.. _data-io_chapter: ============================ Reading and Writing Data ============================ Larch has several built-in functions for reading and writing scientific data. The intention is that the types and varieties of supported files will increase. In addition, because standard Python modules can be used from Larch, many types of standard data and image types can be used by importing the appropriate Python module. This chapter describes the Larch functions for data handling. .. module:: _io :synopsis: Basic Input/Output Functions .. _scipy: http://scipy.org/ .. _h5py: http://code.google.com/p/h5py/ .. _pyepics: http://pyepics.github.com/pyepics/ .. _xdi: https://github.com/XraySpectroscopy/XAS-Data-Interchange Simple ASCII Column Files ============================ A simple way to store small amounts of numerical data, and one that is widely used in the XAFS community, is to store data in plaintext (ASCII encoded) data files, with whitespace delimited numbers layed out as a table, with a fix number of columns and rows indicated by newlines. Typically a comment character such as "#" is used to signify header information. For instance:: # room temperature FeO # data from 20-BM, 2001, as part of NXS school # powder on tape, 4 layers # 2001-08-10T11:10:00 # Si(111), d_spacing: 3.13553 #------------------------ # energy xmu i0 6911.7671 -0.35992590E-01 280101.00 6916.8730 -0.39081634E-01 278863.00 6921.7030 -0.42193483E-01 278149.00 6926.8344 -0.45165576E-01 277292.00 6931.7399 -0.47365589E-01 265707.00 This file and others like it can be read with the builtin :func:`read_ascii` function. .. function:: read_ascii(filename, comentchar='#;*%', labels=None) opens and read an plaintext data file, returning a new group containing the data. :param filename: name of file to read. :type filename: string :param commentchar: string of valid comment characters :type commentchar: string :param labels: string to split for column labels :type labels: string, ``None``, or ``False`` :type ilabels: boolean, column labels 'col1', 'col2', etc if True The commentchar argument (#;% by default) sets the valid comment characters: if the first character in a line matches one of these, the line is marked as a header lines. Header lines continue until a line with '#----' (that is, any commentchar followed by 4 '-' The line immediately following that is read as column labels (space delimited) If the header is of the form:: # KEY : VAL (ie commentchar key ':' value) these key-value pairs (all as strings) will be parsed into an 'attributes' sub-group. If labels has the default value ``None``, column labels from the line following the line of '#----' (if available) will be used. If labels is ``False``, the group will have a *data* variable contain the 2-dimensional data. If ilabels is ``True``, the column labels will be 'col1', 'col2', etc, regardless of the column labels in the data file. Some examples of :func:`read_ascii`:: larch> g = read_ascii('feo_rt1.dat') larch> show(g) == Group ascii_file feo_rt1.dat: 0 methods, 8 attributes == array_labels: ['energy', 'xmu', 'i0'] attrs: data: array energy: array filename: 'feo_rt1.dat' header: ['# room temperature FeO', '# data from 20-BM, 2001, as part of NXS school', ... '#------------------------', '# energy xmu i0'] i0: array xmu: array which reads the data file and sets array names according to the column labels in the file. You can be explicit:: larch> g = read_ascii('feo_rt1.dat', labels='a b c') larch> show(g) == Group ascii_file feo_rt1.dat: 0 methods, 8 attributes == a: array array_labels: ['a', 'b', 'c'] attrs: b: array c: array data: array filename: 'feo_rt1.dat' header: ['# room temperature FeO', '# data from 20-BM, 2001, as part of NXS school', ... '#------------------------', '# energy xmu i0'] Note that the `data` attribute holds the raw 2D array of numbers read in from the file, which can also be useful to rename arrays. .. function:: write_ascii(filename, *args, commentchar='#', label=None, header=None) opens and writes arrays, scalars, and text to an ASCII file. :param commentchar: character for comment ('#') :param label: array label line (autogenerated) :param header: array of strings for header .. function:: write_group(filename, group, scalars=None, arrays=None, arrays_like=None, commentchar='#') write data from a specified group to an ASCII data file. This is pretty minimal and may work poorly for large groups of complex data. Athena Project Files ============================ The popular Athena program for XAFS Analysis uses an "Athena Project File" to store many XAFS spectra and processing parameters. Larch can read and extract the data from these project files, and can also write Athena Project files from existing groups of data. Reading Athena Project Files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: read_athena(filename, match=None, do_preedge=True, do_bkg=True, do_fft=True, use_hashkey=False) open and read an Athena Project File, returning a group of groups, each subgroup corresponding to an Athena Group from the project file. :param filename: name of Athena Project file :param match: string pattern used to limit the imported groups (see Note) :param do_preedge: bool, whether to do pre-edge subtraction :param do_bkg: bool, whether to do XAFS