Software tutorial/Vectors and arrays
Jump to navigation
Jump to search
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(c)
ans =
300 1
>> size(f)
ans =
1 26
|
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)
>>> c.shape
(300, 1)
>>> f.shape
(26,)
|