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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| import * as gc from "./gc.js"; | ||
| import * as wasm from "./wasm.js"; | ||
| import * as utils from "./utils.js"; | ||
| import { ScranMatrix } from "./ScranMatrix.js"; | ||
|
|
||
| /** | ||
| * Initialize a layered sparse matrix from a HDF5 file. | ||
| * | ||
| * @param {string} file Path to the HDF5 file. | ||
| * For browsers, the file should have been saved to the virtual filesystem. | ||
| * @param {string} name Name of the dataset inside the file. | ||
| * This can be a HDF5 Dataset for dense matrices or a HDF5 Group for sparse matrices. | ||
| * For the latter, both H5AD and 10X-style sparse formats are supported. | ||
| * @param {object} [options={}] - Optional parameters. | ||
| * @param {boolean} [options.forceInteger=true] - Whether to coerce all elements to integers via truncation. | ||
| * @param {boolean} [options.layered=true] - Whether to create a layered sparse matrix, see [**tatami_layered**](https://github.com/tatami-inc/tatami_layered) for more details. | ||
| * Only used if the relevant HDF5 dataset contains an integer type and/or `forceInteger = true`. | ||
| * Setting to `true` assumes that the matrix contains only non-negative integers. | ||
| * @param {?(Array|TypedArray|Int32WasmArray)} [options.subsetRow=null] - Row indices to extract. | ||
| * All indices must be non-negative integers less than the number of rows in the sparse matrix. | ||
| * @param {?(Array|TypedArray|Int32WasmArray)} [options.subsetColumn=null] - Column indices to extract. | ||
| * All indices must be non-negative integers less than the number of columns in the sparse matrix. | ||
| * | ||
| * @return {ScranMatrix} Matrix containing sparse data. | ||
| */ | ||
| export function initializeSparseMatrixFromHdf5(file, name, { forceInteger = true, layered = true, subsetRow = null, subsetColumn = null } = {}) { | ||
| const details = extractHdf5MatrixDetails(file, name); | ||
| if (details.format == "dense") { | ||
| // Setting transposed = true as all known dense matrices store the cells in the first dimension and the genes in the last dimension. | ||
| return initializeSparseMatrixFromHdf5DenseArray(file, name, { transposed: true, forceInteger, layered, subsetRow, subsetColumn }); | ||
| } else { | ||
| return initializeSparseMatrixFromHdf5SparseMatrix(file, name, details.rows, details.columns, details.format == "csc", { forceInteger, layered, subsetRow, subsetColumn }); | ||
| } | ||
| } | ||
|
|
||
| function prepare_hdf5_matrix_subset(subsetRow, subsetColumn, fun) { | ||
| var output; | ||
| let wasm_row, wasm_col; | ||
|
|
||
| try { | ||
| let use_row_subset = (subsetRow !== null); | ||
| let row_offset = 0, row_length = 0; | ||
| if (use_row_subset) { | ||
| wasm_row = utils.wasmifyArray(subsetRow, "Int32WasmArray"); | ||
| row_offset = wasm_row.offset; | ||
| row_length = wasm_row.length; | ||
| } | ||
|
|
||
| let use_col_subset = (subsetColumn !== null); | ||
| let col_offset = 0, col_length = 0; | ||
| if (use_col_subset) { | ||
| wasm_col = utils.wasmifyArray(subsetColumn, "Int32WasmArray"); | ||
| col_offset = wasm_col.offset; | ||
| col_length = wasm_col.length; | ||
| } | ||
|
|
||
| output = fun(use_row_subset, row_offset, row_length, use_col_subset, col_offset, col_length); | ||
|
|
||
| } finally { | ||
| utils.free(wasm_row); | ||
| utils.free(wasm_col); | ||
| } | ||
|
|
||
| return output; | ||
| } | ||
|
|
||
| export function initializeSparseMatrixFromHdf5DenseArray(file, name, { transposed = false, forceInteger = false, layered = true, subsetRow = null, subsetColumn = null } = {}) { | ||
| return prepare_hdf5_matrix_subset(subsetRow, subsetColumn, (use_row_subset, row_offset, row_length, use_col_subset, col_offset, col_length) => { | ||
| return gc.call( | ||
| module => module.read_sparse_matrix_from_hdf5_dense_array( | ||
| file, name, transposed, forceInteger, layered, | ||
| use_row_subset, row_offset, row_length, use_col_subset, col_offset, col_length | ||
| ), | ||
| ScranMatrix | ||
| ); | ||
| }); | ||
| } | ||
|
|
||
| export function initializeSparseMatrixFromHdf5SparseMatrix(file, name, numberOfRows, numberOfColumns, byColumn, { forceInteger = false, layered = true, subsetRow = null, subsetColumn = null } = {}) { | ||
| return prepare_hdf5_matrix_subset(subsetRow, subsetColumn, (use_row_subset, row_offset, row_length, use_col_subset, col_offset, col_length) => { | ||
| return gc.call( | ||
| module => module.read_sparse_matrix_from_hdf5_sparse_matrix( | ||
| file, name, numberOfRows, numberOfColumns, byColumn, forceInteger, layered, | ||
| use_row_subset, row_offset, row_length, use_col_subset, col_offset, col_length | ||
| ), | ||
| ScranMatrix | ||
| ); | ||
| }); | ||
| } | ||
|
|
||
| /** | ||
| * Extract the format and dimensions of a HDF5 matrix. | ||
| * | ||
| * @param {string} file Path to the HDF5 file. | ||
| * For browsers, the file should have been saved to the virtual filesystem. | ||
| * @param {string} name Name of the dataset inside the file. | ||
| * This can be a HDF5 Dataset for dense matrices or a HDF5 Group for sparse matrices. | ||
| * For the latter, both H5AD and 10X-style sparse formats are supported. | ||
| * | ||
| * @return {object} An object containing: | ||
| * - `rows`, the number of rows in the matrix. | ||
| * - `columns`, the number of columns. | ||
| * - `format`, whether the matrix is dense, CSR or CSC. | ||
| * - `integer`, whether the matrix data is stored as integers or doubles. | ||
| */ | ||
| export function extractHdf5MatrixDetails(file, name) { | ||
| let output = {}; | ||
| let arr = utils.createInt32WasmArray(5); | ||
| try { | ||
| wasm.call(module => module.extract_hdf5_matrix_details(file, name, arr.offset)); | ||
|
|
||
| let vals = arr.array(); | ||
| if (vals[0] > 0) { | ||
| output.format = "dense"; | ||
| } else if (vals[1] > 0) { | ||
| output.format = "csc"; | ||
| } else { | ||
| output.format = "csr"; | ||
| } | ||
|
|
||
| output.rows = vals[2]; | ||
| output.columns = vals[3]; | ||
| output.integer = vals[4] > 0; | ||
| } finally { | ||
| arr.free(); | ||
| } | ||
| return output; | ||
| } |
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