2018-05-09 16:52

1 files changed, 7 insertions, 7 deletions

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.  %