[SciPy-user] converting hourly series to annual unneccessaryly masks data

[Timmie]

Hello Pierre,

> The documentation is still a bit lacking, sorry. Still, in the  
> docstring of convert, you can see that if you don't precise a func  
> input parameter, the series is converted to 2D, as stated:
> `
>          If ``func`` is not given, the output series group the points  
> of the
>          initial series that share the same new date. For example, if  
> the
>          initial series has a daily frequency and is 1D, the output  
> series is
>          2D.
No problem here.
We discussedit already here:
aggregation of long-term time series
http://article.gmane.org/gmane.comp.python.scientific.user/15584

 
> 1. You're not starting at 01/01:00-00, but 8 days later
Yes, I am aware of it.

> 2. We are using this 366d year: as there are no leap year in your  
> range of years, the last 24 data of each line will be masked.
This explains what I was looking for.
Because it affects how I handle the data later.

I need averages for all hours over the years:

atest.mean(0)
=> this the data array for the new one-year hourly time series (8760 h).
And since the data is masked at the and, I am lacking a day when I build the
timeseries.
Is there a way to handle this generically?

I mean if my long-term years contain a leap year I neeed the masked points but
normally not.

How would you suggest to build the one-year hourly average time series in a
flexible way?

A example case what I am aiming at:
Averge hourly temperatures over 20 years of data.

> 3. You don't finish at 12/31-23:00, but (365-8) days earlier.
I also know this here.

 
> So all is well and works as expected (developer-wise), no need for a  
> ticket (good reflex, though).
Sorry, too fast.
 
> Now, of course, you need to tell us what you were expecting, and what  
> kind of average you wanted to calculate.
See above.