Working with Matlab and Octave

Difficulty level: easy
Time need to lean: 10 minutes or less

MATLAB (Octave)

SoS tries to exchange variables with MATLAB/Octave in the most natural way. For example it converts numpy arrays to MATLAB matrix, and dictionaries to MATLAB struct. It converts Python DataFrame (or R data.frame) to table in MATLAB but to dataframe in Octave because the latter is closer to Python DataFrame.

The convertion of datatype from SoS to MATLAB (Octave) is as followings:

Python	condition	MATLAB/Octave
`None`		`NaN`
`boolean`		`logical`
`integer`		`integer`
`float`		`float`
`complex`		`complex`
`str`		`str`
`char`		`char`
Sequence (`list`, `tuple`, ...)	homogenous type, all numeric	`vector`
Sequence (`list`, `tuple`, ...)	homogenous type, all char	`char`
Sequence (`list`, `tuple`, ...)	multiple types	`cell`
`set`		`cell`
`dict`		`struct`
`numpy.ndarray`		`cell`
`numpy.matrix`		`matrix`
`pandas.DataFrame`		`table` (MATLAB)
		`dataframe` (Octave)

Python objects in other datatypes are transferred as string "Unsupported datatype".

The convertion of datatype from MATLAB (Octave) to SoS is as followings:

MATLAB	length (n)	Python
`NaN`		`None`
`logical`	`1`	`boolean`
`integer`	`1`	`integer`
`numeric`	`1`	`double`
`character`	`1`	`string`
`complex`	`1`	`complex`
`logical`	`n > 1`	`list`
`integer`	`n > 1`	`list`
`complex`	`n > 1`	`list`
`numeric`	`n > 1`	`list`
`character`	`n > 1`	`list`
`struct`	`n > 0`	`numpy.ndarray`
`matrix`	`n > 0`	`numpy.array`
`cell`	`n > 0`	`numpy.ndarray`
`table`	`n > 0`	`pandas.DataFrame`

For example, a R dataframe is transfered as table to MATLAB.

mtcars = dataframe with 32 rows and 11 columns                                  
Src: /var/folders/ys/gnzk0qbx5wbdgm531v82xxljv5yqy8/T/df2oct.csv                
_1    mpg    cyl    disp     hp   drat     wt   qsec     vs     am   gear   carb
Nr double double  double double double double double double double double double
 1 21.000      6 160.000    110 3.9000 2.6200 16.460      0      1      4      4
 2 21.000      6 160.000    110 3.9000 2.8750 17.020      0      1      4      4
 3 22.800      4 108.000     93 3.8500 2.3200 18.610      1      1      4      1
 4 21.400      6 258.000    110 3.0800 3.2150 19.440      1      0      3      1
 5 18.700      8 360.000    175 3.1500 3.4400 17.020      0      0      3      2
 6 18.100      6 225.000    105 2.7600 3.4600 20.220      1      0      3      1
 7 14.300      8 360.000    245 3.2100 3.5700 15.840      0      0      3      4
 8 24.400      4 146.700     62 3.6900 3.1900 20.000      1      0      4      2
 9 22.800      4 140.800     95 3.9200 3.1500 22.900      1      0      4      2
10 19.200      6 167.600    123 3.9200 3.4400 18.300      1      0      4      4
11 17.800      6 167.600    123 3.9200 3.4400 18.900      1      0      4      4
12 16.400      8 275.800    180 3.0700 4.0700 17.400      0      0      3      3
13 17.300      8 275.800    180 3.0700 3.7300 17.600      0      0      3      3
14 15.200      8 275.800    180 3.0700 3.7800 18.000      0      0      3      3
15 10.400      8 472.000    205 2.9300 5.2500 17.980      0      0      3      4
16 10.400      8 460.000    215 3.0000 5.4240 17.820      0      0      3      4
17 14.700      8 440.000    230 3.2300 5.3450 17.420      0      0      3      4
18 32.400      4  78.700     66 4.0800 2.2000 19.470      1      1      4      1
19 30.400      4  75.700     52 4.9300 1.6150 18.520      1      1      4      2
20 33.900      4  71.100     65 4.2200 1.8350 19.900      1      1      4      1
21 21.500      4 120.100     97 3.7000 2.4650 20.010      1      0      3      1
22 15.500      8 318.000    150 2.7600 3.5200 16.870      0      0      3      2
23 15.200      8 304.000    150 3.1500 3.4350 17.300      0      0      3      2
24 13.300      8 350.000    245 3.7300 3.8400 15.410      0      0      3      4
25 19.200      8 400.000    175 3.0800 3.8450 17.050      0      0      3      2
26 27.300      4  79.000     66 4.0800 1.9350 18.900      1      1      4      1
27 26.000      4 120.300     91 4.4300 2.1400 16.700      0      1      5      2
28 30.400      4  95.100    113 3.7700 1.5130 16.900      1      1      5      2
29 15.800      8 351.000    264 4.2200 3.1700 14.500      0      1      5      4
30 19.700      6 145.000    175 3.6200 2.7700 15.500      0      1      5      6
31 15.000      8 301.000    335 3.5400 3.5700 14.600      0      1      5      8
32 21.400      4 121.000    109 4.1100 2.7800 18.600      1      1      4      2