Raxol.Terminal.Graphics.DataVisualization (Raxol v2.0.1)
View SourceAdvanced data visualization component library for terminal graphics.
This module extends the existing chart capabilities with:
- Real-time data streaming visualizations
- Interactive data exploration tools
- Advanced chart types (heatmaps, scatter plots, histograms, etc.)
- Multi-dimensional data visualization
- Export capabilities for graphics
- Performance-optimized rendering for large datasets
Built on top of existing Raxol visualization infrastructure, this module provides enterprise-grade data visualization capabilities.
Features
Advanced Chart Types
- Heatmaps with customizable color scales
- Scatter plots with clustering visualization
- Histograms and distribution plots
- Multi-series time-series charts
- Bubble charts with size/color encoding
- Treemaps for hierarchical data
Real-time Capabilities
- Streaming data visualization with configurable buffers
- Live updates with smooth animations
- Automatic scaling and zooming
- Performance monitoring and throttling
- Memory-efficient data windowing
Interactive Features
- Mouse-driven zoom and pan
- Data point selection and highlighting
- Dynamic filtering and grouping
- Tooltip information on hover
- Click-to-drill-down functionality
Usage
# Create a real-time line chart
{:ok, chart} = DataVisualization.create_streaming_chart(:line, %{
title: "CPU Usage",
max_points: 100,
update_interval: 1000, # 1 second
auto_scale: true
})
# Stream data points
DataVisualization.add_data_point(chart, %{
timestamp: System.system_time(:millisecond),
value: 75.5,
series: "cpu_usage"
})
# Create interactive heatmap
{:ok, heatmap} = DataVisualization.create_heatmap(data, %{
width: 80, height: 24,
color_scale: :viridis,
interactive: true
})Summary
Functions
Adds a data point to a streaming chart.
Adds multiple data points efficiently.
Returns a specification to start this module under a supervisor.
Creates an interactive heatmap visualization.
Creates a histogram from data values.
Creates a scatter plot with optional clustering visualization.
Creates a real-time streaming chart.
Enables interactive features for a chart.
Exports a chart to various formats.
Gets performance statistics for visualization operations.
Types
@type chart_id() :: String.t()
@type chart_state() :: %{ id: chart_id(), type: visualization_type(), config: map(), data_buffer: [data_point()], bounds: map(), graphics_id: non_neg_integer(), last_update: non_neg_integer(), interactive: boolean(), animation_state: map() }
@type data_point() :: %{ timestamp: non_neg_integer(), value: number(), series: String.t(), metadata: map() }
@type streaming_config() :: %{ max_points: non_neg_integer(), update_interval: non_neg_integer(), auto_scale: boolean(), buffer_size: non_neg_integer(), animation_enabled: boolean() }
@type visualization_type() ::
:line
| :bar
| :scatter
| :heatmap
| :histogram
| :bubble
| :treemap
| :sparkline
Functions
@spec add_data_point(chart_id(), data_point()) :: :ok | {:error, term()}
Adds a data point to a streaming chart.
Examples
DataVisualization.add_data_point(chart_id, %{
timestamp: System.system_time(:millisecond),
value: 85.2,
series: "cpu_usage",
metadata: %{core: 0, temperature: 65}
})@spec add_data_points(chart_id(), [data_point()]) :: :ok | {:error, term()}
Adds multiple data points efficiently.
Examples
points = [
%{timestamp: t1, value: 10, series: "cpu"},
%{timestamp: t2, value: 20, series: "cpu"},
%{timestamp: t3, value: 15, series: "memory"}
]
DataVisualization.add_data_points(chart_id, points)Returns a specification to start this module under a supervisor.
See Supervisor.
Creates an interactive heatmap visualization.
Parameters
data- 2D array of numerical dataconfig- Heatmap configuration
Examples
# Create temperature heatmap
data = generate_2d_temperature_data(24, 60) # 24 hours x 60 minutes
{:ok, heatmap_id} = DataVisualization.create_heatmap(data, %{
title: "Temperature Over Time",
x_labels: hour_labels,
y_labels: minute_labels,
color_scale: :thermal,
bounds: %{x: 5, y: 5, width: 70, height: 20},
interactive: true,
tooltip_enabled: true
})Creates a histogram from data values.
Examples
values = [1.2, 1.5, 1.8, 2.1, 2.3, 2.8, 3.1, 3.5]
{:ok, hist_id} = DataVisualization.create_histogram(values, %{
title: "Value Distribution",
bins: 10,
show_statistics: true,
bounds: %{x: 0, y: 0, width: 60, height: 15}
})Creates a scatter plot with optional clustering visualization.
Examples
points = [
%{x: 10, y: 20, size: 5, color: :blue, cluster: 1},
%{x: 15, y: 25, size: 8, color: :red, cluster: 2}
]
{:ok, scatter_id} = DataVisualization.create_scatter_plot(points, %{
title: "Data Clustering",
show_clusters: true,
cluster_colors: [:blue, :red, :green],
interactive: true
})@spec create_streaming_chart(visualization_type(), map()) :: {:ok, chart_id()} | {:error, term()}
Creates a real-time streaming chart.
Parameters
type- Chart type (:line, :bar, :scatter, etc.)config- Chart configuration including streaming settings
Returns
{:ok, chart_id}- Successfully created streaming chart{:error, reason}- Failed to create chart
Examples
{:ok, chart_id} = DataVisualization.create_streaming_chart(:line, %{
title: "Network Throughput",
max_points: 200,
update_interval: 500, # 500ms updates
series: ["download", "upload"],
bounds: %{x: 0, y: 0, width: 80, height: 20},
auto_scale: true,
animation: %{enabled: true, duration: 300}
})Enables interactive features for a chart.
Examples
DataVisualization.enable_interaction(chart_id, %{
zoom: true,
pan: true,
select: true,
hover_tooltip: true
})Exports a chart to various formats.
Examples
# Export as ASCII art
{:ok, ascii_data} = DataVisualization.export_chart(chart_id, :ascii)
# Export as JSON data
{:ok, json_data} = DataVisualization.export_chart(chart_id, :json)
# Export as SVG (if supported)
{:ok, svg_data} = DataVisualization.export_chart(chart_id, :svg)@spec get_performance_stats() :: map()
Gets performance statistics for visualization operations.