# Numpy - multidimensional data arrays

J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/

The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.

The other notebooks in this lecture series are indexed at http://jrjohansson.github.com.

Populating the interactive namespace from numpy and matplotlib


## Introduction

The numpy package (module) is used in almost all numerical computation using Python. It is a package that provide high-performance vector, matrix and higher-dimensional data structures for Python. It is implemented in C and Fortran so when calculations are vectorized (formulated with vectors and matrices), performance is very good.

To use numpy need to import the module it using of example:

In the numpy package the terminology used for vectors, matrices and higher-dimensional data sets is array.

## Creating numpy arrays

There are a number of ways to initialize new numpy arrays, for example from

• a Python list or tuples
• using functions that are dedicated to generating numpy arrays, such as arange, linspace, etc.

### From lists

For example, to create new vector and matrix arrays from Python lists we can use the numpy.array function.

array([1, 2, 3, 4])

array([[1, 2],
[3, 4]])


The v and M objects are both of the type ndarray that the numpy module provides.

(numpy.ndarray, numpy.ndarray)


The difference between the v and M arrays is only their shapes. We can get information about the shape of an array by using the ndarray.shape property.

(4,)

(2, 2)


The number of elements in the array is available through the ndarray.size property:

4


Equivalently, we could use the function numpy.shape and numpy.size

(2, 2)

4


So far the numpy.ndarray looks awefully much like a Python list (or nested list). Why not simply use Python lists for computations instead of creating a new array type?

There are several reasons:

• Python lists are very general. They can contain any kind of object. They are dynamically typed. They do not support mathematical functions such as matrix and dot multiplications, etc. Implementating such functions for Python lists would not be very efficient because of the dynamic typing.
• Numpy arrays are statically typed and homogeneous. The type of the elements is determined when array is created.
• Numpy arrays are memory efficient.
• Because of the static typing, fast implementation of mathematical functions such as multiplication and addition of numpy arrays can be implemented in a compiled language (C and Fortran is used).

Using the dtype (data type) property of an ndarray, we can see what type the data of an array has:

dtype('int64')


We get an error if we try to assign a value of the wrong type to an element in a numpy array:

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-12-a09d72434238> in <module>()
----> 1 M[0,0] = "hello"

ValueError: invalid literal for long() with base 10: 'hello'


If we want, we can explicitly define the type of the array data when we create it, using the dtype keyword argument:

array([[ 1.+0.j,  2.+0.j],
[ 3.+0.j,  4.+0.j]])


Common type that can be used with dtype are: int, float, complex, bool, object, etc.

We can also explicitly define the bit size of the data types, for example: int64, int16, float128, complex128.

### Using array-generating functions

For larger arrays it is impractical to initialize the data manually, using explicit python lists. Instead we can use one of the many functions in numpy that generates arrays of different forms. Some of the more common are:

#### arange

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

array([ -1.00000000e+00,  -9.00000000e-01,  -8.00000000e-01,
-7.00000000e-01,  -6.00000000e-01,  -5.00000000e-01,
-4.00000000e-01,  -3.00000000e-01,  -2.00000000e-01,
-1.00000000e-01,  -2.22044605e-16,   1.00000000e-01,
2.00000000e-01,   3.00000000e-01,   4.00000000e-01,
5.00000000e-01,   6.00000000e-01,   7.00000000e-01,
8.00000000e-01,   9.00000000e-01])


#### linspace and logspace

array([  0.        ,   0.41666667,   0.83333333,   1.25      ,
1.66666667,   2.08333333,   2.5       ,   2.91666667,
3.33333333,   3.75      ,   4.16666667,   4.58333333,
5.        ,   5.41666667,   5.83333333,   6.25      ,
6.66666667,   7.08333333,   7.5       ,   7.91666667,
8.33333333,   8.75      ,   9.16666667,   9.58333333,  10.        ])

array([  1.00000000e+00,   3.03773178e+00,   9.22781435e+00,
2.80316249e+01,   8.51525577e+01,   2.58670631e+02,
7.85771994e+02,   2.38696456e+03,   7.25095809e+03,
2.20264658e+04])


#### mgrid

array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]])

array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]])


