In [1]:

%matplotlib inline

In [2]:

# Waking up Julia takes up to 40 seconds the first time
import julia
j = julia.Julia()

In [3]:

# evaluation using Julia interpreter
print(j.eval("sqrt(1 +31)"))
print(j.sind(90))

5.656854249492381
1.0

In [4]:

# Warning: loading '%load_ext julia.magic' before 'import julia' will change behaviour of julia object
# So here, we do it after
%load_ext julia.magic

Initializing Julia interpreter. This may take some time...

In [5]:

%julia @pyimport matplotlib.pyplot as plt
%julia @pyimport numpy as np

In [6]:

%%julia
# Note how we mix numpy and julia:
t = linspace(0, 2*pi, 1000); # use the julia linspace
s = sin(3 * t + 4 * np.cos(2 * t)); # use the numpy cosine and julia sine

fig = plt.gcf()  # **** WATCH THIS VARIABLE ****
plt.plot(t, s, color="red", linewidth=2.0, linestyle="--")

Out[6]:

[<matplotlib.lines.Line2D at 0xd08d470>]

In [7]:

# fibonacci with each layer being Python than Julia
jfib = %julia jfib(n, fib) = n < 2 ? n : fib(n-1, jfib) + fib(n-2, jfib)
    
def pyfib(n, fib):
    if n < 2:
         return n
    return fib(n-1, pyfib) + fib(n-2, pyfib)

pyfib(20, jfib)

Out[7]:

In [8]:

# original examples from Fernando Perez
# http://nbviewer.ipython.org/github/fperez/talk-1312-nips/blob/master/IPython-Interactive-Computing.ipynb

In [ ]: