diff options
| author | Blaise Thompson <blaise@untzag.com> | 2018-04-16 08:45:36 -0500 |
|---|---|---|
| committer | Blaise Thompson <blaise@untzag.com> | 2018-04-16 08:45:36 -0500 |
| commit | 3671f4e2c92f28319e9c8e44071604e98d8d783f (patch) | |
| tree | eaf532281be6a18d51587d769c59b2482c5ed0e1 /processing | |
| parent | 1df434357600bcae9d11f20a3062cb915704979a (diff) | |
2018-04-16 08:45
Diffstat (limited to 'processing')
| -rw-r--r-- | processing/chapter.tex | 204 | ||||
| -rw-r--r-- | processing/outlier.png | bin | 0 -> 291978 bytes | |||
| -rw-r--r-- | processing/outlier.py | 50 | ||||
| -rw-r--r-- | processing/outliers.data | 624 |
4 files changed, 793 insertions, 85 deletions
diff --git a/processing/chapter.tex b/processing/chapter.tex index 0d89055..1d11788 100644 --- a/processing/chapter.tex +++ b/processing/chapter.tex @@ -507,8 +507,8 @@ WrightTools. % \texttt{wt.data.from\_shimamdzu} & Shimadzu brand spectrometers. \\ \hline
\texttt{wt.data.from\_Tensor27} & Tensor 27 FT-IR. \\ \hline
\end{tabular}
- \caption[CAPTION TODO]{
- CAPTION TODO
+ \caption{
+ WrightTools data import functions.
}
\label{pro:tab:from_functions}
\end{table}
@@ -586,10 +586,11 @@ 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. %
+It also includes ``specialty'' artists which are made to perform certain common plotting
+operations. %
-Currently the artists sub-package is built on-top of the wonderful matplotlib library. %
+Currently the artists sub-package is built on-top of the wonderful matplotlib library
+\cite{HunterJohnD2007a}. %
In the future, other libraries (e.g. Mayavi \cite{Mayavi}), may be incorporated. %
\subsection{Strategies for 2D visualization} % ---------------------------------------------------
@@ -598,19 +599,36 @@ Representing two-dimensional data is an important capability for WrightTools, so 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
+When doing this mapping, choosing an appropriate colormap and interpolation type is crucial. %
\subsubsection{Colormap}
+For the purposes of this discussion, there are two types of colormaps: 1. qualitative 2.
+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). %
+
+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. %
-The black line is the net brightness of each color (larger value means lighter color). %
+The black line is the net lightness 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. %
+The traditional Wright Group colormap (derived from jet) is shown first. i%
Following are two perceptual colormaps, cubehelix from \texcite{GreenDA2009a}, and viridis, the new
matplotlib default.
WrightTools uses the algorithm from \textcite{GreenDA2009a} to define a custom cubehelix colormap
with good perceptual properties and familiar Wright Group coloration. %
+The lightness is not perfectly straight as in viridis and cubehelix, but it is monotonic at
+least. %
+
+\autoref{pro:fig:cmap_comparison} compares the new (top row) and old (bottom row) colormaps in full
+color and greyscale. %
+While the old colormap shows a greater dynamic range, the new colormap is more perceptually
+uniform. %
+In my opinion, users should use logarithmic scaling of an appropriate base if more dynamic range is
+required, rather than using a non-perceptual colormap. %
\subsubsection{Interpolation type}
@@ -635,22 +653,27 @@ plots. % \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}
+ \caption[Colormap lightness.]{
+ Comparison of four different colormaps.
+ R, G, and B components are each shown.
+ Black line is human-perceived lightness.
+ }
\label{pro:fig:cmaps}
\end{figure}
\begin{figure}
\includegraphics[width=\textwidth]{"processing/cmap_comparison"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption{
+ Colormap comparison.
+ }
\label{pro:fig:cmap_comparison}
\end{figure}
\begin{figure}
\includegraphics[width=\textwidth]{"processing/fill_types"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption{
+ Interpolation types in 2D plotting.
+ }
\label{pro:fig:fill_types}
\end{figure}
@@ -684,39 +707,40 @@ These functionalities are derived from \python{wt.Data.chop}, discussed further \begin{figure}
\includegraphics[width=0.5\textwidth]{"processing/quick1D 000"}
\includepython{"processing/quick1D.py"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption[quick1D example.]{
+ Minimal example of the \python{quick1D} function.