background subtraction :param do_fft: bool, whether to do XAFS Fast Fourier transform :param use_hashkey: bool, whether to use Athena's hash key as the group name, instead of the Athena label. :return: group of groups. Notes: 1. To limit the imported groups, use the pattern in `match`, using '*' to match 'all', '?' to match any single character, or [sequence] to match any of a sequence of letters. The match will always be insensitive to case. 2. `do_preedge`, `do_bkg`, and `do_fft` will attempt to reproduce the pre-edge, background subtraction, and FFT from Athena by using the parameters saved in the project file. 3. `use_hashkey=True` will name groups from the internal 5 character string used by Athena, instead of the group label. A simple example of reading an Athena Project file:: larch> hg_prj = read_athena('Hg.prj') larch> show(hg_prj) == Group 0x11b001e50: 0 methods, 5 attributes == HgO: HgS_black: HgS_red: _athena_header: u'# Athena project file -- Demeter version 0.9.26\n# This file created at 2018-06-24T21:55:31\n# Using Demeter 0.9.26 with perl 5.026001 and using Larch X.xx on darwin' _athena_journal: [u'Hg 15nM in 50 mM Na Cacodylate (As-containing buffer) ', u'100 mM NaClO4, pH 6.10', u'Hg 15nM in 50 mM Na Cacodylate (As-containing buffer) ', u'100 mM NaClO4, pH 6.10'] .. function:: extract_athenagroup(datagroup) extracts a group out of an Athena Project File, allowing the file to be closed. :param datagroup: group from athena project :return: group with copy of data, allowing safe closing of project file An example using this function to allow extracting 1 group from an Athena Project would be:: larch> hg_prj = read_athena('Hg.prj') larch> hgo = extract_athenagroup(hg_prj.HgO) larch> del hg_prj Creating and Writing to Athena Project Files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can create an Athena Project File with :func:`create_athena` and then add a group of XAFS data to that pct with the :meth:`add_group` method of that project file. The group is expected to have array names of `energy` and `i0`, and one of `mu`, `mutrans`, or `mufluor`. .. function:: create_athena(filename) Open a new or existing Athena Project File, returning an :class:`AthenaProject` object. That is, a new project file will be created if it does not exist, or an existing project will be opened for reading and writing. :param filename: name of Athena Project file .. class:: AthenaProject(filename) A representation of an Athena Project File .. method:: add_group(group, signal=None) add a group of XAFS data to an Athena Project :param group: group to be added. See note :param signal: string or ``None`` name of array to use as main signal if `signal` is not specified, it will be chosen as `mu`, `mutrans`, or `mufluor` (in that order). .. method:: save(use_gzip=True) save project to file :param use_gzip: bool, whether to use gzip compression for file. .. method:: read(filename=None, match=None, do_preedge=True, do_bkg=True, do_fft=True, use_hashkey=False) read from project. :param filename: name of Athena Project file :param match: string pattern used to limit the imported groups (see Note) :param do_preedge: bool, whether to do pre-edge subtraction :param do_bkg: bool, whether to do XAFS background subtraction :param do_fft: bool, whether to do XAFS Fast Fourier transform :param use_hashkey: bool, whether to use Athena's hash key as the group name, instead of the Athena label. The function :func:`read_athena` above is a wrapper around this method, and the notes there apply here as well. An important difference is that for this method the data is retained in the `groups` attribute which is a Python list of groups for each group in the Athena Project. .. method:: as_group() Return the Athena Project `groups` attribute (as read by :meth:`read`) to a larch Group of groups. As an example creating and saving an Athena Project file:: larch> feo = read_ascii('feo_rt1.dat', label='energy mu i0') larch> autobk(feo, rbkg=1.0, kweight=1) larch> fe2o3 = read_ascii('fe2o3_rt1.xmu') larch> autobk(fe2o3, rbkg=1.0, kweight=1) larch> fe_project = create_athena('FeOxides.prj') larch> fe_project.add_group(feo) larch> fe_project.add_group(fe2o3) larch> fe_project.save() Converting Athena Project Files to HDF5 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ An Athena Project File (.prj) can be easily converted to HDF5 (.h5) with the :func:`athena_to_hdf5`. .. function:: athena_to_hdf5(filename, fileout=None, overwrite=False, match=None, do_preedge=True, do_bkg=True, do_fft=True, use_hashkey=False) convert read an Athena Project File to HDF5 :param filename: name of Athena Project file :param fileout: name of the HDF5 file [None -> filename_root.h5] :param overwrite: bool, whether to overwrite existing outputfile :param match: string pattern used to limit the imported groups (see Note) :param do_preedge: bool, whether to do pre-edge subtraction :param do_bkg: bool, whether to do XAFS background subtraction :param do_fft: bool, whether to do XAFS Fast Fourier transform :param use_hashkey: bool, whether to use Athena's hash key as the group name, instead of the Athena label. :return: None Reading HDF5 Files ======================== HDF5 is an increasingly popular data format for scientific data, as it can efficiently hold very large