#
# Numpy introduction 2 (walkthrough)
#

# using arange to get evenly spaced values in an array:

import numpy as np

a = np.arange(1, 10)
print(a)

# compare to range:
x = range(1,10)
print(x)    # x is an iterator
print(list(x))

# some more arange examples:
x = np.arange(10.4)
print(x)
x = np.arange(0.5, 10.4, 0.8)
print(x)
x = np.arange(0.5, 10.4, 0.8, int)
print(x)

# another related function: linspace

# 50 values between 1 and 10:
print(np.linspace(1, 10))
# 7 values between 1 and 10:
print(np.linspace(1, 10, 7))
# excluding the endpoint:
print(np.linspace(1, 10, 7, endpoint=False))

# dimension of arrays:

x = np.array(42)
print("x: ", x)
print("The type of x: ", type(x))
print("The dimension of x:", np.ndim(x))

F = np.array([1, 1, 2, 3, 5, 8, 13, 21])
V = np.array([3.4, 6.9, 99.8, 12.8])
print("F: ", F)
print("V: ", V)
print("Type of F: ", F.dtype)
print("Type of V: ", V.dtype)
print("Dimension of F: ", np.ndim(F))
print("Dimension of V: ", np.ndim(V))

A = np.array([ [3.4, 8.7, 9.9], 
               [1.1, -7.8, -0.7],
               [4.1, 12.3, 4.8]])
print(A)
print(A.ndim)

# shape of arrays:

print(x.shape)
print(F.shape)
print(V.shape)
print(A.shape)

# reshaping:

x = np.array([ [67, 63, 87],
               [77, 69, 59],
               [85, 87, 99],
               [79, 72, 71],
               [63, 89, 93],
               [68, 92, 78]])
print(np.shape(x))
print(x.shape) #an equivalent array property

x.shape = (3, 6) #reshaped!
print(x)

x.shape = (4,4) #whoops, error!


# more on array indexing:

F = np.array([1, 1, 2, 3, 5, 8, 13, 21])
# print the first element of F, i.e. the element with the index 0
print(F[0])
# print the last element of F
print(F[-1])
B = np.array([ [[111, 112], [121, 122]],
               [[211, 212], [221, 222]],
               [[311, 312], [321, 322]] ])
print(B[0][1][0])

A = np.array([ [3.4, 8.7, 9.9], 
               [1.1, -7.8, -0.7],
               [4.1, 12.3, 4.8]])
print(A[1][0])
print(A[1, 0]) # equivalent syntax

# more slicing examples

S = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print(S[2:5])
print(S[:4])
print(S[6:])
print(S[:])

# multidimensional slicing:

A = np.array([
[11,12,13,14,15],
[21,22,23,24,25],
[31,32,33,34,35],
[41,42,43,44,45],
[51,52,53,54,55]])

print(A[:3,2:])
print(A[3:,:])
print(A[:,4:])

X = np.arange(28).reshape(4,7)
print(X)
print(X[::2, ::3]) # uses the 'step' parameter
print(X[::, ::3])

Z = np.zeros((8,8),dtype=int)
Z[1::2,::2] = 1
Z[::2,1::2] = 1
print(Z)


# shortcuts for array creation:

E = np.ones((2,3))
print(E)
F = np.ones((3,4),dtype=int)
print(F)

Z = np.zeros((2,4))
print(Z)

x = np.array([2,5,18,14,4])
E = np.ones_like(x)
print(E)

I = np.identity(4)
print(I)

J = np.eye(5, 8, k=1, dtype=int)
print(J)


# Broadcasting:

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x)   # Create an empty matrix with the same shape as x

# Add the vector v to each row of the matrix x with an explicit loop (SLOW!)
for i in range(4):
    y[i, :] = x[i, :] + v

# Now y is the following
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
print y

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v  # Add v to each row of x using broadcasting
print y  # Prints "[[ 2  2  4]
         #          [ 5  5  7]
         #          [ 8  8 10]
         #          [11 11 13]]"