Daniele Nicolodi wrote:
> Sure, I realize that, thank for the clarification. The arrays are quite
> small, then the three loops and the temporary take negligible time and
> memory in the overall processing.
If they are small, a Python loop would do the job as well. And if it
doesn't, it is just
On 11/02/2014 18:18, Sturla Molden wrote:
> Daniele Nicolodi wrote:
>
>> That's more or less my current approach (except that I use the fact that
>> the data is evenly samples to use np.where(np.diff(t1) != dt) to detect
>> the regions of continuous data, to avoid the loop.
>
> I hope you realiz
Daniele Nicolodi wrote:
> That's more or less my current approach (except that I use the fact that
> the data is evenly samples to use np.where(np.diff(t1) != dt) to detect
> the regions of continuous data, to avoid the loop.
I hope you realize that np.where(np.diff(t1) != dt) generates three lo
On 11/02/2014 15:38, Sturla Molden wrote:
> Daniele Nicolodi wrote:
>
>> I was probably not that clear: I have two 2xN arrays, one for each data
>> recording, one column for time (taken from the same clock for both
>> measurements) and one with data values. Each array has some gaps.
>
> If you
2014-02-11 14:55 GMT+01:00 Andreas Hilboll :
> On 11.02.2014 14:47, Daniele Nicolodi wrote:
> > On 11/02/2014 14:41, Andreas Hilboll wrote:
> >> On 11.02.2014 14:22, Daniele Nicolodi wrote:
> >>> On 11/02/2014 14:10, Andreas Hilboll wrote:
> On 11.02.2014 14:08, Daniele Nicolodi wrote:
>
Daniele Nicolodi wrote:
> I was probably not that clear: I have two 2xN arrays, one for each data
> recording, one column for time (taken from the same clock for both
> measurements) and one with data values. Each array has some gaps.
If you want all subarrays where both timeseries are sampled,
At 06:07 AM 2/11/2014, you wrote:
>On 11/02/2014 14:56, Sturla Molden wrote:
> > Daniele Nicolodi wrote:
> >
> >> Correct me if I'm wrong, but this assumes that missing data points are
> >> represented with Nan. In my case missing data points are just missing.
> >
> > Then your data cannot be sto
On 11/02/2014 14:56, Sturla Molden wrote:
> Daniele Nicolodi wrote:
>
>> Correct me if I'm wrong, but this assumes that missing data points are
>> represented with Nan. In my case missing data points are just missing.
>
> Then your data cannot be stored in a 2 x N array as you indicated.
I wa
> On 11.02.2014 14:08, Daniele Nicolodi wrote:
>> Hello,
>>
>> I have two time series (2xN dimensional arrays) recorded on the same
>> time basis, but each with it's own dead times (and start and end
>> recording times). I would like to obtain two time series containing
>> only the time overlap
Daniele Nicolodi wrote:
> Correct me if I'm wrong, but this assumes that missing data points are
> represented with Nan. In my case missing data points are just missing.
Then your data cannot be stored in a 2 x N array as you indicated.
Sturla
___
> On 11.02.2014 14:08, Daniele Nicolodi wrote:
>> Hello,
>>
>> I have two time series (2xN dimensional arrays) recorded on the same
>> time basis, but each with it's own dead times (and start and end
>> recording times). I would like to obtain two time series containing
>> only the time overlap
On 11.02.2014 14:47, Daniele Nicolodi wrote:
> On 11/02/2014 14:41, Andreas Hilboll wrote:
>> On 11.02.2014 14:22, Daniele Nicolodi wrote:
>>> On 11/02/2014 14:10, Andreas Hilboll wrote:
On 11.02.2014 14:08, Daniele Nicolodi wrote:
> Hello,
>
> I have two time series (2xN dimension
Daniele Nicolodi wrote:
> I can imagine strategies about how to approach the problem, but none
> that would be efficient. Ideas?
I would just loop from the start and loop from the end and find out where
to clip. Then slice in between.
If Python loops take too much time, JIT compile them with
On 11/02/2014 14:41, Andreas Hilboll wrote:
> On 11.02.2014 14:22, Daniele Nicolodi wrote:
>> On 11/02/2014 14:10, Andreas Hilboll wrote:
>>> On 11.02.2014 14:08, Daniele Nicolodi wrote:
Hello,
I have two time series (2xN dimensional arrays) recorded on the same
time basis, but
On 11.02.2014 14:22, Daniele Nicolodi wrote:
> On 11/02/2014 14:10, Andreas Hilboll wrote:
>> On 11.02.2014 14:08, Daniele Nicolodi wrote:
>>> Hello,
>>>
>>> I have two time series (2xN dimensional arrays) recorded on the same
>>> time basis, but each with it's own dead times (and start and end
>>>
On 11/02/2014 14:10, Andreas Hilboll wrote:
> On 11.02.2014 14:08, Daniele Nicolodi wrote:
>> Hello,
>>
>> I have two time series (2xN dimensional arrays) recorded on the same
>> time basis, but each with it's own dead times (and start and end
>> recording times). I would like to obtain two time s
On 11.02.2014 14:08, Daniele Nicolodi wrote:
> Hello,
>
> I have two time series (2xN dimensional arrays) recorded on the same
> time basis, but each with it's own dead times (and start and end
> recording times). I would like to obtain two time series containing
> only the time overlapping segme
Hello,
I have two time series (2xN dimensional arrays) recorded on the same
time basis, but each with it's own dead times (and start and end
recording times). I would like to obtain two time series containing
only the time overlapping segments of the data.
Does numpy or scipy offer something tha
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