pyspecdata 0.9.5.4

Object-Oriented, Multi-dimensional Spectroscopic Data Processing

To learn more about pyspecdata, you can head over to the documentation.

If you already know that you want to install, see Installation

Please note this package is heavily utilized by three other packages that our lab manages on github:

• ODNP processing scripts

• Classes for communicating with instruments

• SpinCore Extension

We have somewhat recently added fast compiled Fortran functions for things like 2D ILT (Tikhonov regularization with basis set compression) for NMR (Nuclear Magnetic Resonance), so please read the install instructions carefully!

pySpecData

Object-oriented Python package for processing spectral data – or in general, n-dimensional data with labeled axes (i.e. N-dimensional gridded data or “nddata”). It depends on numpy, which provides very fast manipulations of N-dimensional gridded arrays (“ndarray”). This package has some overlap with xarray, but it doesn’t attempt to mimic pandas notation, shooting instead for very compact notation for natural slicing, etc. It mainly focuses on making it easier to quickly write good code for processing spectroscopy data. In particular, it takes care of various features related to fourier transformation, error propagation, and direct products in multidimensional data with little to no interaction from the user.

If you are working in a lab developing new spectroscopic methodologies, then this package is definitely for you. If you deal with multi-dimensional data of some other form, then it’s likely for you. Features include:

Features

• Labeled axes allow one to manipulate datasets (potentially with different dimensions) without having to explicitly keep track of what the different dimensions correspond to. Code becomes more legible. Also, tiling, direct product, and gridding functions become obsolete.

• Fourier transformation with automatic manipulation of axes.

• Automatic error propagation.

• Commands like plot(data) will generate a plot with automatically labeled axes, errors, and units. All of this information is also written to HDF5 when the data is saved.

• Simplified curve fitting that takes advantage of labeled axes and Python’s symbolic algebra package (sympy).

• The code is written so that it can be integrated into a nicely formatted PDF lab notebook.

  • The same code can be run on the command line (to generate pop-up plot windows) and embedded into a LaTeX document.

  • Extension to other output formats, such as HTML or markdown, should be relatively straightforward.

• In a multimedia environment like jupyter, you don’t need a separate plot command. The code can automatically choose a plotting style appropriate to the code (eventually, the general preferences for this can just be configured at the beginning of the jupyter notebook).

More detailed web documentation will be coming soon.

NMR/ESR specific

Because it was written primarily for NMR and ESR data, it also includes:

• Routines for reading commercial raw data (e.g. Bruker, Kea) into nddata objects with all relevant information.

• The object-oriented features make it much easier to process raw phase-cycled data and to simultaneously view multiple (potentially interfering) coherence pathways.

• Contains functions for baseline correction, peak integration, etc.

• (Not yet in packaged version) A basic compiled routine for propagating density matrices that can be used to predict the response to shaped pulses.

Version Notes

Note that the current version is intended just for collaborators, etc. (Though, if you do really want to use it for interesting science, we are happy to work with you to make it work for your purposes.) A public-use version 1.0.0, to be accompanied by useful demonstrations, is planned within a year. (Note that the email currently linked to the PyPI account is infrequently checked –if you have interest in this software, please find J. Franck’s website and contact by that email.)

Roadmap.

Installation

Important note: the package ships Fortran-based extensions that are used to provide fast ILT methods. We believe this is a useful feature. Unfortunately, while the instructions below work for most cases, not everyone’s system is set up equally well for Fortran compilation. If you experience difficulties, please don’t hesitate to reach out to us at jmfranck [at] syr.edu; we would be happy for the opportunity to test distribution on new platforms! In all situations, note that this is a development library that works very well in our hands – we are happy to hear from you and work with you to try to broaden its applicability!

On Windows with Anaconda 3.X, just run conda install -y -c anaconda numpy scipy sympy pyqt pytables matplotlib h5py libpython followed by conda install -y m2w64-toolchain (the libpython and m2w64-toolchain are for building compiled extensions such as the ILT). Then follow the installation for developers below. We have a package on pip, but it currently lags behind the github repo.

On CentOS7, we’ve tested yum install python-matplotlib python-matplotlib-qt4 python-devel sympy h5py python-tables scipy (after running yum install epel-release to install the EPEL distribution). Then follow the installation for developers below.

On Debian (should also work for Ubuntu), we’ve tested sudo apt-get install -y python3 python3-matplotlib libpython3.7 python3-dev python3-sympy python3-h5py python3-tables python3-scipy python3-setuptools gfortran pip. Then follow the installation for developers below.

On MacOS, if you want to install as a developer your python distribution needs to have a working Fortran compiler, since some of the modules use Fortran. We have tested conda install -c conda-forge fortran-compiler, followed by conda install -y -c anaconda numpy scipy sympy pyqt pytables matplotlib h5py. However due to a problem with more recent versions of MacOS/xcode, you need to modify setup.py to tell it where to find the system libraries. At about line 27, you need to add something like following as a keyword arg for the Extension function: library_dirs = ["/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib"], (we recommmend just using keyword completion to find a similar directory that exists). (Feel free to contact us if you have issues with this or would like to test deployment on pip for a Mac).

More generally, these instructions are based on the fact that it’s Highly Recommended that you install the following packages using a good package-management system (conda or linux package manager), rather than relying on pip or setuptools to install them:

• numpy

• scipy

• sympy

• pyqt

• pytables (in future work, we hope to eliminate dependence on this package)

• matplotlib

• h5py

• lmfit

• The python libraries, and a Fortran compiler. Under anaconda, these are supplied by libpython and mingw, respectively.

