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

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)