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.. currentmodule:: qinfer

Interoperability

Introduction

QInfer can be used in conjunction with software written in scientific software platforms other than Python, thanks to the ready availability of interoperability libraries for interacting with Python. In this section, we provide brief examples of using these libraries with QInfer.

MATLAB Interoperability

As of version 2016a, MATLAB includes built-in functions for calling Python-language software. In particular, these functions can be used to use QInfer from within MATLAB. For example, the following MATLAB snippet will generate and analyze frequency estimation data using the :ref:`simple_est` functions provided by QInfer.

>> true_omega = 70.3;
>> n_shots = 400;
>>
>> ts = pi * (1:1:100) / (2 * 100);
>>
>> signal = sin(true_omega * ts / 2) .^ 2;
>> counts = binornd(n_shots, signal);
>>
>> setenv MKL_NUM_THREADS 1
>> data = py.numpy.column_stack({counts ts ...
n_shots * ones(1, size(ts, 2))});
>> est = py.qinfer.simple_est_prec(data, ...
pyargs('freq_min', 0, 'freq_max', 100));

Importantly, the setenv command is required to work around a bug internal to the MATLAB interpreter.

Julia Interoperability

In Julia, interoperability can be achieved using the PyCall.jl package, which provides macros for making Python modules available as Julia variables. To install PyCall.jl, use Julia's built-in package installer:

julia> Pkg.add("PyCall")

After installing PyCall.jl, the example above proceeds in a very similar fashion:

julia> true_omega = 70.3
julia> n_shots = 100
julia>
julia> ts = pi * (1:1:100) / (2 * 100)
julia>
julia> signal = sin(true_omega * ts / 2) .^ 2
julia> counts = map(p -> rand(Binomial(n_shots, p)), signal);
julia> @pyimport numpy as np
julia> @pyimport qinfer as qi
julia>
julia> data = [counts'; ts'; n_shots * ones(length(ts))']'
julia> est_mean, est_cov = qi.simple_est_prec(data, freq_min=0, freq_max=100)