# Add to .npmrc once:
@qe-libs:registry=https://gitlab.com/api/v4/projects/22522458/packages/npm/

# Then install:
npm install @qe-libs/tma-wasm

@qe-libs/tma-wasm

WebAssembly build of libtma — tma's accumulation C++ (stanza windows, context tensors, row connections, weight models), shared with the R package and qe-tma (Python). These functions moved here from @qe-libs/libqe-wasm (libqe's phase 4b split) with the same signatures and results.

Installation

# one-time: tell npm where to find the @qe-libs scope
echo "@qe-libs:registry=https://gitlab.com/api/v4/projects/22522458/packages/npm/" >> ~/.npmrc
npm install @qe-libs/tma-wasm

Usage

import loadTMA from '@qe-libs/tma-wasm';
const tma = await loadTMA();

// Stanza-window accumulation: 3 rows × 3 codes, window 2
const codes = new Float64Array([1,1,0, 1,0,1, 0,1,1]);
const out = tma.accumulate_stanza(codes, 3, 3, 2, 0, true, false);
// → { data: Float64Array, rows: 3, cols: 3 }   (choose_two(3) connections)

// Context-tensor accumulation for one unit, then the unit vector with a weight model
const t = tma.accumulate_tensor_unit([1, 3], [2], [], [], [], new Int32Array(0), 3, 0,
                                     [0, 1, 2], codes, 3, 3, [0, 1, 2], true);
const v = tma.aggregate_row_connections(t.row_connection_counts.data,
                                        t.row_connection_counts.rows,
                                        t.row_connection_counts.cols, 3, false, 'sqrt');

Matrices are flat row-major Float64Arrays with explicit rows/cols; matrix results are { data, rows, cols }.

Function	Notes
`connection_matrix(ground, gn, response, rn, response_weight, ordered)`	one ground/response pair
`accumulate_stanza(data, rows, cols, window_back, window_forward, binary, ordered)`	rENA stanza model
`row_connections(data, rows, cols, binary)`	per-row co-occurrence
`rolling_window_sum(data, rows, cols, window_size)`	backward window sum
`flat_index(indices, dims)`	column-major linear index
`accumulate_unit(codes, rows, cols, unit_rows, decay_fn, ordered)`	ground/response model, JS decay
`accumulate_unit_with_rows(...)`	the same, with per-row networks
`accumulate_tensor_unit(tensor, dims, …, codes, rows, cols, times, ordered)`	context-tensor model
`aggregate_row_connections(row_conn, rows, cols, n_codes, ordered, weight)`	fold + weight model + sum
`finalize_row_connections(row_conn, rows, cols, n_codes, ordered, weight)`	per-row step of the above

weight is 'binary' | 'product' | 'sqrt' | 'log1p' (or the legacy boolean).

Building and testing

npm install
npm run build   # compiles dist/libtma.* — needs emscripten (emcc) and conan
npm test

npm run build (scripts/build.sh) vendors the libtma and libqe headers into include/ (../scripts/sync-headers.sh: libtma from inst/include, libqe from Conan), gets Armadillo from Conan, and builds with emcmake. test/libtma.test.js checks this module against libqe-wasm's own copy of the accumulation functions.