2018-03-30 13:54

19 files changed, 672 insertions, 39 deletions

diff --git a/acquisition/chapter.tex b/acquisition/chapter.tex
index 832e698..bd08edd 100644
--- a/acquisition/chapter.tex
+++ b/acquisition/chapter.tex
@@ -9,28 +9,99 @@
 

 \clearpage

 

-In the Wright Group, \gls{PyCMDS} replaces the old acquisition softwares `ps control', written by

-Kent Meyer and `Control for Lots of Research in Spectroscopy' written by Schuyler Kain.

-

-PyCMDS directly addresses the hardware during experiments.

+MR-CMDS relies on a good number of instrumental capabilities.  %

+Fundamentally, MR-CMDS is about delivering multiple pulses of light to a given sample.  %

+The frequency and relative arrival time (delay) of each pulse must be scanned in the context of a

+basic multidimensional experiment.  %

+Scanning frequency requires using motors to change crystal angles and other optics within Optical

+Parametric Amplifiers (OPAs).  %

+Scanning delay typically involves moving mirrors very small distances such that the optical path

+length of certain beam-lines changes.  %

+In addition to these ``first-order'' controls, the power of MR-CMDS is enhanced with additional

+control of pulse intensity and polarization.  % TODO: citations to motivate this 'enhancement'

+Each of these is an optomechanical device.  %

+An automated monchromator is typically used to spectrally resolve or isolate output signal.  %

+

+An acquisition in MR-CMDS typically means going to a whole series of different positions.  %

+As an example, a three-dimensional ``movie'' might be collected in the following way:

+\begin{codefragment}{python, label=aqn:lst:psuedoaqn}

+w1_points = [14300, 14400, 14500, 14600, 14700]  # wn

+w2_points = [14100, 14200, 14300, 14400, 14500, 14600, 14700]  # wn

+d2_points = [100, 75, 50, 25, 0, 50, 75, 100, 125, 150, 175, 200]  # fs

+for w2 in w2_points:

+    set_w2(w2)

+    for w1 in w1_points:

+        set_w1(w1)

+        for d2 in d2_points:

+            set_d2(d2)

+            measure_signal()

+\end{codefragment}

+In this simple example, there are 5 \python{w1} destinations, 7 \phon{w2} destinations, and 12

+\python{d2} destinations, so there are a total of $5\times7\times12=420$ pixels in the

+three-dimensional scan.  %

+The acquisition software must set the hardware to each of these points and acquire data at each of

+them.  %

+Each hardware motion and signal measurement takes roughly one second, so this acquisition would

+take roughly 7 minutes to complete.  %

+In practice, real scans are composed of $\sim$100 to $\sim$100,000 pixels, and take between 1

+minute and one day to acquire.  %

+

+Because of the highly specialized nature of these experiments, MR-CMDS instruments typically

+require custom software to address all of simultaneous, repeated motor motion that a scan

+requires.  %

+Because MR-CMDS is really a family of techniques which require different kinds of motor motion,

+this acqusition software should be flexable enough to meet the creativity of its users.  %

+Furthermore, because MR-CMDS is a bleeding edge, rapidly evolving technique the instrumental

+software must be built in an extendable way to accommodate the ever-changing hardware

+configurations.  %

+In this chapter I describe how I built such an acquisition software.  %

 

 \section{Overview}  % =============================================================================

 

-PyCMDS has, through software improvements alone, dramatically lessened scan times...

+PyCMDS is a unified software for controlling hardware and collecting data in the Wright Group.  %

+It is written almost entirely in Python, with a graphical user interface (GUI) made using Qt.  %

+It is cross-platform, running on Linux, Windows and macOS.  %

+It is open source, developed on GitHub.  % TODO: cite PyCMDS on github

+PyCMDS is used to drive both of the MR-CMDS instruments maintained by the Wright Group: the ``fs

+table'', focused on semiconductor photophysics, and the ``ps table'', focused on molecular

+systems.  %

+In the Wright Group, \gls{PyCMDS} replaces the old acquisition softwares `ps control', ritten by

+Kent Meyer and `Control for Lots of Research in Spectroscopy' written by Schuyler Kain.

 

-\begin{ditemize}

-  \item simultaneous motor motion

-	\item digital signal processing  % TODO: reference section when it exists

-	\item ideal axis positions \ref{acq:sec:ideal_axis_positions}

-\end{ditemize}

+When PyCMDS starts up, the GUI is constructed out of modules depending on which hardware and

+sensors the user has instructed the program to address.  %

+A screenshot of the PyCMDS GUI, running on the fs table, is shown in

+\autoref{aqn:fig:pycmds_screenshot}.  %

+On the left hand there is a single column displaying the current positions for all loaded

+hardware.  %

+Users may enter new destinations and hit the ``SET'' button.  %

+Positions have units, which are changeable through the pull-down menu next to the control and

+display.  %

+Each hardware knows its own limits, displayed in a tool tip when hovering over the control.  %

+Users cannot type values outside of hardware limits into the controls.  %

+

+The large right-hand section is tabbed...