• If you plan on building the documentation, you also want sphinx_rtd_theme sphinx-gallery

(If you don’t install these packages with your system pip will try to install them, and there is a good chance it will fail – it’s known not to work great with several of these; setuptools should error out and tell you to install the packages.)

mayavi: Mayavi can be used (and gives very nice graphics), but frequently lags behind common Python distros. Therefore, this package was written so that it doesn’t depend on mayavi. Rather, you can just import mayavi.mlab and pass it to any figure list that you initialize: figlist_var(mlab = mayavi.mlab)

Installation for developers

To install pySpecData from github, just git clone https://github.com/jmfranck/pyspecdata.git. Then switch over to the anaconda prompt and move to the directory where setup.py lives (root directory of repository), and type python setup.py develop. Make sure that this terminates with a successful message, and without any compilation errors. In particular:

• If it gives an error about permissions (will happen for a system-wide anaconda install), you need to load the anaconda prompt as admin (right click and run as administrator).

• Near the end (above EXT compiler optimization) it should tell you that you can run pyspecdata_dataconfig. You should do this, unless you’ve installed pyspecdata before on the computer you are working at.

Important notes for conda on Windows:

• Warning Before running the installation for developers, you must first check that the output of conda info on your git bash terminal matches the output of your anaconda prompt.

• For reasons that we don’t understand, the Fortran compiler can give odd errors, depending on which terminal you are using to install. This appears to be Windows’ fault, rather than conda’s (?). We highly recommend trying both the Anaconda prompt, as well as the standard dos prompt (press start: type cmd) if you experience errors related to compilation.

• If you want to build the documentation, run: conda install -y -c conda-forge sphinx_rtd_theme sphinx-gallery

Data File Management

pySpecData is designed to run the same script on different computers, where the required data files might be stored in different paths on the different computers.

The basic strategy is that you enter information on how to find your files in the _pyspecdata config file (typically this is only required once, at setup), then the find_file and search_filename functions can use this info to find your files.

Setting up your _pyspecdata configuration file

Part of the pySpecData package is the datadir module, allowing the user to run the same code on different machines - even thought the location of the raw spectral data might change. This is controlled by the ~/.pyspecdata (unix-like) or ~/_pyspecdata (windows) config file, which looks like the following.

[General]
data_directory = /home/jmfranck/exp_data
qesr conversion = 162.66
qesr diameter = 0.704
qesr q = 4700

[ExpTypes]
odnp_nmr_comp/odnp = /home/jmfranck/exp_data/NMR_comp/ODNP

[mode]
figures = standard

[RcloneRemotes]
nmr_comp/odnp = jmf_teams:General/exp_data/NMR_comp/ODNP/

The General section points to the directory with the datasets of interest whether that is the direct path to the drive with the datasets or if you prefer Rclone, this data_directory points to your local folder of datasets. (This is also a good spot to include, e.g. proportionality constants for QESR, which we have done here, and which are utilized in the proc_scripts repo.)

The ExpTypes section gives the various locations to folders containing the appropriate data sets - either pointing to the cloud storage or pointing to the local directory your rclone adds files to. So when you call odnp_nmr_comp/odnp this will point to the actual location, /home/jmfranck/exp_data/NMR_comp/ODNP

Note that it’s possible to point the different exp_type directly to shared drives, pySpecData also offers a (we think superior) method that downloads local copies of files on-demand using rclone. Obviously, you need to install rclone and add it to your path to do this (see next subsection). Rclone is an amazing tool that can be configured to talk to virtually any type of cloud storage (Google Drive accounts, OneDrive and SharePoint accounts, etc.)

Inside the RcloneRemote section, each key/variable points to a properly configured remote that was set up with rclone– e.g., jmf_teams here is a properly configured remote that shows up in response to the shell command rclone config. Note: as you require datasets from other folders you will need to make new folders locally to match for Rclone. You will receive error messages that guide you to do this, and you should follow them. For example, if you required a dataset from exp_data/francklab_esr/alex you will need to go into your local exp_data folder and add a new folder called francklab_esr/alex

Setting up Rclone

To get set up with Rclone, download Rclone and follow the documentation which should include running the command rclone config enabling you to set up the location and name of the cloud drive you wish to pull from. The documentation of rclone is pretty straightforward and can walk you through this. If you are at an academic institution, we highly recommend asking your IT department for a protocol for connecting rclone to your cloud storage of choice.

Notes on compilation of compiled extensions

We recently added a compiled extension that performs non-negative least-squares for regularization (DOSY/Relaxometry/etc.)

Under linux or mac, you should have a gcc and gfortran compiler installed, and should make sure you have libpython for this to work.

Under anaconda on windows, we have run into some trouble sometimes where it gives you an error 127. We recommend using the normal dos command prompt (cmd) to install pyspecdata, and make sure that your path is set such that where gcc yields a gcc.exe (NOT .bat) file and where python yields the anaconda python executable. (Recent versions of mingw appear to put .bat files in a preferential location in the path, and these .bat files seem to mess everything up, including compatibility with the git bash prompt.)

Further installation notes

Upon upgrading from Python 2.X to 3.X, we made some notes in conda_upgrade.md; this includes some useful (but possibly dated) instructions on how to implement different environments in anaconda, how to deal with AppLocker permissions, and Windows permissions generally, if you run into any of these issues.

Open an issue!

If you have issues with installing or using pyspecdata, don’t hesitate to open an issue on this page!