Plotly Visualization

Plotly Visualization Skill

Overview

This skill provides comprehensive visualization capabilities using both Plotly (interactive) and Matplotlib (static) backends. It enables generation of line plots, scatter plots, polar plots, bar charts, timelines, and multi-series visualizations from pandas DataFrames with YAML-driven configuration.

Key Components

Visualization Class (visualizations.py)

Main matplotlib-based visualization engine:

• generate_time_line(data, plt_settings) - Create timeline visualizations from DataFrame
• from_df_array(df_array, plt_settings) - Plot multiple DataFrames as array
• from_df_columns(df, plt_settings) - Generate line, scatter, polar, or bar plots from DataFrame columns

VisualizationTemplatesPlotly (visualization_templates_plotly.py)

Plotly template generator for interactive charts:

• get_xy_line_df(custom_analysis_dict) - XY line plot templates
• get_x_datetime_input_plotly(custom_analysis_dict) - DateTime-based plot templates

Specialized Modules

• visualization_xy.py - XY coordinate plotting
• visualization_polar.py - Polar coordinate systems
• visualization_common.py - Shared utilities

Usage Patterns

Public-facing risk / incident infographics

When generating infographic statistics from incident, safety, reliability, or risk datasets, treat the evidence taxonomy as part of the visualization contract. Before rendering:

• name each metric with its exact evidence scope;
• persist numerator evidence (matched_incident_ids) and exclusions (excluded_incident_ids);
• show denominators alongside percentages;
• avoid broad substring classifiers that can overcount (weather, sank, overboard are common traps);
• include caveats in both stats JSON and rendered HTML;
• run adversarial review on metric semantics before merging or publishing.

See references/risk-infographic-evidence-taxonomy.md for the checklist and false-positive examples.

YAML Configuration Structure

visualization:
  type: line  # line, scatter, polar, bar
  x_column: timestamp
  y_columns:
    - value1
    - value2
  title: "Analysis Results"
  interactive: true  # Use Plotly vs Matplotlib

Common Workflows

1. Line Plot from DataFrame: Load CSV/Excel → Configure columns → Generate plot
2. Multi-Series Visualization: Prepare df_array → Set plt_settings → Render combined plot
3. Timeline Generation: DataFrame with dates → generate_time_line() → Export

Module Location

• Primary: src/assetutilities/common/visualizations.py
• Templates: src/assetutilities/common/visualization/visualization_templates_plotly.py
• XY Plots: src/assetutilities/common/visualization/visualization_xy.py
• Polar Plots: src/assetutilities/common/visualization/visualization_polar.py

Dependencies

• matplotlib (static plots)
• plotly (interactive plots)
• pandas (DataFrame handling)
• numpy (numerical operations)