 

-% TODO: screenshot

+\begin{figure}

+  \caption{

+    TODO (PyCMDS screenshot, including limits in tool tip)

+  }

+  \label{aqn:fig:pycmds_screenshot}

+\end{figure}

 

-% TODO: modularity

+To perform an acquisition, a user goes to the SOMATIC tab and enters an item into the QUEUE...

 

-% TODO: n-dimensional scans...

+During the acquisition...

+Progress bar...

+Cannot hit SET...

+QUEUE states...

 

-% TODO: calibration

+\begin{figure}

+  \caption{

+    TODO (PyCMDS screenshot while acquiring a 2D delay scan)

+  }

+  \label{aqn:fig:pycmds_screenshot_during_scan}

+\end{figure}

 

 \section{Structure}  % ============================================================================

 

@@ -124,8 +195,56 @@ Note that multithreading is very different from multiprocessing.  %
 

 \subsection{High level objects}  % ----------------------------------------------------------------

 

-PyCMDS is made to be extended and developed by and for immature programmers, so it is crucial to

-create something that is less complicated...

+Towards the goal of stability and extensability, PyCMDS makes heavy use of abstraction and

+inheritance.  %

+

+Abstraction means that complex implementation details are hidden by simple interfaces.  %

+For example, consider the simple case of setting an OPA to a particular color.  %

+For OPAs in the Wright Group, this operation requires the following:

+\begin{ditemize}

+  \item Load the OPA tuning curve, find which motors must move.

+  \item Interpolate the discrete tuning curve, and evaluate that interpolated curve at the desired

+    destination.

+  \item Send each motor towards their new destination.

+  \item Wait for the motors to arrive, check that nothing has gone wrong.

+\end{ditemize}

+This is not even to mention the complexity of spawning and sending information between the main

+thread and working threads.  %

+Through abstraction, PyCMDS is able to wrap all this complexity into the \python{OPA} class and

+it's \python{set_position} method---so doing all of the operations above is as simple as

+\python{opa.set_position(1300, 'nm')}.  %

+Importantly, abstraction does not magically get rid of the complexity.  %

+It simply \emph{hides} the complexity so that it becomes tractable to write simple interfaces to

+accomplish complex things.  %

+

+PyCMDS implements abstraction through inheritance.  %

+In object oriented programming, inheritance is when one class is based on another.  %

+The \emph{child} class acquires all of the properties of the \emph{parent} class.  %

+The child class, then, can modify or extend the properties that it needs, without needing to

+re-implement the properties that it shares with the parent.  %

+Let's consider PyCMDS hardware again.  %

+Every single unique hardware in PyCMDS lives in it's own worker thread, so the basic problem of

+information transfer through queues and Mutexes is shared between them.  %

+A \python{Hardware} class which is parent to \emph{all} hardwares can define the methods and

+attributes necessary to abstract this basic thread communication issue.  %

+Every type of delay stage addressed by PyCMDS needs to handle the basic task of translating between

+``natural'' units (femtosecond, picosecond, nanosecond) and ``native'' units (typically mm).  %

+They all need an attribute \python{zero_position}, in native units, and a method to do the

+conversion.  %

+A class common to all delay stages can abstract away all of these conversion details.  %

+In summary, an inheritance-based system for implementing delay stages might look like this:

+\begin{codefragment}{bash}

+Hardware  # implements basic thread control

+└── Delay  # implements conversion between natural and native units

+    ├── Homemade

+    ├── Thorlabs

+    └── Newport

+\end{codefragment}

+The powerful thing about this strategy is that the three driver-specific classes (...) need only

+implement minimal driver-specific code, typically start, set close...

+This means that code is more maintainable and less buggy...

+Easier to change high-level behavior without redoing low-level code...

+Only implemented once...

 

 At it's most basic PyCMDS defines the following simple data types (derived from

 \python{PyCMDS_object}):

@@ -336,6 +455,8 @@ For DAQ cards, shots and samples...
 

 Power of digital processing...

 

+Old boxcar: 300 ns window, ~10 micosecond delay. Onset of saturation ~2 V.

+

 % TODO: screenshots

 

 \subsection{Sensors as axes}  % -------------------------------------------------------------------

diff --git a/acquisition/hardware_inheritance b/acquisition/hardware_inheritance
index 0a13a1b..3504a8c 100644
--- a/acquisition/hardware_inheritance
+++ b/acquisition/hardware_inheritance
@@ -3,7 +3,6 @@ Hardware
 │   ├── Aerotech
 │   ├── LTS300
 │   ├── MFA
-│   ├── MFA
 │   └── PMC
 ├── Filter
 │   └── Homebuilt
diff --git a/active_correction/chapter.tex b/active_correction/chapter.tex
index ee20859..ef19f40 100644
--- a/active_correction/chapter.tex
+++ b/active_correction/chapter.tex
@@ -18,23 +18,36 @@
 

 \clearpage

 

-\section{Hardware}  % -----------------------------------------------------------------------------

+\section{Introduction}  % =========================================================================

 

-\subsection{Delay Stages}

+MR-CMDS is subject to a number of possible artifacts, many of them stemming from the imperfect

+nature of the frequency-tunable light sources we currently have.  %

+It is self-evidently desirable to correct these artifacts, when possible.  %

+Indeed many of these artifacts, such as OPA power, phase mismatch and absorption effects have

+regularly been corrected for.  % TODO: link to examples in applications section

+These corrections are applied after measurement, typically including information from other sources

+(such as absorption spectra, in the case of absorption effect corrections).

