In [1]:

    

import numpy


    

In [7]:

    

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


    

In [6]:

    

whos


    

    




Variable   Type       Data/Info
-------------------------------
data       ndarray    60x40: 2400 elems, type `float64`, 19200 bytes
numpy      module     <module 'numpy' from '/us<...>kages/numpy/__init__.py'>

In [8]:

    

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.]]

In [9]:

    

print(type(data))


    

    




<class 'numpy.ndarray'>

In [10]:

    

print(data.shape)


    

    




(60, 40)

In [11]:

    

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


    

    




The first value in data:  0.0

In [12]:

    

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


    

    




The middle value in data:  13.0

In [13]:

    

print("An array of data...\n", data[5:10, 0:10])


    

    




An array of data...
 [[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]
 [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]
 [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]
 [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]
 [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]

In [14]:

    

doubledata = data * 2.0


    

In [15]:

    

print(doubledata)


    

    




[[ 0.  0.  2. ...,  6.  0.  0.]
 [ 0.  2.  4. ...,  2.  0.  2.]
 [ 0.  2.  2. ...,  4.  2.  2.]
 ..., 
 [ 0.  2.  2. ...,  2.  2.  2.]
 [ 0.  0.  0. ...,  0.  4.  0.]
 [ 0.  0.  2. ...,  2.  2.  0.]]

In [16]:

    

print('Maximum inflamation: ', data.max())


    

    




Maximum inflamation:  20.0

In [17]:

    

print("Minimum inflamation: ", data.min())


    

    




Minimum inflamation:  0.0

In [18]:

    

print("Standard deviation: ", data.std())


    

    




Standard deviation:  4.61383319712

In [20]:

    

print("Patient 0 Max Inflammation: ", data[0, :].max())


    

    




Patient 0 Max Inflammation:  18.0

In [21]:

    

print("Patient 2 Max Inflammation: ", data[2, :].max())


    

    




Patient 2 Max Inflammation:  19.0

In [26]:

    

print("Mean inflammation per patient: \n", data.mean(axis=1))


    

    




Mean inflammation per patient: 
 [ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525
  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55
  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55
  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75
  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7
  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]

In [24]:

    

print("Mean inflammation per day: \n", data.mean(axis=0))


    

    




Mean inflammation per day: 
 [  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]

In [34]:

    

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


    

In [35]:

    

ave_inflammation_t = data.mean(axis=0)
ave_t_plot = matplotlib.pyplot.plot(ave_inflammation_t)
matplotlib.pyplot.show(ave_t_plot)


    

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