13 files changed, 415 insertions, 39 deletions
diff --git a/processing/chapter.tex b/processing/chapter.tex
index 94ef12e..4f8f029 100644
--- a/processing/chapter.tex
+++ b/processing/chapter.tex
@@ -378,38 +378,129 @@ 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 \cite{Mayavi}), may be incorporated.  %
 
-\subsection{Colormaps}  % -------------------------------------------------------------------------
+\subsection{Strategies for 2D visualization}  % ---------------------------------------------------
 
-% TODO: figure like made by visualize_colormap_components
+Representing two-dimensional data is an important capability for WrightTools, so some special
+discussion about how such representations work is warranted.  %
+WrightTools data is typically very structured, with values recorded at a grid of positions.  %
+To represent two-dimensional data, then, WrightTools needs to map the values onto a color axis.  %
+There are better and worse choices of colormap... % TODO: elaborate
+
+\subsubsection{Colormap}
+
+\begin{figure}
+  \includegraphics[scale=0.5]{"processing/wright_cmap"}
+  \includegraphics[scale=0.5]{"processing/cubehelix_cmap"}
+  \includegraphics[scale=0.5]{"processing/viridis_cmap"}
+  \includegraphics[scale=0.5]{"processing/default_cmap"}
+  \caption[CAPTION TODO]{
+    CAPTION TODO}
+  \label{pro:fig:cmaps}
+\end{figure}
+
+\begin{figure}
+  \includegraphics[width=\textwidth]{"processing/cmap_comparison"}
+  \caption[CAPTION TODO]{
+    CAPTION TODO}
+  \label{pro:fig:cmap_comparison}
+\end{figure}
+
+\autoref{pro:fig:cmaps} shows the red, green, and blue components of four different colormaps.  %
+The black line is the net intensity of each color (larger value means lighter color).  %
+Below each figure is a gray-scale representation of the corresponding colormap.  %
+The r, g, and b components are scaled according to human perception.  % TODO: values, from where
+The traditional Wright Group colormap (derived from jet) is shown first.  % TODO: cite jet
+It is not perceptual...  % TODO: define perceptual
+Following are two perceptual colormaps, cubehelix from Green  % TODO: cite
+and viridis, the new matplotlib default  % TODO: cite
+WrightTools uses the algorithm from Green to define a custom cubehelix colormap with good
+perceptual properties and familiar Wright Group coloration.  %
 
 % TODO: figure like one on wall
 
 % TODO: mention isoluminant
 
-\subsection{Interpolation}  % ---------------------------------------------------------------------
+\subsubsection{Interpolation type}
+
+WrightTools data is defined at discrete points, but an entire 2D surface must be defined in order
+to make a full colored surface.  %
+Defining this surface requires \emph{interpolation}, and there are various strategies that have
+different advantages and disadvantages.  %
+Choosing the wrong type of interpolation can be misleading.  %
+
+In the multidimensional spectroscopy community, the most popular form of interpolation is based on
+deulaney...
 
-% TODO: fill types figure from wright.tools
+\begin{figure}
+  \includegraphics[width=\textwidth]{"processing/fill_types"}
+  \caption[CAPTION TODO]{
+    CAPTION TODO}
+  \label{pro:fig:fill_types}
+\end{figure}
 
 \subsection{Quick}  % -----------------------------------------------------------------------------
 
-\subsubsection{1D}
+To facilitate easy visualization of data, WrightTools offers ``quick'' artist functions which
+quickly generate 1D or 2D representations.  %
+These functions are made to make good representations by default, but they do have certain keyword
+arguments to make popular customization easy.  %
+These are particular useful functions within the context of repls and auto-generated plots in
+acquisition software.  %
+
+Default outputs of \python{wt.artists.quick1D} and \python{wt.artists.quick2D} are shown in
+\autoref{pro:fig:quick1D} and \autoref{pro:fig:quick2D}, respectively.  %
+The full script used to create each image is included in the Figures.  %
+Note that the actual quick functions are each one-liners, and that the supplied keyword arguments
+are necessary only because the images are being saved (not typical for users in interactive
+mode).  %
+
+Perhaps the most powerful feature of \python{quick1D} and \python{quick2D} are their ability to
+treat higher-dimensional datasets by automatically generating multiple figures.  %
+When handing a dataset of higher dimensionality to these artists, the user may choose which axes
+will be plotted against using keyword arguments.  %
+Any axis not plotted against will be iterated over such that an image will be generated at each
+coordinate in that axis.  %
+Users may also provide a dictionary with entries of the form
+\python{{axis_name: [position, units]}} to choose a single coordinate along non-plotted axes.  %
+These functionalities are derived from \python{wt.Data.chop}, discussed further in...  % TODO: link
 
 \begin{figure}
   \includegraphics[width=0.5\textwidth]{"processing/quick1D 000"}
   \includepython{"processing/quick1D.py"}
-  \caption[CAPTION TODO]
-    {CAPTION TODO}
+  \caption[CAPTION TODO]{
+    CAPTION TODO}
+  \label{pro:fig:quick1D}
 \end{figure}
 
