print("test")

test

import numpy

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

array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],
       [ 0.,  1.,  2., ...,  1.,  0.,  1.],
       [ 0.,  1.,  1., ...,  2.,  1.,  1.],
       ..., 
       [ 0.,  1.,  1., ...,  1.,  1.,  1.],
       [ 0.,  0.,  0., ...,  0.,  2.,  0.],
       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])

weight_kg = 55

print(weight_kg)

55

print('weight in pounds:', 2.2 * weight_kg)

weight in pounds: 121.00000000000001

weight_kg = 57.5

print('weight in kilograms is now:', weight_kg)

weight in kilograms is now: 57.5

weight_lb = 2.2 * weight_kg
print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)

weight in kilograms: 57.5 and in pounds: 126.50000000000001

weight_kg = 100
print('weight in kilograms is now:', weight_kg,
      'and weight in pounds is..', weight_lb)

weight in kilograms is now: 100 and weight in pounds is.. 126.50000000000001

weight_kg = "hello"

print(weight_kg)

hello

print(weight_lb)

126.50000000000001

weight_kg = 100

whos

Variable    Type      Data/Info
-------------------------------
numpy       module    <module 'numpy' from '/us<...>kages/numpy/__init__.py'>
weight_kg   int       100
weight_lb   float     126.50000000000001

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

print(data)

[[ 0.  0.  1. ...,  3.  0.  0.]
 [ 0.  1.  2. ...,  1.  0.  1.]
 [ 0.  1.  1. ...,  2.  1.  1.]
 ..., 
 [ 0.  1.  1. ...,  1.  1.  1.]
 [ 0.  0.  0. ...,  0.  2.  0.]
 [ 0.  0.  1. ...,  1.  1.  0.]]

print(type(data))

<class 'numpy.ndarray'>

data.size

2400

data.shape

(60, 40)

print(data.shape)

(60, 40)

print('first value in data:', data[0,0])

first value in data: 0.0

print('middle value in data:', data[30,20])

middle value in data: 13.0

print(data[0:4, 0:10])

[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]
 [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]
 [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]
 [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]

small = data[:3, 36:]
print('small is:')
print(small)

small is:
[[ 2.  3.  0.  0.]
 [ 1.  1.  0.  1.]
 [ 2.  2.  1.  1.]]

doubledata = data * 2.0

print('original:')
print(data[:3, 36:])
print('doubledata:')
print(doubledata[:3, 36:])

original:
[[ 2.  3.  0.  0.]
 [ 1.  1.  0.  1.]
 [ 2.  2.  1.  1.]]
doubledata:
[[ 4.  6.  0.  0.]
 [ 2.  2.  0.  2.]
 [ 4.  4.  2.  2.]]

tripledata = doubledata + data

print(tripledata[:3, 36:])

[[ 6.  9.  0.  0.]
 [ 3.  3.  0.  3.]
 [ 6.  6.  3.  3.]]

print(data.mean())

6.14875

print(doubledata.mean())

12.2975

data.shape

(60, 40)

print(data.max())

20.0

print(data.min())

0.0

print(data.std())

4.61383319712

patient_0 = data[0, :]
print('maximum inflammation for patient 0:', patient_0.max())

maximum inflammation for patient 0: 18.0

print(patient_0)
print(patient_0.mean())

[  0.   0.   1.   3.   1.   2.   4.   7.   8.   3.   3.   3.  10.   5.   7.
   4.   7.   7.  12.  18.   6.  13.  11.  11.   7.   7.   4.   6.   8.   8.
   4.   4.   5.   7.   3.   4.   2.   3.   0.   0.]
5.45

print(data.mean(axis=0))

[  0.           0.45         1.11666667   1.75         2.43333333   3.15
   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9
   8.35         7.73333333   8.36666667   9.5          9.58333333
  10.63333333  11.56666667  12.35        13.25        11.96666667
  11.03333333  10.16666667  10.           8.66666667   9.15         7.25
   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6
   3.3          3.56666667   2.48333333   1.5          1.13333333
   0.56666667]

data.shape

(60, 40)

