DRIADA

Getting Started

  • Installation
    • Requirements
    • Installing from PyPI
    • Installing from source
    • Verifying installation
    • Dependencies
  • Quick start guide
    • Installation
    • Getting started with DRIADA
      • 1. Generate synthetic data for testing
      • 2. Analyze single-neuron selectivity (INTENSE)
      • 3. Estimate intrinsic dimensionality
      • 4. Apply dimensionality reduction
      • 5. Validate manifold quality
      • 6. Integrate single-cell and population analysis
      • 7. Network analysis
      • 8. Working with real data
      • 9. Advanced analysis workflows
    • Next steps
  • Examples
    • INTENSE — Selectivity Detection
    • Dimensionality Reduction
    • Dimensionality Estimation
    • Integration — INTENSE + Dimensionality Reduction
    • Network Analysis
    • Recurrence Analysis
    • RSA — Representational Similarity
    • Synthetic Data
    • Neuron — Spike Reconstruction & Quality
    • Utilities & Data Loading

API Reference

  • INTENSE Module
    • Module Components
      • INTENSE Pipelines
        • substitute_circular_with_2d()
        • compute_cell_feat_significance()
        • compute_feat_feat_significance()
        • compute_cell_cell_significance()
        • compute_embedding_selectivity()
        • Main Functions
        • Usage Example
      • INTENSE Statistics
        • chebyshev_ineq()
        • get_lognormal_p()
        • get_gamma_p()
        • get_gamma_zi_p()
        • get_distribution_function()
        • get_mi_distr_pvalue()
        • reconstruct_stage1_pvals()
        • get_mask()
        • stats_not_empty()
        • criterion1()
        • criterion2()
        • apply_stage_criterion()
        • get_all_nonempty_pvals()
        • get_table_of_stats()
        • merge_stage_stats()
        • merge_stage_significance()
        • Function Groups
      • INTENSE Visualization
        • plot_pc_activity()
        • plot_neuron_feature_density()
        • plot_shadowed_groups()
        • plot_neuron_feature_pair()
        • plot_disentanglement_heatmap()
        • plot_disentanglement_summary()
        • plot_selectivity_heatmap()
        • Function Groups
      • INTENSE Disentanglement
        • DEFAULT_MULTIFEATURE_MAP
        • disentangle_pair()
        • disentangle_all_selectivities()
        • create_multifeature_map()
        • get_disentanglement_summary()
        • Main Functions
        • Constants
      • Delay Optimization
        • calculate_optimal_delays()
        • calculate_optimal_delays_parallel()
        • Functions
      • Input Validation
        • validate_time_series_bunches()
        • validate_metric()
        • validate_common_parameters()
        • Functions
      • FFT Dispatch and Caching
        • FFTCacheEntry
        • get_fft_type()
        • Functions
      • Multiple Comparison Correction
        • get_multicomp_correction_thr()
        • Functions
      • INTENSE Core Implementation
        • StageConfig
        • get_calcium_feature_me_profile()
        • scan_pairs()
        • scan_pairs_parallel()
        • scan_pairs_router()
        • scan_stage()
        • compute_me_stats()
        • Classes
        • Function Groups
      • INTENSE: Information-Theoretic Evaluation of Neuronal Selectivity
        • Overview
        • Key features
        • Quick start
        • Using your own data
        • Mathematical framework
        • Usage example
        • Output
        • Applications
        • Limitations and considerations
        • References
        • Performance considerations
    • Quick Links
  • Dimensionality Reduction Module
    • Module Components
      • Data Structures
        • MVData
        • Embedding
        • ProximityGraph
      • Dimensionality Reduction Algorithms
        • Method Registry
        • Base Classes
        • Sequential Processing
        • Helper Functions
      • Manifold Metrics
        • Key metric categories:
        • KNN-based Metrics Comparison:
        • compute_distance_matrix()
        • knn_preservation_rate()
        • trustworthiness()
        • continuity()
        • geodesic_distance_correlation()
        • stress()
        • circular_structure_preservation()
        • procrustes_analysis()
        • manifold_preservation_score()
        • circular_distance()
        • circular_diff()
        • extract_angles_from_embedding()
        • find_optimal_circular_alignment()
        • compute_circular_correlation()
        • compute_reconstruction_error()
        • compute_embedding_alignment_metrics()
        • train_simple_decoder()
        • compute_embedding_quality()
        • compute_decoding_accuracy()
        • manifold_reconstruction_score()
        • Distance and Structure Metrics
        • Preservation Metrics
        • Circular Manifold Analysis
        • Reconstruction and Alignment
        • Decoding and Quality Assessment
      • Neural Network Methods
        • Standard Architectures
        • Building Blocks
        • Flexible Architectures
        • Loss Functions
        • Data Handling
      • Utilities
        • Data Validation
        • Sequence Validation
        • Specialized Methods
    • Quick Links
    • Usage Example
  • Dimensionality Estimation Module
    • Module Components
      • Linear Dimensionality Methods
        • Functions
        • Usage Examples
        • Implementation Details
      • Effective Dimensionality Estimation
        • Functions
        • Usage Examples
        • Theory
        • Interpretation
      • Intrinsic Dimensionality Estimation
        • Functions
        • Usage Examples
        • Theory
        • Choosing a Method
    • Quick Links
    • Usage Example
  • Integration Module
    • Main Functions
      • get_functional_organization()
      • compare_embeddings()
    • Usage Example
  • Experiment Module
    • Module Components
      • Core Experiment Classes
        • Classes
        • Usage Examples
        • Data Organization
      • Data Loading and Saving
        • Functions
        • Usage Examples
        • Best Practices
      • Spike Reconstruction Methods
        • Functions
        • Usage Examples
        • Method Selection Guide
        • Parameter Guidelines
        • Output Format
      • Wavelet Event Detection
        • Functions
        • Usage Examples
        • Advanced Usage
        • Theory
        • Wavelet Selection
      • Synthetic Data Generation
        • Experiment Generators
        • Signal Generators
        • Usage Examples
        • Ground Truth Information
        • Best Practices
    • Quick Links
    • Usage Example
  • Information Theory Module
    • Module Components
      • Core Information Theory Classes
        • Classes
        • Main Functions
        • Usage Examples
        • Advanced Usage
        • Best Practices
