.0 -> sorted[4] = 5.0
    // Q3: k = 0.75 * 8 = 6.0 -> sorted[6] = 7.0
    assert!((q1 - 3.0).abs() < 0.1);
    assert!((med - 5.0).abs() < 0.1);
    assert!((q3 - 7.0).abs() < 0.1);
}

#[test]
fn test_min_max() {
    assert_eq!(min_max(&[3.0, 1.0, 4.0, 1.0, 5.0]), Some((1.0, 5.0)));
    assert_eq!(min_max(&[]), None);
}

#[test]
fn test_iqr() {
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
    let iqr_val = iqr(&data).unwrap();
    assert!(iqr_val > 0.0);
}

#[test]
fn test_outliers_iqr() {
    let mut data: Vec<f64> = (0..20).map(|x| x as f64).collect();
    data.push(100.0); // Outlier
    data.push(-50.0); // Outlier

    let outliers = outliers_iqr(&data);
    assert!(outliers.contains(&100.0));
    assert!(outliers.contains(&-50.0));
}

#[test]
fn test_compute_bins_auto() {
    let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
    let bins = compute_bins(&data, &BinConfig::Auto);
    assert!(!bins.is_empty());
    assert!(bins.len() <= 100);
}

#[test]
fn test_compute_bins_count() {
    let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
    let bins = compute_bins(&data, &BinConfig::Count(10));
    assert_eq!(bins.len(), 10);
}

#[test]
fn test_compute_bins_width() {
    let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
    let bins = compute_bins(&data, &BinConfig::Width(10.0));
    assert_eq!(bins.len(), 10);
}

#[test]
fn test_compute_bins_edges() {
    let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
    let bins = compute_bins(&data, &BinConfig::Edges(vec![0.0, 25.0, 50.0, 75.0, 100.0]));
    assert_eq!(bins.len(), 4);
}

#[test]
fn test_compute_bins_empty() {
    let bins = compute_bins(&[], &BinConfig::Auto);
    assert!(bins.is_empty());
}

#[test]
fn test_histogram_bin_frequency() {
    let data: Vec<f64> = (0..100).map(|x| x as f64).collect();
    let bins = compute_bins(&data, &BinConfig::Count(10));

    // Sum of frequencies should be approximately 1.0
    let total_freq: f64 = bins.iter().map(|b| b.frequency).sum();
    assert!((total_freq - 1.0).abs() < 0.01);
}