PythonLoopsToFunctions.ipynb

Kernel: Python 3

import numpy

import matplotlib.pyplot

matplotlib.pyplot.plot(data_ave)
matplotlib.pyplot.show()

data = numpy.loadtxt(fname='./data/inflammation-03.csv',
                     delimiter=',')
data_ave = numpy.mean(data, axis=0)
data_max = numpy.max(data, axis=0)
data_min = numpy.min(data, axis=0)
matplotlib.pyplot.plot(data_ave, label='average')
matplotlib.pyplot.plot(data_max, label='max')
matplotlib.pyplot.plot(data_min, label='min')
matplotlib.pyplot.ylabel('inflammation')
matplotlib.pyplot.xlabel('day')
matplotlib.pyplot.title('inflammation-03.csv')
matplotlib.pyplot.legend()
matplotlib.pyplot.show()

word = 'oxygen'
for char in word:
    print(char)

o
x
y
g
e
n

vowels = 0
for vowel in 'aiueo':
    vowels = vowels + 1
print("Now at",vowel)
print("Vowels: ",vowels)

Now at o
Vowels:  5

print("Vowels: ", len('aiueo'))

Vowels:  5

for n in range(1,4):
    print(n)

1
2
3

result = 1
for p in range(3):
    result = result * 5
    
print(result)
print(5 ** 3)

125
125

xs = [1,2,3]
for i, x in enumerate(xs):
    print("i=", i, ", x=", x)

i= 0 , x= 1
i= 1 , x= 2
i= 2 , x= 3

cc = [2, 4, 3]
x = 5
result = 0
for i, c in enumerate(cc):
    result = result + c * x**i
print(result)

97

odds = [1, 3, 5, 7]
print("odds are", odds)

odds are [1, 3, 5, 7]

print("first odd", odds[0])

first odd 1

print("second to last odd", odds[-2])

second to last odd 5

for number in odds:
    print(number)

1
3
5
7

names = ["Curie", "Darwing", "Turing"]
print("names are", names)

names are ['Curie', 'Darwing', 'Turing']

names[1] = "Darwin"

print("names are now", names)

names are now ['Curie', 'Darwin', 'Turing']

name = "Darxin"

name[3] = "w"

-----------------------------------------
TypeErrorTraceback (most recent call last)
<ipython-input-53-a52eec4d30d4> in <module>()
----> 1 name[3] = "w"

TypeError: 'str' object does not support item assignment

salsa = ["peppers", "onions", "cilantro", "tomatoes"]
my_salsa = list(salsa)
my_salsa[0] = "hot peppers"
print("my salsa",my_salsa)

my salsa ['hot peppers', 'onions', 'cilantro', 'tomatoes']

print("original salsa", salsa)

original salsa ['peppers', 'onions', 'cilantro', 'tomatoes']

x = [["pepper", "zucchini", "onion"],
     ["cabbage", "lettuce", "garlic"],
     ["apple", "pear", "banana"]]
print(x[0])

['pepper', 'zucchini', 'onion']

print([x[0]])

[['pepper', 'zucchini', 'onion']]

print(x[0][0])

pepper

x = [1, 2.0, 'ABC', [1, "D"]]
print(x)

[1, 2.0, 'ABC', [1, 'D']]

x[0] = 'more pepper'
print(x)

['more pepper', 2.0, 'ABC', [1, 'D']]

x.append("append this")
print(x)

['more pepper', 2.0, 'ABC', [1, 'D'], 'append this']

del x[2]

print(x)

['more pepper', 2.0, [1, 'D'], 'append this']

x.reverse()
print(x)

['append this', [1, 'D'], 2.0, 'more pepper']

x.pop()
print(x)

['append this', [1, 'D'], 2.0]

odds = [1,3,5,7]
primes = list(odds)
primes.append(2)
print("primes are",primes)

primes are [1, 3, 5, 7, 2]

print("odds are",odds)

odds are [1, 3, 5, 7]

print("h" + "e")

he

word = "hello"
result = []
for char in word:
    result.append(char)
print(result)

['h', 'e', 'l', 'l', 'o']

result2 = ""
for char in result:
    result2 = result2 + char
print(result2)

hello

string_for_slicing = "Observation date: 02-Feb-2013"
list_for_slicing = [["fluorine", "F"],["chlorine", "Cl"],
                    ["bromine", "Br"], ["iodine", "I"],
                    ["astatine", "At"]]
print(string_for_slicing[-4:])
print(list_for_slicing[-4:])

2013
[['chlorine', 'Cl'], ['bromine', 'Br'], ['iodine', 'I'], ['astatine', 'At']]