      • Entropy Estimation
        • Functions
        • Usage Examples
        • Theory
      • Mutual Information Functions
        • Core MI Functions
        • Time-Delayed MI
        • Similarity Measures
        • Gaussian Copula MI (GCMI)
        • KSG Estimators
      • Information Estimators
        • Gaussian Copula Estimators
        • KSG Non-parametric Estimators
        • Utility Functions
      • Information Theory Utilities
        • Data Types
        • Helper Functions
        • JIT-Optimized Functions
    • Quick Links
    • Usage Example
  • Network Analysis Module
    • Module Components
      • Network Core
        • check_matrix_type()
        • check_adjacency()
        • check_directed()
        • check_weights_and_directions()
        • calculate_directionality_fraction()
        • select_construction_pipeline()
        • Network
        • Network Class
        • Validation Functions
      • Graph Utilities
        • get_giant_cc_from_graph()
        • get_giant_scc_from_graph()
        • remove_selfloops_from_graph()
        • remove_isolates_and_selfloops_from_graph()
        • remove_isolates_from_graph()
        • small_world_index()
        • Component Extraction
        • Graph Cleaning
        • Network Metrics
      • Matrix Utilities
        • get_neighbors_from_adj()
        • get_ccs_from_adj()
        • get_sccs_from_adj()
        • get_giant_cc_from_adj()
        • get_giant_scc_from_adj()
        • assign_random_weights()
        • turn_to_partially_directed()
        • get_symmetry_index()
        • symmetric_component()
        • non_symmetric_component()
        • remove_duplicates()
        • adj_input_to_csr_sparse_matrix()
        • remove_selfloops_from_adj()
        • remove_isolates_from_adj()
        • sausage_index()
        • get_laplacian()
        • get_inv_sqrt_diag_matrix()
        • get_norm_laplacian()
        • get_inv_diag_matrix()
        • get_rw_laplacian()
        • get_trans_matrix()
      • Spectral Analysis
        • free_entropy()
        • q_entropy()
        • spectral_entropy()
      • Quantum Network Methods
        • renyi_divergence()
        • get_density_matrix()
        • manual_entropy()
        • js_divergence()
      • Network Randomization
        • adj_random_rewiring_iom_preserving()
        • random_rewiring_complete_graph()
        • random_rewiring_dense_graph()
        • get_single_double_edges_lists()
        • random_rewiring_IOM_preserving()
        • randomize_graph()
      • Network Visualization
        • draw_degree_distr()
        • draw_spectrum()
        • get_vector_coloring()
        • draw_eigenvectors()
        • draw_net()
        • show_mat()
        • plot_lem_embedding()
    • Quick Links
    • Usage Example
  • Recurrence Analysis
    • Embedding
      • takens_embedding()
      • estimate_tau()
      • estimate_embedding_dim()
    • Recurrence Graph
      • RecurrenceGraph
        • RecurrenceGraph.__init__()
        • RecurrenceGraph.construct_adjacency()
        • RecurrenceGraph.theiler_window
        • RecurrenceGraph.recurrence_rate
        • RecurrenceGraph.from_adjacency()
        • RecurrenceGraph.rqa()
    • Recurrence Quantification Analysis
      • compute_rqa()
    • Population Recurrence
      • population_recurrence_graph()
      • pairwise_jaccard_sparse()
    • Visibility Graphs
      • VisibilityGraph
        • VisibilityGraph.__init__()
        • VisibilityGraph.timeseries_data
        • VisibilityGraph.vg_method
    • Ordinal Partition Network
      • OrdinalPartitionNetwork
        • OrdinalPartitionNetwork.__init__()
        • OrdinalPartitionNetwork.permutation_entropy
        • OrdinalPartitionNetwork.missing_patterns
        • OrdinalPartitionNetwork.timeseries_data
    • Plotting
      • plot_recurrence()
  • RSA Module
    • Module Components
      • Core RSA Functions
        • compute_rdm()
        • compute_rdm_from_timeseries_labels()
        • compute_rdm_from_trials()
        • compare_rdms()
        • bootstrap_rdm_comparison()
        • compute_rdm_unified()
        • rsa_compare()
        • JIT-Optimized Functions
      • RSA Integration Functions
        • compute_experiment_rdm()
        • compute_mvdata_rdm()
        • rsa_between_experiments()
      • RSA Visualization
        • plot_rdm()
        • plot_rdm_comparison()
    • Quick Links
    • Usage Example
  • Utilities Module
    • Module Components
      • Data Utilities
        • create_correlated_gaussian_data()
        • populate_nested_dict()
        • nested_dict_to_seq_of_tables()
        • add_names_to_nested_dict()
        • retrieve_relevant_from_nested_dict()
        • rescale()
        • get_hash()
        • phase_synchrony()
        • correlation_matrix()
        • cross_correlation_matrix()
        • norm_cross_corr()
        • to_numpy_array()
        • remove_outliers()
        • check_nonnegative()
        • check_unit()
        • check_positive()
      • Visualization Utilities
        • Visualization utilities for DRIADA
        • plot_embedding_comparison()
        • plot_trajectories()
        • plot_component_interpretation()
        • plot_embeddings_grid()
        • plot_neuron_selectivity_summary()
        • plot_component_selectivity_heatmap()
        • compute_circular_coordinates()
        • visualize_circular_manifold()
        • Plotting Utilities
        • Publication Utilities
        • GIF Creation
      • Signal Processing Utilities
        • Signal Processing Utilities
        • brownian()
        • approximate_entropy()
        • filter_1d_timeseries()
        • filter_signals()
        • adaptive_filter_signals()
      • Spatial Analysis Utilities
        • Spatial Analysis Utilities for Neural Data
        • Visualization Utilities
        • Spatial Information Metrics
        • Metrics Wrapper
        • Experimental Functions
      • Matrix Utilities
        • nearestPD()
        • is_positive_definite()
      • Miscellaneous Utilities
        • Naming Utilities
        • Output Utilities
        • JIT Utilities
    • Quick Links
    • Usage Example
  • Google Drive Integration Module
    • Module Components
      • Authentication
        • Functions
        • Usage Examples
        • Configuration
        • Troubleshooting
        • Security Best Practices
        • API Scopes
      • Download Functions
        • Functions
        • Usage Examples
        • Error Handling
        • Common Use Cases
      • Upload Functions
        • Functions
        • Usage Examples
        • Upload with Version Control
        • Integration Examples
        • Error Handling
        • Best Practices
      • Utility Functions
        • Classes
        • Functions
        • Usage Examples
        • Integration with Other Functions
    • Quick Links
    • Usage Example

