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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
use crate::Precision;
use crate::chart::Chart;
use crate::core::context::PanelContext;
use crate::core::layer::{MarkRenderer, RectConfig, RenderBackend};
use crate::error::ChartonError;
use crate::mark::histogram::MarkHist;
use crate::visual::color::SingleColor;
use polars::prelude::*;
/// Implementation of `MarkRenderer` for Histogram charts.
///
/// This renderer handles both standard vertical histograms and flipped horizontal
/// histograms by checking the coordinate system state.
impl MarkRenderer for Chart<MarkHist> {
fn render_marks(
&self,
backend: &mut dyn RenderBackend,
context: &PanelContext,
) -> Result<(), ChartonError> {
let df_source = &self.data.df;
if df_source.height() == 0 {
return Ok(());
}
let mark_config = self
.mark
.as_ref()
.ok_or_else(|| ChartonError::Mark("MarkHist configuration is missing".into()))?;
// --- STEP 1: RESOLVE ENCODINGS & SCALES ---
let x_enc = self
.encoding
.x
.as_ref()
.ok_or(ChartonError::Encoding("X missing".into()))?;
let y_enc = self
.encoding
.y
.as_ref()
.ok_or(ChartonError::Encoding("Y missing".into()))?;
let x_scale = context.coord.get_x_scale();
let y_scale = context.coord.get_y_scale();
// --- STEP 2: GROUPING ---
// Partition the data if a color aesthetic is present to support grouped histograms.
let group_column = context
.spec
.aesthetics
.color
.as_ref()
.map(|c| c.field.as_str());
let groups = match group_column {
Some(col_name) => df_source.partition_by([col_name], true)?,
None => vec![df_source.clone()],
};
// Calculate the physical "thickness" of the bars based on the X-axis bins.
let bar_thickness = self.calculate_hist_bar_size(context)?;
// Detect if the coordinate system is flipped (e.g., for horizontal histograms).
let is_flipped = context.coord.is_flipped();
// --- STEP 3: RENDER GROUPS ---
for group_df in groups {
let group_color = self.resolve_group_color(&group_df, context, &mark_config.color)?;
let x_series = group_df.column(&x_enc.field)?.as_materialized_series();
let y_series = group_df.column(&y_enc.field)?.as_materialized_series();
// Normalize data to [0, 1] range.
let x_norms = x_scale.scale_type().normalize_series(x_scale, x_series)?;
let y_norms = y_scale.scale_type().normalize_series(y_scale, y_series)?;
// Baseline for frequency is 0.0 in normalized space.
let y_baseline_norm = 0.0;
for (opt_x, opt_y) in x_norms.into_iter().zip(y_norms.into_iter()) {
let x_n = opt_x.unwrap_or(0.0);
let y_n = opt_y.unwrap_or(0.0);
// Transform normalized coordinates to screen pixels.
// In flipped mode: px corresponds to logic Y (frequency), py to logic X (bins).
let (px, py) = context.transform(x_n, y_n);
let (px_base, py_base) = context.transform(x_n, y_baseline_norm);
let rect_config = if !is_flipped {
// --- STANDARD VERTICAL BARS ---
// x-axis is horizontal, bars grow upwards (or downwards).
let h = (py_base - py).abs();
RectConfig {
x: (px - bar_thickness / 2.0) as Precision,
y: py.min(py_base) as Precision,
width: bar_thickness as Precision,
height: h as Precision,
fill: group_color,
stroke: mark_config.stroke,
stroke_width: mark_config.stroke_width as Precision,
opacity: mark_config.opacity as Precision,
}
} else {
// --- FLIPPED HORIZONTAL BARS ---
// x-axis is vertical (bin locations), y-axis is horizontal (frequency).
let w = (px - px_base).abs();
RectConfig {
x: px.min(px_base) as Precision,
y: (py - bar_thickness / 2.0) as Precision,
width: w as Precision,
height: bar_thickness as Precision,
fill: group_color,
stroke: mark_config.stroke,
stroke_width: mark_config.stroke_width as Precision,
opacity: mark_config.opacity as Precision,
}
};
backend.draw_rect(rect_config);
}
}
Ok(())
}
}
// --- HELPER METHODS ---
impl Chart<MarkHist> {
/// Calculates the consistent pixel size (thickness) for bars.
///
/// This method maps the logical width of one bin into physical pixels.
/// It is coordinate-aware: it returns width for vertical charts and height for horizontal charts.
fn calculate_hist_bar_size(&self, context: &PanelContext) -> Result<f64, ChartonError> {
let n_bins = self
.encoding
.x
.as_ref()
.and_then(|x| x.bins)
.ok_or_else(|| ChartonError::Encoding("Bin count not resolved".into()))?
as f64;
let x_field = &self.encoding.x.as_ref().unwrap().field;
let x_scale = context.coord.get_x_scale();
let s = self.data.df.column(x_field)?.as_materialized_series();
let v_min = s
.min::<f64>()?
.ok_or(ChartonError::Data("X column is empty".into()))?;
let v_max = s
.max::<f64>()?
.ok_or(ChartonError::Data("X column is empty".into()))?;
// 4. Calculate the true data-space step between bins.
// If there are N unique bins, the distance from the first center to the last
// center represents (N - 1) full bin widths.
let data_step = if n_bins > 1.0 {
(v_max - v_min) / (n_bins - 1.0)
} else {
// Fallback: if only one bin, we can't measure a step.
// We use a default fraction of the scale's domain.
let (d0, d1) = x_scale.domain();
(d1 - d0) * 0.5
};
// Map two logical points to normalized space to measure the relative span.
let norm0 = x_scale.normalize(v_min);
let norm1 = x_scale.normalize(v_min + data_step);
// Convert normalized positions to pixels.
let (p0_x, p0_y) = context.transform(norm0, 0.0);
let (p1_x, p1_y) = context.transform(norm1, 0.0);
// Determine the pixel distance. If flipped, the bin direction is vertical (Y).
let theoretical_thickness = if context.coord.is_flipped() {
(p1_y - p0_y).abs()
} else {
(p1_x - p0_x).abs()
};
// Return with a visual gap factor (0.95) to prevent bar overlap.
Ok(theoretical_thickness * 0.95)
}
/// Resolves the fill color for a group based on the color encoding or fallback.
fn resolve_group_color(
&self,
df: &DataFrame,
context: &PanelContext,
fallback: &SingleColor,
) -> Result<SingleColor, ChartonError> {
if let Some(ref mapping) = context.spec.aesthetics.color {
let s = df.column(&mapping.field)?.as_materialized_series();
let s_trait = mapping.scale_impl.as_ref();
// Representative color for the group based on the first occurrence.
let first_val_norm = s_trait
.scale_type()
.normalize_series(s_trait, &s.head(Some(1)))?;
let norm = first_val_norm.get(0).unwrap_or(0.0);
Ok(s_trait
.mapper()
.map(|m| m.map_to_color(norm, s_trait.logical_max()))
.unwrap_or_else(|| *fallback))
} else {
Ok(*fallback)
}
}
}