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Multidimensional arrays.
To use in Observable,
ns = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray@umd/browser.js' )The previous example will load the latest bundled code from the umd branch. Alternatively, you may load a specific version by loading the file from one of the tagged bundles. For example,
ns = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray@v0.3.3-umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var ns = require( 'path/to/vendor/umd/ndarray/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.ns;
})();
</script>ndarray namespace.
var o = ns;
// returns {...}The namespace exports the following functions to create multidimensional arrays:
array( [buffer,] [options] ): create a multidimensional array.ndarray( dtype, buffer, shape, strides, offset, order[, options] ): multidimensional array constructor.
The namespace contains the following sub-namespaces:
In addition, the namespace contains the following multidimensional array utility functions:
at( x[, ...indices] ): return anndarrayelement.broadcastArray( x, shape ): broadcast an ndarray to a specified shape.broadcastArrays( ...arrays ): broadcast ndarrays to a common shape.castingModes(): list of ndarray casting modes.dataBuffer( x ): return the underlying data buffer of a provided ndarray.defaults(): default ndarray settings.dispatch( fcns, types, data, nargs, nin, nout ): create an ndarray function interface which performs multiple dispatch.dtype( x ): return the data type of a provided ndarray.dtypes( [kind] ): list of ndarray data types.emptyLike( x[, options] ): create an uninitialized ndarray having the same shape and data type as a provided ndarray.empty( shape[, options] ): create an uninitialized ndarray having a specified shape and data type.FancyArray( dtype, buffer, shape, strides, offset, order[, options] ): fancy multidimensional array constructor.flag( x, name ): return a specified flag for a provided ndarray.flags( x ): return the flags of a provided ndarray.scalar2ndarray( value[, options] ): convert a scalar value to a zero-dimensional ndarray.ind2sub( shape, idx[, options] ): convert a linear index to an array of subscripts.indexModes(): list of ndarray index modes.maybeBroadcastArray( x, shape ): broadcast an ndarray to a specified shape if and only if the specified shape differs from the provided ndarray's shape.maybeBroadcastArrays( arrays ): broadcast ndarrays to a common shape.minDataType( value ): determine the minimum ndarray data type of the closest "kind" necessary for storing a provided scalar value.mostlySafeCasts( [dtype] ): return a list of ndarray data types to which a provided ndarray data type can be safely cast and, for floating-point data types, can be downcast.ndarraylike2ndarray( x[, options] ): convert an ndarray-like object to anndarray.ndims( x ): return the number of ndarray dimensions.nextDataType( [dtype] ): return the next larger ndarray data type of the same kind.numelDimension( x, dim ): return the size (i.e., number of elements) of a specified dimension for a provided ndarray.numel( x ): return the number of elements in an ndarray.offset( x ): return the index offset specifying the underlying buffer index of the first iterated ndarray element.order( x ): return the layout order of a provided ndarray.orders(): list of ndarray orders.outputDataTypePolicies(): list of output ndarray data type policies.promotionRules( [dtype1, dtype2] ): return the ndarray data type with the smallest size and closest "kind" to which ndarray data types can be safely cast.safeCasts( [dtype] ): return a list of ndarray data types to which a provided ndarray data type can be safely cast.sameKindCasts( [dtype] ): return a list of ndarray data types to which a provided ndarray data type can be safely cast or cast within the same "kind".shape( x ): return the shape of a provided ndarray.sliceAssign( x, y, ...s[, options] ): assign element values from a broadcasted inputndarrayto corresponding elements in an outputndarrayview.sliceDimensionFrom( x, dim, start[, options] ): return a read-only shifted view of an inputndarrayalong a specified dimension.sliceDimensionTo( x, dim, stop[, options] ): return a read-only truncated view of an inputndarrayalong a specified dimension.sliceDimension( x, dim, slice[, options] ): return a read-only view of an inputndarraywhen sliced along a specified dimension.sliceFrom( x, ...start[, options] ): return a read-only shifted view of an input ndarray.sliceTo( x, ...stop[, options] ): return a read-only truncated view of an input ndarray.slice( x, ...s[, options] ): return a read-only view of an inputndarray.stride( x, dim ): return the stride along a specified dimension for a provided ndarray.strides( x ): return the strides of a provided ndarray.sub2ind( shape, ...subscripts[, options] ): convert subscripts to a linear index.ndarray2array( x ): convert an ndarray to a generic array.zerosLike( x[, options] ): create a zero-filled ndarray having the same shape and data type as a provided ndarray.zeros( shape[, options] ): create a zero-filled ndarray having a specified shape and data type.
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<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/utils/keys@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray@umd/browser.js"></script>
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</html>This package is part of stdlib, 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 develop stdlib, see the main project repository.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.