refactor: cleanup & refactor implementation

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---

Author: headlessNode

lib/node_modules/@stdlib/blas/ext/index-of/README.md

@@ -53,12 +53,12 @@ The function has the following parameters:
 
 -   **x**: input [ndarray][@stdlib/ndarray/ctor].
 -   **searchElement**: element in an input [ndarray][@stdlib/ndarray/ctor] for which to find an index. May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] same as the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided a scalar value, the value is cast to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, the search element [ndarray][@stdlib/ndarray/ctor] must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], the search element [ndarray][@stdlib/ndarray/ctor] must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor].
--   **fromIndex**: index from which to begin searching (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a numeric or `generic` [data type][@stdlib/ndarray/dtypes]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the the index from which to begin searching is `0`.
+-   **fromIndex**: index from which to begin searching (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having an `integer` or `generic` [data type][@stdlib/ndarray/dtypes]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the the index from which to begin searching is `0`.
 -   **options**: function options (_optional_).
 
 The function accepts the following options:
 
--   **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be a "integer_index" [data type][@stdlib/ndarray/dtypes].
+-   **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be an "integer_index_and_generic" [data type][@stdlib/ndarray/dtypes].
 -   **dims**: list of dimensions over which to perform operation. If not provided, the function performs the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
 -   **keepdims**: boolean indicating whether the reduced dimensions should be included in the returned [ndarray][@stdlib/ndarray/ctor] as singleton dimensions. Default: `false`.
 
@@ -181,8 +181,8 @@ The method has the following parameters:
 
 -   **x**: input [ndarray][@stdlib/ndarray/ctor].
 -   **searchElement**: element in an input [ndarray][@stdlib/ndarray/ctor] for which to find an index. May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] same as the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided a scalar value, the value is cast to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, the search element [ndarray][@stdlib/ndarray/ctor] must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] initial value must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor].
--   **fromIndex**: index from which to begin searching (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a numeric or `generic` [data type][@stdlib/ndarray/dtypes]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the the index from which to begin searching is `0`.
--   **out**: output [ndarray][@stdlib/ndarray/ctor]. Must have a numeric or generic [data type][@stdlib/ndarray/dtypes].
+-   **fromIndex**: index from which to begin searching (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having an `integer` or `generic` [data type][@stdlib/ndarray/dtypes]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] containing the index from which to begin searching must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the the index from which to begin searching is `0`.
+-   **out**: output [ndarray][@stdlib/ndarray/ctor]. Must have an `integer` or `generic` [data type][@stdlib/ndarray/dtypes].
 -   **options**: function options (_optional_).
 
 The method accepts the following options:
@@ -221,7 +221,7 @@ var xbuf = discreteUniform( 10, 0, 20, {
 });
 
 // Wrap in an ndarray:
-var x = new ndarray( 'float64', xbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
+var x = new ndarray( 'float64', xbuf, [ 5, 2 ], [ 2, 1 ], 0, 'row-major' );
 console.log( ndarray2array( x ) );
 
 // Find index:

lib/node_modules/@stdlib/blas/ext/index-of/lib/assign.js

@@ -21,7 +21,8 @@
 // MODULES //
 
 var hasOwnProp = require( '@stdlib/assert/has-own-property' );
-var isNumber = require( '@stdlib/assert/is-number' ).isPrimitive;
+var objectAssign = require( '@stdlib/object/assign' );
+var isInteger = require( '@stdlib/assert/is-integer' ).isPrimitive;
 var isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
 var broadcastScalar = require( '@stdlib/ndarray/base/broadcast-scalar' );
 var maybeBroadcastArray = require( '@stdlib/ndarray/base/maybe-broadcast-array' );
@@ -47,16 +48,17 @@ var DEFAULT_DTYPE = defaults.get( 'dtypes.integer_index' );
 * @name assign
 * @type {Function}
 * @param {ndarrayLike} x - input ndarray
-* @param {(ndarrayLike|number)} searchElement - search element
-* @param {(ndarrayLike|number)} [fromIndex] - index from which to begin searching
+* @param {(ndarrayLike|*)} searchElement - search element
+* @param {(ndarrayLike|integer)} [fromIndex] - index from which to begin searching
 * @param {ndarrayLike} out - output ndarray
 * @param {Options} [options] - function options
 * @param {IntegerArray} [options.dims] - list of dimensions over which to perform operation
 * @param {boolean} [options.keepdims=false] - boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions
 * @param {string} [options.dtype] - output ndarray data type
+* @throws {TypeError} function must be provided at least two arguments
 * @throws {TypeError} first argument must be an ndarray-like object
-* @throws {TypeError} search element argument must be either an ndarray-like object or a numeric value
-* @throws {TypeError} from index argument must be either an ndarray-like object or a numeric value
+* @throws {TypeError} third argument must be either an ndarray-like object or an integer
+* @throws {TypeError} fourth argument must be an ndarray-like object
 * @throws {TypeError} options argument must be an object
 * @throws {RangeError} dimension indices must not exceed input ndarray bounds
 * @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
@@ -66,7 +68,6 @@ var DEFAULT_DTYPE = defaults.get( 'dtypes.integer_index' );
 * @example
 * var Float64Array = require( '@stdlib/array/float64' );
 * var zeros = require( '@stdlib/ndarray/zeros' );
-* var ndarray2array = require( '@stdlib/ndarray/to-array' );
 * var ndarray = require( '@stdlib/ndarray/ctor' );
 *
 * // Create data buffers:
@@ -107,118 +108,62 @@ function assign( x, searchElement, fromIndex, out, options ) {
 	var sh;
 
