| name | plotly-visualization |
| version | 1.0.0 |
| category | data |
| description | Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support. |
| type | reference |
| globs | ["src/assetutilities/common/visualization/**","src/assetutilities/common/visualizations.py","src/assetutilities/common/visualization.py"] |
| alwaysApply | false |
| tags | [] |
| scripts_exempt | true |
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
x_column: timestamp
y_columns:
- value1
- value2
title: "Analysis Results"
interactive: true
Common Workflows
- Line Plot from DataFrame: Load CSV/Excel → Configure columns → Generate plot
- Multi-Series Visualization: Prepare df_array → Set plt_settings → Render combined plot
- : DataFrame with dates → generate_time_line() → Export