npm install @stdlib/ndarray-map

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varmap=require('@stdlib/ndarray-map');

Applies a callback function to elements in an inputndarray and assigns results to elements in a new outputndarray.

varFloat64Array=require('@stdlib/array-float64');varndarray=require('@stdlib/ndarray-ctor');varndarray2array=require('@stdlib/ndarray-to-array');functionscale(z){returnz*10.0;}varbuffer=newFloat64Array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0]);varshape=[2,3];varstrides=[6,1];varoffset=1;varx=ndarray('float64',buffer,shape,strides,offset,'row-major');// returns <ndarray>vary=map(x,scale);// returns <ndarray>vararr=ndarray2array(y);// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]

The function accepts the following arguments:

• x: inputndarray.
• options: function options(optional).
• fcn: callback to apply.
• thisArg: callback execution context(optional).

The function accepts the following options:

• dtype: output ndarraydata type. If not specified, the output ndarraydata type is inferred from the inputndarray.

By default, the output ndarraydata type is inferred from the inputndarray. To return an ndarray with a differentdata type, specify thedtype option.

varFloat64Array=require('@stdlib/array-float64');varndarray=require('@stdlib/ndarray-ctor');vardtype=require('@stdlib/ndarray-dtype');varndarray2array=require('@stdlib/ndarray-to-array');functionscale(z){returnz*10.0;}varbuffer=newFloat64Array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0]);varshape=[2,3];varstrides=[6,1];varoffset=1;varx=ndarray('float64',buffer,shape,strides,offset,'row-major');// returns <ndarray>varopts={'dtype':'float32'};vary=map(x,opts,scale);// returns <ndarray>vardt=dtype(y);// returns 'float32'vararr=ndarray2array(y);// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]

To set the callback function execution context, provide athisArg.

varFloat64Array=require('@stdlib/array-float64');varndarray=require('@stdlib/ndarray-ctor');varndarray2array=require('@stdlib/ndarray-to-array');functionscale(z){this.count+=1;returnz*10.0;}varbuffer=newFloat64Array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0]);varshape=[2,3];varstrides=[6,1];varoffset=1;varx=ndarray('float64',buffer,shape,strides,offset,'row-major');// returns <ndarray>varctx={'count':0};vary=map(x,scale,ctx);// returns <ndarray>vararr=ndarray2array(y);// returns [ [ 20.0, 30.0, 40.0 ], [ 80.0, 90.0, 100.0 ] ]varcount=ctx.count;// returns 6

The callback function is provided the following arguments:

• value: current array element.
• indices: current array element indices.
• arr: the inputndarray.

• The function doesnot perform explicit casting (e.g., from a real-valued floating-point number to a complex floating-point number). Any such casting should be performed by a provided callback function.

  varFloat64Array=require('@stdlib/array-float64');varndarray=require('@stdlib/ndarray-ctor');varComplex128=require('@stdlib/complex-float64-ctor');varndarray2array=require('@stdlib/ndarray-to-array');functiontoComplex(z){returnnewComplex128(z,0.0);}varbuffer=newFloat64Array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0]);varshape=[2,3];varstrides=[6,1];varoffset=1;varx=ndarray('float64',buffer,shape,strides,offset,'row-major');// returns <ndarray>varopts={'dtype':'complex128'};vary=map(x,opts,toComplex);// returns <ndarray>

• The functionalways returns anndarray having the same shape andorder as the inputndarray.

• For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a callback function in order to achieve better performance.

• @stdlib/ndarray-filter:return a shallow copy of an ndarray containing only those elements which pass a test implemented by a predicate function.
• @stdlib/ndarray-filter-map:filter and map elements in an input ndarray to elements in a new output ndarray according to a callback function.
• @stdlib/ndarray-for-each:invoke a callback function once for each ndarray element.
• @stdlib/ndarray-slice:return a read-only view of an input ndarray.

This package is part ofstdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to developstdlib, see the main projectrepository.

SeeLICENSE.

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