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replace "multi-dimensional" with "multivariate" in README.md

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TaiSakuma committed Nov 18, 2018
1 parent 24df389 commit 30e71dfe46ffaf9b3ec649b936c20c3031706983
Showing with 5 additions and 5 deletions.
  1. +5 −5 README.md
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---

A python library for summarizing event data into multi-dimensional categorical data
A python library for summarizing event data into multivariate categorical data

### Description
_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.
_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](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. 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](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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@@ -55,8 +55,8 @@ _AlphaTwirl_ is a python library that summarizes event data into multi-dimension

#### Output format

- **Multi-dimensional categorical data**: output data of alphatwirl
are multi-dimensional categorical data
- **Multivariate categorical data**: output data of alphatwirl
are multivariate categorical data
- They are usually small because they only have as many entries as categories.
- Often small enough to be stored as text files in a laptop computer.
- **Fixed width format**: text files with fixed width format have been
@@ -88,7 +88,7 @@ process htbin njetbin minChi n
strategy_](https://www.jstatsoft.org/article/view/v040i01) on event data.
- _split_ event data into groups by categories, _apply_ a
function to data in each group, and _combine_ the results as a
**table** of multi-dimensional categorical data.
**table** of multivariate categorical data.
- Histograms can be created in this strategy—split data into
bins, count the number of entries in each bin, and combine the
results as a table.

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