> There might be an easier way to do this with numpy dtypes. In pseudo-
> code:
>
> np.dtype([(colname, np.int16) for colname in colnames])
>
Can we use time and enum kinds that way as well?
This makes me think I should probably flatten my table.
Having nested columns is quite natural to group
Hello,
I 've started to write a parser to convert ASTERIX data to HDF5, but I have
some problem to represent all the data.
I use table objects. I've defined a class for each category record (a record is
made of different data items).
See below as an example for category 30.
1. Some data items
+1
/Benjamin
> -Ursprungligt meddelande-
> Från: Anthony Scopatz [mailto:scop...@gmail.com]
> Skickat: den 24 juli 2012 18:39
> Till: Discussion list for PyTables
> Ämne: [Pytables-users] [POLL] Fully Adopt PEP8 Proposal - Please
> respond!
>
> Dear PyTables Community,
>
> The next vers
> Hello Benjamin,
>
> Not knowing to much about the ASTERIX format, other than what you said
> and what is in the links, I would say that this is a good fit for HDF5
> and PyTables. PyTables will certainly help you read in the data and
> manipulate it.
>
> However, before you abandon hachoir com
Hi,
I'm working with Air Traffic Management and would like to perform checks /
compute statistics on ASTERIX data.
ASTERIX is an ATM Surveillance Data Binary Messaging Format
(http://www.eurocontrol.int/asterix/public/standard_page/overview.html)
The data consist of a concatenation of consecuti