 

-% TODO: discuss _all 3_ delay configurations.... implications for sign conventions etc

+A more interesting class of corrections are ``active'' corrections---that is, corrections that must

+be actively applied during acquisition and cannot be applied in post processing.  %

+These corrections are more insidious, and they are often neglected because the hardware and/or

+software does not allow for them.  %

 

-\section{Signal Acquisition}

+In this chapter I explore some of these active correction strategies that are useful in the context

+of MR-CMDS.  %

+Some of these strategies have already been implemented, others are partially implemented, and

+others are still just ideas.  %

+I hope to show that active correction is a particularly useful strategy in MR-CMDS.  %

 

-Old boxcar: 300 ns window, ~10 micosecond delay. Onset of saturation ~2 V.

+\section{Spectral delay correction}  % ============================================================

 

-\subsection{Digital Signal Processing}

+\section{Poynting correction}  % ==================================================================

 

-% TODO:

+\section{Excitation power correction}  % ==========================================================

 

-\section{Artifacts and Noise}  % ------------------------------------------------------------------

+\section{Chopping}  % =============================================================================

 

-\subsection{Scatter}

+\subsection{Scatter}  % ---------------------------------------------------------------------------

 

 Scatter is a complex microscopic process whereby light traveling through a material elastically

 changes its propagation direction.  %

@@ -270,10 +283,6 @@ Now, substituting in BRAZARD formalism:
     \frac{<D_{PR}>D}{D_{PR}}}{<PR><PU>}

 \end{equation}

 

-\section{Light Generation}  % ---------------------------------------------------------------------

+\section{Fibrillation}  % =========================================================================

 

-\subsection{Automated OPA Tuning}

-

-\section{Optomechanics}  % ------------------------------------------------------------------------

-

-\subsection{Automated Neutral Density Wheels}

+\section{Conclusions}  % ==========================================================================
\ No newline at end of file
diff --git a/dissertation.tex b/dissertation.tex
index 5d6d456..30290b0 100644
--- a/dissertation.tex
+++ b/dissertation.tex
@@ -67,12 +67,12 @@ This dissertation is approved by the following members of the Final Oral Committ
 

 % chapters ----------------------------------------------------------------------------------------

 

-%\include{introduction/chapter}

+\include{introduction/chapter}

 

 \part{Background}

-%\include{spectroscopy/chapter}

-%\include{materials/chapter}

-%\include{software/chapter}

+\include{spectroscopy/chapter}

+\include{materials/chapter}

+\include{software/chapter}

 

 \part{Development}

 \include{processing/chapter}

diff --git a/opa/autotune_preamp.png b/opa/autotune_preamp.png
new file mode 100644
index 0000000..570d4fb
--- /dev/null
+++ b/opa/autotune_preamp.png
diff --git a/opa/c2.png b/opa/c2.png
new file mode 100644
index 0000000..a546d44
--- /dev/null
+++ b/opa/c2.png
diff --git a/opa/chapter.tex b/opa/chapter.tex
index b92f62a..75459b5 100644
--- a/opa/chapter.tex
+++ b/opa/chapter.tex
@@ -1,4 +1,7 @@
-\chapter{A robust, fully automated algorithm to collect high quality OPA tuning curves}

+% TODO: DONALDSON HAS AUTOMATED OPA TUNING DOWN?

+

+\chapter{A robust, fully automated algorithm to collect high quality OPA tuning

+  curves} \label{cha:opa}

 

 \begin{dquote}

   Principle design features of the new EVV 2DIR optical delivery system include the following:

@@ -13,6 +16,138 @@
 

 \clearpage

 

-This chapter pasted from publication...

+In frequency-domain Multi-Resonant Coherent Multidimensional Spectroscopy (MR-CMDS), automated

+Optical Parametric Amplifiers (OPAs) are used to actively scan excitation color axes. %

+To accomplish these experiments, exquisite OPA performance is required. %

+During the experiment, motors inside the OPA move to pre-recorded positions to optimize output at

+the desired color. %

+Parametric conversion (``mixing'') strategies are now readily avalible, extending the 800 nm pumped

+OPA tuning range into the visible, near-infrared, and mid-infrared. %

+

+OPAs are very sensitive to changes in upstream lasers and lab conditions, so OPA tuning is

+regularly required. %

+Manual OPA tuning can easily take a full day. %

+Automated OPA tuning makes OPA upkeep easier, faster and more reproducible, facilitating frequency

+domain experiments. %

+The major challenges in automated OPA tuning are:

+\begin{enumerate}

+  \item Expensive to take high resoltion data.