 	nargs = arguments.length;
+	if ( nargs < 3 ) {
+		throw new TypeError( format( 'invalid argument. The function must be provided at least two arguments. Value: `%s`.', nargs ) );
+	}
+
+	if ( !isndarrayLike( x ) ) {
+		throw new TypeError( format( 'invalid argument. The first argument must be an ndarray-like object. Value: `%s`.', x ) );
+	}
 
 	// Resolve input ndarray meta data:
 	dt = getDType( x );
 	ord = getOrder( x );
 
+	// Initialize options object:
+	opts = {};
+
+	// Case: assign( x, searchElement, out )
 	if ( nargs === 3 ) {
-		// Case: assign( x, searchElement, out )
 		out = fromIndex;
-
-		if ( isndarrayLike( out ) ) {
-			// Case: indexOf( x, searchElement_ndarray, out )
-			if ( isndarrayLike( searchElement ) ) {
-				return base( x, searchElement, broadcastScalar( 0, DEFAULT_DTYPE, [], ord ), out ); // eslint-disable-line max-len
-			}
-			// Case: indexOf( x, searchElement_scalar, out )
-			if ( isNumber( searchElement ) ) {
-				return base( x, broadcastScalar( searchElement, dt, [], ord ), broadcastScalar( 0, DEFAULT_DTYPE, [], ord ), out ); // eslint-disable-line max-len
-			}
-			// Check for other scalars than `Number` i.e. bool
-		} else {
-			throw new TypeError( format( 'invalid argument. Third argument must be an ndarray. Value: `%s`.', out ) );
-		}
+		fromIndex = 0;
 	}
-	// Case: assign( x, searchElement, fromIndex, out )
-	else if ( nargs < 5 ) {
-		if ( isndarrayLike( out ) ) {
-			// Case: indexOf( x, searchElement_ndarray, fromIndex_ndarray, out )
-			if ( isndarrayLike( searchElement ) & isndarrayLike( fromIndex ) ) {
-				return base( x, searchElement, fromIndex, out );
-			}
-			// Case: indexOf( x, searchElement_scalar, fromIndex_scalar out )
-			if ( isNumber( searchElement ) && isNumber( fromIndex ) ) {
-				return base( x, broadcastScalar( searchElement, dt, [], ord ), broadcastScalar( fromIndex, DEFAULT_DTYPE, [], ord ), out ); // eslint-disable-line max-len
-			}
-			// Case: indexOf( x, searchElement_ndarray, fromIndex_scalar, out )
-			if ( isndarrayLike( searchElement ) & isNumber( fromIndex ) ) {
-				return base( x, searchElement, broadcastScalar( fromIndex, DEFAULT_DTYPE, [], ord ), out ); // eslint-disable-line max-len
-			}
-			// Case: indexOf( x, searchElement_scalar, fromIndex_ndarray out )
-			if ( isNumber( searchElement ) && isndarrayLike( fromIndex ) ) {
-				return base( x, broadcastScalar( searchElement, dt, [], ord ), fromIndex, out ); // eslint-disable-line max-len
-			}
-			// Check for other scalars than `Number` i.e. bool
-
-			// Case: assign( x, searchElement, out, options )
-			out = fromIndex;
-			opts = out;
-
-			// Case: indexOf( x, searchElement_ndarray, out, options )
-			if ( isndarrayLike( searchElement ) ) {
-				// When not provided `dims`, the operation is performed across all dimensions and `v` is assumed to be a zero-dimensional ndarray; when `dims` is provided, we need to broadcast `v` to match the shape of the non-core dimensions...
-				if ( hasOwnProp( opts, 'dims' ) ) {
-					searchElement = maybeBroadcastArray( searchElement, nonCoreShape( getShape( x ), opts.dims ) ); // eslint-disable-line max-len
-					fromIndex = broadcastScalar( 0, DEFAULT_DTYPE, nonCoreShape( getShape( x ), opts.dims ), ord ); // eslint-disable-line max-len
-				}
-			}
-			// Case: indexOf( x, searchElement_scalar, opts )
-			else if ( isNumber( searchElement ) ) {
-				if ( hasOwnProp( opts, 'dims' ) ) {
-					sh = nonCoreShape( getShape( x ), opts.dims );
-				} else {
-					sh = [];
-				}
-				searchElement = broadcastScalar( searchElement, dt, sh, getOrder( x ) ); // eslint-disable-line max-len
-				fromIndex = broadcastScalar( 0, DEFAULT_DTYPE, sh, ord );
-			} else {
-				throw new TypeError( format( 'invalid argument. Second argument must be either an ndarray or a numeric scalar value. Value: `%s`.', searchElement ) );
-			}
-			return base( x, searchElement, fromIndex, out, opts );
-		}
+	// Case: assign( x, searchElement, out, options )
+	else if (
+		nargs === 4 &&
+		isndarrayLike( fromIndex ) &&
+		!isndarrayLike( out )
+	) {
+		opts = objectAssign( opts, out );
+		out = fromIndex;
+		fromIndex = 0;
+	} else if ( nargs === 5 ) {
+		opts = objectAssign( opts, options );
 	}
 
