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* @license Apache-2.0
*
* Copyright (c) 2025 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* eslint-disable no-restricted-syntax, no-invalid-this */
'use strict';
// MODULES //
var isWebAssemblyMemory = require( '@stdlib/assert/is-wasm-memory' );
var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
var inherits = require( '@stdlib/utils/inherit' );
var WasmModule = require( '@stdlib/wasm/module-wrapper' );
var format = require( '@stdlib/string/format' );
var wasmBinary = require( './binary.js' );
// MAIN //
/**
* BLAS routine WebAssembly module wrapper constructor.
*
* @constructor
* @param {Object} memory - WebAssembly memory instance
* @throws {TypeError} must provide a WebAssembly memory instance
* @returns {Module} module instance
*
* @example
* var Memory = require( '@stdlib/wasm/memory' );
* var oneTo = require( '@stdlib/array/one-to' );
*
* // Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
* var mem = new Memory({
* 'initial': 10,
* 'maximum': 100
* });
*
* // Create a BLAS routine:
* var dnanasumors = new Module( mem );
* // returns <Module>
*
* // Initialize the routine:
* dnanasumors.initializeSync();
*
* // Define a vector data type:
* var dtype = 'float64';
*
* // Specify a vector length:
* var N = 3;
*
* // Define a pointer (i.e., byte offset) for storing the input vector:
* var xptr = 0;
*
* // Write vector values to module memory:
* dnanasumors.write( xptr, oneTo( N, dtype ) );
*
* // Perform computation:
* var v = dnanasumors.main( N, xptr, 1 );
* // returns 6.0
*/
function Module( memory ) {
if ( !( this instanceof Module ) ) {
return new Module( memory );
}
if ( !isWebAssemblyMemory( memory ) ) {
throw new TypeError( format( 'invalid argument. Must provide a WebAssembly memory instance. Value: `%s`.', memory ) );
}
// Call the parent constructor:
WasmModule.call( this, wasmBinary, memory, {
'env': {
'memory': memory
}
});
return this;
}
// Inherit from the parent constructor:
inherits( Module, WasmModule );
/**
* Compute the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation.
*
* @name main
* @memberof Module.prototype
* @readonly
* @type {Function}
* @param {PositiveInteger} N - number of indexed elements
* @param {NonNegativeInteger} xptr - input array pointer (i.e., byte offset)
* @param {integer} strideX - stride length
* @returns {number} sum
*
* @example
* var Memory = require( '@stdlib/wasm/memory' );
* var oneTo = require( '@stdlib/array/one-to' );
*
* // Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
* var mem = new Memory({
* 'initial': 10,
* 'maximum': 100
* });
*
* // Create a BLAS routine:
* var dnanasumors = new Module( mem );
* // returns <Module>
*
* // Initialize the routine:
* dnanasumors.initializeSync();
*
* // Define a vector data type:
* var dtype = 'float64';
*
* // Specify a vector length:
* var N = 3;
*
* // Define a pointer (i.e., byte offset) for storing the input vector:
* var xptr = 0;
*
* // Write vector values to module memory:
* dnanasumors.write( xptr, oneTo( N, dtype ) );
*
* // Perform computation:
* var v = dnanasumors.main( N, xptr, 1 );
* // returns 6.0
*/
setReadOnly( Module.prototype, 'main', function dnanasumors( N, xptr, strideX ) {
return this._instance.exports.stdlib_strided_dnanasumors( N, xptr, strideX ); // eslint-disable-line max-len
});
/**
* Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics.
*
* @name ndarray
* @memberof Module.prototype
* @readonly
* @type {Function}
* @param {PositiveInteger} N - number of indexed elements
* @param {NonNegativeInteger} xptr - input array pointer (i.e., byte offset)
* @param {integer} strideX - stride length
* @param {NonNegativeInteger} offsetX - starting index
* @returns {number} sum
*
* @example
* var Memory = require( '@stdlib/wasm/memory' );
* var oneTo = require( '@stdlib/array/one-to' );
*
* // Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
* var mem = new Memory({
* 'initial': 10,
* 'maximum': 100
* });
*
* // Create a BLAS routine:
* var dnanasumors = new Module( mem );
* // returns <Module>
*
* // Initialize the routine:
* dnanasumors.initializeSync();
*
* // Define a vector data type:
* var dtype = 'float64';
*
* // Specify a vector length:
* var N = 3;
*
* // Define a pointer (i.e., byte offset) for storing the input vector:
* var xptr = 0;
*
* // Write vector values to module memory:
* dnanasumors.write( xptr, oneTo( N, dtype ) );
*
* // Perform computation:
* var v = dnanasumors.ndarray( N, xptr, 1, 0 );
* // returns 6.0
*/
setReadOnly( Module.prototype, 'ndarray', function dnanasumors( N, xptr, strideX, offsetX ) {
return this._instance.exports.stdlib_strided_dnanasumors_ndarray( N, xptr, strideX, offsetX ); // eslint-disable-line max-len
});
// EXPORTS //
module.exports = Module;
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