-\subsubsection{2D}
-
 \begin{figure}
   \includegraphics[width=0.5\textwidth]{"processing/quick2D 000"}
   \includepython{"processing/quick2D.py"}
-  \caption[CAPTION TODO]
-    {CAPTION TODO}
+  \caption[CAPTION TODO]{
+    CAPTION TODO}
+  \label{pro:fig:quick1D}
 \end{figure}
 
+% TODO: signed data (with and without dynamic_range=True)
+
+\subsection{Specialty}   % ------------------------------------------------------------------------
+
+\subsection{API}  % -------------------------------------------------------------------------------
+
+The artists sub-package offers a thin wrapper on the default matplotlib object-oriented figure
+creation API.  %
+The wrapper allows WrightTools to add the following capabilities on top of matplotlib:
+\begin{ditemize}
+  \item More consistent multi-axes figure layout.
+  \item Ability to plot data objects directly.
+\end{ditemize}
+Each of these is meant to lower the barrier to plotting data.  %
+Without going into every detail of matplotlib figure generation capabilities, this section
+introduces the unique strategy that the WrightTools wrapper takes.  %
+
+\subsection{Gotchas}  % ---------------------------------------------------------------------------
+
+% TODO: mention gotcha of apparently narrowing linewidths with wigners (how to READ colormaps)
+
 \section{Variables and channels}  % ===============================================================
 
 Data objects are made up of many component channels and variables, each array having the same
@@ -454,10 +545,8 @@ From a quick inspection, one can see that \python{w1} and \python{wm} were scann
 \python{w3}, \python{d0}, and \python{d1} were not moved at all, yet their coordinates are still
 propagated.  %
 
-
 \section{Axes}  % =================================================================================
 
-
 The axes have the joint shape of their component variables.  %
 Although not shown in this example, channels also may have axes with length 1.
 
@@ -472,14 +561,15 @@ squeezed and broadcasted array, respectively.  %
     CAPTION TODO}
 \end{figure}
 
-
 \section{Math}  % =================================================================================
 
 Now that we know the basics of how the WrightTools \python{Data} class stores data, it's time to do
 some data manipulation.  %
 Let's start with some elementary algebra.  %
 
-\subsection{In-place operators}
+% TODO: mention chunkwise strategy
+
+\subsection{In-place operators}  % ----------------------------------------------------------------
 
 In Python, operators are symbols that carry out some computation.  %
 Consider the following:
@@ -529,7 +619,7 @@ data.created at /tmp/tdyvfxu8.wt5::/
   range: 2500.0 to 700.0 (nm)
   size: 1801
 >>> data.signal
-<WrightTools.Channel 'signal' at /tmp/tdyvfxu8.wt5::/signal>
+<WrightTools.Channel 'signal'' at /tmp/tdyvfxu8.wt5::/signal>
 >>> data.signal.min(), data.signal.max()
 (0.10755, 1.58144)
 >>> data.signal /= 2
@@ -538,17 +628,83 @@ data.created at /tmp/tdyvfxu8.wt5::/
 \end{codefragment}
 Variables also support in-place operators.  %
 