-	opts = options;
-
-	// Case: assign( x, searchElement_ndarray, fromIndex_ndarray, out, options )
-	if ( isndarrayLike( searchElement ) && isndarrayLike( fromIndex ) ) {
-		// When not provided `dims`, the operation is performed across all dimensions and `v` is assumed to be a zero-dimensional ndarray; when `dims` is provided, we need to broadcast `v` to match the shape of the non-core dimensions...
-		if ( hasOwnProp( opts, 'dims' ) ) {
-			searchElement = maybeBroadcastArray( searchElement, nonCoreShape( getShape( x ), opts.dims ) ); // eslint-disable-line max-len
-			fromIndex = maybeBroadcastArray( fromIndex, nonCoreShape( getShape( x ), opts.dims ) ); // eslint-disable-line max-len
-		}
-	}
-	// Case: assign( x, searchElement_scalar, fromIndex_scalar, out, options )
-	else if ( isNumber( searchElement ) && isNumber( fromIndex ) ) {
-		if ( hasOwnProp( opts, 'dims' ) ) {
-			sh = nonCoreShape( getShape( x ), opts.dims );
-		} else {
-			sh = [];
-		}
-		searchElement = broadcastScalar( searchElement, dt, sh, getOrder( x ) );
-		fromIndex = broadcastScalar( fromIndex, dt, sh, getOrder( x ) );
-	}
-	// Case: assign( x, searchElement_ndarray, fromIndex_scalar, out, options )
-	else if ( isndarrayLike( searchElement ) && isNumber( fromIndex ) ) {
-		// When not provided `dims`, the operation is performed across all dimensions and `v` is assumed to be a zero-dimensional ndarray; when `dims` is provided, we need to broadcast `v` to match the shape of the non-core dimensions...
-		if ( hasOwnProp( opts, 'dims' ) ) {
-			searchElement = maybeBroadcastArray( searchElement, nonCoreShape( getShape( x ), opts.dims ) ); // eslint-disable-line max-len
-			fromIndex = broadcastScalar( fromIndex, dt, nonCoreShape( getShape( x ), opts.dims ), ord ); // eslint-disable-line max-len
-		}
+	// Resolve shape for broadcasting
+	if ( hasOwnProp( opts, 'dims' ) ) {
+		sh = nonCoreShape( getShape( x ), opts.dims );
+	} else {
+		sh = [];
 	}
-	// Case: assign( x, searchElement_scalar, fromIndex_ndarray, out, options )
-	else if ( isNumber( searchElement ) && isndarrayLike( fromIndex ) ) {
-		if ( hasOwnProp( opts, 'dims' ) ) {
-			sh = nonCoreShape( getShape( x ), opts.dims );
-		} else {
-			sh = [];
-		}
+
+	// Normalize search element:
+	if ( isndarrayLike( searchElement ) ) {
+		searchElement = maybeBroadcastArray( searchElement, sh );
+	} else {
 		searchElement = broadcastScalar( searchElement, dt, sh, ord );
-		fromIndex = maybeBroadcastArray( fromIndex, dt, sh, ord );
+	}
+
+	// Normalize from index:
+	if ( isndarrayLike( fromIndex ) ) {
+		fromIndex = maybeBroadcastArray( fromIndex, sh );
+	} else if ( isInteger( fromIndex ) ) {
+		fromIndex = broadcastScalar( fromIndex, DEFAULT_DTYPE, sh, ord );
 	} else {
-		throw new TypeError( format( 'invalid argument. Second and third arguments must be either an ndarray or a scalar value. Value: `%s`.', searchElement, fromIndex ) );
+		throw new TypeError( format( 'invalid argument. Third argument must be either an ndarray or an integer. Value: `%s`.', fromIndex ) );
 	}
+
 	return base( x, searchElement, fromIndex, out, opts );
 }
 

lib/node_modules/@stdlib/blas/ext/index-of/lib/index.js

@@ -26,7 +26,6 @@
 * @example
 * var Float64Array = require( '@stdlib/array/float64' );
 * var ndarray = require( '@stdlib/ndarray/ctor' );
-* var ndarray2array = require( '@stdlib/ndarray/to-array' );
 * var indexOf = require( '@stdlib/blas/ext/index-of' );
 *
 * // Create a data buffer:
@@ -48,8 +47,8 @@
 * var out = indexOf( x, 4.0 );
 * // returns <ndarray>
 *
-* var idx = out.data;
-* // returns [ 3 ]
+* var idx = out.get();
+* // returns 3
 */
 
 // MODULES //