Flow Tree:

A model for navigator dynamics and environmental complexity

Environment setup (mac m3):

• make conda-osx-arm.lock

• conda create --name mazedyn-test --fil conda-osx-arm64.lock

• conda activate mazedyn-test

• make poetry.lock

• poetry add notebook

• conda install pydot

Data download:

• Data is located here under Maze-Learning Task: https://osf.io/dujhy/
• Download the following files and put in mazedyn/data/
  • MLINDIV_behavioral_full.csv
  • MLINDIV_subject_info.csv
  • MLINDIV_train_full.csv

Code structure:

The code is organised into seven main notebooks, each of which concern a main application of Flow Trees. Flow Tree implementation is in flow_tree_utils.py.

• 00_animations_and_traces.ipynb -- visualisation of maze and navigation traces (Figures 2, 4)
• 0_dataset_statistics.ipynb -- dataset analysis (Figure 3)
• 1_flow_trees_and_regression.ipynb -- feature computation and regression (Figures 4, 5)
• 2_hypothesis_testing.ipynb -- hypothesis tests (Table 1)
• 3_dynamic_prediction.ipynb -- predicting individual results from group data (Figure 8)
• 4_backbone.ipynb -- backbone-specific analysis (Figure 6)
• 5_ego_v_allocentric.ipynb -- additional exploration of discretisation
• 6_start_node_end_node_trees.ipynb -- node Flow Trees and analysis (Figure 7)
• flow_tree_utils.py