-\subsection{Clip}
+\subsection{Clip}  % ------------------------------------------------------------------------------
+
+Clip allows users to exclude values outside of a certain range.  %
+This can be particularly useful in cases like fitting.  %
+See section ... for an example.  % TODO: link to section
+
+It's also useful for when noise in a certain region of a spectrum obscures useful data...
+Particularly true for normalized and signed data.  %
+
+\subsection{Symmetric root}  % --------------------------------------------------------------------
+
+Homodyne vs heterodyne-detected data need to be scaled appropriately for comparison.  %
+Much of the data that we collect in the Wright Group is homodyne detected, so it goes as $N^2$.  %
+To compare with the majority of other experiments, including basic linear experiments like
+absorption and Raman spectroscopy, need to plot on ``amplitude level'', that is
+$\mathsf{amplitude=\sqrt{signal}}$.  %
+
+Due to things like leveling, chopping, baseline subtraction, and simple noise even homodyne
+detected data typically include negative numbers.  %
+Symmetric root treats these values as cleanly as possible by applying the same relative scaling to
+positive and negative values, and keeping the sign of each pixel, as the following psudocode
+shows.  %
+\begin{codefragment}{python}
+def symmetric_root(value):
+    return sign(value) * sqrt(abs(value))
+\end{codefragment}
+
+For generality, \python{wt.Channel.symmetric_root} accepts any root as an argument.  %
+The default is 2, for the common case of going from intensity scaling to amplitude scaling.  %
 
-% TODO
+Any other power can be applied to a channel using the in-place \python{**=} syntax.  %
 
-\subsection{Symmetric root}
+\subsection{Log}  % -------------------------------------------------------------------------------
 
-% TODO
+The method \python{wt.Channel.log} applies logarithmic scaling to a channel.  %
+The base of the log is settable by keyword argument, with a default of $\me$.  %
+There are also methods \python{wt.Channel.log10} and \python{wt.Channel.log2}, which accept no
+keyword arguments.  %
+These may be slightly faster than \python{channel.log(base=10)} and
+\python{channel.log(base=2)}.  %
 
-\subsection{Log}
+\subsection{Level}  % -----------------------------------------------------------------------------
 
-% TODO
+% TODO: figure from wright.tools
+
+\subsection{Trim}  % ------------------------------------------------------------------------------
+
+Trim uses statistical treatment to find and remove outliers from a dataset.  %
+It is useful in cases where the naive strategy employed by \python{wt.Channel.clip} is not
+sufficient, and when preparing for fitting.  %
+
+Currently \python{trim} only supports one statistical treatment: the z-test.  %
+Z-testing compares each pixel to its multidimensional neighborhood of pixels.  %
+If the pixel is more than $n$ standard deviations outside of the neighborhood mean (using the
+neighborhood standard deviation) it is either masked, replaced with \python{np.nan}, or replaced
+with the neighborhood mean.  %
+All outliers are found before any outliers are modified, so the algorithm is not directional.  %
+
+% TODO: z-test citation
+
+\python{wt.Channel.trim} can easily be enhanced with other statistical methods as needed.  %
+
+\subsection{Smooth}  % ----------------------------------------------------------------------------
+
+\python{wt.Channel.smooth} essentially passes the channel through a low-pass filter.  %
+It does this by convolving the channel with an n-dimensional Kaiser–Bessel window.  %
+
+% TODO: define Kaiser window
+% TODO: citations
+% TODO: motivate use of Kaiser window over other choices
+
+Smoothing is a highly destructive process, and can be very dangerous if used unthinkingly.  %
+However it can be useful when noisy data is collected in high resolution.  %
+By taking many more pixels than required to capture the relevant spectral or temporal features, one
+can confidently smooth collected data in post to achieve clean results.  %
+This strategy is similar to that accomplished in time domain CMDS where a low-pass filter is
+applied on the very high resolution raw data.  %
 
 \section{Dimensionality manipulation}  % ==========================================================
 
@@ -560,7 +716,7 @@ Also consider using the fit sub-package.  % TODO: more info, link to section
 Chop is one of the most important methods of data, although it is typically not called directly by
 users of WrightTools.  %
 Chop takes n-dimensional data and ``chops'' it into all of it's lower dimensional components.  %
-Consider a 3D dataset in \python{('wm', 'w2', 'w1''''')}.  %
+Consider a 3D dataset in \python{('wm', 'w2''', 'w1''''')}.  %
 This dataset can be chopped to it's component 2D \python{('wm'', 'w1')} spectra.  %
 \begin{codefragment}{python, label=test_label}
 >>> import WrightTools as wt; from WrightTools import datasets
@@ -607,33 +763,81 @@ This same syntax used in artists...  % TODO
 
 \subsection{Collapse}  % --------------------------------------------------------------------------
 
+\python{wt.Data.collapse} reduces the dimensionality of the data object by exactly 1 using some
+mathematical operation.  %
+Currently supported methods are integrate, average, sum, max, and min, with integrate as
+default.  %
+Collapsing a dataset is a very simple and powerful method of dimensionality reduction.  %
+It allows users to inspect the net dependency along a set of axes, without being opinionated about
+the coordinate in other dimensions.  %
+It can also be used as a method of noise reduction.  %
+
 \subsection{Split}  % -----------------------------------------------------------------------------
 
