From: Zach Laine (whatwasthataddress_at_[hidden])
Date: 2007-07-31 10:21:32
Here are the review guidelines from
What to include in Review Comments
Your comments may be brief or lengthy, but basically the Review
Manager needs your evaluation of the library. If you identify
problems along the way, please note if they are minor, serious, or
Here are some questions you might want to answer in your review:
* What is your evaluation of the design?
* What is your evaluation of the implementation?
* What is your evaluation of the documentation?
* What is your evaluation of the potential usefulness of the library?
* Did you try to use the library? With what compiler? Did you
have any problems?
* How much effort did you put into your evaluation? A glance? A
quick reading? In-depth study?
* Are you knowledgeable about the problem domain?
And finally, every review should answer this question:
* Do you think the library should be accepted as a Boost library?
Be sure to say this explicitly so that your other comments don't
obscure your overall opinion.
On 7/31/07, John Phillips <phillips_at_[hidden]> wrote:
> My apologies for the delay in this posting, but the review period for
> the Time Series library submitted by Eric Neibler runs from Monday, July
> 30 until Wednesday, August 8. From the documentation:
> The purpose of the Boost.Time_series library is to provide data
> structures, numerical operators and algorithms to operate on time
> series. A time series is a series of data points, sampled at regular
> intervals. The library provides numerous time series containers, each
> with different time/space trade-offs, and a hierarchy of concepts which
> allow the time series to be manipulated generically. The library also
> provides operators and algorithms which use the generic interfaces to
> perform calculations on time series and accumulate various statistics
> about them.
> Boost.Time_series does not yet contain all the algorithms one might
> want in order to perform full time series analysis. However, the key
> contribution of Boost.Time_series is the framework and the rich
> hierarchy of concepts with which such algorithms can be written to
> efficiently and generically process series of data with widely
> divergent in-memory representations and performance characteristics.
> Boost.Time_series provides several such series containers, as well as
> mechanisms for defining additional series types and algorithms that fit
> within the framework. Some examples of series types that are provided
> are: dense, sparse, piecewise constant, heaviside and others, as well
> as adaptors for providing shifted, scaled and clipped series views.
> Please notice that the Time Series library uses some features of
> boost that will be part off the 1.35 release, but are not I the 1.34.1
> release. For testing, you will need to either test against CVS Head
> (Which will not be available for much of Tuesday.) or use backports
> provided in the time series download files to update your 1.34.x
> The library is available from
> or by following the link provided on the review schedule.
> Thanks to all for your time and effort on this review.
> John Phillips
> Review Manager
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