%matplotlib inline
import matplotlib.pyplot
image = matplotlib.pyplot.imshow(data)
matplotlib.pyplot.show()

ave_inflammation = data.mean(axis=0)
ave_plot = matplotlib.pyplot.plot(ave_inflammation)
matplotlib.pyplot.show(ave_plot)

max_plot = matplotlib.pyplot.plot(data.max(axis=0))
matplotlib.pyplot.show()

min_plot = matplotlib.pyplot.plot(data.min(axis=0))
matplotlib.pyplot.show(min_plot)

import numpy
import matplotlib.pyplot

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

fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))

axes1 = fig.add_subplot(1,3,1)
axes2 = fig.add_subplot(1,3,2)
axes3 = fig.add_subplot(1,3,3)

axes1.set_ylabel('average')
axes1.plot(data.mean(axis=0))

axes2.set_ylabel('max')
axes2.plot(data.max(axis=0))

axes3.set_ylabel('min')
axes3.plot(data.min(axis=0))

fig.tight_layout()

matplotlib.pyplot.show()

import numpy
import matplotlib.pyplot

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

fig = matplotlib.pyplot.figure(figsize=(10.0,3.0))

axes1 = fig.add_subplot(1,4,1)
axes2 = fig.add_subplot(1,4,2)
axes3 = fig.add_subplot(1,4,3)
axes4 = fig.add_subplot(1,4,4)

axes1.set_ylabel('Average')
axes1.plot(data.mean(axis=0))

axes2.set_ylabel('Maximum')
axes2.plot(data.max(axis=0))

axes3.set_ylabel('Minimum')
axes3.plot(data.min(axis=0))

axes4.set_ylabel('Standard Deviation')
axes4.plot(data.std(axis=0))

fig.tight_layout()

matplotlib.pyplot.show()

word = 'lead'
print(word[0])
print(word[1])
print(word[2])
print(word[3])

l
e
a
d

print(word)

lead

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

o
x
y
g
e
n

length = 0
for vowel in 'aeiouhippo':
    length = length + 1

    print('There are', length, 'vowels, and the vowel is', vowel)

There are 1 vowels, and the vowel is a
There are 2 vowels, and the vowel is e
There are 3 vowels, and the vowel is i
There are 4 vowels, and the vowel is o
There are 5 vowels, and the vowel is u
There are 6 vowels, and the vowel is h
There are 7 vowels, and the vowel is i
There are 8 vowels, and the vowel is p
There are 9 vowels, and the vowel is p
There are 10 vowels, and the vowel is o

my_string = 'kjasgoijaefjihadfjohoidafjhjadfh'
print(len(my_string))

32

my_string = 'hello'
for x in range(len(my_string)):
    print(my_string[x])
    
for x in my_string:
    print(x)

h
e
l
l
o
h
e
l
l
o

print(5 ** 3)

125

power = 4
base = 2
result = base

for x in range(1,power):
    result = result * base
    
print(result)

16

res = 1
for x in range(3):
    res = res*5
    
print(res)

125

exponent = 3
value = 3
result = 1
for i in range(1,exponent+1) :
    result = result * value
    print(result)

3
9
27

exp = 5
num = 10
result = 1

for x in range(exp):
    result = result*num

print(result)

100000

challenge = 'Newton'
reverse = ''

for ??? in ???:
    ???

print(reverse)

# notweN

100000

challenge[0]

'N'

challenge = 'Newton'
long = len(challenge)

for x in range(long-1,-1,-1):
    #print(-x)
    now = challenge[-x]
    print(now, end='')

ewtonN

challenge = 'Newton'
length = 0

for char in challenge:
    length = length + 1
    count = 0 - length
    print(count)
    #print(challenge[count], end='')

-1
-2
-3
-4
-5
-6

word = "Newton"
reverse = ''
for i in range(len(word)-1, -1, -1):
    reverse = reverse + word[i]
    
print(reverse)

notweN

odds = [1, 3, 5, 7]

len(odds)

4

print('first andlast: ', odds[0], odds[-2])

first andlast:  1 5

for number in odds:
    print(number)

1
3
5
7

somelist = ["bla", 11234, 1.124124]

b = "ASDFG"

b[1] = "Z"

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-222-f8422f3486af> in <module>()
----> 1 b[1] = "Z"

TypeError: 'str' object does not support item assignment

a = ['A', 'S', 'D', 'F']

a[1] = 'Z'

print(a)

['A', 'Z', 'D', 'F']

''.join(a)

'AZDF'

print(list(b))

['A', 'S', 'D', 'F', 'G']

b

'ASDFG'

x = [['pepper', 'zucchini', 'onion'],
     ['cabbage', 'lettuce', 'garlic'],
     ['apple', 'pear', 'banana']]

print(x)