+ }
\label{pro:fig:quick1D}
\end{figure}
\begin{figure}
\includegraphics[width=0.5\textwidth]{"processing/quick2D 000"}
\includepython{"processing/quick2D.py"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption[quick2D example.]{
+ Minimal example of the \python{quick2D} function.
+ }
\label{pro:fig:quick2D}
\end{figure}
% TODO: signed data (with and without dynamic_range=True)
-\subsection{API} % -------------------------------------------------------------------------------
+% \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. %
+% 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} % -------------------------------------------------------------------------
+% % \subsection{Gotchas} % -------------------------------------------------------------------------
% TODO: mention gotcha of apparently narrowing linewidths with wigners (how to READ colormaps)
-\clearpage
\section{Variables and channels} % ===============================================================
Data objects are made up of many component channels and variables, each array having the same
@@ -728,7 +752,7 @@ may not have even moved across some (or any) dimensions. % To avoid unnecessarily large arrays, WrightTools allows Channels and Variables to have different
sizes than the parent data. %
As an example, consider the following object.
-\begin{codefragment}{bash} % TODO: need to use bash here because of box charachters :-(
+\begin{codefragment}{bash}
>>> import WrightTools as wt; from WrightTools import datasets
>>> data = wt.data.from_COLORS(datasets.COLORS.v2p1_MoS2_TrEE_movie)
>>> data.print_tree()
@@ -763,18 +787,34 @@ propagated. % \section{Axes} \label{pro:sec: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.
+Axes are the primary interface to coordinate positions in WrightTools. %
+Axes are \emph{not} arrays, although they do behave like arrays. %
+They are merely \emph{interfaces} into the information stored in one or more variables. %
+
+Each axis has an expression, like \python{'w1'}, \python{'d1=d2'}, \python{2*w3} or
+\python{'w1+w2-wm'}. %
+These expressions describe an unambiguous mathematical operation involving one or more
+variables. %
+The axis computes these expressions on the fly when needed, giving users real flexability over how
+they would like to represent and process their results. %
Axes, variables, and channels are array-likes, so they support slicing operations. %
+The axes have the joint shape of their component variables. %
In addition, all three classes have \python{points} and \python{full} attributes that return the
squeezed and broadcasted array, respectively. %
+One of the coolest features enabled by this approach to axes is \emph{transformation}. %
+\autoref{pro:fig:transform} shows how easy it is to transform data from one coordinate system into
+another. %
+Transforming is essentially free, since axes compute coordinates on the fly. %
+
\begin{figure}
- \includegraphics[width=\textwidth]{"processing/fringes_transform"}
+ \includegraphics[width=0.8\textwidth]{"processing/fringes_transform"}
\includepython{"processing/fringes_transform.py"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption{
+ Example using transform.
+ }
+ \label{pro:fig:transform}
\end{figure}
\section{Math} % =================================================================================
@@ -783,8 +823,6 @@ Now that we know the basics of how the WrightTools \python{Data} class stores da some data manipulation. %
Let's start with some elementary algebra. %
-% TODO: mention chunkwise strategy
-
\subsection{In-place operators} \label{pro:sec:in_place} % ---------------------------------------
In Python, operators are symbols that carry out some computation. %
@@ -847,10 +885,11 @@ Variables also support in-place operators. % \subsection{Clip} % ------------------------------------------------------------------------------
Clip allows users to exclude values outside of a certain range. %
+Excluded values can be replaced with a user-specified value or with not-a-number. %
This can be particularly useful in cases like fitting. %
-See section ... for an example. % TODO: link to section
+See \autoref{pro:sec:fit} for an example. %
-It's also useful for when noise in a certain region of a spectrum obscures useful data...
+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} % --------------------------------------------------------------------
@@ -885,7 +924,7 @@ keyword arguments. % These may be slightly faster than \python{channel.log(base=10)} and
\python{channel.log(base=2)}. %
-\subsection{Level} % -----------------------------------------------------------------------------
+% \subsection{Level} % -----------------------------------------------------------------------------
% TODO: figure from wright.tools
@@ -894,6 +933,7 @@ These may be slightly faster than \python{channel.log(base=10)} and 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. %
+See \autoref{pro:fig:outlier} for an example of outlier detection. %
Currently \python{trim} only supports one statistical treatment: the z-test. %
Z-testing compares each pixel to its multidimensional neighborhood of pixels. %
@@ -902,8 +942,6 @@ neighborhood standard deviation) it is either masked, replaced with \python{np.n 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} % ----------------------------------------------------------------------------
@@ -922,13 +960,20 @@ 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. %
+\begin{figure}
+ \includegraphics[width=0.8\textwidth]{"processing/outlier"}
+ \caption{
+ Outlier detection using a z-test.
