Pythesis Plot

Python scientific plotting tool for thesis/dissertation scenarios. Workflow: data upload → analysis → recommendations → confirmation → generation. Triggers when users upload data files (CSV/Excel/TXT) and ask for plots, charts, figures, or data visualization for academic publications.

Audits

Pass

ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install pythesis-plot

PyThesisPlot

Python scientific plotting workflow tool supporting the complete process from data upload to figure generation for academic publications.

Workflow

[User Uploads Data] → [Auto-save to output dir] → [Data Analysis]
                                           ↓
[Generate Images to output dir] ← [Code Generation] ← [User Confirms Scheme]

Required Steps

Data Reception: User uploads data file (txt/md/xlsx/csv)
Auto-save: Rename to timestamp-original_filename, save to output/YYYYMMDD-filename/
Data Analysis: Analyze dimensions, types, statistical features, column relationships
Chart Recommendations: Recommend chart schemes based on data characteristics (type, quantity, layout)
User Confirmation: Display analysis report, must wait for user confirmation before generation
Generation & Delivery: Python code + chart images, save to same output directory

Core Scripts

1. Main Workflow Script

python scripts/workflow.py --input data.csv --output-dir output/

2. Data Analysis

python scripts/data_analyzer.py --input data.csv

Output: Data characteristics report + chart recommendation scheme

3. Chart Generation

python scripts/plot_generator.py --config plot_config.json --output-dir output/

File Management Standards

Directory Structure

output/
└── 20250312-145230-data.csv/          # Named with timestamp + filename
    ├── 20250312-145230-data.csv       # Original data file (renamed)
    ├── analysis_report.md             # Data analysis report
    ├── plot_config.json               # Chart configuration (generated after user confirmation)
    ├── 20250312-145230_plot.py        # Generated Python code
    ├── 20250312-145230_fig1_line.png  # Chart (PNG image)
    └── 20250312-145230_fig2_bar.png

Naming Conventions

File Type	Naming Format	Example
Data File	`{timestamp}-{original}`	`20250312-145230-data.csv`
Analysis Report	`analysis_report.md`	`analysis_report.md`
Python Code	`{timestamp}_plot.py`	`20250312-145230_plot.py`
Chart PNG	`{timestamp}_fig{n}_{type}.png`	`20250312-145230_fig1_line.png`

Usage

Scenario 1: Complete Workflow

When user uploads a data file:

Auto-save File

# Rename and save to output/{timestamp}-{filename}/
save_uploaded_file(input_file, output_base="output/")

Execute Data Analysis

# Analyze data characteristics, generate report
python scripts/data_analyzer.py --input output/20250312-data/data.csv

Display Analysis Report to User

## Data Analysis Report

### Data Overview
- File: data.csv
- Dimensions: 120 rows × 5 columns
- Types: 3 numeric + 2 categorical columns

### Column Details
| Column | Type | Description |
|-----|------|-----|
| date | datetime | 2023-01 to 2023-12 |
| sales | numeric | mean=1250, std=320 |
| region | categorical | 4 categories: N/S/E/W |

### Chart Recommendations
Based on data characteristics, the following schemes are recommended:

**Scheme 1: Time Trend Analysis** ⭐Recommended
- Chart Type: Line plot
- Content: Sales trend over time
- Reason: Time series data, most intuitive for showing trends

**Scheme 2: Regional Comparison**
- Chart Type: Grouped bar chart
- Content: Sales comparison across regions
- Reason: Categorical comparison, suitable for showing differences

**Scheme 3: Comprehensive Dashboard**
- Chart Type: 2×2 subplot layout
- Includes: Trend line + Bar chart + Box plot + Correlation heatmap
- Reason: Rich data dimensions, comprehensive display

Please tell me what you want:
- "Generate schemes 1 and 2"
- "Generate all"
- "Modify scheme 3..." (provide your modification suggestions)

Wait for User Confirmation ⚠️ Critical Step
- User may say: "Generate scheme 1" / "Generate all" / "Modify XX..."
- Must wait for explicit instruction before entering generation phase

Generate and Save

# Generate Python code
python scripts/plot_generator.py --config plot_config.json

# Output to same directory
output/20250312-data/
├── 20250312-145230_plot.py        # Code
├── 20250312-145230_fig1_line.png  # Chart
└── 20250312-145230_fig2_bar.png

Scenario 2: Data Analysis Only

python scripts/data_analyzer.py --input data.csv --output report.md

Scenario 3: Generate from Config

python scripts/plot_generator.py --config config.json --output-dir ./

Chart Recommendation Logic

Data Characteristics	Recommended Chart	Application
Time series + Numeric	Line plot	Trend display
Categorical + Single numeric	Bar chart	Category comparison
Categorical + Distribution	Box/Violin plot	Distribution display
Two numeric (correlated)	Scatter (+regression)	Correlation analysis
Multiple numeric (correlated)	Heatmap	Correlation matrix
Single numeric distribution	Histogram/Density	Distribution characteristics
Multi-dimensional rich data	2×2 subplots	Comprehensive display

Supported File Formats

CSV: .csv (Recommended)
Excel: .xlsx, .xls
Text: .txt, .md (table format)

Dependencies

pandas >= 1.3.0
matplotlib >= 3.5.0
seaborn >= 0.11.0
openpyxl >= 3.0.0  # Excel support
numpy >= 1.20.0
scipy >= 1.7.0

Reference Documents

Workflow Guide - Complete workflow instructions
Chart Types - Detailed chart type descriptions
Style Guide - Color schemes, fonts, size standards
Examples - Complete code examples

Important Notes

User confirmation is mandatory: Must wait for user confirmation after analysis, cannot generate directly
Unified file management: All output files saved to same output/{timestamp}-{filename}/ directory
High-resolution output: Generate PNG at 300 DPI (suitable for publication)
Code traceability: Generated Python code also saved to same directory for user modification
Academic style: Charts follow top journal standards (Nature/Science/Lancet style)