+  \item Need smooth curves for interpolation, especially at edges where output is low.

+  \item Optimization metrics are not necessarily separable along motor dimensions.

+\end{enumerate}

+

+\section{Introduction}  % =========================================================================

+

+\section{TOPAS-C}  % ==============================================================================

+

+% TODO: introduction to the internal design of the OPA

+

+\section{Preamp}  % ===============================================================================

+

+\begin{figure}

+  \includegraphics[width=\textwidth]{opa/preamp}

+  \caption{

+    CAPTION TODO

+  }

+  \label{opa:fig:preamp}

+\end{figure}

+

+In TOPAS-C OPAs, a small portion of input light is used to generate a signal seed in a BBO crystal

+``C1''.  %

+A motorized delay stage ``D1'' is used to temporally overlap a particular color in chirped white

+light with 800 nm pump.  %

+C1 angle is tuned to optimize phase matching.  %

+Measured seed intensity and color for all combinations of C1 and D1 position are shown in

+\autoref{fig:preamp}.  %

+

+Output color and intensity are not separable along the preamp motor axes.  %

+We therefore use a multidimensional fitting strategy to find the best preamp motor positions, as

+shown below.  %

+

+% TODO: procedure

+

+\begin{figure}

+  \includegraphics[width=\linewidth]{opa/autotune_preamp}

+  \caption{

+    CAPTION TODO

+  }

+  \label{opa:fig:autotune_preamp}

+\end{figure}

+

+A representative preamp tune procedure output image is shown in \autoref{fig:autotune_preamp}.  %

+The thick black line is the final output curve.  %

+The dark grey lines are the contours of constant color.  %

+The colorbar shows the Delaunay-interpolated intensity values for each motor position.  %

+

+Preamp tuning takes less than 20 minutes, in large part due to a NIR array detector which collects

+the full spectrum at each motor position.  %

+

+\section{Poweramp}  % =============================================================================

+

+\begin{figure}

+  \includegraphics[width=\linewidth]{opa/poweramp}

+  \caption{

+    CAPTION TODO

+  }

+  \label{opa:fig:poweramp}

+\end{figure}

+

+Once generated, the seed goes on to be amplified in a second BBO crystal ``C2'' with the rest of

+the 800 nm pump.  %

+Optimizing this amplification step is primarily a matter of setting C2 angle.  %

+A small delay correction ``D2'' is necessary to account for dispersion in the seed optics.  %

+To fully explore poweramp behavior, we need to tak a C2-D23 scan for each seed color.  %

+Measured output intensity and color in this 3D space is represented in \autoref{fig:poweramp}.  %

+Note that the motor axes are scans about the previously recorded tuning curve value.  %

+

+The best position (zero displacement along both axes) is chosen to maximize output intensity while

+keeping the output color identical to the seed color.  %

+Optimizing for zero detuining rather than simply for output intensity has led to better OPA

+performance and stability.  %

+Like in the preamp case, color and intensity are not fully separable along the poweramp motor

+dimensions (this is especially true at the edge output colors).  %

+In the poweramp, the increased dimensionaity makes it too expensive to do a full multidimensional

+tuning procedure.  %

+Instead we emply an iterative procedure as diagrammed below.  %

+

+% TODO: procedure

+

+We always end the iteration(s) with C2 so that the OPA's color calibration is as good as

+possible.  %

+Typically only one iteration is required but multiple iterations may be necessary if dramatic OPA

+realignment has occurred.  %

+In total, poweramp tuning typically takes less than 1 hour. %

+Representative procedure output images for D2 (\autoref{op:fig:d2}) and C2 (\autoref{opa:fig:c2})

+are shown.  %

+

+\begin{figure}

+  \includegraphics[width=\textwidth]{opa/d2}

+  \caption{

+    CAPTION TODO

+  }

+  \label{opa:fig:d2}

+\end{figure}

+

+\begin{figure}

+  \includegraphics[width=\textwidth]{opa/c2}}

+  \caption{

+    CAPTION TODO

+  }

+  \label{opa:fig:c2}

+\end{figure}

+

+For the D2 figure, the lower panel shows the intensity of the data taken.  %

+Note the thick grey line, which represents the chosen points before the final spline step.  %

+The top panel compares the old tuning curve (thin) with the output tuning curve (thick).  %

+For the C2 image, the bottom panel represents the color of each fit mapped onto detuning.  %

+Each separate marker color represents a different setpoint.  %

+As with D2, the C2 upper panel compares the old tuning curve (thin black) with the output tuning

+curve (colored X's).  %

+

+\section{Mixers}  % ===============================================================================

+

+\section{Generalizability}  % =====================================================================

+

+\section{Future directions}  % ====================================================================

 