+ }
+ \label{pro:fig:outlier}
+\end{figure}
+
% TODO: figure: example of smoothed data
-\clearpage
\section{Dimensionality manipulation} % ==========================================================
WrightTools offers several strategies for reducing the dimensionality of a data object. %
-Also consider using the fit sub-package. % TODO: more info, link to section
+Also consider using the fit sub-package described in \autoref{pro:sec:fit}. %
\subsection{Chop} \label{pro:sec:chop} % ---------------------------------------------------------
@@ -941,7 +986,7 @@ This dataset can be chopped to it's component 2D \python{('wm'', 'w1')} spectra. >>> import WrightTools as wt; from WrightTools import datasets
>>> data = wt.data.from_PyCMDS(datasets.PyCMDS.wm_w2_w1_000)
data created at /tmp/lzyjg4au.wt5::/
- axes ('wm', 'w2', 'w1')
+ axes ('wm'', 'w2', 'w1')
shape (35, 11, 11)
>>> chopped = data.chop('wm', 'w1')
chopped data into 11 piece(s) in ('wm', 'w1'')
@@ -977,8 +1022,8 @@ chopped data into 1 piece(s) in ('wm', 'w1') >>> chopped.w2.points
array([1580.0])
\end{codefragment}
-Note the [0]... % TODO
-This same syntax used in artists... % TODO
+% Note the [0]... % TODO
+% This same syntax used in artists... % TODO
\subsection{Collapse} % --------------------------------------------------------------------------
@@ -1016,8 +1061,9 @@ To do this, the variable and channel names must agree. % \begin{figure}
\includegraphics[width=\textwidth]{"processing/join_example"}
- \caption[CAPTION TODO]{
- CAPTION TODO}
+ \caption{
+ Join example.
+ }
\label{pro:fig:join_example}
\end{figure}
@@ -1032,7 +1078,7 @@ Rather than attempt to visualize \python{w1, w2} at some specific value of \pyth 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
+greatly simplify automated instrumental calibration (see \autoref{cha:opa}). %
WrightTools employs some simple tricks to enable these kind of fit operations, described here. %
% TODO: consider inserting figures that demonstrate this story (need to use wt2?)
@@ -1043,22 +1089,16 @@ One challenge of slice-by-slice fitting is making a good intial guess to optimiz 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...
-
-[USERS CAN WRITE THEIR OWN FUNCTION OBJECTS]
+As an example, consider the \python{wt.fit.Expontial} class.
+It can be used directly, as shown in \autoref{pro:fig:fit_function}
\begin{figure}
\includegraphics[width=0.5\textwidth]{"processing/fit_function"}
\includepython{"processing/fit_function.py"}
- \caption[CAPTION TODO]{
- CAPTION TODO
+ \caption{
+ Fitting a Gaussian.
}
+ \label{pro:fig:fit_function}
\end{figure}
\subsection{Fitter} % ----------------------------------------------------------------------------
@@ -1117,13 +1157,11 @@ the fit parameters, in this case amplitude, tau, and offset. Accordingly, outs i than the original data object. model contains the fit evaluated at each coordinate of the original
dataset—it’s really useful for inspecting the quality of your fit procedure.
-[ALSO GOOD FOR WORKUP OF TUNING DATA: SEE SECTION ...]
-
\begin{figure}
\includegraphics[width=0.4\textwidth]{"processing/fit_amplitude"}
\includegraphics[width=0.4\textwidth]{"processing/fit_tau"}
- \caption[CAPTION TODO]{
- CAPTION TODO
+ \caption{
+ Fitting as dimensionality reduction.
