Julia motivation why werent numpy scipy numba good enough

using Pkg
Pkg.add("PyCall")
Pkg.add("PyPlot")
Pkg.add("Numba")
Pkg.add("SciPy")
Pkg.add("NumPy")

Option 1: Using PyCall and PyPlot

One way to solve the problem of Julia not having numpy, scipy, and numba is to use the PyCall and PyPlot packages. PyCall allows Julia to call Python functions, while PyPlot provides a plotting interface similar to matplotlib in Python.

To use PyCall and PyPlot, first install them by running the following commands:

using Pkg
Pkg.add("PyCall")
Pkg.add("PyPlot")

Once installed, you can import the necessary Python libraries and use them in your Julia code. For example, to use numpy, scipy, and numba, you can do the following:

using PyCall
numpy = pyimport("numpy")
scipy = pyimport("scipy")
numba = pyimport("numba")

Now you can use numpy, scipy, and numba functions in your Julia code as if they were native Julia functions. For example:

x = numpy.array([1, 2, 3])
y = scipy.sin(x)
z = numba.jit(y)

This option allows you to leverage the power of numpy, scipy, and numba in your Julia code, but it comes with the overhead of calling Python functions from Julia, which may introduce some performance penalties.

Option 2: Using SciPy and NumPy

If you prefer to use the actual numpy and scipy libraries in your Julia code, you can install and use them directly using the SciPy and NumPy packages for Julia.

To install SciPy and NumPy, run the following commands:

using Pkg
Pkg.add("SciPy")
Pkg.add("NumPy")

Once installed, you can import the necessary modules and use them in your Julia code. For example, to use numpy and scipy, you can do the following:

using SciPy
using NumPy

x = NumPy.array([1, 2, 3])
y = SciPy.sin(x)

This option allows you to use numpy and scipy directly in your Julia code without the need for calling Python functions. However, keep in mind that the functionality provided by the SciPy and NumPy packages for Julia may not be as extensive as the original Python libraries.

Option 3: Using Numba

If your main concern is performance and you want to leverage the just-in-time (JIT) compilation capabilities of numba, you can use the Numba package for Julia.

To install Numba, run the following command:

using Pkg
Pkg.add("Numba")

Once installed, you can use the @njit macro provided by Numba to compile your Julia functions just-in-time. For example:

using Numba

@njit
function my_function(x)
    # Your code here
end

This option allows you to achieve performance improvements by compiling your Julia functions just-in-time using numba. However, keep in mind that not all Julia code can be optimized using numba, and the performance gains may vary depending on the specific use case.

Overall, the best option depends on your specific needs and preferences. If you want to leverage the full functionality of numpy, scipy, and numba, option 1 using PyCall and PyPlot may be the most suitable. If you prefer to use the actual numpy and scipy libraries, option 2 using SciPy and NumPy is a good choice. If performance is your main concern, option 3 using Numba can provide significant performance improvements.