import numpy as np

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

print lst[0], arr[0]

print lst[-1], arr[-1]

print lst[2:], 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=np.float)

np.zeros(3, dtype=np.int)

print '5 ones:', np.ones(5, dtype=np.int)

a = np.empty(4, dtype=np.float)
a.fill(5.5)
a

np.array(256, dtype=np.uint8)

float_info = ('{finfo.dtype}: max={finfo.max:<18}, '
              'approx decimal precision={finfo.precision};')
print float_info.format(finfo=np.finfo(np.float32))
print float_info.format(finfo=np.finfo(np.float64))

np.array(1, dtype=np.uint8).astype(np.float32)

np.arange(10, dtype=np.float64)

np.arange(5, 7, 0.1)

print "A linear grid between 0 and 1:"
print np.linspace(0, 1, 5)

print "A logarithmic grid between 10**2 and 10**4:"
print np.logspace(2, 4, 3)

import numpy as np
import numpy.random

print np.random.randn(5)

norm10 = np.random.normal(10, 3, 5)
print 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, 5, 6]]
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print arr2
print arr2.shape

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

print lst2[0:2][1]
print arr2[0:2, 1]

np.zeros((2, 3))

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

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

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

# Print the "view" array from reshape.
print 'Before\n', arr_view

# Update the first element of the original array.
arr[0] = 1000

# Print the "view" array from reshape again,
# noticing the first value has changed.
print 'After\n', arr_view

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

print ('Second element from dimension 0, '
       'last 2 elements from dimension one: \n'),
print arr[1, 2:]

print ('All elements bar the last from dimension 0, '
       'third element from dimension one: \n'),
print arr[:-1, 2]

print ('Second element from dimension 0 (maintaining its dimension), '
       'all elements from dimension one: \n'),
print arr[1:2, :]

print 'First row:  ', arr[0], 'is equivalent to', arr[0, :]
print 'Second row: ', arr[1], 'is equivalent to', arr[1, :]

arr1 = np.empty((4, 6, 3))
print 'Orig shape: ', arr1.shape
print arr1[...].shape
print arr1[..., 0:2].shape
print arr1[2:4, ..., ::2].shape
print arr1[2:4, :, ..., ::-1].shape

x = np.linspace(0, 9, 3)
y = np.linspace(-8, 4, 3)
x2d, y2d = np.meshgrid(x, y)
print x2d
print y2d

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', arr
print 'The sum of elements along the rows is    :', arr.sum(axis=1)
print 'The sum of elements along the columns is :', arr.sum(axis=0)

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

print 'Array:\n', arr
print 'Transpose:\n', arr.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)

a = np.arange(3).reshape((3, 1))
b = np.arange(3)
print a, '+', b, '=\n', a + b

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

# arr1 + arr2

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

# arr1 + arr2

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

# arr1 + arr2

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

# arr1 + arr2







np.arange(6).reshape((1, -1))

np.arange(6).reshape((2, -1))

arr = np.arange(6)
print arr[np.newaxis, :, np.newaxis].shape

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

x = np.arange(-5, 5.5, 0.5)
y = np.exp(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, '\n'
print np.dot(A, A.T)

print np.dot(A.T, A)

print A, 'x', v1, '=', np.dot(A, v1)