def fahr_to_kelvin(temp):
    return ((temp - 32) * (5/9)) + 273.15

print 'freezing point of water:', fahr_to_kelvin(32)
print 'boiling point of water:', fahr_to_kelvin(212)

# We'll use temp = 212, the boiling point of water, which was incorrect
print "212 - 32:", 212 - 32

print "(212 - 32) * (5/9):", (212 - 32) * (5/9)

5/9

print '10/3 is:', 10/3

print '10.0/3 is:', 10.0/3

def fahr_to_kelvin(temp):
    return ((temp - 32) * (5.0/9.0)) + 273.15

print 'freezing point of water:', fahr_to_kelvin(32)
print 'boiling point of water:', fahr_to_kelvin(212)

def kelvin_to_celsius(temp):
    return temp - 273.15

print 'absolute zero in Celsius:', kelvin_to_celsius(0.0)

def fahr_to_celsius(temp):
    temp_k = fahr_to_kelvin(temp)
    result = kelvin_to_celsius(temp_k)
    return result

print 'freezing point of water in Celsius:', fahr_to_celsius(32.0)

original = 32.0
final = fahr_to_celsius(original)

print 'final value of temp after all function calls:', temp

import numpy

def span(a):
    diff = a.max() - a.min()
    return diff

data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
print 'span of data', span(data)

diff = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
print 'span of data:', span(diff)

def center(data, desired):
    return (data - data.mean()) + desired

z = numpy.zeros((2,2))
print center(z, 3)

data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
print center(data, 0)

print 'original min, mean, and max are:', data.min(), data.mean(), data.max()
centered = center(data, 0)
print 'min, mean, and and max of centered data are:', centered.min(), centered.mean(), centered.max()

print 'std dev before and after:', data.std(), centered.std()

print 'difference in standard deviations before and after:', data.std() - centered.std()

# center(data, desired): return a new array containing the original data centered around the desired value.
def center(data, desired):
    return (data - data.mean()) + desired

def center(data, desired):
    '''Return a new array containing the original data centered around the desired value.'''
    return (data - data.mean()) + desired

help(center)

def center(data, desired):
    '''Return a new array containing the original data centered around the desired value.
    Example: center([1, 2, 3], 0) => [-1, 0, 1]'''
    return (data - data.mean()) + desired

help(center)

numpy.loadtxt('inflammation-01.csv', delimiter=',')

numpy.loadtxt('inflammation-01.csv', ',')

def center(data, desired=0.0):
    '''Return a new array containing the original data centered around the desired value (0 by default).
    Example: center([1, 2, 3], 0) => [-1, 0, 1]'''
    return (data - data.mean()) + desired

test_data = numpy.zeros((2, 2))
print center(test_data, 3)

more_data = 5 + numpy.zeros((2, 2))
print 'data before centering:', more_data
print 'centered data:', center(more_data)

def display(a=1, b=2, c=3):
    print 'a:', a, 'b:', b, 'c:', c

print 'no parameters:'
display()
print 'one parameter:'
display(55)
print 'two parameters:'
display(55, 66)

print 'only setting the value of c'
display(c=77)

help(numpy.loadtxt)