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

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

Core Scripts

1. Main Workflow Script

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

2. Data Analysis

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

Output: Data characteristics report + chart recommendation scheme

3. Chart Generation

```bash 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:

1. Auto-save File
2. ```python
3. # Rename and save to output/{timestamp}-{filename}/
4. save_uploaded_file(input_file, output_base="output/")
5. ```

1. Execute Data Analysis
2. ```python
3. # Analyze data characteristics, generate report
4. python scripts/data_analyzer.py --input output/20250312-data/data.csv
5. ```

1. Display Analysis Report to User
2. ```markdown
3. ## Data Analysis Report
4. ### Data Overview
5. - File: data.csv
6. - Dimensions: 120 rows × 5 columns
7. - Types: 3 numeric + 2 categorical columns
8. ### Column Details
9. | Column | Type | Description |
10. |-----|------|-----|
11. | date | datetime | 2023-01 to 2023-12 |
12. | sales | numeric | mean=1250, std=320 |
13. | region | categorical | 4 categories: N/S/E/W |
14. ### Chart Recommendations
15. Based on data characteristics, the following schemes are recommended:
16. Scheme 1: Time Trend Analysis ⭐Recommended
17. - Chart Type: Line plot
18. - Content: Sales trend over time
19. - Reason: Time series data, most intuitive for showing trends
20. Scheme 2: Regional Comparison
21. - Chart Type: Grouped bar chart
22. - Content: Sales comparison across regions
23. - Reason: Categorical comparison, suitable for showing differences
24. Scheme 3: Comprehensive Dashboard
25. - Chart Type: 2×2 subplot layout
26. - Includes: Trend line + Bar chart + Box plot + Correlation heatmap
27. - Reason: Rich data dimensions, comprehensive display
28. Please tell me what you want:
29. - "Generate schemes 1 and 2"
30. - "Generate all"
31. - "Modify scheme 3..." (provide your modification suggestions)
32. ```

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

1. Generate and Save
2. ```python
3. # Generate Python code
4. python scripts/plot_generator.py --config plot_config.json
5. # Output to same directory
6. output/20250312-data/
7. ├── 20250312-145230_plot.py # Code
8. ├── 20250312-145230_fig1_line.png # Chart
9. └── 20250312-145230_fig2_bar.png
10. ```

Scenario 2: Data Analysis Only

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

Scenario 3: Generate from Config

```bash 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

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