+\python{wt.Data.split} is not a proper method of dimensionality reduction, but it is a crucial tool
+for interacting with the dimensionality of a data object.  %
+\python{split} allows users to access a portion of the dataset.  %
+The most common use-case is certainly in fitting operations.  %
+In population spectroscopies like transient absorption and transient grating it has become typical
+to take three-dimensional ``movies'' in \python{('w1', 'w2', 'd2')}, where \python{w1} is a probe,
+\python{'w2'} is a pump, and \python{'d2'} is a population delay.  %
+It can be informative to fit each \python{d2} trace to a model (often single exponential), but such
+a fit will not do well to describe the signal through zero delay and for positive \python{d2}
+values (into the coherence pathways).  %
+\python{data.split(d2=0.)} will return two data objects, one for the positive delays and one for
+negative.  %
+You can then pass the data object with only population response into your fitting routine.  %
+
 \subsection{Join}  % ------------------------------------------------------------------------------
 
-\section{Specialty visualizations}  % =============================================================
+Like \python{split}, \python{wt.data.join} is not a method of dimensionality reduction.  %
+It is also not a method of the \python{Data} class, it is a bare function.  %
+Join accepts multiple data objects and attempts to join them together.  %
+To do this, the variable and channel names must agree.  %
 
-\subsection{Specialty}  % -------------------------------------------------------------------------
+% TODO: join example
 
-\subsection{Artists API}  % -----------------------------------------------------------------------
+\section{Fitting}  % ==============================================================================
 
-The artists sub-package offers a thin wrapper on the default matplotlib object-oriented figure
-creation API.  %
-The wrapper allows WrightTools to add the following capabilities on top of matplotlib:
-\begin{ditemize}
-  \item More consistent multi-axes figure layout.
-  \item Ability to plot data objects directly.
-\end{ditemize}
-Each of these is meant to lower the barrier to plotting data.  %
-Without going into every detail of matplotlib figure generation capabilities, this section
-introduces the unique strategy that the WrightTools wrapper takes.  %
+Like the rest of WrightTools, the \python{fit} sub-package is made to play as nicely as possible
+with high-dimensional data.  %
+WrightTools uses fitting as a method of dimensionality reduction.  %
+For example, consider a three-dimensional \python{('w1', 'w2', 'd2')} ``movie'', where \python{d2}
+is a population delay that can be well approximated by a single exponential decay with offset.  %
+Rather than attempt to visualize \python{w1, w2} at some specific value of \python{d2}, it can be
+powerful to instead consider the parameters (amplitude, offset, and time constant) of an
+exponential fit at each \python{w1, w2} coordinate.  %
+On a more practical note, this kind of slice-by-slice dimensionality reduction via fitting can
+greatly simplify automated instrumental calibration (see ...)  % TODO: link to opa chapter
+WrightTools employs some simple tricks to enable these kind of fit operations, described here.  %
 
-% TODO: finish discussion
+% TODO: consider inserting figures that demonstrate this story (need to use wt2?)
 
-\section{Fitting}  % ==============================================================================
+\subsection{Function objects}  % ------------------------------------------------------------------
+
+One challenge of slice-by-slice fitting is making a good intial guess to optimize from.  %
+It is not tractable to ask the user to provide a guess for each slice, so some kind of reasonable
+automated guessing must be used.  %
+WrightTools ``function'' objects are self contained describers of a particular function.  %
+As an example, consider the \python{wt.fit.Expontial} class...
+It has parameters...
+Fit...
+Evaluate...
+Guess...
+
+Can be used directly...
+
+\subsection{Fitter}  % ----------------------------------------------------------------------------
+
+Loops through...
+Returns model and outs...
 
 \section{Construction and maintenance}  % =========================================================
 
+\subsection{Collaborative development}  % ---------------------------------------------------------
+
+\subsection{Version control}  % -------------------------------------------------------------------
+
 \subsection{Unit tests}  % ------------------------------------------------------------------------
 
 \section{Distribution and licensing} \label{pro:sec:disbribution}  % ==============================
@@ -642,4 +846,6 @@ WrightTools is MIT licensed.  %
 
 WrightTools is distributed on PyPI and conda-forge.
 