}
\label{pro:fig:fitted_movie}
\end{figure}
@@ -1197,12 +1235,12 @@ returned to again and again. % Developers can collaborate together to edit the software by making incremental changes that are
easy to review. %
-WrightTools uses git \cite{git} for source control, and the package is hosted on GitHub
-\cite{GitHub, WrightToolsGitHub}. %
+WrightTools uses git for source control, and the package is hosted on GitHub
+\cite{GitHub}. %
As of 2018-04-08, WrightTools has 1,018 commits from seven developers, as shown in
\autoref{pro:tab:commits}. %
-In addition to simply hosting the git repo, GitHub gives us issue tracking and automated
-integration with zenodo. %
+In addition to simply hosting the git repository, GitHub gives us issue tracking, continuous
+integration, and Zenodo support. %
The WrightTools package has a developer controlled version as well, following the semantic
versioning convention \cite{SemanticVersioning}. %
@@ -1223,9 +1261,8 @@ These attributes can be accessed through python: \python{wt.__version__} and Daniel Kohler & 3 & 113 & 29 \\ \hline
Rachel Swedin & 1 & 5,197 & 0 \\ \hline
\end{tabular}
- \caption[CAPTION TODO]{
+ \caption[Commits to WrightTools.]{
Commits to WrightTools by individual, ordered by number of commits.
- Wright Stuff is...
Note that datasets...
}
\label{pro:tab:commits}
@@ -1260,7 +1297,8 @@ of the tests and receive a report on what failed and why. % If a future programmer unintentionally breaks \python{convert}, the above test will fail and alert
her to the unexpected side effect of her modification. %
-[PARAGRAPH ABOUT CONTINUOUS INTEGRATION]
+WrightTools uses continuous integration services to run unit tests every time a pull request is
+made on GitHub. %
\subsection{Distribution} \label{pro:sec:distribution} % ------------------------------------------
@@ -1272,8 +1310,7 @@ Luckily for us, distribution is fairly simple within the Python ecosystem. % The same tools that are used to distribute hugely popular packages like numpy are also available
for anyone else, including WrightTools. %
-The Python Package Index (PyPI), affectionately known as the cheese shop, is the official third
-party software repository for Python. %
+The Python Package Index (PyPI) is the official third party software repository for Python. %
It is community maintained, and supported by the Python Software Foundation and The Python
Packaging Group. %
As of 2018-04-08 PyPI hosts 134,758 Python packages, all for free. %
@@ -1291,10 +1328,9 @@ Since the scientific Python ecosystem has so many non-Python binary dependencies popular choice---especially on Windows where the necessary compilers are not typically
pre-installed. %
Unlike pip, conda is not tied to a single repository. %
-There is the official repository, maintained by Anaconda, the company that develops conda.
-[CITE] %
+There is the official repository, maintained by Anaconda, the company that develops conda. %
There is also the popular conda-forge repository, which is maintained by the community via
-GitHub. [CITE] %
+GitHub. %
WrightTools is on conda-forge: %
\begin{codefragment}{bash}
conda config --add channels conda-forge
@@ -1308,13 +1344,11 @@ using such a thing immediately. % To this end, it is important to have easy to use, searchable documentation with end-users in
mind. %
-wright.tools
-
-Built and hosted by readthedocs
-
-Both master and development versions...
+I have built a website for WrightTools documentation at \href{http://wright.tools/}. %
+The documentation is built and hosted by Read the Docs. \cite{ReadTheDocs}. %
+Both master and development versions are built, so users of both can get documentation that is up
+to date for them. %
-% \clearpage
% \section{Future directions} % ====================================================================
% Single variable decomposition. %
diff --git a/processing/outlier.png b/processing/outlier.png Binary files differnew file mode 100644 index 0000000..6e96ec7 --- /dev/null +++ b/processing/outlier.png diff --git a/processing/outlier.py b/processing/outlier.py new file mode 100644 index 0000000..520aa60 --- /dev/null +++ b/processing/outlier.py @@ -0,0 +1,50 @@ +### import ####################################################################
+
+
+import os
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+import WrightTools as wt
+
+
+### define ####################################################################
+
+
+directory = os.path.dirname(__file__)
+google_drive_directory = wt.kit.get_path_matching('Google Drive')
+
