'''
   20211116
   
   Robin Dawes
   
   NumPy
'''


import numpy as np
import random

# variations of using the np.loadtxt() function to create a 2-dimensional array from a file

# See the test.txt file: each row of the array is on a separate line

# load the file as an array of integers
table_1 = np.loadtxt("test.txt", dtype = int)
# The default type is float, but we use dtype to override this
print("\ntable_1")
print(table_1)  

# use skiprows to ignore lines at the top of the file
# use usecols to select specific columns
table_2 = np.loadtxt("test.txt", dtype = int, skiprows=1, usecols=(0,2,4,5))
print("\ntable_2")
print(table_2)

# we can use a range() to select the columns we want
table_3 = np.loadtxt("test.txt", dtype = int, usecols=range(3,6))
print("\ntable_3")
print(table_3)

# create an array from a set of values using the np.array() function

# NB - this function takes ONE parameter.  The values/rows of the table 
# must be enclosed in parentheses

# create a one-dimensional array
table_4 = np.array([4, 6, 1, 2, 3] )
print("\ntable_4")
print(table_4)

# create a two-dimensional array
table_5 = np.array([ [1,2,3,4],
                                 [8,7,6,5],
                                 [10, 10, 10, 10]
                               ]
                              )
print("\ntable_5")
print(table_5)

# create an array initialized to all 0's
# Note that the entries are floats (0.) because we didn't specify integers
table_6 = np.zeros((5,4))
print("\ntable_6")
print(table_6)

# create an array initialized to all 1's
# Note that the entries are integers because we specified the dtype
table_7 = np.ones((3,4), dtype = int)
print("\ntable_7")
print(table_7)

# create an arry initialized to whatever was in the memory addresses
# being used - why would you ever use this??
table_8 = np.empty((2,5))
print("\ntable_8")
print(table_8)

print("\ntable_5 again")
print(table_5)

# we can find the shape of an array
print("\ntable_5.shape")
print(table_5.shape)

# we can sum the elements of an array
print("\nsum(table_5)")
print(np.sum(table_5))

# we can sum a single row of an array
print("\nsum(table_5[1])")
print(np.sum(table_5[1]))

# we can access/change a single value in an array
table_5[2,3] = 99       # row 2, column 3
print("\ntable_5 with one element changed")
print(table_5)

# we can iterate over the rows of an array
print("\nsum each row in table_5")
for r in table_5:
   print(np.sum(r))
   
# we can multiply (or add, subtract, divide) an array by a value
print("\nmultiplying all elements of table_5 by 10")
table_9 = table_5*10
print(table_9)
   
# we can add (multiply, subtract, divide) two tables together   ...
print("\nadding table_7 and table_9")
table_10 = table_7 + table_9
print(table_10)

# ... but only if they have the same shape
#~ print("\ntrying to add table_1 and table_2")
#~ table_11 = table_1 + table_2                          # fails because they have different shape
#~ print(table_11)

# numpy has hundreds of pre-defined operations - we are just 
# scratching the surface here.  You should check out the online
# documentation!