-\section{Future directions}  % ====================================================================
-\ No newline at end of file
+\section{Future directions}  % ====================================================================
+
+Single variable decomposition.
+\ No newline at end of file
diff --git a/processing/cmap_comparison.png b/processing/cmap_comparison.png
new file mode 100644
index 0000000..232cd4d
--- /dev/null
+++ b/processing/cmap_comparison.png
diff --git a/processing/cmap_comparison.py b/processing/cmap_comparison.py
new file mode 100644
index 0000000..03b9a11
--- /dev/null
+++ b/processing/cmap_comparison.py
@@ -0,0 +1,44 @@
+import os
+
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+import WrightTools as wt
+from WrightTools import datasets
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+fig, gs = wt.artists.create_figure(width='double', cols=[1, 1, 'cbar'], nrows=2)
+
+p = datasets.COLORS.v2p1_MoS2_TrEE_movie
+data = wt.data.from_COLORS(p, verbose=False)
+data.level(0, 2, -3)
+data.convert('eV')
+data.ai0.symmetric_root(0.5)
+data = data.chop('w1=wm', 'w2', at={'d2': [-600, 'fs']})[0]
+data.ai0.normalize()
+
+
+def fill_row(row, cmap):
+    # greyscale
+    ax = plt.subplot(gs[row, 0])
+    ax.pcolor(data, cmap=wt.artists.grayify_cmap(cmap))
+    # color
+    ax = plt.subplot(gs[row, 1])
+    ax.pcolor(data, cmap=cmap)
+    # cbar
+    cax = plt.subplot(gs[row, 2])
+    wt.artists.plot_colorbar(cax=cax, label='amplitude', cmap=cmap)
+
+
+cmap = wt.artists.colormaps['default']
+fill_row(0, cmap)
+cmap = wt.artists.colormaps['wright']
+fill_row(1, cmap)
+
+# label
+wt.artists.set_fig_labels(xlabel=data.w1__e__wm.label, ylabel=data.w2.label)
+
+# save
+p = os.path.join(here, 'cmap_comparison.png')
+wt.artists.savefig(p)
diff --git a/processing/cubehelix_cmap.png b/processing/cubehelix_cmap.png
new file mode 100644
index 0000000..6f8f50a
--- /dev/null
+++ b/processing/cubehelix_cmap.png
diff --git a/processing/cubehelix_cmap.py b/processing/cubehelix_cmap.py
new file mode 100644
index 0000000..bb6e641
--- /dev/null
+++ b/processing/cubehelix_cmap.py
@@ -0,0 +1,5 @@
+import WrightTools as wt
+import matplotlib.pyplot as plt
+cmap = wt.artists.colormaps['cubehelix']
+wt.artists.plot_colormap_components(cmap)
+wt.artists.savefig('cubehelix_cmap.png')
diff --git a/processing/default_cmap.png b/processing/default_cmap.png
new file mode 100644
index 0000000..c8f8cf9
--- /dev/null
+++ b/processing/default_cmap.png
diff --git a/processing/default_cmap.py b/processing/default_cmap.py
new file mode 100644
index 0000000..30d546c
--- /dev/null
+++ b/processing/default_cmap.py
@@ -0,0 +1,5 @@
+import WrightTools as wt
+import matplotlib.pyplot as plt
+cmap = wt.artists.colormaps['default']
+wt.artists.plot_colormap_components(cmap)
+wt.artists.savefig('default_cmap.png')
diff --git a/processing/fill_types.png b/processing/fill_types.png
new file mode 100644
index 0000000..2a852f6
--- /dev/null
+++ b/processing/fill_types.png
diff --git a/processing/fill_types.py b/processing/fill_types.py
new file mode 100644
index 0000000..50123e2
--- /dev/null
+++ b/processing/fill_types.py
@@ -0,0 +1,105 @@
+import os
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+import WrightTools as wt
+from WrightTools import datasets
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+cmap = wt.artists.colormaps['default']
+
+fig, gs = wt.artists.create_figure(width='double', nrows=2, cols=[1, 1, 1, 1, 'cbar'])