+### workspace #################################################################
+
+
+# get data
+p = os.path.join(google_drive_directory, 'MX2', 'CMDS', '2017-01-15', '2017-01-18', '2', '000 [wm=w1, d1]', '000.data')
+d = wt.data.from_PyCMDS(p)
+d.convert('eV')
+
+# clean up axes
+d.axes[0].name = 'w1'
+d._update()
+d.transpose()
+d.flip('d1')
+
+# turn into DT/T
+d.divide(d, 'signal_diff', 'signal_mean')
+d.signal_diff.znull = 0
+d.signal_diff.signed = True
+
+# trim
+if True:
+ # higher factors are more permissive
+ d.trim('signal_diff', w1=6, d1=0, replace='nan', factor=3)
+ d.trim('signal_diff', w1=6, d1=0, replace='nan', factor=3)
+
+# heal
+if False:
+ d.heal('signal_diff')
+
+# plot
+a = wt.artists.mpl_2D(d)
+a.plot(contours=0)
diff --git a/processing/outliers.data b/processing/outliers.data new file mode 100644 index 0000000..d052a00 --- /dev/null +++ b/processing/outliers.data @@ -0,0 +1,624 @@ +# PyCMDS version: '0.5.15.6f4971e'
+# system name: 'fs'
+# file created: '2017-01-13T15:48:57-06:00'
+# data name: 'PEDOT'
+# data info: ''
+# data origin: 'SCAN'
+# queue url: 'https://drive.google.com/open?id=0BzJTClorMBuwOVE2RmNPM1RFTlk'
+# acquisition url: 'https://drive.google.com/open?id=0BzJTClorMBuwWlJMQmY5R2ZJNDQ'
+# scan url: 'https://drive.google.com/open?id=0BzJTClorMBuwam15VUY5VENKcWc'
+# axis names: ['d1']
+# axis identities: ['d1']
+# axis units: ['ps']
+# axis interpolate: [False]
+# d1 points: [-1. -0.99 -0.98 -0.97 -0.96 -0.95 -0.94 -0.93 -0.92 -0.91 -0.9 -0.89 -0.88 -0.87 -0.86 -0.85 -0.84 -0.83 -0.82 -0.81 -0.8 -0.79 -0.78 -0.77 -0.76 -0.75 -0.74 -0.73 -0.72 -0.71 -0.7 -0.69 -0.68 -0.67 -0.66 -0.65 -0.64 -0.63 -0.62 -0.61 -0.6 -0.59 -0.58 -0.57 -0.56 -0.55 -0.54 -0.53 -0.52 -0.51 -0.5 -0.49 -0.48 -0.47 -0.46 -0.45 -0.44 -0.43 -0.42 -0.41 -0.4 -0.39 -0.38 -0.37 -0.36 -0.35 -0.34 -0.33 -0.32 -0.31 -0.3 -0.29 -0.28 -0.27 -0.26 -0.25 -0.24 -0.23 -0.22 -0.21 -0.2 -0.19 -0.18 -0.17 -0.16 -0.15 -0.14 -0.13 -0.12 -0.11 -0.1 -0.09 -0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0. 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1. 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.2 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29 1.3 1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 1.39 1.4 1.41 1.42 1.43 1.44 1.45 1.46 1.47 1.48 1.49 1.5 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59 1.6 1.61 1.62 1.63 1.64 1.65 1.66 1.67 1.68 1.69 1.7 1.71 1.72 1.73 1.74 1.75 1.76 1.77 1.78 1.79 1.8 1.81 1.82 1.83 1.84 1.85 1.86 1.87 1.88 1.89 1.9 1.91 1.92 1.93 1.94 1.95 1.96 1.97 1.98 1.99 2. 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.1 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.2 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.4 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.5 2.51 2.52 2.53 2.54 2.55 2.56 2.57 2.58 2.59 2.6 2.61 2.62 2.63 2.64 2.65 2.66 2.67 2.68 2.69 2.7 2.71 2.72 2.73 2.74 2.75 2.76 2.77 2.78 2.79 2.8 2.81 2.82 2.83 2.84 2.85 2.86 2.87 2.88 2.89 2.9 2.91 2.92 2.93 2.94 2.95 2.96 2.97 2.98 2.99 3. 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19 3.2 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 3.3 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39 3.4 3.41 3.42 3.43 3.44 3.45 3.46 3.47 3.48 3.49 3.5 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58 3.59 3.6 3.61 3.62 3.63 3.64 3.65 3.66 3.67 3.68 3.69 3.7 3.71 3.72 3.73 3.74 3.75 3.76 3.77 3.78 3.79 3.8 3.81 3.82 3.83 3.84 3.85 3.86 3.87 3.88 3.89 3.9 3.91 3.92 3.93 3.94 3.95 3.96 3.97 3.98 3.99 4. 4.01 4.02 4.03 4.04 4.05 4.06 4.07 4.08 4.09 4.1 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19 4.2 4.21 4.22 4.23 4.24 4.25 4.26 4.27 4.28 4.29 4.3 4.31 4.32 4.33 4.34 4.35 4.36 4.37 4.38 4.39 4.4 4.41 4.42 4.43 4.44 4.45 4.46 4.47 4.48 4.49 4.5 4.51 4.52 4.53 4.54 4.55 4.56 4.57 4.58 4.59 4.6 4.61 4.62 4.63 4.64 4.65 4.66 4.67 4.68 4.69 4.7 4.71 4.72 4.73 4.74 4.75 4.76 4.77 4.78 4.79 4.8 4.81 4.82 4.83 4.84 4.85 4.86 4.87 4.88 4.89 4.9 4.91 4.92 4.93 4.94 4.95 4.96 4.97 4.98 4.99 5. ]
+# constant names: []
+# constant identities: []
+# channel signed: [False False]
+# NI 6251 shots: 3000.0
+# kind: [None None 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'hardware' 'channel' 'channel']
+# tolerance: [None 0.01 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 None None]
+# label: ['' 'lab' '1' 'Crystal_1' 'Delay_1' 'Crystal_2' 'Delay_2' 'Mixer_1' 'Mixer_2' 'Mixer_3' '2' 'Crystal_1' 'Delay_1' 'Crystal_2' 'Delay_2' 'Mixer_1' 'Mixer_2' 'Mixer_3' 'm' '21' '21' '21' '22\'' '22\'' '22\'' '21' '21' '21' '0' '0' '0' '0' '0' '0' '' '']