[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]

print(x[0])

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

print(x[0][0])

pepper

odds.append(9)

print(odds)

[1, 3, 5, 7, 9]

del odds[0]

print(odds)

[3, 5, 7, 9]

odds.reverse()

print(odds)

[9, 7, 5, 3]

odds.extend(['a', 'c', 'b'])

print(odds)

[9, 7, 5, 3, 'a', 'c', 'b']

odds =[1,3,5,7]

primes = odds

primes += [2]

print('this is primes: ', primes)
print('this is odds: ', odds)

this is primes:  [1, 3, 5, 7, 2]
this is odds:  [1, 3, 5, 7, 2]

primes = list(odds)

primes[0] = 'bla'

print('this is primes: ', primes)
print('this is odds: ', odds)

this is primes:  ['bla', 3, 5, 7, 2]
this is odds:  [1, 3, 5, 7, 2]

hello = 'hello'
hello_list = ['h', 'e', 'l', 'l', 'o']
my_list = []

my_list = ['e']
for c in hello:
    my_list
print(my_list)

h  -> nope
e  -> yep
l  -> nope
l  -> nope
o  -> nope
['e']

string_to_break = 'hello'
string_list = []
for letter in string_to_break:
    string_list.append(letter)
print(string_list)

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

string_list.sort()

string_list.pop()

'o'

string_list.pop()

'l'

string_list.pop()

'l'

string_list.pop()

'h'

string_list.pop()

'e'

string_list.pop()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-266-e5ccaa9b37c5> in <module>()
----> 1 string_list.pop()

IndexError: pop from empty list

this_tuple = 1, 2, 3

a, b, c = this_tuple

print(a)

1

left = 'L'
right = 'R'

temp = left
left = 'BLA'
# left = right
right = temp

print('left =', left)
print('right =', right)
print('temp = ', temp)

left = BLA
right = L
temp =  L

left = 'L'
right = 'R'

left, right = (right, left)

print('left =', left)
print('right =', right)

left = R
right = L

import glob

print(glob.glob('data/inflammation*.csv'))

['data/inflammation-02.csv', 'data/inflammation-01.csv', 'data/inflammation-09.csv', 'data/inflammation-05.csv', 'data/inflammation-08.csv', 'data/inflammation-12.csv', 'data/inflammation-04.csv', 'data/inflammation-07.csv', 'data/inflammation-10.csv', 'data/inflammation-11.csv', 'data/inflammation-03.csv', 'data/inflammation-06.csv']

%matplotlib inline

import numpy
import matplotlib.pyplot

filenames = glob.glob('data/inflammation*.csv')
filenames.sort()
filenames = filenames[0:3]
for f in filenames:
    print(f)
    
    data = numpy.loadtxt(fname=f, delimiter=',')
    
    if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:
        print("Suspicious looking maxima !!!1!")
    elif data.min(axis=0).sum() == 0:
        print("Minima add up to zero !")
    else:
        print("Seems okay. :)")
        
    
    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
    
    axes1 = fig.add_subplot(1 ,3, 1)
    axes2 = fig.add_subplot(1, 3, 2)
    axes3 = fig.add_subplot(1, 3, 3)
    
    axes1.set_ylabel('average')
    axes1.plot(data.mean(axis=0))
    
    axes2.set_ylabel('max')
    axes2.plot(data.max(axis=0))
    
    axes3.set_ylabel('min')
    axes3.plot(data.min(axis=0))
    
    fig.tight_layout()
    
    matplotlib.pyplot.show()
    
    
    

data/inflammation-01.csv
Suspicious looking maxima !!!1!

data/inflammation-02.csv
Suspicious looking maxima !!!1!

data/inflammation-03.csv
Minima add up to zero !

print(filenames)

['data/inflammation-02.csv', 'data/inflammation-01.csv', 'data/inflammation-09.csv']

type(data)

numpy.ndarray

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

not greater
done

num = 53
print ('before')
if num > 100:
    print('53 is greater than 100')
print('after')

before
after

num = -3
if num > 0:
    print(num, 'is +ve')
elif num == 0:
    print(num, 'is zero')
else:
    print(num, 'is -ve')

-3 is -ve

if (1 < 0) and (-1 < 0): # False and True
    print('both parts are True')
else:
    print('at least one part is False')

at least one part is False

if (1 < 0) or (-1 < 0): # False and True
    print('at least one test is True')

at least one test is True

if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:
    print("Suspicious looking maxima !!!1!")

data = numpy.loadtxt(fname='data/inflammation-03.csv', delimiter=',')

if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:
    print("Suspicious looking maxima !!!1!")
elif data.min(axis=0).sum() == 0:
    print("Minima add up to zero !")
else:
    print("Seems okay. :)")

Minima add up to zero !

