Hi there,
I have some generic functions that take time series data with 2 numpy array
arguments, time and value, and return 2 numpy arrays of time and value.
I would like to place these arrays into a Numpy structured array or
directly into a new pytables table with fields, time and value.
Now Iv
Hi Jason,
A key-value store pattern is definitely supported. However, be forewarned
that groups are implemented using B-trees, not hash tables. However, with
data of your size most of the access time will be in the leaf nodes and not
getting the group. I'd say try it out and see.
Be Well
Anthon
Thanks Anthony, I think I will give a try, apprently at some stage I would
like to flush the data into disk :p
cheers,
Chao
On Wed, Aug 7, 2013 at 6:44 PM, Anthony Scopatz wrote:
> On Wed, Aug 7, 2013 at 5:44 AM, Chao YUE wrote:
>
>> Dear all,
>>
>> I have a hierachical nested python dictiona
oops sorry, seem auto-correction of my email client created some typo for me :
P
here's the corrections,
On 8 Aug, 2013, at 2:33 AM, Xianli Xu wrote:
> Hi all,
>
> I'm developing data processing service and evaluating if Pytable. Since hdf5
> supports hierarchical data like a tree of folder
Hi all,
I'm developing data processing service and evaluating if Pytable. Since hdf5
supports hierarchical data like a tree of folder, can I use such a tree-like
structure as a K-V store like possibly store million of tables or arrays under
one group and randomly access any one of them in O(1)
On Wed, Aug 7, 2013 at 4:39 AM, Gabriel J.L. Beckers <
pytables-u...@gbeckers.nl> wrote:
> Hi,
>
> I don't know if this is related in any way to Gergo's problem, but I
> have slow responses when querying which children a group contains, if
> that group contains big leafs. I am using pytables 2.5 a
On Wed, Aug 7, 2013 at 5:44 AM, Chao YUE wrote:
> Dear all,
>
> I have a hierachical nested python dictionaries with the end of the branch
> as either pandas dataframe, or np.ndarray or list or plain scalars.
>
> let's say the different levels of keys are:
>
> 1st level: ['top1', 'top2', 'top3']
Dear all,
I have a hierachical nested python dictionaries with the end of the branch
as either pandas dataframe, or np.ndarray or list or plain scalars.
let's say the different levels of keys are:
1st level: ['top1', 'top2', 'top3']
2nd level: ['mid1','mid2','mid3']
3rd level: ['bot1','bot2','bo
Hi,
I don't know if this is related in any way to Gergo's problem, but I
have slow responses when querying which children a group contains, if
that group contains big leafs. I am using pytables 2.5 and hdf5 1.8.9
on linux 64 bit.
Specifically, I found that using the _g_get_objinfo method (w
