ENH: Enhanced the problem set building on the image handing and arrays.
Illustrated dtypes, casting and their importance along with an example
using RGBA images. Also introduce edge detection.
import math
def linspace(a, b, N):
lns = []
step = (float(b) - float(a)) / float(N - 1)
print step
for i in range(N):
lns.append(a + i*step)
return lns
def sin_func():
x = linspace(0, 5, 11)
sin_list = []
for i in x:
sin_list.append(math.sin(i))
print sin_list
def sinsin_func():
x = linspace(0, 5, 11)
sin_list = []
for i in x:
sin_list.append(math.sin(i) + math.sin(10*i))
print sin_list
sinsin_func()