function filterMap( x, options, fcn, thisArg ) {
	var hasOpts;
	var ndims;
	var cache;
	var clbk;
	var opts;
	var ctor;
	var ctx;
	var ord;
	var dim;
	var idx;
	var buf;
	var dt;
	var sh;
	var N;
	var y;
	var v;
	var i;
	if ( !isndarrayLike( x ) ) {
		throw new TypeError( format( 'invalid argument. First argument must be an ndarray-like object. Value: `%s`.', x ) );
	}
	if ( arguments.length < 3 ) {
		clbk = options;
	} else if ( arguments.length === 3 ) {
		if ( isFunction( options ) ) {
			clbk = options;
			ctx = fcn;
		} else {
			hasOpts = true;
			opts = options;
			clbk = fcn;
		}
	} else {
		hasOpts = true;
		opts = options;
		clbk = fcn;
		ctx = thisArg;
	}
	if ( !isFunction( clbk ) ) {
		throw new TypeError( format( 'invalid argument. Callback argument must be a function. Value: `%s`.', clbk ) );
	}
	if ( hasOpts ) {
		if ( !isPlainObject( opts ) ) {
			throw new TypeError( format( 'invalid argument. Options argument must be an object. Value: `%s`.', opts ) );
		}
		if ( hasOwnProp( opts, 'dtype' ) ) {
			dt = opts.dtype;
		} else {
			dt = getDType( x );
		}
		if ( hasOwnProp( opts, 'order' ) ) {
			if ( !isOrder( opts.order ) ) {
				throw new TypeError( format( 'invalid option. `%s` option must be a recognized order. Option: `%s`.', 'order', opts.order ) );
			}
			ord = opts.order;
		}
	} else {
		dt = getDType( x );
	}
	// Resolve an output array buffer constructor:
	ctor = ctors( dt );
	if ( ctor === null ) {
		// The only way we should get here is if the user provided an unsupported data type, as `getDType` should error if the input array has an unrecognized/unsupported data type...
		throw new TypeError( format( 'invalid option. `%s` option must be a recognized data type. Option: `%s`.', 'dtype', opts.dtype ) );
	}
	// Resolve the iteration order:
	if ( ord === void 0 ) {
		ord = getOrder( x );
	}
	// Resolve the input array shape:
	sh = getShape( x );

	// Compute the number of array elements:
	N = numel( sh );

	// Retrieve the number of dimensions:
	ndims = sh.length;

	// Resolve the dimension in which indices should iterate fastest:
	if ( ord === 'row-major' ) {
		dim = ndims - 1;
	} else { // ord === 'column-major'
		dim = 0;
	}
	// Initialize an index array workspace:
	idx = zeros( ndims );

	// Initialize a value cache for those elements which pass a callback function (note: unfortunately, we use an associative array here, as no other good options. If we use a "generic" array, we are limited to 2^32-1 elements. If we allocate, say, a Float64Array buffer for storing indices, we risk materializing lazily-materialized input ndarray values again (e.g., lazy accessor ndarray), which could be expensive. If we allocate a workspace buffer of equal size to the input ndarray to store materialized values, we'd then need to perform another copy in order to shrink the buffer, as, otherwise, could be holding on to significantly more memory than needed for the returned ndarray. There are likely other options, but all involve complexity, so the simplest option is used here.):
	cache = {
		'length': 0
	};

	// Filter and map elements according to a callback function...
	for ( i = 0; i < N; i++ ) {
		if ( i > 0 ) {
			idx = nextCartesianIndex( sh, ord, idx, dim, idx );
		}
		v = clbk.call( ctx, x.get.apply( x, idx ), idx.slice(), x );
		if ( v !== void 0 ) {
			cache[ cache.length ] = v;
			cache.length += 1;
		}
	}
	// Retrieve the number of cached elements:
	N = cache.length;

	// Allocate an output array buffer:
	buf = new ctor( N );

	// Copy cached elements to the output array buffer:
	gcopy( N, cache, 1, buf, 1 );

	// Create an output ndarray:
	y = new x.constructor( dt, buf, [ N ], [ 1 ], 0, ord );

	return y;
}
Filters and maps elements in an input ndarray to elements in a new output ndarray according to a callback function.

@param {ndarray} x - input ndarray
@param {Options} [options] - function options
@param {string} [options.dtype] - output array data type
@param {string} [options.order] - index iteration order
@param {Callback} fcn - callback function
@param {*} [thisArg] - callback execution context
@throws {TypeError} first argument must be an ndarray-like object
@throws {TypeError} callback argument must be a function
@throws {TypeError} options argument must be an object
@returns {ndarray} output ndarray

@example
var Float64Array = require( '@stdlib/array/float64' );
var ndarray = require( '@stdlib/ndarray/ctor' );
var ndarray2array = require( '@stdlib/ndarray/to-array' );

function fcn( v ) {
    if ( v > 5.0 ) {
        return v * 10.0;
    }
}

var buffer = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var shape = [ 2, 3 ];
var strides = [ 6, 1 ];
var offset = 1;

var x = ndarray( 'float64', buffer, shape, strides, offset, 'row-major' );
// returns <ndarray>

var y = filterMap( x, fcn );
// returns <ndarray>

var arr = ndarray2array( y );
// returns [ 80.0, 90.0, 100.0 ]