Here you will find some examples of usage of SciDart.
import 'package:scidart/numdart.dart';
void main() {
// 1D array creation
var a = Array([1.0, 2.0, 3.0]);
print(a); // print vector
// 2D array creation
var mA = Array2d.empty();
var line = Array([1.0, 2.0, 3.0]);
mA.add(line);
mA.add(line);
mA.add(line);
print(mA); // print matrix
// 3D array creation
var book = Array3d.empty();
var page = Array2d.empty();
page.add(Array([1.0, 2.0, 3.0]));
page.add(Array([1.0, 2.0, 3.0]));
page.add(Array([1.0, 2.0, 3.0]));
book.add(page);
book.add(page);
book.add(page);
print(book);
// Create a array of complex numbers
var aComplex = ArrayComplex([
Complex(real: 2.0, imaginary: 2.0),
Complex(real: 2.0, imaginary: 2.0),
Complex(real: 2.0, imaginary: 2.0)
]);
print(aComplex);
}import 'package:scidart/numdart.dart';
void main() {
var ar = arange(step: 0, stop: 100); // generate a array 0 to 99
print(ar);
var lin =
linspace(0, 10, num: 40); // generate a array 0 to 10 with 40 elements
print(lin);
var on =
ones(10); // generate a array with length 10 where all elements are 1
print(on);
var zer =
zeros(5); // generate a array with length 5 where all elements are 0
print(zer);
// generate a 1Hz sine wave with sample frequency (fs) of 128Hz
var N = 50.0;
var fs = 128;
var n = linspace(0, N, num: (N * fs).toInt(), endpoint: false);
var f1 = 1; // 1Hz
var sg = arraySin(arrayMultiplyToScalar(n, 2 * pi * f1));
print(sg);
}import 'package:scidart/numdart.dart';
void main() {
// generate a array with 10 random elements 0 to 0.99
var r = randomArray(10);
print(r);
// generate a 2D 3x3 with random elements
var r2d = randomArray2d(3, 3);
print(r2d);
// generate ArrayComplex with 10 random elements
var rAc = randomArrayComplex(10);
print(rAc);
}import 'package:scidart/numdart.dart';
void main() {
var a = Array([1.0, 2.0, 3.0]);
var b = Array([1.12121212, 2.12121212, 3.12121212]);
var aComplex = ArrayComplex([
Complex(real: 2.0, imaginary: 2.0),
Complex(real: 2.0, imaginary: 2.0),
Complex(real: 2.0, imaginary: 2.0)
]);
print(a + b);
print(a - b);
print(a * b);
print(a / b);
print(arrayAddToScalar(a, 2));
print(arrayArgMax(a));
print(arrayConcat(a, b)); // array concatenation
print(arrayCos(a));
print(arrayCumSum(a));
print(arrayDiff(a));
print(arrayDivisionToScalar(a, 2));
print(arrayLog10(a));
print(arrayLog(a));
print(arrayMax(a));
print(arrayMin(a));
print(arrayMultiplyToScalar(a, 2));
print(arrayPadStart(a, 2)); // add zeros at start of array
print(arrayPow(a, 2));
print(arrayReshapeToMatrix(a, 2));
print(arrayReverse(a)); // array a reversed
print(arraySin(a)); // compute sin for all elements of array
print(arraySinc(a)); // compute sinc function for all elements of array
print(arraySqrt(a));
print(arraySubToScalar(a, 2));
print(arraySum(a));
print(arrayToColumnMatrix(a));
print(arrayToComplexArray(a));
print(arrayTruncateEachElement(b, 4, returnNewArray: true)); // truncate all values
print(arrayTruncateLast(a));
print(a.getRangeArray(0, 2));
print(a == c);
// Complex array operations
print(arrayComplexAbs(aComplex));
print(arrayComplexConjugate(aComplex));
print(arrayComplexCos(aComplex));
print(arrayComplexDivisionToScalar(aComplex, 2));
print(arrayComplexMultiplyToScalar(aComplex, 2));
print(arrayComplexPadStart(aComplex, 2));
print(arrayComplexReverse(aComplex));
print(arrayComplexSum(aComplex, aComplex));
print(arrayComplexTruncateEachElement(aComplex, 4));
print(arrayComplexTruncateLast(aComplex));
}import 'package:scidart/numdart.dart';
void main() {
var mA = Array2d.empty();
var line = Array([1.0, 2.0, 3.0]);
mA.add(line);
mA.add(line);
mA.add(line);
var mB = Array2d([
Array([1.0, 2.0, 3.0]),
Array([4.0, 5.0, 6.0]),
Array([7.0, 8.0, 10.0])
]);
var mC = Array2d([
Array([1]),
Array([2]),
Array([3])
]);
print(matrixDeterminant(mA)); // find matrix determinant
print(matrixRank(mA)); // matrix rank
print(matrixTranspose(mA)); // matrix transposition
print(matrixInverse(mC)); // matrix inverse
print(matrixDot(mA, mC)); // Matrix product
print(matrixQR(mA).Q()); // Matrix Q of QR decomposition
print(matrixSolve(mB, mC)); // solve a linear system
print(array2dAddToScalar(mA, 2, returnNewArray: true));