+
+# get data
+p = datasets.COLORS.v0p2_d1_d2_diagonal
+data = wt.data.from_COLORS(p, invert_d1=False)
+data.level(0, 0, 3)
+data.ai0.symmetric_root(0.5)
+data.ai0.normalize()
+
+
+def dot_pixel_centers(ax, xi, yi):
+    for x in xi:
+        ax.scatter([x] * len(xi), yi, edgecolor=None, s=5, color='k')
+
+
+def decorate(ax):
+    ax.set_xlim(-150, 150)
+    ax.set_ylim(-150, 150)
+
+
+# pcolor
+ax = plt.subplot(gs[0, 0])
+ax.pcolor(data, cmap=cmap)
+ax.set_title('pcolor', fontsize=20)
+decorate(ax)
+ax = plt.subplot(gs[1, 0])
+ax.pcolor(data, cmap=cmap, edgecolors='k')
+dot_pixel_centers(ax, data.d1.points, data.d2.points)
+decorate(ax)
+
+# tripcolor
+xi = data.d1.points
+yi = data.d2.points
+zi = data.channels[0][:].T
+ax = plt.subplot(gs[0, 1])
+points = [xi, yi]
+x, y = tuple(np.meshgrid(*points, indexing='ij'))
+ax.tripcolor(x.flatten(), y.flatten(), zi.T.flatten(), cmap=cmap, vmin=0, vmax=1)
+decorate(ax)
+ax.set_title('tripcolor', fontsize=20)
+ax = plt.subplot(gs[1, 1])
+ax.tripcolor(x.flatten(), y.flatten(), zi.T.flatten(), edgecolor='k', cmap=cmap, vmin=0, vmax=1)
+decorate(ax)
+dot_pixel_centers(ax, xi, yi)
+
+
+def plot_delaunay_edges(ax, xi, yi, zi):
+    x, y = tuple(np.meshgrid(xi, yi, indexing='ij'))
+    tri = matplotlib.tri.Triangulation(x.flatten(), y.flatten())
+    for i, j in tri.edges:
+        plt.plot([x.flatten()[i], x.flatten()[j]],
+                 [y.flatten()[i], y.flatten()[j]],
+                 c='k', lw=0.25)
+        ax.set_xlim(-125, 125)
+        ax.set_ylim(-125, 125)
+
+
+# contourf
+ax = plt.subplot(gs[0, 2])
+levels = np.linspace(0, 1, 200)
+ax.contourf(data)
+decorate(ax)
+ax.set_title('contourf', fontsize=20)
+ax = plt.subplot(gs[1, 2])
+ax.contourf(data)
+plot_delaunay_edges(ax, xi, yi, zi)
+dot_pixel_centers(ax, xi, yi)
+
+# contour
+ax = plt.subplot(gs[0, 3])
+levels = np.linspace(0, 1, 11)
+ax.contour(data)
+decorate(ax)
+ax.set_title('contour', fontsize=20)
+ax = plt.subplot(gs[1, 3])
+ax.contour(data)
+decorate(ax)
+plot_delaunay_edges(ax, xi, yi, zi)
+dot_pixel_centers(ax, xi, yi)
+
+# label
+ticks = [-100, 0, 100]
+wt.artists.set_fig_labels(xlabel=data.d1.label, ylabel=data.d2.label, xticks=ticks, yticks=ticks)
+
+# colorbar
+cax = plt.subplot(gs[:, -1])
+wt.artists.plot_colorbar(cax=cax, label='amplitude')
+
+# save
+p = os.path.join(here, 'fill_types.png')
+wt.artists.savefig(p)
diff --git a/processing/viridis_cmap.png b/processing/viridis_cmap.png
new file mode 100644
index 0000000..a0f0fa1
--- /dev/null
+++ b/processing/viridis_cmap.png
diff --git a/processing/viridis_cmap.py b/processing/viridis_cmap.py
new file mode 100644
index 0000000..6107ba7
--- /dev/null
+++ b/processing/viridis_cmap.py
@@ -0,0 +1,6 @@
+import WrightTools as wt
+from matplotlib import cm
+import matplotlib.pyplot as plt
+cmap = cm.get_cmap('viridis')
+wt.artists.plot_colormap_components(cmap)
+wt.artists.savefig('viridis_cmap.png')
diff --git a/processing/wright_cmap.png b/processing/wright_cmap.png
new file mode 100644
index 0000000..9258c7a
--- /dev/null
+++ b/processing/wright_cmap.png
diff --git a/processing/wright_cmap.py b/processing/wright_cmap.py
new file mode 100644
index 0000000..44f7922
--- /dev/null
+++ b/processing/wright_cmap.py
@@ -0,0 +1,5 @@
+import WrightTools as wt
+import matplotlib.pyplot as plt
+cmap = wt.artists.colormaps['wright']
+wt.artists.plot_colormap_components(cmap)
+wt.artists.savefig('wright_cmap.png')