4 = 5
if 4 > 5:
    print('A')
elif 4 == 5:
    print('B')
elif 4 < 5:
    print('C')
    
# Hmmmmmm. Will it print A ? Nope, 4 is not greater than 5
# Will it print B ?

  File "<ipython-input-326-c216428900eb>", line 1
    4 = 5
         ^
SyntaxError: can't assign to literal

this_is_a_bool = True
an_another = False

if '':
    print('empty string is True')
if 'word':
    print('word is True')
if []:
    print('empty list is True')
if [1,2,4]:
    print('list with stuff is True')
if 0:
    print('the number zero is True')
if 1:
    print('the number one is True')
if None:
    print('None is True')

word is True
list with stuff is True
the number one is True

bla = "  blblbl  "
print(bla)
print(bla.strip())

  blblbl  
blblbl

if not bla.strip():
    print("bla is empty")

bla is empty

a = 100
b = 95
print(a == b)
print(a > b * 2)
print((a > 10 * b) and (a < 2 * b))
# print( (a ? < .. ?) and (a ? > ..?))

False
False
False

threshold = 2
if a > b*(1 - threshold/100) and a < b*(1 + threshold/100):
    print('a is within %s percent of b' % threshold)
else:
    print('a is not within %s percent of b' % threshold)

a is not within 2 percent of b

x = 1
x += 1  # x = x + 1
print(x)
x *= 10
print(x)

2
20

nums = [-1, -3, -5, 2, 4, 8]
pos_sum = 0
neg_sum = 0
for n in nums:
    # something in here that uses in-place += operator ...
    pass

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))

freezing point of water: 273.15
boiling point of water: 373.15

def kelvin_to_celsius(temp_k):
    return temp_k - 273.15

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

absolute zero in celsius: -273.15

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

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

freezing point of water in celsius: 0.0

def analyze(filename):
    data = numpy.loadtxt(fname=filename, delimiter=',')
    
    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
    
    axes1 = fig.add_subplot(1, 3, 1)
    axes2 = fig.add_subplot(1, 3, 2)
    axes3 = fig.add_subplot(1, 3, 3)
    
    axes1.set_ylabel('average')
    axes1.plot(data.mean(axis=0))
    
    axes2.set_ylabel('max')
    axes2.plot(data.max(axis=0))
    
    axes3.set_ylabel('min')
    axes3.plot(data.min(axis=0))
    
    fig.tight_layout()
    
    matplotlib.pyplot.show()

def detect_problems(filename):
    data = numpy.loadtxt(fname=filename, delimiter=',')
    
    if data.max(axis=0)[0] == 0 and data.max(axis=0)[20] == 20:
        print("Suspicious looking maxima !!!1!")
    elif data.min(axis=0).sum() == 0:
        print("Minima add up to zero !")
    else:
        print("Seems okay. :)")

import numpy
import matplotlib.pyplot

filenames = glob.glob('data/inflammation*.csv')
filenames.sort()
filenames = filenames[0:3]
for f in filenames:
    print(f)
    analyze(f)
    detect_problems(f)

data/inflammation-01.csv

Suspicious looking maxima !!!1!
data/inflammation-02.csv

Suspicious looking maxima !!!1!
data/inflammation-03.csv

Minima add up to zero !

def center(data, desired=0.0):
    '''Return a new array containing the original data centered around the desired value'''
    return (data - data.mean()) + desired

help(data.max)

Help on built-in function max:

max(...) method of numpy.ndarray instance
    a.max(axis=None, out=None)
    
    Return the maximum along a given axis.
    
    Refer to `numpy.amax` for full documentation.
    
    See Also
    --------
    numpy.amax : equivalent function

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

[[ 0.  0.]
 [ 0.  0.]]

print(center(z, 3))

[[ 3.  3.]
 [ 3.  3.]]

