Matlab

ndims

For scalars and vectors, ndims always returns 2.

```>> ndims(0)
ans =
2
```
```>> ndims([1 2 3 4])
ans =
2
```

isvector

```>> isvector(1)
ans =
1
```

structures as matrices

Even structures are matrices:

```s.field = 1;
```

You can index them and query the dimensions (which is also two):

```s(1)
ndims(s)
```

So every thing in mat(trix)lab is a matrix; Even function handle (tested in octave):

```ismatrix (@(x) x)
ans =  1
```

datestr

Matlab 7.10.0.499 (R2010a):

```>> datestr([1495 1 4  0  0 0])
ans =
03-Feb-0004
01-Jan-0000
04-Jan-0000
00-Jan-0000
00-Jan-0000
00-Jan-0000

>> datestr([1495 1 4  0  0 1])
ans =
04-Jan-1495 00:00:01
```

What happened the 4th January 1495? But then:

```>> datestr([1495 1 5 0 0 0])

ans =
05-Jan-1495
```

Appending data to an empty array

For a vector is works as expected:

```>> A = [];
>> A(end+1) = 1

A =
1

>> whos A
Name      Size            Bytes  Class     Attributes
A         1x1                 8  double
```

However, for a 3d-array, we get 2 elements (instead of 1) in matlab R2013a.

```>> A = [];
>> A(1,1,end+1) = 1;

A(:,:,1) =
0

A(:,:,2) =
1

>> whos A
Name      Size             Bytes  Class     Attributes
A         1x1x2               16  double
```

Python

In numpy version 1.6.1, numpy.sqrt is also applied to masked values and returns a warning if these are negative:

```import numpy
norm2 = numpy.ma.array([1, -2, 3], mask = [0, 1, 0])
numpy.sqrt(norm2)
/usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt
```

This behavior is inconsistent since the ** operators applies only on non-masked value:

```b = ma.array([1, -2, 3], mask = [0, 1, 0])
b2 = b**2
b2.data
Out[5]: array([ 1, -2,  9])
```

Thus numpy.sqrt(b**2) returns this warning.

A work-around:

```valid = numpy.logical_not(norm2.mask)
norm2[valid] = numpy.sqrt(norm2[valid])
```

Numpy: arr.max() can return NaN

Numpy's max can return a NaN as maximum value:

```In [1]: array([1.,NaN]).max()
Out[1]: nan
```

Unlike python building max function:

```In [10]: max([1.,NaN])
Out[10]: 1.0
```

Also unlike Fortran and Matlab. For a sane behaviour, one should use numpy.nanmax().

```i=010
let i=\$i+1
echo \$i
9
```