From 2514e7edee8cd66f6bc2e966082168b9663f7450 Mon Sep 17 00:00:00 2001
From: Blaise Thompson <blaise@untzag.com>
Date: Wed, 9 May 2018 16:52:28 -0500
Subject: 2018-05-09 16:52

---
 processing/chapter.tex | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'processing')

diff --git a/processing/chapter.tex b/processing/chapter.tex
index 2bb569e..cfcaba8 100644
--- a/processing/chapter.tex
+++ b/processing/chapter.tex
@@ -197,7 +197,7 @@ As examples:
   \item The \python{closest_pair} function finds the pair(s) of indices corresponding to the
     closest elements in an array.  %
 \end{ditemize}
-    
+
 \begin{table}
   \begin{tabular}{c | c | l}
     & type & description \\ \hline
@@ -526,7 +526,7 @@ The primary attributes and methods of \python{Collection} are
   \item attribute \python{item_names}
   \item attribute \python{fullpath}
 \end{ditemize}
-% TODO: finish adding attributes and methodsd  
+% TODO: finish adding attributes and methodsd
 
 Collections are useful because they allow WrightTools users to ``carry around'' several associated
 data objects in the same file.  %
@@ -608,7 +608,7 @@ perceptual.  %
 Qualitative colormaps have random orderings of color.  %
 They are best used to represent unordered things, and they typically have high dynamic range.  %
 Perceptual colormaps are monotonic in lightness, and are best at representing ordered information
-(like signal levels in MR-CMDS).  %
+(like signal levels in MR-CMDS). \cite{LiuYang2018a}  %
 
 Historically the Wright Group has used a qualitative colormap for all plotting.  %
 \autoref{pro:fig:cmaps} shows the red, green, and blue components of four different colormaps.  %
@@ -646,7 +646,7 @@ In \autoref{pro:fig:fill_types} the edges of the Delaunay triangles are drawn fo
 Such interpolation methods result in \emph{smoother} looking spectra, but they can look strange and
 cause visual artifacts.  %
 ``pcolor'' is a much more direct approach that results in \emph{blocky} but honest two-dimensional
-plots.  % 
+plots.  %
 
 \begin{figure}
   \includegraphics[scale=0.5]{"processing/wright_cmap"}
@@ -991,7 +991,7 @@ This dataset can be chopped to it's component 2D \python{('wm'', 'w1')} spectra.
 data created at /tmp/lzyjg4au.wt5::/
   axes ('wm'', 'w2', 'w1')
   shape (35, 11, 11)
->>> chopped = data.chop('wm', 'w1')  
+>>> chopped = data.chop('wm', 'w1')
 chopped data into 11 piece(s) in ('wm', 'w1'')
 >>> chopped.chop000
 <WrightTools.Data 'chop000' ('wm', 'w1') at /tmp/935c2v5a.wt5::/chop000>
@@ -1164,7 +1164,7 @@ dataset—it’s really useful for inspecting the quality of your fit procedure.
   \includegraphics[width=0.4\textwidth]{"processing/fit_amplitude"}
   \includegraphics[width=0.4\textwidth]{"processing/fit_tau"}
   \caption{
-    Fitting as dimensionality reduction. 
+    Fitting as dimensionality reduction.
   }
   \label{pro:fig:fitted_movie}
 \end{figure}
@@ -1323,7 +1323,7 @@ pip (``pip installs packages'', ``pip installs python'', or ``preferred installe
 can be used to install packages directly from PyPI:  %
 \begin{codefragment}{bash}
 pip install WrightTools
-\end{codefragment} 
+\end{codefragment}
 
 Conda is a multilingual package manager that handles virtual environments and dependencies, even
 binary dependencies, in a hassle-free way.  %
-- 
cgit v1.2.3