import numpy as np

lst = [10, 20, 30, 40]
arr = np.array([10, 20, 30, 40])

lst[0]

arr[0]

arr[-1]

arr[2:]

lst[-1] = 'a string inside a list'
lst

arr[-1] = 'a string inside an array'

arr.dtype

arr[-1] = 1.234
arr

np.zeros(5, dtype=float)

np.zeros(3, dtype=int)

np.zeros(3, dtype=complex)

print '5 ones:', np.ones(5)

a = np.empty(4)
a.fill(5.5)
a

np.arange(5)

print "A linear grid between 0 and 1:"
print np.linspace(0, 1, 4)

print "A logarithmic grid between 10**1 and 10**3:"
print np.logspace(1, 3, 4)

np.random.randn(5)

norm10 = np.random.normal(10, 3, 5)
norm10

mask = norm10 > 9
mask

print 'Values above 9:', norm10[mask]

print 'Resetting all values above 9 to 0...'
norm10[mask] = 0
print norm10

lst2 = [[1, 2], [3, 4]]
arr2 = np.array([[1, 2], [3, 4]])
arr2

print lst2[0][1]
print arr2[0,1]

np.zeros((2,3))

np.random.normal(10, 3, (2, 4))

arr = np.arange(8).reshape(2, 4)
print arr

arr = np.arange(8)
arr2 = arr.reshape(2, 4)

arr[0] = 1000
print arr
print arr2

print 'Slicing in the second row:', arr2[1, 2:4]
print 'All rows, third column   :', arr2[:, 2]

print 'First row:  ', arr2[0]
print 'Second row: ', arr2[1]

print 'Data type                :', arr.dtype
print 'Total number of elements :', arr.size
print 'Number of dimensions     :', arr.ndim
print 'Shape (dimensionality)   :', arr.shape
print 'Memory used (in bytes)   :', arr.nbytes

print 'Minimum and maximum             :', arr.min(), arr.max()
print 'Sum and product of all elements :', arr.sum(), arr.prod()
print 'Mean and standard deviation     :', arr.mean(), arr.std()

print 'For the following array:\n', arr2
print 'The sum of elements along the rows is    :', arr2.sum(axis=1)
print 'The sum of elements along the columns is :', arr2.sum(axis=0)

np.zeros((3,4,5,6)).sum(2).shape

print 'Array:\n', arr2
print 'Transpose:\n', arr2.T

arr1 = np.arange(4)
arr2 = np.arange(10, 14)
print arr1, '+', arr2, '=', arr1+arr2

print arr1, '*', arr2, '=', arr1*arr2

1.5 * arr1

print np.arange(3)
print np.arange(3) + 5

np.ones((3, 3)) + np.arange(3)

np.arange(3).reshape((3, 1)) + np.arange(3)

arr1 = np.ones((2, 3))
arr2 = np.ones((2, 1))

arr1 + arr2

arr1 = np.ones((2, 3))
arr2 = np.ones(2)

arr1 + arr2

arr1 = np.ones((2, 3))
arr2 = np.ones((2, 1))

arr1 + arr2



A = np.arange(1, 9).reshape((2, 4))
B = np.arange(1, 3)
A + B.reshape((2, 1))

print B.shape
print B[:, np.newaxis].shape

A + B[:, np.newaxis]

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

v1 = np.array([2, 3, 4])
v2 = np.array([1, 0, 1])

print v1, '.', v2, '=', np.dot(v1, v2)

A = np.arange(6).reshape(2, 3)
print A, 'x', v1, '=', np.dot(A, v1)

print np.dot(A, A.T)

print np.dot(A.T, A)

arr = np.arange(10).reshape(2, 5)
np.savetxt('test.out', arr, fmt='%.2e', header="My dataset")
!cat test.out

arr2 = np.loadtxt('test.out')
print arr2

np.save('test.npy', arr2)
# Now we read this back
arr2n = np.load('test.npy')
# Let's see if any element is non-zero in the difference.
# A value of True would be a problem.
print 'Any differences?', np.any(arr2-arr2n)

np.savez('test.npz', arr, arr2)
arrays = np.load('test.npz')
arrays.files

np.savez('test.npz', array1=arr, array2=arr2)
arrays = np.load('test.npz')
arrays.files

print 'First row of first array:', arrays['array1'][0]
# This is an equivalent way to get the same field
print 'First row of first array:', arrays.f.array1[0]