#### random data

array([[ 0.20067261,  0.31903074,  0.50413339,  0.17916857,  0.80098158],
[ 0.4605664 ,  0.0889817 ,  0.89913197,  0.65326837,  0.95042471],
[ 0.50638764,  0.93865793,  0.27431355,  0.12660373,  0.2375227 ],
[ 0.11655293,  0.21264332,  0.1865033 ,  0.58306332,  0.75787615],
[ 0.39529707,  0.58368816,  0.86195759,  0.48969116,  0.70358949]])

array([[-0.38628886, -0.25183044, -0.75108899, -0.24938967,  0.04214111],
[ 2.58066361, -1.0318666 ,  0.06503565,  0.09726207,  1.63467302],
[ 0.43746396, -1.6740317 ,  0.70424676,  0.48901995, -0.7179185 ],
[ 0.6081299 ,  0.15481145,  0.78895533,  0.60471688, -1.05199935],
[ 0.49851063,  0.71784166, -1.18752626,  1.03286227,  0.57774408]])


#### diag

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

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


#### zeros and ones

array([[ 0.,  0.,  0.],
[ 0.,  0.,  0.],
[ 0.,  0.,  0.]])

array([[ 1.,  1.,  1.],
[ 1.,  1.,  1.],
[ 1.,  1.,  1.]])


## File I/O

### Comma-separated values (CSV)

A very common file format for data files are the comma-separated values (CSV), or related format such as TSV (tab-separated values). To read data from such file into Numpy arrays we can use the numpy.genfromtxt function. For example,

1800  1  1    -6.1    -6.1    -6.1 1
1800  1  2   -15.4   -15.4   -15.4 1
1800  1  3   -15.0   -15.0   -15.0 1
1800  1  4   -19.3   -19.3   -19.3 1
1800  1  5   -16.8   -16.8   -16.8 1
1800  1  6   -11.4   -11.4   -11.4 1
1800  1  7    -7.6    -7.6    -7.6 1
1800  1  8    -7.1    -7.1    -7.1 1
1800  1  9   -10.1   -10.1   -10.1 1
1800  1 10    -9.5    -9.5    -9.5 1

(77431, 7)


Using numpy.savetxt we can store a Numpy array to a file in CSV format:

array([[ 0.33005805,  0.83865056,  0.57961497],
[ 0.25206785,  0.8369909 ,  0.8509039 ],
[ 0.82622818,  0.08387398,  0.73442222]])

3.300580456122463868e-01 8.386505645635993211e-01 5.796149703411075382e-01
2.520678545950313376e-01 8.369908984836819110e-01 8.509038976540077570e-01
8.262281797172411624e-01 8.387398055365946625e-02 7.344222208274131525e-01

0.33006 0.83865 0.57961
0.25207 0.83699 0.85090
0.82623 0.08387 0.73442


### Numpy’s native file format

Useful when storing and reading back numpy array data. Use the functions numpy.save and numpy.load:

random-matrix.npy: data

array([[ 0.33005805,  0.83865056,  0.57961497],
[ 0.25206785,  0.8369909 ,  0.8509039 ],
[ 0.82622818,  0.08387398,  0.73442222]])


## More properties of the numpy arrays

8

72

2


## Manipulating arrays

### Indexing

We can index elements in an array using the square bracket and indices:

1

0.83699089848368191


If we omit an index of a multidimensional array it returns the whole row (or, in general, a N-1 dimensional array)

array([[ 0.33005805,  0.83865056,  0.57961497],
[ 0.25206785,  0.8369909 ,  0.8509039 ],
[ 0.82622818,  0.08387398,  0.73442222]])

array([ 0.25206785,  0.8369909 ,  0.8509039 ])


The same thing can be achieved with using : instead of an index:

array([ 0.25206785,  0.8369909 ,  0.8509039 ])

array([ 0.83865056,  0.8369909 ,  0.08387398])


We can assign new values to elements in an array using indexing:

array([[ 1.        ,  0.83865056,  0.57961497],
[ 0.25206785,  0.8369909 ,  0.8509039 ],
[ 0.82622818,  0.08387398,  0.73442222]])

array([[ 1.        ,  0.83865056, -1.        ],
[ 0.        ,  0.        , -1.        ],
[ 0.82622818,  0.08387398, -1.        ]])


### Index slicing

Index slicing is the technical name for the syntax M[lower:upper:step] to extract part of an array:

array([1, 2, 3, 4, 5])

array([2, 3])


Array slices are mutable: if they are assigned a new value the original array from which the slice was extracted is modified:

array([ 1, -2, -3,  4,  5])


We can omit any of the three parameters in M[lower:upper:step]:

array([ 1, -2, -3,  4,  5])

array([ 1, -3,  5])

array([ 1, -2, -3])

array([4, 5])


Negative indices counts from the end of the array (positive index from the begining):

5

array([3, 4, 5])