%cd /home/jule/proj/stim

/home/jule/proj/stim

import glob

files = glob.glob('./data/inflammation-*.csv')
files.sort()
for f in files:
    data = numpy.loadtxt(fname=f,
                         delimiter=',')
    data_ave = numpy.mean(data, axis=0)
    data_max = numpy.max(data, axis=0)
    data_min = numpy.min(data, axis=0)
    matplotlib.pyplot.plot(data_ave, label='average')
    matplotlib.pyplot.plot(data_max, label='max')
    matplotlib.pyplot.plot(data_min, label='min')
    matplotlib.pyplot.ylabel('inflammation')
    matplotlib.pyplot.xlabel('day')
    matplotlib.pyplot.title(f)
    matplotlib.pyplot.legend()
    matplotlib.pyplot.show()

files = glob.glob('./data/inflammation-*.csv')
files.sort()
data0 = numpy.loadtxt(fname=files[0], delimiter=',')
data1 = numpy.loadtxt(fname=files[1], delimiter=',')
data0_ave = numpy.mean(data0, axis=0)
data1_ave = numpy.mean(data1, axis=0)
diff = data0_ave - data1_ave
matplotlib.pyplot.plot(diff)
matplotlib.pyplot.title("Difference of averages")
matplotlib.pyplot.show()

filenames = glob.glob('./data/inflammation*.csv')
composite_data = numpy.zeros((60,40))
for f in filenames:
    data = numpy.loadtxt(fname=f, delimiter=',')
    composite_data += data
composite_data /= len(filenames)

data_ave = numpy.mean(composite_data, axis=0)
data_max = numpy.max(composite_data, axis=0)
data_min = numpy.min(composite_data, axis=0)
matplotlib.pyplot.plot(data_ave, label='average')
matplotlib.pyplot.plot(data_max, label='max')
matplotlib.pyplot.plot(data_min, label='min')
matplotlib.pyplot.ylabel('inflammation')
matplotlib.pyplot.xlabel('day')
matplotlib.pyplot.title('inflammation-averaged.csv')
matplotlib.pyplot.legend()
matplotlib.pyplot.show()

num = 37
if num > 100:
    print("greater")
else:
    print("not greater")

not greater

num = 53
if num > 100:
    print("greater")
elif num == 0:
    print("it's zero")
elif num == 42:
    print("it's the answer")
else:
    print("?")

?

if (1 > 0) or (-1 < 0):
    print("one of them is true (or both)")
else:
    print("none is true")

one of them is true (or both)

data = numpy.loadtxt(fname="./data/inflammation-01.csv",
                     delimiter=',')
data_max = numpy.max(data, axis=0)
data_min = numpy.min(data, axis=0)
if data_max[0] == 0 and data_max[20] == 20:
    print("This data has suspicious maxima")
elif numpy.sum(data_min) == 0:
    print("Minima add up to 0")
else:
    print("Seems Ok")

This data has suspicious maxima

if not '':
    print('not (empty string) is true')
if 'word':
    print('word is true')
if []:
    print('empty list is true')
if [1, 2, 3]:
    print('non-empty list is true')
if 0:
    print('zero is true')
if -1:
    print('-one is true')

not (empty string) is true
word is true
non-empty list is true
-one is true

3 > 2 > 1

True

3 > 2 < 4 "==" 3 > 2 and 2 < 4

True

a = data_max[0]
b = data_max[1]
#if .9*b < a < 1.1*b:
if abs(b-a) <= .1*b:
    print("True")
else:
    print("False")

False

def fahr_to_celsius(temp):
    return ((temp - 32) * (5/9))

print("freezing point of water", fahr_to_celsius(32),"C")
print("boiling point of water", fahr_to_celsius(212),"C")

freezing point of water 0.0 C
boiling point of water 100.0 C

def celsius_to_kelvin(temp_c):
    return temp_c + 273.15

print("freezing point of water", celsius_to_kelvin(0),"K")

freezing point of water 273.15 K

def fahr_to_kelvin(temp_f):
    temp_c = fahr_to_celsius(temp_f)
    temp_k = celsius_to_kelvin(temp_c)
    return temp_k

print("freezing point of water", fahr_to_kelvin(32),"K")

freezing point of water 273.15 K

def plot_inflammation(f):
    """
    Plot the averages, maxima, and minima of the columns
    in the file called f.
    """
    data = numpy.loadtxt(fname=f,
                         delimiter=',')
    data_ave = numpy.mean(data, axis=0)
    data_max = numpy.max(data, axis=0)
    data_min = numpy.min(data, axis=0)
    matplotlib.pyplot.plot(data_ave, label='average')
    matplotlib.pyplot.plot(data_max, label='max')
    matplotlib.pyplot.plot(data_min, label='min')
    matplotlib.pyplot.ylabel('inflammation')
    matplotlib.pyplot.xlabel('day')
    matplotlib.pyplot.title(f)
    matplotlib.pyplot.legend()
    matplotlib.pyplot.show()
    
def check_data(f):
    data = numpy.loadtxt(fname=f, delimiter=',')
    data_max = numpy.max(data, axis=0)
    data_min = numpy.min(data, axis=0)
    if data_max[0] == 0 and data_max[20] == 20:
        print("This data has suspicious maxima")
    elif numpy.sum(data_min) == 0:
        print("Minima add up to 0")
    else:
        print("Seems Ok")
    
files = glob.glob('./data/inflammation-*.csv')
files.sort()
for f in files:
    plot_inflammation(f)
    check_data(f)

This data has suspicious maxima

This data has suspicious maxima

Minima add up to 0

This data has suspicious maxima

This data has suspicious maxima

This data has suspicious maxima

This data has suspicious maxima

Minima add up to 0

This data has suspicious maxima

This data has suspicious maxima

Minima add up to 0

This data has suspicious maxima

help(plot_inflammation)

Help on function plot_inflammation in module __main__:

plot_inflammation(f)
    Plot the averages, maxima, and minima of the columns
    in the file called f.