print(array2dDivisionToScalar(mA, 2, returnNewArray: true));
print(array2dInverseOfEachElement(mA, returnNewArray: true));
print(array2dMultiplyToScalar(mA, 2, returnNewArray: true));
print(array2dPow(mA, 2, returnNewArray: true));
print(array2dSubToScalar(mA, 2, returnNewArray: true));
var mD = Array2d([
Array([1.121212121212]),
Array([2.121212121212]),
Array([3.121212121212])
]);
print(array2dTruncateEachElement(mA, 4));
print(mD);
print(mA + mB);
print(mA - mB);
print(mA * mB);
print(mA / mB);
print(mA == mB);
print(matrixNormOne(mA));
print(matrixNormTwo(mA));
// indexing only columns
var onlyOneColumn = mA.getColumn(1);
print(onlyOneColumn);
// subArray2d (submatrix)
var subMatrix = mA.subArray2d(1, 2, 1, 2);
print(subMatrix);
}import 'package:scidart/numdart.dart';
void main() {
print(bitReverse(1, 3)); // rotate 000001 3 times and get 00100
print(highestOneBit(130)); // highest one bit. 0b10000010 => 1H: 2^8 = 128
print(intToBool(130)); // int to bool
print(boolToInt(false)); // bool to int
print(isEven(0)); // even
print(isOdd(1)); // odd
print(isEvenDouble(1.1141, 3)); // check if double is even after truncation
print(isOddDouble(1.4474, 3)); // check if double is odd after truncation
var c1 = Complex(real: 1.1111, imaginary: 1);
var c2 = Complex(real: 1, imaginary: 1);
print(c1 + c2); // complex sum
print(c1 - c2); // complex sub
print(c1 * c2); // complex mul
print(c1 / c2); // complex div
print(complexAbs(c1)); // complex absolute
print(complexConjugate(c1)); // complex conjugate
print(complexCos(c1)); // complex cos
print(complexDivideScalar(c1, 2)); // complex div to scalar
print(complexTruncate(c1, 2)); // complex truncate
}import 'package:scidart/numdart.dart';
void main() {
var a = Complex(real: 1.121212, imaginary: 2.22222);
var b = Complex.ri(4.0, 8.0);
print(a + b);
print(a - b);
print(a * b);
print(a / b);
print(complexAbs(a));
print(complexConjugate(a));
print(complexCos(a));
print(complexDivideScalar(a, 2));
print(complexMultiplyScalar(a, 2));
print(complexTruncate(a, 2));
}import 'package:scidart/numdart.dart';
void main() {
var y = Array([-1, -1, -1, -1, 1, 1, 1, 1]); // function y points
var x = arange(stop: 8); // function x points
// array integration
print(trapzArray(y, x: x)); // integration with trapezoidal rule
print(simpsArray(y, x: x)); // integration with Simpson's rule
// cumulative integration using trapezoidal rule, useful for signals
print(cumIntegration(y, x: x));
// function integration
var f = (x) => pow(x, 2); // function definition: y = x^2
var a = 0.0; // start integration interval
var b = 10.0; // end integration interval
var n = 10; // number of point between a and b
print(trapzFunction(a, b, n, f)); // integration using trapezoidal rule
print(simpsFunction(a, b, n, f)); // integration using Simpson's rule
// array differentiation
print(differentiateArray(y, x)); // return a array with the differentiation
// function differentiation
var px = 10.0; // point where the function will be differentiated
// return the differentiation at point x=10.0
print(differentiateFunction(px, f));
}import 'package:scidart/numdart.dart';
void main() {
// generate a x range -3 to 3
var x = linspace(-3, 3, num: 30);
// generate parabolic equation: y = 5 - 0.555*x^2
var y = Array.fixed(x.length, initialValue: 5) -
arrayMultiplyToScalar(arrayPow(x, 2), 0.555);
// estimate the inter-sample points vx and vy around the maximum point
// of y function
var xMax = arrayArgMax(y);
var yMax = arrayMax(y);
print("Array points:");
print("${xMax}, ${yMax}");
print("Estimated points:");
print(parabolic(y, xMax));
}import 'package:scidart/numdart.dart';
void main() {
// some point to estimate a polynomial regression
var y = Array([0, 1, 4, 3, 2, 5, 0]); // y axis points
var x = Array([0, 1, 2, 3, 4, 5, 6]); // x axis points
var degree = 10; // suggested polynomial degree
var p = PolyFit(x, y, degree); // polynomial regression
print(p); // print polynomial
// print the current polynomial degree, the function reduces the degree
// to get the ideal degree rank for the actual points
print(p.polyDegree());
print(p.coefficients()); // polynomial coefficients
