diff options
Diffstat (limited to 'processing/chapter.tex')
| -rw-r--r-- | processing/chapter.tex | 14 |
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. %
|