Getting started

SciDart brings the core of NumPy and SciPy to Dart. It is written in pure Dart, so the same code runs on the Dart VM, in Flutter apps (Android, iOS, desktop) and on the web.

The package has two libraries:

• package:scidart/numdart.dart is the NumPy side: arrays, linear algebra, polynomials, statistics, calculus, constants.
• package:scidart/scidart.dart is the SciPy side: FFT, signal processing, special functions, distributions and optimization. It also exports everything from numdart, so most programs only need this import.

Install

dart pub add scidart

In a Flutter project, use flutter pub add scidart. See Install for the SDK requirements.

First program

import 'dart:math' as math;

import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';

void main() {
  var fs = 128.0;
  var f0 = 3.7; // between two FFT bins
  var n = linspace(0, 4, num: (4 * fs).toInt(), endpoint: false);
  var signal = arraySin(arrayMultiplyToScalar(n, 2 * math.pi * f0));

  var windowed = signal * blackmanharris(signal.length);
  var spectrum = arrayComplexAbs(rfft(windowed));
  var i = arrayArgMax(spectrum);
  var refined = parabolic(arrayLog(spectrum), i)[0];

  print('naive estimate: ${fs * i / windowed.length} Hz');
  print(
      'refined estimate: ${(fs * refined / windowed.length).toStringAsFixed(4)} Hz');
}

naive estimate: 3.75 Hz
refined estimate: 3.6993 Hz

How the docs are organized

• Each guide covers one module, with examples whose output comes from running them against the released version of scidart when this site was built.
• Some guides have interactive demos. They call the library compiled to JavaScript, the same code your app runs on the web.
• Names in code link to the API reference, generated with dart doc.
• Function names follow NumPy and SciPy where possible, so their documentation is a good second reference.