IntelliCage Analysis Toolkit documentationο
The IntelliCage Analysis Toolkit is a Python package for analyzing IntelliCage
experiments. It reads exported Visits.txt and Nosepokes.txt tables,
combines them with script-defined experiment and subject metadata, computes behavioral metrics, and creates
publication-oriented summary plots.
The toolkit is designed around a central Experiment object that contains all
the relevant data and metadata for a given experiment. The toolkit provides methods
to load data, prepare it for analysis, compute metrics, and generate plots. Its
main goal is to make IntelliCage data analysis more standardized, reproducible, and
accessible:
import ic_analysis as ic
my_pl_exp = ic.experiment(EXPERIMENT=EXPERIMENT, PHASES=PHASES, SUBJECTS=SUBJECTS)
my_pl_exp.load()
my_pl_exp.prepare_analysis()
my_pl_exp.plot_mice_activity()
Contents
- Overview
- Installation
- Usage
- Preparing IntelliCage data
- Place learning and reversal example
- Experiment protocol
- Getting the example data
- Expected input layout
- Step 1: Define experiment metadata
- Step 2: define subject metadata
- Step 3: Create and load the experiment object
- Pipeline settings
- General plotting arguments
- Step 4: Prepare the analysis
- Age distribution
- General activity
- Drinking behavior
- Bottle preference
- Learning and reversal behavior
- Day-wise endpoints
- Binned learning curves
- Reversal components
- Learning onset and experience
- Threshold onsets
- Derived ratios
- Cumulative corner preferences
- Reading the synthetic phenotype
- Adapting the script
- API reference
- Experiment creation
- Experiment definitions
- The experiment object
- Analysis and plotting functions
configure_plot_style()set_group_colors()sanitize_filename_part()plot_experiment_overview()plot_experiment_overview_groups()plot_bottle_preference_groups()plot_bottle_preference_day_violin()plot_phase2_adaptation()plot_phase2_adaptation_groups()plot_phase_learning_counts()plot_phase_learning_rate()plot_phase_learning_rate_groups()plot_phase_learning_counts_groups()plot_experiment_dual_metric_bars()plot_experiment_dual_metric_groups()plot_phase4_reversal_components()plot_phase_segment_rate_groups()plot_group_day_violin()plot_cumulative_role_curves()plot_visit_learning_curve_groups()plot_onset_violin()plot_phase_activity_boxplot()
- The PlotLayout object
- Data loading and time alignment
- Metric tables
flag_iqr_outliers()infer_phase_boundaries()build_phase_time_limit_table()suggest_common_phase_limits()filter_visits_by_phase_limits()filter_by_dayphase()build_analysis_phase_window_table()compute_experiment_visit_bins()compute_experiment_drinking_visit_bins()compute_experiment_nosepoke_count_bins()compute_experiment_lick_count_bins()compute_bottle_preference_bins()compute_binned_group_statistics()compute_phase2_adaptation_bins()compute_place_learning_count_bins()compute_phase_visit_count_bins()compute_place_learning_rate_bins()compute_phase4_reversal_rate_bins()compute_phase4_reversal_count_bins()compute_phase_segment_rate_tables()compute_phase_segment_error_rate_tables()compute_awake_day_rate_tables()compute_awake_day_error_rate_tables()compute_awake_day_ratio_tables()compute_first_hours_rate_table()compute_threshold_responder_table()compute_responder_group_statistics()compute_binomial_glm_group_statistics()compute_binomial_gee_group_statistics()compute_group_day_violin_statistics()compute_role_cumulative_curves()compute_time_window_learning_curves()compute_visit_window_learning_curves()compute_onset_group_statistics()compute_phase_activity_medians()compute_phase_activity_statistics()
- Whatβs new?
- IntelliCage Analysis Toolkit Changelog
- π IntelliCage Analysis Toolkit v0.1.1
- π IntelliCage Analysis Toolkit v0.1.0
- π IntelliCage Analysis Toolkit v0.0.4
- π IntelliCage Analysis Toolkit v0.0.3
- π IntelliCage Analysis Toolkit v0.0.2
- π IntelliCage Analysis Toolkit v0.0.1
- π IntelliCage Analysis Toolkit v0.0.0
- Contributing and community guidelines