API References

A Julia interface module for SciPy

• GitHub repository: AtsushiSakai/SciPy.jl: Julia interface for SciPy
• Stable Doc
• Dev Doc

scipy.cluster module

• Clustering package (scipy.cluster) Reference Guide

Examples

You can do k-means clustering using this module:

julia> features  = [[ 1.9  2.3];
                    [ 1.5 2.5];
                    [ 0.8 0.6];
                    [ 0.4 1.8];
                    [ 0.1 0.1];
                    [ 0.2 1.8];
                    [ 2.0 0.5];
                    [ 0.3 1.5];
                    [ 1.0 1.0]]
9×2 Array{Float64,2}:
 1.9  2.3
 1.5  2.5
 0.8  0.6
 0.4  1.8
 0.1  0.1
 0.2  1.8
 2.0  0.5
 0.3  1.5
 1.0  1.0

julia> whitened = SciPy.cluster.vq.whiten(features)
9×2 Array{Float64,2}:
 2.7396    2.91001
 2.16284   3.16306
 1.15351   0.759134
 0.576757  2.2774
 0.144189  0.126522
 0.288379  2.2774
 2.88379   0.632612
 0.432568  1.89784
 1.44189   1.26522

julia> SciPy.cluster.vq.kmeans(whitened, [whitened[1,:] whitened[3,:]] )
([1.1174670798453024 1.8345740800894272; 2.8837860125040065 0.6326117517549749], 1.073399
3090584457)

scipy.fft module

• Discrete Fourier transforms (scipy.fft) Reference Guide

Examples

You can use FFT (Fast Fourier Transform):

julia> SciPy.fft.fft(exp.(π/8 * collect(1:8)))
8-element Array{Complex{Float64},1}:
   68.17385416403044 - 0.0im
   1.408601300061675 + 31.248171041435185im
 -10.268363617931092 + 15.207165888808841im
 -12.695027025520982 + 6.493878653648949im
 -13.216494113330363 - 0.0im
 -12.695027025520982 - 6.493878653648949im
 -10.268363617931092 - 15.207165888808841im
   1.408601300061675 - 31.248171041435185im

scipy.integrate module

• Integration and ODEs (scipy.integrate) Reference Guide

Examples

You can compute a definite integral:

julia> f(x) = x^2
f (generic function with 1 method)

julia> SciPy.integrate.quad(f, 0, 4)
(21.333333333333336, 2.368475785867001e-13)

scipy.io module

• Input and output (scipy.io) Reference Guide

Examples

You can save a MATLAB-style .mat file:

julia> mdic = Dict([("a", 100), ("label", "experiment")])
Dict{String,Any} with 2 entries:
  "label" => "experiment"
  "a"     => 100

julia> SciPy.io.savemat("sample_data.mat", mdic)

scipy.linalg module

• Linear algebra (scipy.linalg) Reference Guide

Examples

You can compute the matrix exponential using Pade approximation:

julia> SciPy.linalg.expm(zeros((2,2)))
2×2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0

scipy.ndimage module

• Multi-dimensional image processing (scipy.ndimage) Reference Guide

Examples

You can compute a multidimensional convolution:

julia> k = [[1 1 1];[1 1 0];[1 0 0]]
3×3 Array{Int64,2}:
 1  1  1
 1  1  0
 1  0  0

julia> a = [[1 2 0 0];
            [5 3 0 4];
            [0 0 0 7];
            [9 3 0 0]]
4×4 Array{Int64,2}:
 1  2  0  0
 5  3  0  4
 0  0  0  7
 9  3  0  0

julia> SciPy.ndimage.convolve(a, k, mode="constant", cval=0.0)
4×4 Array{Int64,2}:
 11  10   7   4
 10   3  11  11
 15  12  14   7
 12   3   7   0

scipy.odr module

• Orthogonal distance regression (scipy.odr) Reference Guide

Examples

You can calculate orthogonal distance regression with an exponential model:

julia> x = collect(0.0:5.0);

julia> y = -10.0 .+ exp.(0.5*x);

julia> data = SciPy.odr.Data(x, y)
PyObject <scipy.odr.odrpack.Data object at 0x7fe5fda4ccc0>

julia> data = SciPy.odr.Data(x, y);

julia> odr_obj = SciPy.odr.ODR(data, SciPy.odr.exponential);

julia> output = odr_obj.run();

julia> println(output.beta)
[-10.0, 0.5]

scipy.signal module

• Signal processing (scipy.signal) Reference Guide

Examples

You can compute the Kaiser parameter beta, given the attenuation a:

julia> SciPy.signal.kaiser_beta(65)
6.20426

scipy.sparse module

• Sparse matrices (scipy.sparse) Reference Guide

Examples

You can do sparse matrix calculation:

julia> A = SciPy.sparse.csc_matrix([[1.0 0.0];
                                   [1.0 2.0]]);

julia> Ainv = SciPy.sparse.linalg.inv(A);

julia> A.dot(Ainv).todense()
2×2 Array{Float64,2}:
 1.0  0.0
 0.0  1.0

scipy.special module

• Special functions (scipy.special) Reference Guide

Module initialization function

Print configurations:

• Julia version
• Python version
• Python path
• scipy version