-% DONALDSON HAS AUTOMATED OPA TUNING DOWN?
\ No newline at end of file
+% TODO: discuss Attune
\ No newline at end of file
diff --git a/opa/curve.png b/opa/curve.png
new file mode 100644
index 0000000..6290ce0
--- /dev/null
+++ b/opa/curve.png
diff --git a/opa/d2.png b/opa/d2.png
new file mode 100644
index 0000000..3c1cf87
--- /dev/null
+++ b/opa/d2.png
diff --git a/opa/figures.py b/opa/figures.py
new file mode 100644
index 0000000..efc8d45
--- /dev/null
+++ b/opa/figures.py
@@ -0,0 +1,369 @@
+### import ####################################################################

+

+

+import os

+import collections

+

+import numpy as np

+

+import matplotlib

+import matplotlib.pyplot as plt

+matplotlib.rcParams['font.size'] = 14

+

+import WrightTools as wt

+import WrightData as wd

+

+

+### define ####################################################################

+

+

+width = 14

+

+gd_path = wt.kit.get_path_matching('Google Drive')

+

+data_path = os.path.join(gd_path, 'Blaise', 'unpublished Thompson - Automated OPA Tuning')

+

+

+### recent examples ###########################################################

+

+

+if False:

+    # prepare figure

+    wf = 0.01

+    cols = ['cbar', 1, 1, 'cbar']

+    aspects = [[[0, 1], 0.5]]

+    fig, gs = wt.artists.create_figure(width=width/2., nrows=2, cols=cols, hspace=0.5, wspace=0.5, aspects=aspects)

+    # dOD colorbar

+    cax = plt.subplot(gs[:, 0])

+    limit = 0.05

+    ticks = np.linspace(-limit, limit, 5)

+    wt.artists.plot_colorbar(cax=cax, cmap='signed', label='dOD', ticklocation='left', ticks=ticks)

+    # example 1 (MoS2 TA)

+    ax = plt.subplot(gs[0, 1])

+    p = os.path.join(gd_path, 'MX2', 'CMDS', '2016.05.26', 'SCAN [w2, wmw1, d2] 2016.05.30 18_17_55 TA', 'SCAN [w2, wmw1, d2] 2016.05.30 18_17_55 TA.data')

+    data = wt.data.from_PyCMDS(p, verbose=False)

+    data = data.chop('w2', 'wmw1', verbose=False)[11]

+    data = data.split('wmw1', 13250, verbose=False)[1]

+    data.smooth(2)

+    data.dOD('dI', 'I')

+    data.dI.clip(-limit, limit)

+    xi = data.axes[1].points

+    yi = data.axes[0].points

+    zi = data.channels[0].values

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    ax.pcolor(X, Y, Z, vmin=-limit, vmax=limit, cmap=wt.artists.colormaps['signed'])

+    wt.artists.diagonal_line(xi, yi)

+    ax.grid()

+    plt.setp(ax.get_xticklabels(), visible=False)

+    plt.setp(ax.get_yticklabels(), visible=False)

+    ax.set_xlabel('$\\mathsf{\\bar\\nu_{probe}=\\bar\\nu_{m}}$', fontsize=18)

+    ax.set_ylabel('$\\mathsf{\\bar\\nu_{pump}}$', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(yi.min(), yi.max())

+    # example 2 (MoS2 TrEE)

+    ax = plt.subplot(gs[0, 2])

+    data = wd.get('2015.12 Czech', check_remote=False)['movie']

+    data = data.chop('w1', 'd2')[29]

+    data.transpose()

+    xi = data.axes[1].points

+    yi = data.axes[0].points

+    zi = data.channels[0].values

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    ax.pcolor(X, Y, Z, cmap=wt.artists.colormaps['default'])

+    ax.grid()

+    ax.axhline(0, c='k', lw=1)

+    ax.axvline(data.w2.points, c='k', lw=4, alpha=0.25)

+    plt.setp(ax.get_xticklabels(), visible=False)

+    plt.setp(ax.get_yticklabels(), visible=False)

+    ax.set_xlabel('$\\mathsf{\\bar\\nu_{1}=\\bar\\nu_{m}}$', fontsize=18)

+    ax.set_ylabel('$\\mathsf{\\tau_{21}}$', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(yi.min(), yi.max())

+    # example 3 (PbSe TrEE)

+    ax = plt.subplot(gs[1, 1])

+    data = wt.data.from_pickle('PbSe.p')

+    data = data.chop('w2', 'w1')[12]

+    xi = data.axes[1].points

+    yi = data.axes[0].points

+    zi = data.channels[0].values

+    zi -= zi.min()

+    zi += 0.001

+    zi /= zi.max()

+    zi **= 0.5

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    ax.pcolor(X, Y, Z, cmap=wt.artists.colormaps['default'])

+    wt.artists.diagonal_line(xi, yi)

+    ax.grid()

+    plt.setp(ax.get_xticklabels(), visible=False)

+    plt.setp(ax.get_yticklabels(), visible=False)

+    ax.set_xlabel('$\\mathsf{\\bar\\nu_{1}=\\bar\\nu_{m}}$', fontsize=18)

+    ax.set_ylabel('$\\mathsf{\\bar\\nu_{2}}$', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(yi.min(), yi.max())