print(p.predict(3.5)); // estimate a y value for the point x = 3.5
}import 'package:scidart/numdart.dart';
void main() {
var a = randomArray(100); // generate a random array
print(a); // print array
print(mean(a)); // mean of array
print(median(a)); // median of array
print(mode(a)); // mode of array
print(standardDeviation(a)); // standard deviation of array
print(variance(a)); // variance of array
}import 'package:scidart/numdart.dart';
void main() {
// the random function to measure the computation time
var func = () {
var x = arange(stop: 99);
var y = randomArray(99);
var py = PolyFit(x, y, 10);
print(py);
print(py.predict(55.5));
};
// the time measure the computation time if func and return
var dur = timeit(func);
print(dur); // print the computation time
}import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';
void main() {
// generate the signal for test
// 1Hz sine wave
var N = 50.0;
var fs = 128.0;
var n = linspace(0, N, num: (N * fs).toInt(), endpoint: false);
var f1 = 1.0; // 1Hz
var sw = arraySin(arrayMultiplyToScalar(n, 2 * pi * f1));
// return the fft of the signal
var swF = fft(arrayToComplexArray(sw));
print(swF);
// return the real fft, in other words,
// only the components with of positive frequencies
var swFr = rfft(sw);
print(swFr);
// return the frequency scale of FFT in Hz
var swFScale = fftFreq(swF.length, d: 1 / fs);
print(swFScale);
// return only the real frequencies of FFT, useful to use with rFFT
swFScale = fftFreq(swF.length, d: 1 / fs, realFrequenciesOnly: true);
print(swFScale);
print(ifft(swF)); // inverse FFT
print(rifft(swFr)); // inverse FFT of a real FFT
print(dbfft(sw, fs)); // Return the frequency and fft in DB scale
}import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';
void main() {
// sample signals for test
var x = arrayConcat(zeros(8), ones(8));
var y = arrayConcat(ones(8), zeros(8));
//-------- convolution -----------//
// simple numeric convolution
print('convolution');
print(convolution(x, y));
// compute the convolution using FFT, that will be more fast only if:
// x.length + y.length is equal power of 2;
print(convolution(x, y, fast: true));
// compute the convolution for complex signals using FFT
print(convolutionComplex(arrayToComplexArray(x), arrayToComplexArray(y)));
// compute the circular convolution for the complex signals
print(convolutionCircularComplex(
arrayToComplexArray(x), arrayToComplexArray(y)));
// compute cross-relation for two singnals
print(correlate(x, y));
// compute cross-relation for two complex singnals
correlateComplex(arrayToComplexArray(x), arrayToComplexArray(y));
//-------- FIR filter -----------//
// generate the test signal for filter
// a sum of sine waves with 1Hz and 10Hz, 50 samples and
// sample frequency (fs) 128Hz
var N = 50.0;
var fs = 128;
var n = linspace(0, N, num: (N * fs).toInt(), endpoint: false);
var f1 = 1; // 1Hz
var sg1 = arraySin(arrayMultiplyToScalar(n, 2 * pi * f1));
var f2 = 10; // 10Hz
var sg2 = arraySin(arrayMultiplyToScalar(n, 2 * pi * f2));
var sg = sg1 + sg2;
// design a FIR filter low pass with cut frequency at 1Hz, the objective is
// remove the 10Hz sine wave signal
var nyq = 0.5 * fs; // nyquist frequency
var fc = 1; // cut frequency 1Hz
var normal_fc = fc / nyq; // frequency normalization for digital filters
var numtaps = 30; // attenuation of the filter after cut frequency
// generation of the filter coefficients
var b = firwin(numtaps, Array([normal_fc]));
print('FIR');
print(b);
//-------- Digital filter application -----------//
print('digital filter application');
// Apply the filter on the signal using lfilter function
// lfilter uses direct form II transposed, for FIR filter
// the a coefficient is 1.0
var sgFiltered = lfilter(b, Array([1.0]), sg);
print(sg); // show the original signal
print(sgFiltered); // show the filtered signal
//-------- peaks -----------//
print('peaks');
var pk = findPeaks(sgFiltered);
print(pk[0]);// print the indexes of the peaks found in the signal
print(pk[1]);// print the values of the peaks found in the signal
//-------- window functions -----------//
// generate a backmanharris window with x length
print('window functions');
