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Welcome to the IDTxl wiki!

Installation

For more details read the Installation and Requirements page.

Your first Information Network Inference with IDTxl

You can test your installation of IDTxl and run your first Information Network Inference on a small simulated dataset.

# Import classes
from idtxl.multivariate_te import MultivariateTE
from idtxl.data import Data
from idtxl.visualise_graph import print_res_to_console, plot_network

# 1) Generate test data
dat = Data()
dat.generate_mute_data(n_samples=1000, n_replications=5)

# 2) Initialise analysis object
network_analysis = MultivariateTE()

# 3) Define analysis settings
settings = {'cmi_estimator': 'JidtGaussianCMI',
            'max_lag_sources': 5,
            'min_lag_sources': 1}

# 4) Run analysis on whole network
res = network_analysis.analyse_network(settings, dat)

# 5) Plot inferred network to console and via matplotlib
print_res_to_console(dat, res)
plot_network(res=res, n_nodes=dat.n_processes)

In order to run a meaningful analysis on your dataset, make sure you understand the key theoretical concepts underlying IDTxl algorithms.

Theoretical background

Start with a short theoretical introduction before continuing to the tutorial below.

If you are looking for a full and detailed picture of the theoretical background, refer to the Literature.

Tutorials

Algorithms for network inference
- Multivariate Transfer Entropy / Granger causality
  1. Import data
    - The Data Class
  2. Define analysis settings
  3. Initialise analysis object and define lags
    - Source and Target Lags
  4. Run analysis
  5. Plot inferred network
    - networkx interface
- Bivariate TE
Algorithms for node dynamics
- Active Information Storage
- Entropy
Core estimators
- General usage
Input/Output
- MATLAB importer
- FieldTrip importer
- Neo importer
- Export networkx graph object
- Export to BrainNet viewer
Generate Synthetic Test Data
- Coupled Autoregressive processes
- MuTE network
- Coupled Logistic maps