+    # example 4 (TRSF)

+    ax = plt.subplot(gs[1, 2])

+    data = wd.get('2013.10 Boyle', check_remote=False)['full 2D TRSF']

+    xi = data.axes[1].points

+    yi = data.axes[0].points

+    zi = data.channels[0].values

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    ax.pcolor(X, Y, Z, cmap=wt.artists.colormaps['default'])

+    wt.artists.diagonal_line(xi, yi)

+    ax.grid()

+    plt.setp(ax.get_xticklabels(), visible=False)

+    plt.setp(ax.get_yticklabels(), visible=False)

+    ax.set_xlabel('$\\mathsf{\\bar\\nu_{1}}$', fontsize=18)

+    ax.set_ylabel('$\\mathsf{\\bar\\nu_{2}}$', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(yi.min(), yi.max())

+    # homodyne colorbar

+    cax = plt.subplot(gs[:, -1])

+    wt.artists.plot_colorbar(cax=cax, label='amplitude')

+    # finish

+    plt.savefig('frequency domain examples.png', dpi=300, transparent=True, pad_inches=1)

+    plt.close(fig)

+

+    

+### curve #####################################################################

+

+

+if False:

+    # prepare figure

+    fig, gs = wt.artists.create_figure(width=width/2., nrows=2, cols=[1],

+                                       default_aspect=0.3)

+    # get curve

+    d = os.path.join(gd_path, 'Development', 'OPA tuning', 'tuning records', 'TOPAS-C 10743', 'signal', '2016.05.28', 'OPA1 (10743) curves')

+    curves = [os.path.join(d, 'OPA1 (10743) base - 2016.05.28 12_56_14.crv'),

+              os.path.join(d, 'OPA1 (10743) mixer1 - 2015.11.09'),

+              os.path.join(d, 'OPA1 (10743) mixer2 - 2016.05.15 10_23_48'),

+              os.path.join(d, 'OPA1 (10743) mixer3 - 2013.06.01'),]

+    curve = wt.tuning.curve.from_TOPAS_crvs(curves, 'TOPAS-C', 'NON-NON-NON-Sig')

+    # preamp

+    ax = plt.subplot(gs[0, 0])

+    plt.setp(ax.get_xticklabels(), visible=False)

+    ax.grid()

+    ax.set_xticks([1200, 1400, 1600])

+    # C1

+    ax.scatter(curve.colors, curve.motors[0].positions, c='b', edgecolor='none')

+    ax.plot(curve.colors, curve.motors[0].positions, c='b', lw=2)

+    gone = [0, 7]

+    for i, tl in enumerate(ax.get_yticklabels()):

+        tl.set_color('b')

+        if i in gone:

+            tl.set_visible(False)

+    ax.set_ylabel('C1 (deg)', color='b', fontsize=18)

+    # D1

+    ax = ax.twinx()

+    ax.scatter(curve.colors, curve.motors[1].positions, c='r', edgecolor='none')

+    ax.plot(curve.colors, curve.motors[1].positions, c='r', lw=2)

+    gone = [5]

+    for i, tl in enumerate(ax.get_yticklabels()):

+        tl.set_color('r')

+        if i in gone:

+            tl.set_visible(False)

+    ax.set_ylabel('D1 (mm)', color='r', fontsize=18)

+    # poweramp

+    ax = plt.subplot(gs[1, 0])

+    ax.grid()

+    xi = np.linspace(1100, 1700, 4)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_xticks([1200, 1400, 1600])

+    ax.set_xlabel('setpoint (nm)', fontsize=18)

+    # C2

+    ax.scatter(curve.colors, curve.motors[2].positions, c='b', edgecolor='none')

+    ax.plot(curve.colors, curve.motors[2].positions, c='b', lw=2)

+    gone = [0, 9]

+    for i, tl in enumerate(ax.get_yticklabels()):

+        tl.set_color('b')

+        if i in gone:

+            tl.set_visible(False)

+    ax.set_ylabel('C2 (deg)', color='b', fontsize=18)

+    # D2

+    ax = ax.twinx()

+    ax.scatter(curve.colors, curve.motors[3].positions, c='r', edgecolor='none')

+    ax.plot(curve.colors, curve.motors[3].positions, c='r', lw=2)

+    gone = [0, 5]

+    for i, tl in enumerate(ax.get_yticklabels()):

+        tl.set_color('r')

+        if i in gone:

+            tl.set_visible(False)

+    ax.set_ylabel('D2 (deg)', color='r', fontsize=18)

+    yticks = ax.yaxis.get_major_ticks()

+    # finish

+    plt.savefig('curve.png', dpi=300, transparent=True, pad_inches=1)

+    plt.close(fig)

+

+

+### tuning range ##############################################################

+

+

+if False:

+    # prepare figure

+    fig, gs = wt.artists.create_figure(width=width, nrows=1, cols=[1],

+                                       default_aspect=0.15)

+    # populate

+    ax = plt.subplot(gs[0, 0])

+    cs = ['k', 'grey', 'orange', 'r', 'c', 'm', 'g', 'b', 'k']

