#Examples for Week 3
import numpy as np
##################################
#Basic if, elif, and else statements (remember these can be nested)
x = 5
y = 4 
z = 2*x + y

if z < y: 
	print 'This will never print.'
	print 'Something Else'

elif z = y:
	z += 1

elif ((2*z < x**2) or y>x) and (3*z > y**2):
	print 'Wow!'

else: 
	print 'case 4'

if x > y:
	print 'yay'

elif x<y:
		print "why?"
elif x==y:
		print "ok"



###################################
#Print a countdown 
x = 100
while x > 5:
	print x
	x = x - 1
	if timer > 20:
		x=0

###################################
#Iterating over a range, and checking it against the index of the list
list1 = [1,56,2,6,3,5,2,6,8,19]
for i in range(len(list1)): #Range(10) = [0,1,2,3,4,5,6,7,8,9]
	if i < 5:
		print list1[i]
	elif i > 5: 
		print list1[i-1]
	print i**2

####################################
#Iterating over elements in a list/array
arr = np.array([1.,5,7,4,2,9,76,4])
arr_2 = np.array([4,6,4,7,4,2])
for i in arr:
	print i**2
	if i not in arr_2:
		print 'Not in array 2'
	elif i in arr_2:
		print 'Aw Yis'

#Nested For Loop
one = np.arange(100)
two = np.ones(100)*25
empty_list = []
empty_array = np.array([])
for i in range(len(one)):
	for j in range(len(two)):
		if one[i]==two[j]:
			empty.append([i,j,one[i]]) #[12,45,2]
			np.append(empty_array, [i,j,one[i]])

#####################################
x = np.arange(10, 10, 2) 
y = np.sin(x) #input arr_like

def avg(arr1,arr2):
	avg = (arr1 + arr2) / 2.
	return avg
x1 = avg(arr1, array_2) 

def age_calc(birth_year):
	current = time.asctime( time.localtime(time.time()) )
	current_year = current[-1:-4] 
	age = current_year - birth_year
	return age
my_age= age_calc(1994)
cousin_age= age_calc(1975)

def normalize(type, array):
	'''
	A function to normalize arrays. 
	INPUT: (type, array), where type is either "max1", "mean1", or "median1"
	OUTPUT: A normalized array
	'''
	if type=='max1':
		new_array = array/np.max(array)
		
	elif type=='mean1':
		new_array = array/np.mean(array)
	
	elif type=='median1':
		new_array = array/np.median(array)
	return new_array

spectrum = np.array([1,2,3,4,5,...,1,4,6,4,2]) #Not real, but could be some long spectrum
normed_spectrum = normalize('median1', spectrum)
normed_by_mean = normalize('mean1', spectrum)
help(normalize)
#####################################