npmpackage.info

Gathering detailed insights and metrics for @stdlib/ndarray

Other packages similar to @stdlib/ndarray

@stdlib/ndarray-dtypes

0.3.0

List of ndarray data types.

@stdlib/ndarray-ctor

0.2.2

Multidimensional array constructor.

@stdlib/ndarray-orders

0.2.2

List of ndarray orders.

@stdlib/ndarray-base-ctor

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Base multidimensional array.

Gathering detailed insights and metrics for @stdlib/ndarray

@stdlib/ndarray

Multidimensional arrays.

0.3.3

Apache-2.0

3,698,797 5.1

Installations

npm install @stdlib/ndarray

Pull Requests

Open

0

Total

0

Closed

0

Issues

Open

0

Total

0

Closed

0

Releases

Unable to fetch releases

Developer

stdlib-js

Developer Guide

BETA

Module System

CommonJS

Min. Node Version

>=0.10.0

Typescript Support

Yes

Node Version

20.18.0

NPM Version

10.8.2 Statistics

10 Stars

423 Commits

3 Forks

5 Watching

5 Branches

10 Contributors

Updated on 21 Nov 2024

Languages

C (66.73%)

JavaScript (31.14%)

TypeScript (2.08%)

Python (0.05%)

Total Downloads

Cumulative downloads

Total Downloads

3,698,797

Last day

10.7%

2,341

Compared to previous day

Last week

-21.7%

11,902

Compared to previous week

Last month

5.5%

76,608

Compared to previous month

Last year

19.8%

1,447,238

Compared to previous year

Daily Downloads

Weekly Downloads

Monthly Downloads

Yearly Downloads

Dependencies

@stdlib/array @stdlib/assert @stdlib/bigint @stdlib/blas @stdlib/boolean @stdlib/buffer @stdlib/complex @stdlib/constants @stdlib/math @stdlib/number @stdlib/object @stdlib/proxy @stdlib/slice @stdlib/string @stdlib/symbol @stdlib/types @stdlib/utils

Versions

About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

ndarray

Multidimensional arrays.

Installation

1npm install @stdlib/ndarray

Alternatively,

To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
If you are using Deno, visit the deno branch (see README for usage intructions).
For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

1var ns = require( '@stdlib/ndarray' );

ns

ndarray namespace.

1var o = ns;
2// 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:

base: base ndarray.
iter: multidimensional array iterators.

In addition, the namespace contains the following multidimensional array utility functions:

at( x[, ...indices] ): return an ndarray element.
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 an ndarray.
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 input ndarray to corresponding elements in an output ndarray view.
sliceDimensionFrom( x, dim, start[, options] ): return a read-only shifted view of an input ndarray along a specified dimension.
sliceDimensionTo( x, dim, stop[, options] ): return a read-only truncated view of an input ndarray along a specified dimension.
sliceDimension( x, dim, slice[, options] ): return a read-only view of an input ndarray when 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 input ndarray.
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.

Examples

1var objectKeys = require( '@stdlib/utils/keys' );
2var ns = require( '@stdlib/ndarray' );
3
4console.log( objectKeys( ns ) );

Notice

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.

Community

License

See LICENSE.

Copyright

No vulnerabilities found.

10

Dangerous-Workflow

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10

Maintained

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10

License

Determines if the project has defined a license.

10

Vulnerabilities

Determines if the project has open, known unfixed vulnerabilities.

10

Binary-Artifacts

Determines if the project has generated executable (binary) artifacts in the source repository.

9

Security-Policy

Determines if the project has published a security policy.

3

Pinned-Dependencies

Determines if the project has declared and pinned the dependencies of its build process.

Reason

dependency not pinned by hash detected -- score normalized to 3

Details

Warn: third-party GitHubAction not pinned by hash: .github/workflows/productionize.yml:85: update your workflow using https://app.stepsecurity.io/secureworkflow/stdlib-js/ndarray/productionize.yml/main?enable=pin
Warn: third-party GitHubAction not pinned by hash: .github/workflows/productionize.yml:275: update your workflow using https://app.stepsecurity.io/secureworkflow/stdlib-js/ndarray/productionize.yml/main?enable=pin
Warn: third-party GitHubAction not pinned by hash: .github/workflows/productionize.yml:449: update your workflow using https://app.stepsecurity.io/secureworkflow/stdlib-js/ndarray/productionize.yml/main?enable=pin
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Warn: third-party GitHubAction not pinned by hash: .github/workflows/productionize.yml:623: update your workflow using https://app.stepsecurity.io/secureworkflow/stdlib-js/ndarray/productionize.yml/main?enable=pin
Warn: npmCommand not pinned by hash: .github/workflows/benchmark.yml:58
Warn: npmCommand not pinned by hash: .github/workflows/benchmark.yml:58
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Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:157
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Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:269
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:269
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:269
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:436
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:436
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:436
Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:617
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Warn: npmCommand not pinned by hash: .github/workflows/productionize.yml:617
Warn: npmCommand not pinned by hash: .github/workflows/test.yml:76
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Warn: npmCommand not pinned by hash: .github/workflows/test.yml:83
Warn: npmCommand not pinned by hash: .github/workflows/test_bundles.yml:72
Warn: npmCommand not pinned by hash: .github/workflows/test_coverage.yml:67
Warn: npmCommand not pinned by hash: .github/workflows/test_coverage.yml:67
Warn: npmCommand not pinned by hash: .github/workflows/test_coverage.yml:67
Warn: npmCommand not pinned by hash: .github/workflows/test_coverage.yml:74
Warn: npmCommand not pinned by hash: .github/workflows/test_install.yml:75
Warn: npmCommand not pinned by hash: .github/workflows/test_install.yml:75
Warn: npmCommand not pinned by hash: .github/workflows/test_install.yml:75
Warn: npmCommand not pinned by hash: .github/workflows/test_published_package.yml:93
Info: 33 out of 33 GitHub-owned GitHubAction dependencies pinned
Info: 19 out of 24 third-party GitHubAction dependencies pinned
Info: 0 out of 32 npmCommand dependencies pinned

0

Code-Review

Determines if the project requires human code review before pull requests (aka merge requests) are merged.

0

CII-Best-Practices

Determines if the project has an OpenSSF (formerly CII) Best Practices Badge.

0

Token-Permissions

Determines if the project's workflows follow the principle of least privilege.

0

SAST

Determines if the project uses static code analysis.

0

Branch-Protection

Determines if the default and release branches are protected with GitHub's branch protection settings.

0

Fuzzing

Determines if the project uses fuzzing.

Score

5.1

/10

Last Scanned on 2024-11-18

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