Index slicing works exactly the same way for multidimensional arrays:

array([[ 0,  1,  2,  3,  4],
[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34],
[40, 41, 42, 43, 44]])

array([[11, 12, 13],
[21, 22, 23],
[31, 32, 33]])

array([[ 0,  2,  4],
[20, 22, 24],
[40, 42, 44]])


### Fancy indexing

Fancy indexing is the name for when an array or list is used in-place of an index:

array([[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34]])

array([11, 22, 34])


We can also index masks: If the index mask is an Numpy array of with data type bool, then an element is selected (True) or not (False) depending on the value of the index mask at the position each element:

array([0, 1, 2, 3, 4])

array([0, 2])

array([0, 2])


This feature is very useful to conditionally select elements from an array, using for example comparison operators:

array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,
5.5,  6. ,  6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5])

array([False, False, False, False, False, False, False, False, False,
False, False,  True,  True,  True,  True, False, False, False,
False, False], dtype=bool)

array([ 5.5,  6. ,  6.5,  7. ])


## Performance

Before going any further let’s have a look at how Numpy compares to pure Python lists in terms of performance.

To do this we implement a few examples and compare the execution speed.

numpy: 5.634e-06 seconds
list:  4.472e-05 seconds


## Functions for extracting data from arrays and creating arrays

### where

The index mask can be converted to position index using the where function

(array([11, 12, 13, 14]),)

array([11, 12, 13, 14])


### diag

With the diag function we can also extract the diagonal and subdiagonals of an array:

array([ 0, 11, 22, 33, 44])

array([10, 21, 32, 43])


### take

The take function is similar to fancy indexing described above:

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

array([-2,  0,  2])

array([-2,  0,  2])


But take also works on lists and other objects:

array([-2,  0,  2])


### choose

Constructs and array by picking elements form several arrays:

array([ 5, -2,  5, -2])


## Linear algebra

Vectorizing code is the key to writing efficient numerical calculation with Python/Numpy. That means that as much as possible of a program should be formulated in terms of matrix and vector operations, like matrix-matrix multiplication.

### Scalar-array operations

We can use the usual arithmetic operators to multiply, add, subtract, and divide arrays with scalar numbers.

array([0, 2, 4, 6, 8])

array([2, 3, 4, 5, 6])

(array([[ 0,  2,  4,  6,  8],
[20, 22, 24, 26, 28],
[40, 42, 44, 46, 48],
[60, 62, 64, 66, 68],
[80, 82, 84, 86, 88]]), array([[ 2,  3,  4,  5,  6],
[12, 13, 14, 15, 16],
[22, 23, 24, 25, 26],
[32, 33, 34, 35, 36],
[42, 43, 44, 45, 46]]))


### Element-wise array-array operations

When we add, subtract, multiply and divide arrays with each other, the default behaviour is element-wise operations:

array([[   0,    1,    4,    9,   16],
[ 100,  121,  144,  169,  196],
[ 400,  441,  484,  529,  576],
[ 900,  961, 1024, 1089, 1156],
[1600, 1681, 1764, 1849, 1936]])

array([ 0,  1,  4,  9, 16])


If we multiply arrays with compatible shapes, we get an element-wise multiplication of each row:

((5, 5), (5,))

array([[  0,   1,   4,   9,  16],
[  0,  11,  24,  39,  56],
[  0,  21,  44,  69,  96],
[  0,  31,  64,  99, 136],
[  0,  41,  84, 129, 176]])


### Matrix algebra

What about matrix mutiplication? There are two ways. We can either use the dot function, which applies a matrix-matrix, matrix-vector, or inner vector multiplication to its two arguments:

array([[ 300,  310,  320,  330,  340],
[1300, 1360, 1420, 1480, 1540],
[2300, 2410, 2520, 2630, 2740],
[3300, 3460, 3620, 3780, 3940],
[4300, 4510, 4720, 4930, 5140]])

array([ 30, 130, 230, 330, 430])

30


Alternatively, we can cast the array objects to the type matrix. This changes the behavior of the standard arithmetic operators +, -, * to use matrix algebra.

matrix([[0],
[1],
[2],
[3],
[4]])

matrix([[ 300,  310,  320,  330,  340],
[1300, 1360, 1420, 1480, 1540],
[2300, 2410, 2520, 2630, 2740],
[3300, 3460, 3620, 3780, 3940],
[4300, 4510, 4720, 4930, 5140]])

matrix([[ 30],
[130],
[230],
[330],
[430]])

matrix([[30]])

matrix([[ 30],
[131],
[232],
[333],
[434]])


If we try to add, subtract or multiply objects with incomplatible shapes we get an error:

((5, 5), (6, 1))

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

----> 1 M * v

/is/ei/btabibian/anaconda/lib/python2.7/site-packages/numpy/matrixlib/defmatrix.pyc in __mul__(self, other)
339         if isinstance(other, (N.ndarray, list, tuple)) :
340             # This promotes 1-D vectors to row vectors
--> 341             return N.dot(self, asmatrix(other))
342         if isscalar(other) or not hasattr(other, '__rmul__') :
343             return N.dot(self, other)

ValueError: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)


See also the related functions: inner, outer, cross, kron, tensordot. Try for example help(kron).

