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add small chep 2018 poster image

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TaiSakuma committed Aug 3, 2018
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A python library for summarizing event data into multi-dimensional categorical data
### Description
_AlphaTwirl_ is a python library that loops over event data and summarizes them into multi-dimensional 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](https://root.cern.ch/) [TTrees](https://root.cern.ch/doc/master/classTTree.html) with one entry per collision event of an [LHC](https://home.cern/topics/large-hadron-collider) experiment at [CERN](http://home.cern/). Event data are often large—too large to be loaded in memory—because they have as many entries as events. Multi-dimensional 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](https://www.r-project.org/) and [pandas](http://pandas.pydata.org/), 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.
_AlphaTwirl_ is a python library that summarizes event data into multi-dimensional 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](https://root.cern.ch/) [TTrees](https://root.cern.ch/doc/master/classTTree.html) with one entry per collision event of an [LHC](https://home.cern/topics/large-hadron-collider) experiment at [CERN](http://home.cern/). Event data are often large—too large to be loaded in memory—because they have as many entries as events. Multi-dimensional 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](https://www.r-project.org/) and [pandas](http://pandas.pydata.org/), 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.
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