From 8c9fc50d417809d7c5ad1b512f55c83307a83364 Mon Sep 17 00:00:00 2001 From: Blaise Thompson Date: Tue, 27 Mar 2018 00:25:35 -0500 Subject: 2018-03-27 00:25 --- acquisition/chapter.tex | 156 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 155 insertions(+), 1 deletion(-) (limited to 'acquisition/chapter.tex') diff --git a/acquisition/chapter.tex b/acquisition/chapter.tex index 8e75cc1..1525b9e 100644 --- a/acquisition/chapter.tex +++ b/acquisition/chapter.tex @@ -26,8 +26,156 @@ PyCMDS has, through software improvements alone, dramatically lessened scan time \item ideal axis positions \ref{acq:sec:ideal_axis_positions} \end{ditemize} +% TODO: screenshot + +% TODO: modularity + +% TODO: n-dimensional scans... + +% TODO: calibration + +\section{Structure} % ============================================================================ + +I think of PyCMDS as a central program with three kinds of modular ``plugins'' that can be extended +indefinitely: +\begin{ditemize} + \item Hardware: things that can be set to a position (\autoref{aqn:sec:hardware}). + \item Sensors: things that can be used to measure a signal (\autoref{aqn:sec:sensors}). + \item Acquisition modules: things that can be used to define and carry out an acquisition, and + associated post-processing (\autoref{aqn:sec:somatic}). +\end{ditemize} +The first design rule for PyCMDS is that these three things should be easy for the average +(motivated) user to add by herself. % +Modularity and extensibility is important for all software projects, but it is of paramount +importance for acquisition software simply because the diversity of hardware and experimental +configurations is so great. % +It is conceivable to imagine 90\% overlap between the data processing and simulation needs of one +spectroscopist to the next, but there is almost no overlap between hardware configurations even in +the two primary instruments maintained by the Wright Group. % +Besides the extendable modular pieces, the rest of PyCMDS is a mostly-static code-base that accepts +modules and does the necessary things to handle display of information from, and communication +between them. % + +For the kinds of acquisitions that the Wright Group has done the acquisition software spends the +vast majority of its run-time waiting---waiting for user input through mouse clicks or keyboard +presses, waiting for hardware to finish moving or for sensors to finish reading and return +signals. % +Despite all of this downtime, it is crucial that the software respond very quickly when +instructions or signals are recieved. % +A multi-threaded implementation is necessary to achieve this. % +The main thread handles the graphical user interface (GUI) and other top level things. % +Everything else happens in child threads. % +Each hardware instance (e.g. a delay stage) lives in its own thread, as does each sensor. % +Since only one scan happens at a time, all acquisition modules share a single thread that handles +the orchestration of hardware motion, sensor operation, and data processing that the chosen +acquisition requires. % + +Threads are powerful because they allow for ``semi-synchronous'' operation. % +Imagine PyCMDS is in the middle of a 2D delay-delay scan, and the scan thread has just told each of +the two delay stages to head to their positions. % +PyCMDS must sit in a tight loop to keep track of the position as closely as possible during motor +motion. % +In a single-threaded configuration, this tight loop would only run for one delay at a time, such +that PyCMDS would have to finish shepherding one delay stage before turning its attention to the +second. % +In a multi-threaded configuration, each thread will run simultaniously, switching off CPU cycles at +a low level far faster than human comprehension. % +This switching is handled in an OS and hardware specific way---luckily it is all abstracted through +platform-agnostic Qt threads. % + +Threads are dangerous because it is hard to pass information between them. % +Thus, mutexes... signals and slots... + +Without getting into details, let's investigate the key ``signals and slots'' that hardware and +sensors have. % +% TODO: elaborate + +The central loop of scans in PyCMDS. % +\begin{codefragment}{python, label=aqn:lst:loop_simple} +for coordinates in list_of_coordinates: + for hardware, coordinate in zip(hardwares, coordinates): + hardware.set(coordinate) + for hardware in hardwares: + hardware.wait_until_still() + for sensor in sensors: + sensor.read() + for sensor in sensors: + sensor.wait_until_done() +\end{codefragment} + +\subsection{Conditional validity} % -------------------------------------------------------------- + +The central conceit of the PyCMDS modular hardware abstraction is that experiments can be boiled +down to a set of orthogonal axes that can be set separately from other hardware and sensor +status. % +This requirement is loosened by the autonomic and expression systems, such that any experiment +could \emph{probably} be forced into PyCMDS, but still the conceit stands---PyCMDS is probably +\emph{not} the correct framework if your experiment cannot be reduced in this way. % +From this we can see that it is useful to talk about the conditional validity of the modular +hardware abstraction. % + +The important axis is hardware complexity vs measurement complexity. + +For hardware-complex problems, the challenge is coordination. % +MR-CMDS is the perfect example of a hardware-complex problem. % +MR-CMDS is composed of a collection (typically 5 to 10 members) of relatively simple hardware +components. % +The challenge is that experiments involve complex ``dances'', including expressions, of the +component hardwares. % + +For measurement-complex problems, the challenge is, well, the measurement. % +These are experiments where every little piece of the instrument is tied together into a complex +network of inseparable parts. % +These are often time-domain or ``single shot'' measurements. % +Consider work of (GRAPES INVENTOR). % +Such instruments are typically much faster at data acquisition and more reliable. % +This comes at a price of flexibility: often such instruments cannot be modified or enhanced without +touching everything. % + +From an acquisition software perspective, measurement-complex problems are not amenable to general +purpose modular software design. % +The instrument is so custom that it certainly requires entirely custom software. % + +Measurements can be neither hardware-complex nor software-complex (simple) or both (expensive). % +Conceptually, can imagine a 4 quadrant system. % + +Thus PyCMDS can be proud to try and generalize the hardware-complex part of acquisition software +because indeed that is all that can be generalized. % + +% TODO: 4 quadrants of complexity figure + +\section{Hardware} \label{aqn:sec:hardware} % ==================================================== + +\section{Sensors (devices)} \label{aqn:sec:sensors} % ============================================ + +\subsection{Sensors as axes} % ------------------------------------------------------------------- + +\section{Autonomic} % ============================================================================ + +% TODO: concept of additive offsets + +\section{Somatic} \label{aqn:sec:somatic} % ======================================================= + +\subsection{Acquisition modules} % --------------------------------------------------------------- + +% TODO: the aqn file + +% TODO: list of modules, descriptions thereof + +\subsection{Queue manager} % --------------------------------------------------------------------- + +\subsection{The central loop of PyCMDS} % -------------------------------------------------------- + +\subsection{The data file} % --------------------------------------------------------------------- + +\subsection{Automatic processing} % -------------------------------------------------------------- + \section{Future directions} % ==================================================================== +\subsection{Spectral delay correction module} % -------------------------------------------------- + +\subsection{``Headless'' hardware, sensors} % ---------------------------------------------------- + \subsection{Ideal Axis Positions} \label{acq:sec:ideal_axis_positions} % ------------------------- Frequency domain multidimensional spectroscopy is a time-intensive process. % @@ -138,4 +286,10 @@ S_n &=& (1-c)\left(\frac{n}{N}\right)^{\frac{\tau_{\mathrm{step}}}{\tau_{\mathrm \subsubsection{Lorentzian} -\subsubsection{Bimolecular} \ No newline at end of file +\subsubsection{Bimolecular} + +\subsection{Simultanious acquisitions} % --------------------------------------------------------- + +\subsection{Enhanced modularity} % --------------------------------------------------------------- + +\subsection{wt5 savefile} % ---------------------------------------------------------------------- \ No newline at 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