plotly-visualization

Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.

5 stars

byvamseeachanta

View on GitHub Installation ↓

Best use case

plotly-visualization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.

Teams using plotly-visualization should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/plotly-visualization/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/data/visualization/plotly-visualization/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/plotly-visualization/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How plotly-visualization Compares

Feature / Agent	plotly-visualization	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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
```yaml
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)

Related Skills

orcaflex-visualization

from vamseeachanta/workspace-hub

Generate visualizations from OrcaFlex and OrcaWave simulations using the shared OrcFxAPI — model views (SaveModelView), time series plots, range graphs, and interactive HTML reports. Covers both .dat/.sim (OrcaFlex) and .owd (OrcaWave) files via the same API surface.