+    ranges = collections.OrderedDict()

+    ranges['Idler'] = [2600, 1600]

+    ranges['Signal'] = [1600, 1150]

+    ranges['SHI'] = [800, 1200]

+    ranges['SHS'] = [800, 580]

+    ranges['SFI'] = [600, 533]

+    ranges['SFS'] = [533, 480]

+    ranges['4HI'] = [590, 400]

+    ranges['4HS'] = [400, 290]

+    height = 0

+    for name, limits in ranges.items():

+        ax.plot(limits, [height, height], lw=10, label=name, c=cs[height+1])

+        height += 1

+    labels = [''] + ranges.keys() + ['']

+    [i.set_color(c) for i, c in zip(plt.gca().get_yticklabels(), cs)]

+    ax.set_yticklabels(labels)

+    ax.set_xlim(290, 2000)

+    ax.set_ylim(-1, len(ranges))

+    ax.set_xlabel('OPA output (nm)', fontsize=18)

+    ax.grid()

+    # finish

+    plt.savefig('ranges.png', dpi=300, transparent=True, pad_inches=1)

+    plt.close(fig)

+

+

+### huge preamp ###############################################################

+

+

+if False:

+    # prepare figure

+    fig, gs = wt.artists.create_figure(width=width, nrows=1, hspace=1,

+                                       cols=[ 1, 'cbar', 0.1, 1, 'cbar'],

+                                       aspects=[[[0, 0], 1]])

+    # get data

+    p = 'TOPAS_C_full_preamp.p'

+    data = wt.data.from_pickle(p)

+    #data.transpose()

+    # clip based on intensity

+    data.amplitude.clip(zmin=0.1, zmax=5)

+    # clip based on width

+    data.width.clip(zmin=10, zmax=100)

+    # clip based on center

+    data.center.clip(zmin=1140, zmax=1620)

+    # share NaNs

+    data.share_nans()

+    data.heal('center')

+    data.heal('amplitude')

+    # intensity

+    ax = plt.subplot(gs[0, 0])

+    cax = plt.subplot(gs[0, 1])

+    xi = data.c1.points

+    yi = data.d1.points

+    zi = data.amplitude.values

+    zi /= np.nanmax(zi)

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    cmap=wt.artists.colormaps['default']

+    cmap.set_under([0.75]*3, 1)

+    mappable = ax.pcolor(X, Y, Z, vmin=0, vmax=1, cmap=cmap)

+    plt.colorbar(mappable=mappable, cax=cax, ticklocation='right')

+    cax.set_ylabel('intensity', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(1.35, 1.8)

+    ax.contour(xi, yi, zi, 5, colors='k')

+    ax.set_xlabel('C1 (deg)', fontsize=18)

+    ax.set_ylabel('D1 (mm)', fontsize=18)

+    ax.grid()

+    # color

+    ax = plt.subplot(gs[0, 3])

+    cax = plt.subplot(gs[0, 4])

+    xi = data.c1.points

+    yi = data.d1.points

+    zi = data.center.values

+    X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+    cmap=wt.artists.colormaps['rainbow']

+    cmap.set_under([0.75]*3, 1)

+    mappable = ax.pcolor(X, Y, Z, vmin=np.nanmin(Z), vmax=np.nanmax(Z), cmap=cmap)

+    plt.colorbar(mappable=mappable, cax=cax)

+    cax.set_ylabel('color (nm)', fontsize=18)

+    ax.set_xlim(xi.min(), xi.max())

+    ax.set_ylim(1.35, 1.8)   

+    ax.contour(xi, yi, zi, 25, colors='k')

+    ax.set_xlabel('C1 (deg)', fontsize=18)

+    plt.setp(ax.get_yticklabels(), visible=False)

+    ax.grid()

+    # finish

+    plt.savefig('preamp.png', dpi=300, transparent=True, pad_inches=1)

+    plt.close(fig)

+

+

+### actual preamp #############################################################

+

+

+if False:

+    p = os.path.join(gd_path, 'Development', 'OPA tuning', 'tuning records', 'TOPAS-C 10743', 'signal', '2016.05.14', 'TOPAS-C AUTOTUNE [w1, w1_Delay_1, wa] 2016.05.14 20_25_26', 'TOPAS-C AUTOTUNE [w1, w1_Delay_1, wa] 2016.05.14 20_25_26.data')

+    d = r'C:\Users\blais\Google Drive\Development\OPA tuning\tuning records\TOPAS-C 10743\signal\2016.04.25\OPA1 (10743) curves'    

+    curves = [os.path.join(d, 'OPA1 (10743) base - 2016.04.25 22_46_02.crv'),

+              os.path.join(d, 'OPA1 (10743) mixer1 - 2015.11.09'),

+              os.path.join(d, 'OPA1 (10743) mixer2 - 2016.03.22 23_31_12'),

+              os.path.join(d, 'OPA1 (10743) mixer3 - 2013.06.01'),]

+    wt.tuning.TOPAS_C.process_preamp_motortune(1, p, curves, save=True)

+

+

+### huge poweramp #############################################################

+

+

+if False:

+    # prepare figure

+    fig, gs = wt.artists.create_figure(width=width, nrows=1, hspace=1,

+                                       cols=[ 1, 'cbar', 0.1, 1, 'cbar'],

+                                       aspects=[[[0, 0], 4/3.]])

