This is a control, helps splitting the page and loads the same sequentially.
Thus, Sequence splitter control helps splitting the loading duration for a page and sequences them. Its provides a way to slice the complete loading duration into multiple chunks and thus allowing the page to load in
... [More] steps. Thus it reduces the initial loading duration and enhances user experience. Apart from dividing the entire load duration into chunks, this also allows to set priorities for each chunk. So that the page is initially loaded with primary content and following which, the remaining portions of the page is loaded sequentially.
The concept behind the control is to create small chunks from the entire page content and load them in a sequential manner. Each of these chunks would be assigned a load order or a priority value, based on which the loading of the content would be ordered. Therefore, each time a chunk is loaded, page makes a new request to load next chunk. This process continues until the last chunk of lowest priority is rendered in the page.
Visit [url:this|http://www.codeplex.com/SequenceSplitter/SourceControl/FileView.aspx?itemId=227565&changeSetId=6191] for a step by step guide and more information on using SequenceSplitter.
Visit [url:this|http://www.codeplex.com/SequenceSplitter/SourceControl/FileView.aspx?itemId=227564&changeSetId=6191] for a detailed example on SequenceSplitter.
Refer chm file included as part of the source code for help on SequenceSplitter APIs. [Less]
functools.partial allows for the currying of arguments. However, being implemented in C, functools.partial objects are not pickleable. This package implements a wrapper class for the partial objects, such that enables pickling of the objects.
You can download and install currypy using easy_install as follows
Partial Least Squares (PLS), was first introduced to the neuroimaging community in 1996 (McIntosh et al., 1996), for measuring distributed task responses (Mean-Centering PLS and Non-Rotated Task PLS). It has also been applied to measuring distributed patterns that impact on task performance (Regular
... [More] Behav PLS, Non-Rotated Behav PLS and Multiblock PLS) and finally to both task-dependent and resting state regional connectivity (McIntosh and Lobaugh, 2004).
The NPAIRS (Nonparametric, Prediction, Activation, Influence, Reproducibility, re-Sampling) package was first introduced with canonical variates analysis (i.e., linear discriminant analysis) and a reproducibilty metric (Strother et al., 1997) followed by the addition of prediction metrics (Strother et al., 2002). NPAIRS uses a penalized PCA basis (PCA denoising) adapted to optimize the reproducibility and prediction metrics for CVA. In addition to measuring distributed task and resting state responses NPAIRS provides a statistical resampling framework with basic building blocks for benchmarking and comparing preprocessing and data analysis, (i.e., processing pipeline) choices (Strother et al., 2004).
Both PLS and NPAIRS/CVA have proven to be robust methods for extracting distributed signal changes related to changing task demands in neuroimaging. Their relative strengths and weaknesses are currently being evaluated at the Rotman Research Institute.
Note that the code is currently in beta development (version 184.108.40.206).
See what changes have been made in the latest version. Install and run the latest version of plsnpairs (beta 220.127.116.11). Alternatively, download the jar file and run it from the command line using Java 1.6. New! Run plsnpairs via batch mode! See User Guide Batch PLSNPAIRS for details. See How To Run PLSNPAIRS for details. [Less]