Installation & Setup¶

This guide covers installing and setting up Cute Plot on your system.

System Requirements¶

Operating System¶

macOS: macOS 10.15 (Catalina) or later
Windows: Windows 10 or later
Linux: Most modern distributions

Hardware Requirements¶

RAM: 4GB minimum, 8GB+ recommended for large datasets
Storage: 500MB for installation, additional space for data files
GPU: OpenGL 3.3 compatible graphics card

Python Requirements¶

Python: 3.8 or later
Dependencies: Automatically handled by package manager

Installation Methods¶

Method 1: Using pip (Recommended)¶

pip install cuteplot

Method 2: From Source¶

Clone the repository:

git clone https://github.com/your-repo/cuteplot.git
cd cuteplot

Create virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:
```
pip install -r requirements.txt
```
Run the application:
```
python cuteplot_dcg.py
```

First Launch Setup¶

License Configuration¶

Cute Plot uses a licensing system. On first launch:

The application will display license status in the top-right corner
For trial or licensed versions, follow the licensing prompts
Contact support for license information

Configuration Directory¶

Cute Plot creates a configuration directory at: - macOS/Linux: ~/.cuteplot/ - Windows: %USERPROFILE%\.cuteplot\

This directory contains: - templates/ - Saved plot templates - recent_templates.json - Recently used templates - Configuration files and logs

Initial Settings¶

The application includes several default settings that can be customized:

Query Options¶

Access via Settings → Query Options: - Basic statistics (Min, Max, Mean, Std, Count) - Advanced calculations (Frequency, RMS, Rise Time, etc.) - FFT Analysis options

Theme Settings¶

Access via Settings → Theme: - Dark Theme (default) - Light Theme
- Custom themes

Verifying Installation¶

Quick Test¶

Launch Cute Plot
Load a sample CSV file using File → Open
Drag series from the sidebar to the plot area
Verify plotting and interaction work correctly

Performance Test¶

For large file performance: 1. Load a file larger than 2GB (should use lazy loading) 2. Verify smooth plotting and navigation 3. Test zoom and pan operations

Troubleshooting¶

Common Issues¶

Application Won't Start¶

Check Python version: python --version
Verify all dependencies are installed
Check system logs for error messages

Poor Performance¶

Increase virtual memory/swap space
Close unnecessary applications
Consider upgrading RAM for very large datasets

File Loading Issues¶

Verify file format is supported (CSV, PSCAD .inf/.out, PSS/E .out)
Check file permissions
Try smaller sample files first

Graphics Issues¶

Update graphics drivers
Verify OpenGL 3.3+ support
Try different display scaling settings

Log Files¶

Check application logs for detailed error information: - Location: Configuration directory /.cuteplot/logs/ - Access: Settings → Logger menu option

Getting Help¶

Built-in Feedback: Use Leave Feedback menu option
Documentation: Check relevant documentation sections
Support: Contact support with system information and error logs

Uninstallation¶

Remove Application¶

pip uninstall cuteplot

Remove Configuration¶

Delete the configuration directory: - macOS/Linux: rm -rf ~/.cuteplot/ - Windows: Delete %USERPROFILE%\.cuteplot\

Advanced Installation¶

Development Setup¶

For development and customization:

Fork the repository
Clone your fork
Install in development mode:
```
pip install -e .
```
Set up pre-commit hooks:
```
pre-commit install
```

Custom Dependencies¶

For specific environments, you may need additional packages:

# For enhanced performance
pip install numba

# For additional file formats
pip install openpyxl xlrd

# For development
pip install pytest black flake8

Next Steps¶

After successful installation:

Read the User Interface Overview
Follow the Quick Start Guide
Learn about File Loading

Installation support: Use the built-in feedback system for installation issues