Difference between revisions of "Software tutorial/Vectors and arrays"

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One difference though with MATLAB, is that a Numpy array with one dimension, in other words a vector, has a shape property of <tt>(n,)</tt>: indicating the vector has <tt>n</tt>-elements.  It is not like MATLAB where one of the dimensions is a <tt>1</tt> (one).
One difference though with MATLAB, is that a Numpy array with one dimension, in other words a vector, has a shape property of <tt>(n,)</tt>: indicating the vector has <tt>n</tt>-elements.  It is not like MATLAB where one of the dimensions is a <tt>1</tt> (one).


If you require the vector to be either a row vector or a column vector, then you must do so explicitly:
If you require the vector to be either a row vector or a column vector, then you must do so explicitly.  The following code demonstrates this:
<syntaxhighlight lang="python">
<syntaxhighlight lang="python">
general_vector = np.zeros(300).shape
np.zeros(300).shape       # (300,)     a general vector
(300,)
np.zeros((300, 1)).shape # (300, 1)   a column vector
row_vector = np.zeros((300, 1)).shape
np.zeros((1, 300)).shape # (1, 300)   a row vector
(300, 1)
column_vector =  np.zeros((1, 300)).shape
(1, 300)
</syntaxhighlight>
</syntaxhighlight>
Unlike MATLAB
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Revision as of 15:54, 26 September 2010

In this section we will focus on containers for your numbers: vectors and arrays. For the purposes of this section you should use this terminology:

Vector
A one-dimensional list of numbers
Array
A multi-dimensional arrangement of numbers
Matrix
A two-dimensional array

In this course we will mainly use vectors and matrices, however, arrays are extremely prevalent in process modelling.

Creating vectors

We will create these vectors:

  • \(a = [4.0, \, 5, \,6, \,-2,\, 3, \text{NaN}, \infty]\)
  • \(b = [0, 0, \ldots, 0]\) with 300 columns of zeros, one row
  • \(c = [1, 1, \ldots, 1]^T\) with 300 rows of ones in a single column
  • \(d = [2.6, 2.6, \ldots, 2.6]^T\) with 300 entries of 2.6 in one column
  • \(e = [4.5, 4.6, 4.7, \ldots, 10.5 \), equi-spaced entries
  • \(f = \) 26 equi-spaced entries starting from 3.0, going down -4.0
MATLAB Python
a = [4, 5, 6, -2, 3, NaN, inf];
b = zeros(1, 300);
c = ones(300, 1);
d = ones(300, 1) .* 2.6;
e = 4.5:0.1:10.5;
f = linspace(3.0, -4.0, 26);

>> size(a)
ans =
     1     7
>> size(c)
ans =
   300     1
>> size(f)
ans =
     1    26

In MATLAB, everything is an array, even a scalar is just a \(1 \times 1\) array, and vectors are either a \(1 \times n\) or \(n \times 1\) array.

import numpy as np
a = np.array([4, 5, 6, -2, 3, np.nan, np.inf])
b = np.zeros((1, 300))   # note the extra brackets!
c = np.ones((300, 1))
d = np.ones((300, 1)) * 2.6
e = np.arange(4.5, 10.5001, 0.1);  # type help(np.arange) to understand why
f = np.linspace(3.0, -4.0, 26)

>>> a.shape
(7,)
>>> c.shape
(300, 1)
>>> f.shape
(26,)

The NumPy library in Python supports two different data containers: arrays and matrices. We will not use NumPy matrices in this course, because they cannot deal with multiple dimensions (e.g. 3 dimensional arrays). And even though we will probably not use multi-dimensional arrays in this course, it is not worth learning about NumPy matrices, because they are fairly limited. So we will focus on NumPy arrays, which while a bit harder to get used to, are much more powerful than NumPy matrices. A NumPy array is roughly equivalent to a MATLAB array.

One difference though with MATLAB, is that a Numpy array with one dimension, in other words a vector, has a shape property of (n,): indicating the vector has n-elements. It is not like MATLAB where one of the dimensions is a 1 (one).

If you require the vector to be either a row vector or a column vector, then you must do so explicitly. The following code demonstrates this:

np.zeros(300).shape       # (300,)      a general vector
np.zeros((300, 1)).shape  # (300, 1)    a column vector
np.zeros((1, 300)).shape  # (1, 300)    a row vector