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# requirements.txt — Haber_PP_MEA
#
# This is a MATLAB codebase. Unlike Python, MATLAB dependencies are
# installed via the MathWorks installer or the Add-On Explorer, not by a
# package manager. This file is informational: it lists every external
# dependency the pipeline needs to run end-to-end. To verify that your
# local machine satisfies all of these, run:
#
# ./scripts/run.sh smoke
#
# which includes explicit toolbox-availability checks.
# =============================================================================
# Runtime
# =============================================================================
matlab >= R2023a # tested on R2023b (9.15)
# =============================================================================
# Required MathWorks toolboxes
# =============================================================================
# Signal Processing Toolbox
# functions used : filtfilt, butter, xcorr
# note : 60 Hz notch uses an in-repo biquad implementation
# (src/utils/build_notch_filter.m) to avoid a DSP
# System Toolbox dependency (iirnotch /
# designNotchPeakIIR both live there).
# used by : src/utils/load_and_filter.m
# src/utils/build_bandpass_filter.m
# src/utils/build_notch_filter.m
# src/analysis/connectivity_xcorr.m
# src/figures/fig_connectivity_exemplar.m
# required for : preprocess_and_save, all connectivity analysis
signal_processing_toolbox >= 9.2 # R2023a ships 9.2
# Statistics and Machine Learning Toolbox
# functions used : signrank, tiedrank, ksdensity, prctile, chi2cdf
# used by : src/analysis/paired_stats.m (signrank, tiedrank)
# src/figures/fig_connectivity_summary.m (ksdensity, prctile)
# src/figures/fig_stats_bootstrap.m (prctile)
# src/figures/fig_rate_change_bar.m (chi2cdf)
# src/utils/plot_pct_change_violin.m (ksdensity)
# required for : all statistical reporting, KDE panels, rate-change bar
statistics_and_machine_learning_toolbox >= 12.5 # R2023a ships 12.5
# =============================================================================
# Vendored (shipped in-tree, no separate install required)
# =============================================================================
# Tucker-Davis Technologies MATLAB SDK
# path : <repo>/TDTMatlabSDK/
# functions used : TDTbin2mat, TDTthresh
# used by : src/utils/load_and_filter.m
# src/utils/detect_spikes.m
# src/pipeline/preprocess_and_save.m
# added to path : automatically by preprocess_and_save via
# addpath(genpath(cfg.paths.tdt_sdk))
tdt_matlab_sdk == vendored # see TDTMatlabSDK/
# =============================================================================
# Optional
# =============================================================================
# Brain Connectivity Toolbox (Rubinov & Sporns 2010)
# homepage : https://sites.google.com/site/bctnet/
# canonical git : https://github.com/brainlife/BCT
# status : NOT currently required. network_metrics.m provides
# native implementations of density, clustering, path
# length, global efficiency, modularity, and
# small-worldness sigma. Vendor BCT at
# src/external/BCT/ only if you want literal BCT
# citations in the paper or BCT-implemented
# rich-club / assortativity metrics.
brain_connectivity_toolbox >= 2019.03.03 # optional
# =============================================================================
# NOT required (installed on test machine but unused)
# =============================================================================
# image_processing_toolbox # unused
# parallel_computing_toolbox # unused
# =============================================================================
# Install notes (macOS / Linux / Windows)
# =============================================================================
# 1. Install MATLAB R2023a or later from https://www.mathworks.com/downloads/
# 2. During the installer, select BOTH:
# [x] Signal Processing Toolbox
# [x] Statistics and Machine Learning Toolbox
# Or add them later via the MATLAB Add-On Explorer (HOME > Add-Ons).
# 3. Clone this repository. The TDT MATLAB SDK is already included at
# TDTMatlabSDK/; no separate download is needed.
# 4. Verify the install with:
# ./scripts/run.sh smoke
# The final line should read "Result: 38 PASS, 0 FAIL".
# 5. Run the pipeline:
# ./scripts/run.sh preprocess doi
# ./scripts/run.sh figures doi
#
# See scripts/run.sh --help for the full command list.