Better documentation of multi-dimensional arrays in MDSplus about mdsplus HOT 3 CLOSED

Hi all,

I was wondering how the MDSplus arrays work when I casually found this issue. I think it is not just matter of documentation: people do not understand how they works because they do not work as expected.

A very common operation one normally wants to apply to multi-dimensional arrays is slicing. MDSplus slicing seems not to work as the experienced user expects both in an MDSplus expression and on the Python mdsarray object. What I mean is that in the example below one expects d3 to have shape (4,1).

It seems to me that mdsplus arrays work more like list of lists than like multidimensional arrays. What about returning numpy arrays directly? That's what users need.

Sincerely

from MDSplus import *
c = Connection("somemachine.someplace.comecountry")
c.openTree("test", 11)
d1 = c.get("SIG1")
d2 = c.get("SIG1[0, *]")
d3 = c.get("SIG1[*, 0]")

In[11]: d1.shape
Out[12]: (69, 4)
In[13]: d2.shape
Out[14]: (69, 1)
In[15]: d3.shape
Out[16]: (69,)
In[17]: type(d1)
Out[18]: MDSplus.mdsarray.Float32Array

from mdsplus.

MDSplus arrays are implemented using numpy arrays. To get to the numpy array you just use the data() method. In the example above, d1.data() would return the numpy array.

from mdsplus.

More documentation on MDSplus arrays has been added by Gabriele to the MDSplus web site at: http://www.mdsplus.org/index.php/Documentation:Tutorial:MdsObjects#More_on_Arrays

from mdsplus.