### Array/Matrix transformations

Above we have used the .T to transpose the matrix object v. We could also have used the transpose function to accomplish the same thing.

Other mathematical functions that transforms matrix objects are:

matrix([[ 0.+1.j,  0.+2.j],
[ 0.+3.j,  0.+4.j]])

matrix([[ 0.-1.j,  0.-2.j],
[ 0.-3.j,  0.-4.j]])


Hermitian conjugate: transpose + conjugate

matrix([[ 0.-1.j,  0.-3.j],
[ 0.-2.j,  0.-4.j]])


We can extract the real and imaginary parts of complex-valued arrays using real and imag:

matrix([[ 0.,  0.],
[ 0.,  0.]])

matrix([[ 1.,  2.],
[ 3.,  4.]])


Or the complex argument and absolute value

array([[ 0.78539816,  1.10714872],
[ 1.24904577,  1.32581766]])

matrix([[ 1.,  2.],
[ 3.,  4.]])


### Matrix computations

#### Inverse

matrix([[ 0.+2.j ,  0.-1.j ],
[ 0.-1.5j,  0.+0.5j]])

matrix([[  1.00000000e+00+0.j,   4.44089210e-16+0.j],
[  0.00000000e+00+0.j,   1.00000000e+00+0.j]])


#### Determinant

(2.0000000000000004+0j)

(0.50000000000000011+0j)


### Data processing

Often it is useful to store datasets in Numpy arrays. Numpy provides a number of functions to calculate statistics of datasets in arrays.

For example, let’s calculate some properties data from the Stockholm temperature dataset used above.

(77431, 7)


#### mean

6.1971096847515854


The daily mean temperature in Stockholm over the last 200 year so has been about 6.2 C.

#### standard deviations and variance

(8.2822716213405734, 68.596023209663414)


#### min and max

-25.800000000000001

28.300000000000001


#### sum, prod, and trace

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

45

3628800

array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45])

array([      1,       2,       6,      24,     120,     720,    5040,
40320,  362880, 3628800])

110


### Computations on subsets of arrays

We can compute with subsets of the data in an array using indexing, fancy indexing, and the other methods of extracting data from an array (described above).

For example, let’s go back to the temperature dataset:

1800  1  1    -6.1    -6.1    -6.1 1
1800  1  2   -15.4   -15.4   -15.4 1
1800  1  3   -15.0   -15.0   -15.0 1


The dataformat is: year, month, day, daily average temperature, low, high, location.

If we are interested in the average temperature only in a particular month, say February, then we can create a index mask and use the select out only the data for that month using:

array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
12.])

-3.2121095707365961


With these tools we have very powerful data processing capabilities at our disposal. For example, to extract the average monthly average temperatures for each month of the year only takes a few lines of code:

### Calculations with higher-dimensional data

When functions such as min, max, etc., is applied to a multidimensional arrays, it is sometimes useful to apply the calculation to the entire array, and sometimes only on a row or column basis. Using the axis argument we can specify how these functions should behave:

array([[ 0.43485521,  0.04517144,  0.15983549],
[ 0.06160665,  0.01117206,  0.95997496],
[ 0.60084606,  0.62310207,  0.673428  ]])

0.9599749615722134

array([ 0.60084606,  0.62310207,  0.95997496])

array([ 0.43485521,  0.95997496,  0.673428  ])


Many other functions and methods in the array and matrix classes accept the same (optional) axis keyword argument.