+    # get data

+    p = 'TOPAS_C_full_poweramp_moments.p'

+    data = wt.data.from_pickle(p, verbose=False)

+    setpoints = np.linspace(1160, 1600, 12)

+    # clip, share NaNs

+    data.integral.clip(zmin=0.1, zmax=500)

+    data.one.clip(zmin=1140, zmax=1620)

+    # plot method

+    def plot(subplot_spec, cax, channel_index, cmap, yticklabels=False):

+        inner_gs = matplotlib.gridspec.GridSpecFromSubplotSpec(4, 3, subplot_spec=subplot_spec, wspace=0.0, hspace=0.0)

+        # fill in

+        for i, g in enumerate(inner_gs):

+            ax = plt.subplot(g)

+            if not ax.is_last_row():

+                plt.setp(ax.get_xticklabels(), visible=False)

+            if not ax.is_first_col():

+                plt.setp(ax.get_yticklabels(), visible=False)

+            if not yticklabels:

+                plt.setp(ax.get_yticklabels(), visible=False)

+            ax.grid()

+            if i == 10:

+                ax.set_xlabel('$\mathsf{\Delta}$C2 (deg)', fontsize=18)

+            cm = wt.artists.colormaps[cmap]

+            cm.set_under([0.75]*3)

+            setpoint = setpoints[i]

+            d = data.chop('d2', 'c2', {'w1': [setpoint, 'nm']})[0]

+            xi = d.c2.points

+            yi = d.d2.points

+            zi = d.channels[channel_index].values

+            vmin = data.channels[channel_index].min()

+            vmax = data.channels[channel_index].max()

+            X, Y, Z = wt.artists.pcolor_helper(xi, yi, zi)

+            ax.pcolor(X, Y, Z, vmin=vmin, vmax=vmax, cmap=cm)

+            ax.set_xlim(xi.min(), xi.max())

+            ax.set_ylim(yi.min(), yi.max())

+            wt.artists.corner_text(int(setpoint), fontsize=18, background_alpha=0.33)

+            ax.grid()

+    # intensity

+    subplot_spec = gs[0, 0]

+    cax = plt.subplot(gs[0, 1])

+    plot(subplot_spec, cax, 0, 'default', yticklabels=True)

+    wt.artists.plot_colorbar(cax=cax, cmap='default', label='intensity')

+    # color

+    subplot_spec = gs[0, 3]

+    cax = plt.subplot(gs[0, 4])

+    plot(subplot_spec, cax, 1, 'rainbow')

+    ticks = np.linspace(1140, 1620, 9)

+    wt.artists.plot_colorbar(cax=cax, cmap='rainbow', label='color (nm)', ticks=ticks)

+    # y label

+    fig.text(0.01, 0.55, '$\mathsf{\Delta}$D2 (deg)', fontsize=18, rotation=90)

+    # finish

+    plt.savefig('poweramp.png', dpi=300, transparent=True, pad_inches=1)

+    plt.close(fig)

diff --git a/opa/frequency domain examples.png b/opa/frequency domain examples.png
new file mode 100644
index 0000000..c86bb8f
--- /dev/null
+++ b/opa/frequency domain examples.png
diff --git a/opa/introduction.png b/opa/introduction.png
new file mode 100644
index 0000000..869c073
--- /dev/null
+++ b/opa/introduction.png
diff --git a/opa/poweramp flowchart.png b/opa/poweramp flowchart.png
new file mode 100644
index 0000000..7ab1dc4
--- /dev/null
+++ b/opa/poweramp flowchart.png
diff --git a/opa/poweramp.png b/opa/poweramp.png
new file mode 100644
index 0000000..3f0fdb7
--- /dev/null
+++ b/opa/poweramp.png
diff --git a/opa/preamp flowchart.png b/opa/preamp flowchart.png
new file mode 100644
index 0000000..01b04f1
--- /dev/null
+++ b/opa/preamp flowchart.png
diff --git a/opa/preamp.png b/opa/preamp.png
new file mode 100644
index 0000000..abf3a24
--- /dev/null
+++ b/opa/preamp.png
diff --git a/opa/ranges.png b/opa/ranges.png
new file mode 100644
index 0000000..07bbbcb
--- /dev/null
+++ b/opa/ranges.png
diff --git a/opa/reproducability.png b/opa/reproducability.png
new file mode 100644
index 0000000..83ad67f
--- /dev/null
+++ b/opa/reproducability.png
diff --git a/opa/signal_and_idler_motortune.png b/opa/signal_and_idler_motortune.png
new file mode 100644
index 0000000..092eb72
--- /dev/null
+++ b/opa/signal_and_idler_motortune.png