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))