## Reshaping, resizing and stacking arrays

The shape of an Numpy array can be modified without copying the underlaying data, which makes it a fast operation even for large arrays.

array([[ 0,  1,  2,  3,  4],
[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34],
[40, 41, 42, 43, 44]])

array([[ 0,  1,  2,  3,  4, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,
32, 33, 34, 40, 41, 42, 43, 44]])

array([[ 5,  5,  5,  5,  5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,
32, 33, 34, 40, 41, 42, 43, 44]])

array([[ 5,  5,  5,  5,  5],
[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34],
[40, 41, 42, 43, 44]])


We can also use the function flatten to make a higher-dimensional array into a vector. But this function create a copy of the data.

array([ 5,  5,  5,  5,  5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,
32, 33, 34, 40, 41, 42, 43, 44])

array([10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,
32, 33, 34, 40, 41, 42, 43, 44])

array([[ 5,  5,  5,  5,  5],
[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34],
[40, 41, 42, 43, 44]])


## Adding a new dimension: newaxis

With newaxis, we can insert new dimensions in an array, for example converting a vector to a column or row matrix:

(3,)

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

(3, 1)

(1, 3)


## Stacking and repeating arrays

Using function repeat, tile, vstack, hstack, and concatenate we can create larger vectors and matrices from smaller ones:

### tile and repeat

array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])

array([[1, 2, 1, 2, 1, 2],
[3, 4, 3, 4, 3, 4]])


### concatenate

array([[1, 2],
[3, 4],
[5, 6]])

array([[1, 2, 5],
[3, 4, 6]])


### hstack and vstack

array([[1, 2],
[3, 4],
[5, 6]])

array([[1, 2, 5],
[3, 4, 6]])


## Copy and “deep copy”

To achieve high performance, assignments in Python usually do not copy the underlaying objects. This is important for example when objects are passed between functions, to avoid an excessive amount of memory copying when it is not necessary (techincal term: pass by reference).

array([[1, 2],
[3, 4]])

array([[10,  2],
[ 3,  4]])

array([[10,  2],
[ 3,  4]])


If we want to avoid this behavior, so that when we get a new completely independent object B copied from A, then we need to do a so-called “deep copy” using the function copy:

array([[-5,  2],
[ 3,  4]])

array([[10,  2],
[ 3,  4]])


## Iterating over array elements

Generally, we want to avoid iterating over the elements of arrays whenever we can (at all costs). The reason is that in a interpreted language like Python (or MATLAB), iterations are really slow compared to vectorized operations.

However, sometimes iterations are unavoidable. For such cases, the Python for loop is the most convenient way to iterate over an array:

1
2
3
4

('row', array([1, 2]))
1
2
('row', array([3, 4]))
3
4


When we need to iterate over each element of an array and modify its elements, it is convenient to use the enumerate function to obtain both the element and its index in the for loop:

('row_idx', 0, 'row', array([1, 2]))
('col_idx', 0, 'element', 1)
('col_idx', 1, 'element', 2)
('row_idx', 1, 'row', array([3, 4]))
('col_idx', 0, 'element', 3)
('col_idx', 1, 'element', 4)

array([[ 1,  4],
[ 9, 16]])


## Vectorizing functions

As mentioned several times by now, to get good performance we should try to avoid looping over elements in our vectors and matrices, and instead use vectorized algorithms. The first step in converting a scalar algorithm to a vectorized algorithm is to make sure that the functions we write work with vector inputs.

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-166-6658efdd2f22> in <module>()
----> 1 Theta(array([-3,-2,-1,0,1,2,3]))

<ipython-input-165-9a0cb13d93d4> in Theta(x)
3     Scalar implemenation of the Heaviside step function.
4     """
----> 5     if x >= 0:
6         return 1
7     else:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


OK, that didn’t work because we didn’t write the Theta function so that it can handle with vector input…

To get a vectorized version of Theta we can use the Numpy function vectorize. In many cases it can automatically vectorize a function:

array([0, 0, 0, 1, 1, 1, 1])


We can also implement the function to accept vector input from the beginning (requires more effort but might give better performance):

array([0, 0, 0, 1, 1, 1, 1])

(0, 1)


## Using arrays in conditions

When using arrays in conditions in for example if statements and other boolean expressions, one need to use one of any or all, which requires that any or all elements in the array evalutes to True:

array([[ 1,  4],
[ 9, 16]])

at least one element in M is larger than 5

all elements in M are not larger than 5


## Type casting

Since Numpy arrays are statically typed, the type of an array does not change once created. But we can explicitly cast an array of some type to another using the astype functions (see also the similar asarray function). This always create a new array of new type:

dtype('int64')

array([[  1.,   4.],
[  9.,  16.]])

dtype('float64')

array([[ True,  True],
[ True,  True]], dtype=bool)


• http://numpy.scipy.org
• http://scipy.org/Tentative_NumPy_Tutorial
• http://scipy.org/NumPy_for_Matlab_Users - A Numpy guide for MATLAB users.

## Versions

SoftwareVersion
Python2.7.8 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
IPython3.0.0
OSLinux 3.13.0 45 generic x86_64 with debian jessie sid
numpy1.9.1
Tue Apr 14 12:19:45 2015 CEST

Tags:

Categories:

Updated: