!git log -1

!git pull origin master

!git checkout -b my_branch

!git branch

Use the issue tracker

%matplotlib inline
%load_ext autoreload
%autoreload 2

import numpy as np
import matplotlib.pyplot as plt
import fipy as fp
import pymks
import sklearn
import numexpr
import line_profiler
import pyfftw

pymks.test()
plt.plot(np.sin(np.linspace(0, 2 * np.pi, 1000)))

my_list = ['a', 1, 2, 1.1, 'a']
type(my_list)

my_tuple = ('a', 1, 2, 1.1, 'a')
type(my_tuple)

my_dict = {'a':  'a', 'b' : 1, 1 : 2,  2.1 : 1.1,  'e' : 'a'}
type(my_dict)

my_set = set(my_list)
print my_set
type(my_set)

my_list[1] = 2
my_list

[i**2 for i in range(4)]

my_tuple[1] = 2

print my_dict

{[1] : 'a'}

{(1, 2) : 'b'}

my_dict['key'] = 'item'
del my_dict['a']
print my_dict

x = [1, 2, 3]
y = x
print x
print y

x[2] = 4

print x
print y

class A:
    pass

x = A()
y = x
print x
print y

x = A()

print x
print y

np.array((0., 1, 2, 3, 1.1))

np.array((0, 1, 2, 2, 1)).dtype


N = 100000
a = [1., 0.2] * N
b = [0.5, 0.4] * N
print a[:10]
print b[:10]

def multiply(a, b):
    for i in xrange(len(a)):
        c = a[i] * b[i]

%timeit multiply(a, b)

import numpy as np

a = np.array(a)
b = np.array(b)

%timeit c = a * b

a = np.arange(10)**3
print a
print a[2]
print a[2:5]

a[:6:2] = 1000.
print a

print a[::-1]

%%bash --out cpuinfo

cat /proc/cpuinfo | grep processor

print cpuinfo
Nproc = len(cpuinfo.splitlines())
print Nproc

%%bash --out notebooks

ls -al | grep [^\ ].ipynb

print notebooks

sorted(notebooks.splitlines(), key=lambda k: int(k.split()[4]))[::-1]

from IPython.parallel import Client
engines = Client()
engines

dview = engines[:]
print "engines IDs",engines.ids
dview.block = True
dview.activate()

N = 1000
M = 4
np.random.seed(201)
A = np.random.random((N, M, M))
Evec = np.zeros((N, M), dtype=complex)
print type(A)
print A.dtype
print A.shape

?np.linalg.eig

np.linalg.eig([[1, 0], [0, 0.5]])

for i in xrange(N):
    Evec[i] = np.linalg.eig(A[i])[0]

Evec_zip = np.array(zip(*map(np.linalg.eig, A))[0])

print np.allclose(Evec_zip, Evec)

dview.scatter('A', A)

for a in dview['A']:
    print a.shape

?px

%px import numpy as np

%px Evec = np.array(zip(*map(np.linalg.eig, A))[0])

%px print Evec.shape

Evec_parallel = dview.gather('Evec')

Evec_parallel.shape

np.allclose(Evec, Evec_parallel)