var wBh = blackmanharris(x.length);
print(x * wBh); // apply the windows on the signal
// the windows available can be get with getWindow
print(getWindow('hann', x.length)); // hann window
// for kaiser window, we need pass beta (double value)
print(getWindow(['kaiser', 2.0], x.length));
}import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';
void main() {
// modified Bessel function of order 0
print(besselI0(10));
// modified Bessel function of order 0 for a Array
var a = randomArray(10);
print(arrayBesselI0(a));
}import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';
void main() {
// generate the signals for test
// 1Hz sine wave with incomplete with result a spectral leakage
// in the frequency domain
var N = 20.0;
var fs = 128.0;
var n = linspace(0, N, num: (N * fs).toInt(), endpoint: false);
var f1 = 1.0; // 1Hz
var sg = arraySin(arrayMultiplyToScalar(n, 2 * pi * f1));
sg = sg.getRangeArray(0, sg.length - 201);
sg = arrayConcat(sg, zeros(200));
// reduce spectral leakage with a window function
var sgWindowed = blackmanharris(sg.length) * sg;
print(dbfft(sg, fs)); // fft of original signal
print(dbfft(sgWindowed, fs)); // fft of windowed signal
}import 'package:scidart/numdart.dart';
import 'package:scidart/scidart.dart';
void main() {
// generate the signals for test
// 1Hz sine wave
var N = 50.0;
var fs = 128.0;
var n = linspace(0, N, num: (N * fs).toInt(), endpoint: false);
var f1 = 1.0; // 1Hz
var sg1 = arraySin(arrayMultiplyToScalar(n, 2 * pi * f1));
var fEstimated = freqFromFft(sg1, fs);
print('The original and estimated frequency need be very close each other');
print('Original frequency: ${f1}');
print('Estimated frequency: ${fEstimated}');
// both are equal if truncated
expect(truncate(f1, 4), truncate(fEstimated, 4));
}
double freqFromFft(Array sig, double fs) {
// Estimate frequency from peak of FFT
// Compute Fourier transform of windowed signal
// Avoid spectral leakage: https://en.wikipedia.org/wiki/Spectral_leakage
var windowed = sig * blackmanharris(sig.length);
var f = rfft(windowed);
var fAbs = arrayComplexAbs(f);
// Find the peak and interpolate to get a more accurate peak
var i = arrayArgMax(fAbs); // Just use this for less-accurate, naive version
// Parabolic approximation is necessary to get the exactly frequency of a discrete signal
// since the frequency can be in some point between the samples.
var true_i = parabolic(arrayLog(fAbs), i)[0];
// Convert to equivalent frequency
return fs * true_i / windowed.length;
}import 'package:scidart/numdart.dart';
import 'package:scidart_io/scidart_io.dart';
void main() async {
// read stock_data.csv file in the same directory of the current
// script
// the delimiter of this file is ',' but can be any character
// the reader skip one line in the header of the file and
// one line at the end of the file
var data = await readCSV('stock_data.csv', delimiter: ',', skipHeader: 1, skipFooter: 1);
print(data); // show the data
// generate a 2d array data for test
var data2 = Array2d([
Array([1, 2, 3, 4, 5]),
Array([2, 3, 4, 5, 6]),
Array([3, 4, 5, 6, 7]),
Array([4, 5, 6, 7, 8]),
Array([5, 6, 7, 8, 9]),
]);
// define a file name
var fileName = 'data_array.csv';
// save the data in a CSV file
await writeLinesCSV(data2, fileName);
// read the same data again and convert to Array2d again
var data2Read = await readCSV(fileName, convertToArray2d: true);
// show the data
print(data2Read);
}import 'package:scidart/numdart.dart';
import 'package:scidart_io/scidart_io.dart';
void main() async {
// define a data for tests
var data = Array2d([
Array([1, 2, 3, 4, 5]),
Array([2, 3, 4, 5, 6]),
Array([3, 4, 5, 6, 7]),
Array([4, 5, 6, 7, 8]),
Array([5, 6, 7, 8, 9]),
]);
// define a file name
var fileName = 'data_array.txt';
// write lines in the txt files
await writeLinesTxt(data.toString().split('\n'), fileName);
// read the line again
var dataRead = await readLinesTxt(fileName);
// show in the terminal
print(dataRead);
// to write plain string in a file, just use writeTxt
// write string in the txt files
await writeTxt(data.toString(), fileName);
// read a string again
dataRead = await readTxt(fileName);
print(dateRead);
}