Package: eyeris 3.2.0.9001

🎯 One obvious, correct entry point | 🧭 An interface that resists hallucination | 🔧 Expert defaults mean fewer iterations | 🔒 Output an agent can trust | 🧩 Composable with grammars an agent already knows | 🤖 Agent quickstart | 🗂 For LLM indexers | 📚 Citing eyeris

The one concept to know: block is run | The pattern | The equivalent: label the run in bidsify() instead | What you get | Handling missing or non-sequential runs | Fixing or re-running a single run | Sanity check: confirm one run per file | The other case: multiple runs inside one file | ✨ Summary | 📚 Citing eyeris

1 Load and Preprocess Your Data | 2 Extract Data Epochs | Example A: Fixed Time Epochs Around a Matched Event | Example B: Metadata Parsing with Custom Labels | Example C: Epoch with Subtractive Baselining | Example D: Start/End Event Pair Epoching | 3 Export to a BIDS-like Format | 💡 Data Previews and QC with Interactive Reports | ✨ Summary | 📚 Citing eyeris

1 Setup | 2 Generating the Interactive HTML Reports | 3 Previewing your Entire Pupil Timeseries | 4 Data QC of Extracted Pupil Epochs with Interactive Reports | 5 Gaze Heatmaps | Standalone Gaze Heatmaps | Manual Gaze Heatmap Creation | Epoch-Level Gaze Heatmaps | 📚 Citing eyeris

📦 Key Components | 🧱 Building Blocks Under the Hood | The Default glassbox() Steps and Parameters, Deconstructed: | 📚 Citing eyeris

📦 Introduction | 🔎 The Glass Box Function | Installing eyeris | Loading eyeris Package | Loading Your Raw Data | Using the Demo Dataset | Loading Your Own Custom Data | Running the Fully-Automated Pipeline | Running the Pipeline Interactively | Overriding the Default Parameters | Example | Pipeline Steps with Overridable Parameters | Advanced: Building the Pipeline Manually | 💬 Caveats | Detrend Step | I received this message... what does it mean and what should I do? | Some additional notes about preventing live filtering in future recordings... | 📚 Citing eyeris

Introduction | Why Use Databases Instead of CSV Files? | The Case for Database Storage | When to Use CSV vs a Database | Getting Started: Creating Your First eyeris Database | Basic Database Creation | Database-Only Workflow (Cloud Optimized) | Batch Processing Multiple Subjects | Connecting to and Exploring eyeris Databases | Basic Database Connection | eyeris Database Overview and Exploration | Data Extraction: From Simple to Advanced | Simple Data Extraction | Targeted Data Extraction | Working with Binocular Data | Epoch-Specific Extraction | Output Format Options | Advanced Database Operations | Direct SQL Queries | Reading Individual Tables | Real-World Analysis Examples | Example 1: Pupil Response Analysis Across Subjects | Example 2: Quality Control and Confounds Analysis | Performance Comparison: Database vs CSV | Speed and Memory Benchmarks | Best Practices and Tips | Database Management | Cloud Computing Optimization | Error Handling and Debugging | Migration and Interoperability | Converting Existing CSV Data to Database | Exporting Database Data Back to CSV | Conclusion | Session Information

Introduction | Overview | Core Processing Functions | Blink Removal | deblink_pupil() | Transient Artifact Removal | detransient_pupil() | speed() | Interpolation | interpolate_pupil() | Filtering and Signal Processing | lpfilt_pupil() | downsample_pupil() | bin_pupil() | Statistical Processing | detrend_pupil() | zscore_pupil() | get_zscores() | Epoching Functions | Core Epoching Logic | epoch_pupil() | epoch_and_baseline_block() | process_epoch_and_baselines() | Epoching Strategies | epoch_manually() | epoch_only_start_msg() | epoch_start_msg_and_limits() | epoch_start_end_msg() | Confounds Analysis Functions | Core Confounds Calculation | get_confounds_for_step() | calculate_epoched_confounds() | Data Quality Metrics | tag_blinks() | calc_euclidean_dist() | normalize_gaze_coords() | tag_gaze_coords() | Export Functions | export_confounds_to_csv() | Logging System | Core Logging Functions | get_log_timestamp() | log_message() | log_info(), log_success(), log_warn(), log_error() | Validation and Quality Control | Input Validation | check_input() | check_data() | check_time_monotonic() | is_binocular_object() | Directory Management | check_and_create_dir() | Database Functions | Connection Management | connect_eyeris_database() | disconnect_eyeris_database() | Data Management | create_table_name() | write_eyeris_data_to_db() | write_csv_and_db() | Database Export & Management | create_temp_eyeris_database() | merge_temp_database() | cleanup_temp_database() | Utility Functions | String Processing | clean_string() | sanitize_event_tag() | Data Parsing | get_block_numbers() | BIDS Compliance | make_bids_fname() | run_bidsify() | Progress and Error Handling | Progress Bars | progress_bar() | counter_bar() | tick() | Error Handling | error_handler() | Report Generation | R Markdown Reports | render_report() | make_report() | Advanced Usage Notes | Function Chaining | Performance Considerations | Development Guidelines

Introduction | Prerequisites | Basic Usage | Simple Export with Default Settings | Understanding the Output Structure | Advanced Configuration | Controlling File Sizes | Exporting Specific Data Types | Using Parquet Format | Working with the Exported Files | Reading Single Files Back into R | Combining Multiple Split Files | Advanced Use Cases | Custom Chunk Processing | Handling Very Large Databases | Performance Tips | Optimizing Chunk Size | Choosing Output Format | File Size Considerations | Troubleshooting | Memory Issues | SQL Query Length Errors | Column Structure Mismatches | File Access Issues | Getting Help | Summary

🧩 How the Pipeline Works | 🛠 Creating a Custom Extension for eyeris | 1) Write the core operation function | To illustrate: | 2) Create the wrapper using the eyeris::pipeline_handler() | 3) Understanding Call Stack Tracking | 4) Function Structure Breakdown | 🎉 And that's it! | 💪 Best Practices | 🔍 Advanced: Custom Call Info Handling | ✨ Summary | 📚 Citing eyeris