File Loading & Format Support¶

Cute Plot provides comprehensive support for multiple file formats with intelligent loading strategies optimized for performance.

Supported File Formats¶

CSV Files (.csv)¶

Headers: Automatic header detection
Separators: Comma, semicolon, tab (auto-detected)
Date/Time: Automatic datetime parsing and conversion
Large files: Lazy loading for files > 2GB
Performance: Optimized with direct Float32 casting

PSCAD Files (.inf, .out)¶

Info files: PSCAD .inf metadata files
Output files: PSCAD .out time-series data
Auto-detection: Intelligent format recognition
Power systems: Specialized handling for power system data

PSS/E Files (.out)¶

Dynamic output: PSS/E dynamic simulation results
Format detection: Distinguished from PSCAD .out files
Time-series: Full support for PSS/E time-series data

Loading Methods¶

Click File → Open
Select one or multiple files
Files process automatically

Drag & Drop¶

OS Integration: Drag files from file explorer
Multiple files: Drop multiple files simultaneously
Template support: Automatic template application when available

Supported Locations¶

Local files: Full file system access
Network drives: Mapped network locations
Removable media: USB drives, external storage

Loading Strategies¶

Intelligent Loading Decision¶

Cute Plot automatically chooses the optimal loading strategy:

# Loading decision logic
if file_size > 2GB and supports_lazy_loading:
    use_lazy_loading()
else:
    use_normal_loading()

Normal Loading¶

For smaller files (< 2GB): - Complete load: Entire file loaded into memory - Full processing: All data columns processed - Immediate access: All series immediately available - Performance: Optimized with Float32 casting

Lazy Loading¶

For large files (≥ 2GB): - Preview mode: Load first 200 rows as preview - On-demand: Full data loaded when series selected - Memory efficient: Minimal memory footprint - Progress feedback: Loading progress indicators

Data Processing Pipeline¶

1. File Detection & Reading¶

# Format detection
loader = get_loader(file_path)  # Auto-detects format
result = loader.load()          # Uses appropriate strategy

2. Column Processing¶

Header detection: Automatic identification
Name cleaning: Remove quotes, spaces
Duplicate handling: Auto-rename duplicate columns
Type conversion: Convert to Float32 for performance

3. Index Creation¶

Time column: First column becomes time index
Datetime parsing: Automatic format detection
Zero offset: Time series normalized to start at 0
Format support: Multiple datetime formats

4. Data Validation¶

Column validation: Check for empty or invalid columns
Data quality: Identify and report issues
Error handling: Graceful handling of format issues

File Management¶

File Widgets¶

Each loaded file appears as a FileWidget in the sidebar: - Display name: Editable file name - Expansion: Click to show/hide series - Context menu: Right-click for options

Series Widgets¶

Individual data columns appear as SeriesWidget entries: - Column name: Original data column name - Selection: Click to select/deselect - Draggable: Drag to plots for visualization

File Operations¶

Rename Files¶

Right-click file in sidebar
Select Rename File
Enter new name
Press Enter to confirm

Close Files¶

Right-click file in sidebar
Select Close File
File and all series removed from session

Performance Optimization¶

Loading Performance¶

Parallel processing: Multi-threaded file processing
Memory optimization: Efficient data structures
Progress tracking: Real-time loading progress
Error recovery: Graceful handling of loading issues

Processing Optimizations¶

# Direct Float32 casting during CSV read
schema = {col: pl.Float32 for col in columns}
df = pl.read_csv(file_path, schema=schema)

Memory Management¶

Pyramid downsampling: Multi-level data representation
Lazy evaluation: Process data only when needed
Garbage collection: Automatic cleanup of unused data

Troubleshooting¶

Common Loading Issues¶

File Format Not Recognized¶

Check extension: Ensure file has correct extension
Content validation: Verify file contains expected data format
Format examples: See format specifications below

Large File Performance¶

Memory: Ensure sufficient RAM (8GB+ recommended)
Patience: Large files may take time to process
Lazy loading: Files >2GB automatically use lazy loading

Corrupted or Invalid Data¶

Partial loading: Files with some invalid data may partially load
Error messages: Check notifications for specific issues
Data validation: Verify source data integrity

File Format Specifications¶

CSV Requirements¶

Time,Series1,Series2,Series3
0.0,1.23,4.56,7.89
0.1,1.25,4.58,7.91
0.2,1.27,4.60,7.93

- First column: Time or index values - Headers: Column names in first row - Data: Numeric values only

PSCAD Requirements¶

.inf files: Valid PSCAD metadata format
.out files: PSCAD binary output format
Paired files: .inf and .out files should be in same directory

PSS/E Requirements¶

.out files: Valid PSS/E dynamic simulation output
Format validation: Must contain recognizable PSS/E headers

Advanced Loading Options¶

Lazy Loading Control¶

Override automatic lazy loading decision:

# Force normal loading for large files (advanced users)
loader.should_use_lazy_loading = False

Custom Processing¶

For specialized data formats: 1. Extend BaseFileLoader class 2. Implement custom load_normal() and load_lazy() methods 3. Register new loader in LOADERS registry

Batch Loading¶

For multiple related files: 1. Select multiple files in file dialog 2. Files process in sequence 3. Progress tracking for entire batch

File Loading Best Practices¶

File Organization¶

Consistent naming: Use clear, descriptive file names
Directory structure: Organize related files together
File sizes: Consider breaking very large files into segments

Performance Tips¶

Close unused files: Remove files not currently needed
Memory monitoring: Watch memory usage with large files
Template preparation: Prepare templates for repeated workflows

Data Preparation¶

Clean data: Remove invalid rows/columns before loading
Consistent formats: Ensure consistent datetime formats
Column names: Use descriptive, unique column names

Next Steps¶

After loading files successfully: 1. Learn Series Management 2. Explore Plotting & Visualization 3. Try Data Querying

Loading tip: Use drag & drop for quick file loading, especially with templates