Contents¶

Home¶

AlphaTwirl is a python library that summarizes event data into multivariate categorical data as data frames. Event data, input to AlphaTwirl, are data with one entry (or row) for one event: for example, data in ROOT TTrees with one entry per collision event of an LHC experiment at CERN. Event data are often large—too large to be loaded in memory—because they have as many entries as events. Multivariate categorical data, the output of AlphaTwirl, have one row for one category. They are usually small—small enough to be loaded in memory—because they only have as many rows as categories. Users can, for example, import them as data frames into R and pandas, which usually load all data in memory, and can perform categorical data analyses with a rich set of data operations available in R and pandas.

API reference¶

binning¶

`Binning`
`Echo`
`Round`
`RoundLog`

loop¶

`Collector`
`CollectorComposite`
`CollectorDelegate`
`EventLoop`
`DatasetIntoEventBuildersSplitter`
`EventDatasetReader`
`EventLoopRunner`
`MPEventLoopRunner`
`ReaderComposite`
`NullCollector`
`ReaderComposite`
`splitfuncs.create_files_start_length_list`

roottree¶

`Branch`
`Events`

selection¶

`build_selection`
`modules.All`
`modules.Any`
`modules.Not`
`modules.AllwCount`
`modules.AnywCount`
`modules.NotwCount`
`modules.Count.Count`
`modules.LambdaStr.LambdaStr`

summary¶

`BackrefMultipleArrayReader`
`Count`
`KeyValueComposer`
`NextKeyComposer`
`Reader`
`Scan`
`Sum`
`Summarizer`
`WeightCalculatorOne`

progressbar¶

`BProgressMonitor`
`NullProgressMonitor`
`ProgressBar`
`ProgressMonitor`
`ProgressPrint`
`ProgressReport`
`ProgressReportPickup`
`ProgressReporter`

concurrently¶

`CommunicationChannel`
`CommunicationChannel0`
`HTCondorJobSubmitter`
`MultiprocessingDropbox`
`SubprocessRunner`
`TaskPackage`
`TaskPackageDropbox`
`Worker`
`WorkingArea`