+# units: [None 's' 'nm' None None None None None None None 'nm' None None None None None None None 'nm' 'ps' 'mm' 'mm' 'fs' 'mm' 'mm' 'fs' 'mm' 'mm' 'OD' 'deg' 'deg' 'OD' 'deg' 'deg' 'V' 'V']
+# name: ['d1_index' 'time' 'w1' 'w1_Crystal_1' 'w1_Delay_1' 'w1_Crystal_2' 'w1_Delay_2' 'w1_Mixer_1' 'w1_Mixer_2' 'w1_Mixer_3' 'w2' 'w2_Crystal_1' 'w2_Delay_1' 'w2_Crystal_2' 'w2_Delay_2' 'w2_Mixer_1' 'w2_Mixer_2' 'w2_Mixer_3' 'wm' 'd0' 'd0_position' 'd0_zero' 'd1' 'd1_position' 'd1_zero' 'd2' 'd2_position' 'd2_zero' 'nd1' 'nd1_position' 'nd1_home' 'nd2' 'nd2_position' 'nd2_home' 'signal_diff' 'signal_mean']
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+1.000000 1484344145.032000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -989.640882 19.838760 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.001977 1.528738
+2.000000 1484344148.507000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -979.619761 19.840430 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000462 1.514524
+3.000000 1484344152.259000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -969.898673 19.842050 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000471 1.562892
+4.000000 1484344155.686000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -959.757539 19.843740 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 -0.000293 1.573652
+5.000000 1484344159.082000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -949.916438 19.845380 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000991 1.578171
+6.000000 1484344162.512000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -939.595283 19.847100 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000416 1.542350
+7.000000 1484344166.003000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -929.394142 19.848800 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000420 1.497681
+8.000000 1484344169.421000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -919.853075 19.850390 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.004564 1.526523
+9.000000 1484344172.843000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -909.891960 19.852050 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 -0.001325 1.559860
+10.000000 1484344176.296000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -899.870839 19.853720 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.003068 1.560093
+11.000000 1484344179.667000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -889.789712 19.855400 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 -0.001189 1.551451
+12.000000 1484344183.087000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -879.708584 19.857080 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.001387 1.548940
+13.000000 1484344186.436000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -869.747470 19.858740 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.001740 1.510552
+14.000000 1484344189.821000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -859.846362 19.860390 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.003657 1.512683
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+17.000000 1484344199.844000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -829.602979 19.865430 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 -0.000698 1.615434
+18.000000 1484344203.148000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -819.701871 19.867080 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.004321 1.584717
+19.000000 1484344206.505000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -809.620743 19.868760 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.000242 1.539278
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+23.000000 1484344219.838000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -769.896299 19.875380 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.002795 1.538247
+24.000000 1484344223.164000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -759.815172 19.877060 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 0.006261 1.535438
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+31.000000 1484344247.102000 1176.470588 -9.408167 2.185313 7.100000 55.941667 0.000000 1.666667 0.000000 1333.333333 3.387500 2.492812 5.340833 56.145500 0.000000 0.000000 0.000000 1176.477100 0.000165 20.000025 20.000000 -689.907351 19.888710 20.003682 -0.174686 20.006590 20.006561 1.009342 74.993750 125.000000 nan -0.015625 125.000000 -0.000063 1.596045
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