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

[[-6.14875 -6.14875 -5.14875 ..., -3.14875 -6.14875 -6.14875]
 [-6.14875 -5.14875 -4.14875 ..., -5.14875 -6.14875 -5.14875]
 [-6.14875 -5.14875 -5.14875 ..., -4.14875 -5.14875 -5.14875]
 ..., 
 [-6.14875 -5.14875 -5.14875 ..., -5.14875 -5.14875 -5.14875]
 [-6.14875 -6.14875 -6.14875 ..., -6.14875 -4.14875 -6.14875]
 [-6.14875 -6.14875 -5.14875 ..., -5.14875 -5.14875 -6.14875]]

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

original min, mean and max are:  0.0 6.14875 20.0
min, mean and max of centered data are: -6.14875 2.84217094304e-16 13.85125

print(data.std())

4.61383319712

print(centered.std())

4.61383319712

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

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-383-7924d6313a1e> in <module>()
----> 1 numpy.loadtxt('data/inflammation-01.csv', ',')

/usr/local/lib/python3.4/dist-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)
    871     try:
    872         # Make sure we're dealing with a proper dtype
--> 873         dtype = np.dtype(dtype)
    874         defconv = _getconv(dtype)
    875 

TypeError: data type "," not understood

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

display()

a: 1 b: 2 c: 3

display(55, c=66)

a: 55 b: 2 c: 66

help(numpy.loadtxt)

Help on function loadtxt in module numpy.lib.npyio:

loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)
    Load data from a text file.
    
    Each row in the text file must have the same number of values.
    
    Parameters
    ----------
    fname : file or str
        File, filename, or generator to read.  If the filename extension is
        ``.gz`` or ``.bz2``, the file is first decompressed. Note that
        generators should return byte strings for Python 3k.
    dtype : data-type, optional
        Data-type of the resulting array; default: float.  If this is a
        structured data-type, the resulting array will be 1-dimensional, and
        each row will be interpreted as an element of the array.  In this
        case, the number of columns used must match the number of fields in
        the data-type.
    comments : str or sequence, optional
        The characters or list of characters used to indicate the start of a
        comment;
        default: '#'.
    delimiter : str, optional
        The string used to separate values.  By default, this is any
        whitespace.
    converters : dict, optional
        A dictionary mapping column number to a function that will convert
        that column to a float.  E.g., if column 0 is a date string:
        ``converters = {0: datestr2num}``.  Converters can also be used to
        provide a default value for missing data (but see also `genfromtxt`):
        ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.
    skiprows : int, optional
        Skip the first `skiprows` lines; default: 0.
    usecols : sequence, optional
        Which columns to read, with 0 being the first.  For example,
        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
        The default, None, results in all columns being read.
    unpack : bool, optional
        If True, the returned array is transposed, so that arguments may be
        unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured
        data-type, arrays are returned for each field.  Default is False.
    ndmin : int, optional
        The returned array will have at least `ndmin` dimensions.
        Otherwise mono-dimensional axes will be squeezed.
        Legal values: 0 (default), 1 or 2.
    
        .. versionadded:: 1.6.0
    
    Returns
    -------
    out : ndarray
        Data read from the text file.
    
    See Also
    --------
    load, fromstring, fromregex
    genfromtxt : Load data with missing values handled as specified.
    scipy.io.loadmat : reads MATLAB data files
    
    Notes
    -----
    This function aims to be a fast reader for simply formatted files.  The
    `genfromtxt` function provides more sophisticated handling of, e.g.,
    lines with missing values.
    
    .. versionadded:: 1.10.0
    
    The strings produced by the Python float.hex method can be used as
    input for floats.
    
    Examples
    --------
    >>> from io import StringIO   # StringIO behaves like a file object
    >>> c = StringIO("0 1\n2 3")
    >>> np.loadtxt(c)
    array([[ 0.,  1.],
           [ 2.,  3.]])
    
    >>> d = StringIO("M 21 72\nF 35 58")
    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
    ...                      'formats': ('S1', 'i4', 'f4')})
    array([('M', 21, 72.0), ('F', 35, 58.0)],
          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
    
    >>> c = StringIO("1,0,2\n3,0,4")
    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
    >>> x
    array([ 1.,  3.])
    >>> y
    array([ 2.,  4.])

def fence(original, wrapper):
    result = wrapper + original + wrapper
    return result

print(fence('name', '*'))

*name*

print("{first_name} {last_name}".format(first_name="john", last_name="smith"))

john smith