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\chapter{Processing}
% TODO: cool quote, if I can think of one
\clearpage
From a data science perspective, CMDS has several unique challenges:
\begin{ditemize}
\item Dimensionality of datasets can typically be greater than two, complicating
\textbf{representation}.
\item Shape and dimensionality change...
\item Data can be large (over one million points). % TODO: contextualize large (not BIG DATA)
\end{ditemize}
I have designed a software package that directly addresses these issues. %
WrightTools is a software package at the heart of all work in the Wright Group. %
% TODO: more intro
WrightTools is written in Python, and endeavors to have a ``pythonic'', explicit and ``natural''
application programming interface (API). %
To use WrightTools, simply import:
\begin{codefragment}{python}
>>> import WrightTools as wt
>>> wt.__version__
3.0.0
\end{codefragment}
I'll discuss more about how exactly WrightTools packaging, distribution, and instillation works in
\autoref{sec:processing_distbribution}.
We can use the builtin Python function \mintinline{python}{dir} to interrogate the contents of the
WrightTools package. %
\begin{codefragment}{python}
>>> dir(wt)
['Collection',
'Data',
'__branch__',
'__builtins__',
'__cached__',
'__doc__',
'__file__',
'__loader__',
'__name__',
'__package__',
'__path__',
'__spec__',
'__version__',
'__wt5_version__',
'_dataset',
'_group',
'_open',
'_sys',
'artists',
'collection',
'data',
'diagrams',
'exceptions',
'kit',
'open',
'units']
\end{codefragment}
Many of these are dunder (double underscore) attributes---Python internals that are not normally
used directly. %
The ten attributes that do not start with underscore are the public API that users of WrightTools
typically use. %
Within the public API are two classes, \mintinline{python}{Collection} \&
\mintinline{python}{Data}, which are the two main classes in the WrightTools object model. %
\mintinline{python}{Data} stores spectra directly as multidimensional arrays, and
\mintinline{python}{Collection} stores \textit{groups} of data objects (and other collection
objects) in a hierarchical way for internal organization purposes. %
\section{Data object model} % ====================================================================
WrightTools uses a programming strategy called object oriented programming (OOP). %
It contains a central data ``container'' that is capable of storing all of the information about
each multidimensional (or one-dimensional) spectra: the \mintinline{python}{Data} class. %
It also defines a \mintinline{python}{Collection} class that contains data objects, collection
objects, and other pieces of metadata in a hierarchical structure. %
Let's first discuss \mitinline{python}{Data}.
All spectra are stored within WrightTools as multidimensional arrays. %
Arrays are containers that store many instances of the same data type, typically numerical
datatypes. %
These arrays have some \mintinline{python}{shape}, \mintinline{python}{size}, and
\mintinline{python}{size}. %
In the context of WrightTools, they can contain floats, integers, complex numbers and NaNs. %
The \mintinline{python}{Data} class contains everything that is needed to define a single spectra
from a single experiment (or simulation). %
To do this, each data object contains several multidimensional arrays (typically 2 to 50 arrays,
depending on the kind of data). %
There are two kinds of arrays, instances of \mintinline{python}{Variable} and
\mintinline{python}{Channel}. %
Variables are coordinate arrays that define the position of each pixel in the multidimensional
spectrum, and channels are each a particular kind of signal within that spectrum. %
Typical variables might be \mintinline{python}{[w1, w2, w3, d1, d2]}, and typical channels
\mintinline{python}{[pmt, pyro1, pyro2, pyro3]}. %
\subsection{Dimensionality manipulation} % -------------------------------------------------------
\subsubsection{Chop}
\subsubsection{Collapse}
\subsubsection{Split}
\section{Artists} % ==============================================================================
After importing and manipulating data, one typically wants to create a plot. %
The artists sub-package contains everything users need to plot their data objects. %
This includes both ``quick'' artists, which generate simple plots as quickly as possible, and a
full figure layout toolkit that allows users to generate full publication quality figures. %
It also includes ``specialty'' artists which are made to perform certain popular plotting
operations, as I will describe below. %
Currently the artists sub-package is built on-top of the wonderful matplotlib library. %
In the future, other libraries (e.g. mayavi), may be incorporated. %
\subsection{Quick} % -----------------------------------------------------------------------------
\subsubsection{1D}
\subsubsection{2D}
\subsection{Specialty} % -------------------------------------------------------------------------
\subsection{Artists API} % -----------------------------------------------------------------------
\subsection{Colormaps} % -------------------------------------------------------------------------
\subsection{Interpolation} % ---------------------------------------------------------------------
\section{Fitting} % ==============================================================================
\section{Distribution and licensing} \label{sec:processing_disbribution} % =======================
\section{Future directions} % ====================================================================
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