Thank you for these e-mails with so many useful tips! This is
definitely a start. I will report what I find!
Cheers,
-รก.
On Fri, Mar 16, 2012 at 15:00, Francesc Alted wrote:
> On Mar 16, 2012, at 1:55 AM, Alvaro Tejero Cantero wrote:
>
>> Thanks Francesc, we're getting there :).
>>
>> Some mo
Hi Alvaro,
I just want to second what Francesc is saying. You do have to get
used to the fact that you can't do *everything* that numpy can do
inside of PyTables. However, you can do the important stuff which
lets you pull out only the data that you are interested in. In fact,
PyTables
lets you
On Mar 16, 2012, at 1:55 AM, Alvaro Tejero Cantero wrote:
> Thanks Francesc, we're getting there :).
>
> Some more precise questions below.
>
>> Here it is how you can do that in PyTables:
>>
>> my_condition = '(col1>0.5) && (col2<24) && (col3 == "novel")'
>> mycol4_values = [ r['col4'] for r i
Thanks Francesc, we're getting there :).
Some more precise questions below.
> Here it is how you can do that in PyTables:
>
> my_condition = '(col1>0.5) && (col2<24) && (col3 == "novel")'
> mycol4_values = [ r['col4'] for r in tbl.where(my_condtion) ]
ok, but having data upon which I want to ope
Hola Alvaro,
On Mar 15, 2012, at 4:58 PM, Alvaro Tejero Cantero wrote:
> Hi Anthony and Francesc,
>
> please bear with me for one more.
>
> I was thinking of having this huge array in memory and be able to
> write nice indexing expressions, the kind that one writes all the time
> with numpy; e.
Hi Anthony and Francesc,
please bear with me for one more.
I was thinking of having this huge array in memory and be able to
write nice indexing expressions, the kind that one writes all the time
with numpy; e.g.
arr[ fast && novel && checked, 1:4]
where fast, novel and checked are boolean arra
On Mar 15, 2012, at 1:43 PM, Anthony Scopatz wrote:
> Hello Alvaro
>
> On Thu, Mar 15, 2012 at 1:20 PM, Alvaro Tejero Cantero
> wrote:
> Hi!
>
> Thanks for the prompt answer. Actually I am not clear about switching
> from NxM array to N columns (64 in my case). How do I make a
> rectangular se
Hello Alvaro
On Thu, Mar 15, 2012 at 1:20 PM, Alvaro Tejero Cantero wrote:
> Hi!
>
> Thanks for the prompt answer. Actually I am not clear about switching
> from NxM array to N columns (64 in my case). How do I make a
> rectangular selection with columns? With an NxM array I just have to
> do arr
Hi!
Thanks for the prompt answer. Actually I am not clear about switching
from NxM array to N columns (64 in my case). How do I make a
rectangular selection with columns? With an NxM array I just have to
do arr[1:2,1:4] to select columns 1,2,3 and time samples 1
to 2.. While it is
Hello Alvaro,
Thanks for your excitement!
On Thu, Mar 15, 2012 at 7:52 AM, Alvaro Tejero Cantero wrote:
> Hi everybody!
>
> I plan to start using PyTables for an application at the University of
> Oxford where data is collected in sessions of 2Gb Int16 data organized
> as 64 parallel time series
Hi everybody!
I plan to start using PyTables for an application at the University of
Oxford where data is collected in sessions of 2Gb Int16 data organized
as 64 parallel time series (64 detectors), each holding 15 million
points (15M).
I could handle this sessions separately, but ideally I would
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