Additional Information

  • Changelog
    • Changelog
      • [1.1.0] - 2026-03-15
        • Recurrence analysis (new module)
        • INTENSE features
        • INTENSE bug fixes
        • Dimensionality reduction
        • Performance
        • Tutorial notebook
        • Documentation
      • [1.0.0] - 2026-03-01
        • INTENSE performance
        • INTENSE features
        • INTENSE refactoring
        • Experiment & data loading
        • Tutorial notebooks (new)
        • Documentation overhaul
        • Test quality
        • Bug fixes
        • Code quality & refactoring
        • Tooling
        • Project governance (new)
      • [0.7.2] - 2026-01-21
        • Performance
        • Features
        • Fixed
        • Changed
      • [0.7.1] - 2026-01-17
        • Performance
        • Features
        • Fixed
        • Added
      • [0.7.0] - 2026-01-13
        • Performance
        • Critical Fixes
        • Added
        • Changed
        • Statistics
      • [0.6.6] - 2025-12-01
        • Added
        • Changed
        • Fixed
      • [0.6.5] - 2025-12-01
        • Added
        • Fixed
        • Changed
      • [0.6.4] - 2025-11-01
        • Added
        • Changed
        • Fixed
        • Improved
      • [0.6.1] - 2025-10-01
        • Added
        • Fixed
        • Changed
      • [0.6.0] - 2025-09-01
        • Added
        • Fixed
        • Changed
      • [0.5.1] - 2025-08-01
        • Added
        • Fixed
      • [0.4.0] - 2025-07-01
        • Added
        • Changed
        • Fixed
        • Removed
      • [0.2.0] - 2025-03-01
        • Added
        • Changed
        • Fixed
      • [0.1.x] - 2024-2025
        • INTENSE Module (Information-Theoretic Evaluation of Neuronal Selectivity)
        • Dimensionality Reduction Framework
        • Calcium Imaging Pipeline
        • Experiment Framework
        • Infrastructure
    • Version History
  • Contributing to DRIADA
    • Contributing to DRIADA
      • Types of Contributions
      • Getting Started
        • Development Setup
      • Running Tests
      • Code Style
      • Building Documentation
      • Submitting Changes
        • For Larger Contributions
      • Reporting Issues
      • Questions?
      • License
    • Quick links
    • Development setup
    • Running tests
    • Code style
    • Submitting changes
  • License
    • Full License Text
    • Citation
    • Contributing
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