def script():
print('in script')
#############Load Vars#############
#If we saved the vars in MATLAB then we load them here.
a = np.load('a.npy')
b = np.load('b.npy')
#If you want a as ints or floats for your julia scripts use the conversions below,
#(provided these inputs are single no's when defined and converted to numpy arrays in MATLAB e.g. "a=1")
#a=int(a);
#a=float(a);
#############Python bit#############
#Testing we can actually use python (not needed)
c = np.add(a, b)
print('NumPy Sum result')
print(c)
#############Julia bit#############
#Compute something in Julia (from a module)
from julia import Main
print('Make sure you cd to correct dir here')
Main.include(string(pwd(), "SumArrays.jl"))
d = Main.SumArrays(a, b)
print('Julia Sum result')
print(d)
#############Save bit#############
#Now save as MATLAB matricies.
scipy.io.savemat('PythonOutput.mat', dict(a=a, b=b, c=c, d=d))
def script(a, b):
#############Python bit#############
#Testing we can actually use python (not needed)
c = np.add(a, b)
print('NumPy Sum result')
print(c)
#############Julia bit#############
#Compute something in Julia (from a module)
from julia import Main
print('Make sure you cd to correct dir here')
Main.include(string(pwd(), "SumArrays.jl"))
d = Main.SumArrays(a, b)
print('Julia Sum result')
print(d)
#############Save bit#############
#Now save as MATLAB matricies.
scipy.io.savemat('PythonOutput.mat', dict(a=a, b=b, c=c, d=d))