arrays in a heirarchical format that holds "metadata" about the data, and can be explored with a variety of tools. The interface used in Larch is based on `h5py`_, which should be consulted for further documentation. .. function:: h5_group(filename) opens and maps and HDF5 file to a Larch Group, with HDF5 Groups map as Larch Groups. Note that the full set of data is not read and copied. Instead, the HDF5 file is kept open and data accessed from the file as needed. An example using :func:`h5_group` shows that one can browse through the data heirarchy of the HDF5 file, and pick out the needed data:: larch> g = h5group('test.h5') larch> show(g) == Group test.h5: 3 symbols == attrs: {u'Collection Time': ': Sat Feb 4 13:29:00 2012', u'Version': '1.0.0', u'Beamline': 'GSECARS, 13-IDC / APS', u'Title': 'Epics Scan Data'} data: h5_file: larch>show(g.data) == Group test.h5/data: 5 symbols == attrs: {u'scan_prefix': '13IDC:', u'start_time': ': Sat Feb 4 13:29:00 2012', u'correct_deadtime': 'True', u'dimension': 2, u'stop_time': ': Sat Feb 4 13:44:52 2009'} environ: full_xrf: merged_xrf: scan: larch> g.data.scan.sums larch> imshow(g.data.scan.sums[8:,:,:]) This interface is general-purpose but somewhat low-level. As HDF5 formats and schemas become standardized, better interfaces can easily be made on top of this approach. Reading NetCDF Files ============================ NetCDF4 is an older and less flexible file format than HDF5, but is efficient for storing array data and still in wide use. .. function:: netcdf_group(filename) returns a group with data from a NetCDF4 file. .. function:: netcdf_file(filename, mode='r') opens and returns a netcdf file. Reading TIFF Images ============================ TIFF is a popular image format used by many cameras and detectors. The interface used in Larch is based on code from Chrisoph Gohlke. .. function:: read_tiff(fname) reads a TIFF image from a TIFF File. This returns just the image data as an array, and does return any metadata. .. function:: tiff_object(fname) opens and returns a TIFF file. This is useful for extracting metadata and multiple series. Working with Epics Channel Access =================================== Many synchrotron facilities use the Epics control system. If the Epics Channel Access layer, which requires network access and configuration discussed elsewhere, are set correcty, then Larch can read and write data from Epics Process Variables (PVs). The interface used in Larch is based on `pyepics`_, which should be consulted for further documentation. The access is encapsulated into three functions: .. function:: caget(PV_name, as_string=False) get the value of the Process Variable. The optional ``as_string`` argument ensures the returned value is the string representation for the variable. .. function:: caput(PV_name, value, wait=False) set the value of the Process Variable. If the optional ``wait`` is ``True``, the function will not return until the put "completes". For some types of data, this may wait for some process (moving a motor, triggering a detector) to finish before returning. .. function:: PV(PV_name) create and return an Epics PV object for a Process Variable. This will have get() and put() methods, and allows you to add callback functions which will be run with new values everytime the PV value changes. Reading Scan Data from APS Beamlines =========================================== This list is minimal, but can be expanded easily to accomodate more facilities and beamlines. .. function:: read_mda(filename, maxdim=4) read a binary MDA (multi-Dimensional Array) file from the Epics SScan Record, and return a group based on the scans it contains. This is not very well tested -- use with caution! .. function:: read_gsescan(filename) read a (old-style) GSECARS Escan data file into a group. .. function:: read_stepscan(filename) read a GSECARS StepScan data file into a group. Reading XAFS Data Interchange (XDI) Files ============================================= The X-ray Data Interchange Format has been developed as part of an effort to standardize the format of XAFS data files (see `xdi`_.) .. function:: read_xdi(filename) read an XDI data file into a Larch group. Saving and Restoring Larch Groups ========================================= It is often useful to save groups of data and be able to open them again later. The :func:`save` / :func:`restore` mechanism here allows you to save the state of a number of Larch groups and use them in another session. Some precautions should be kept in mind, as not all Larch data is easily transferrable. Most importantly, Python functions cannot be saved to any sort of data that can be recovered in a meaningful way. This is actually not as big of a problem as you might expect: you want to save **data**, and the functions will be present in the later session. All the built-in Larch groups and data structures can be saved and restored. .. function:: save(filename, list_of_groups) save a set of Larch groups and data into an HDF5 file. .. function:: restore(filename, group=None) recover groups from a Larch 'save' file. If ``group`` is None, the groups in the save file will be returned (in the order in which they were saved). If ``group`` is an existing Larch group, the groups in the save file will be put inside that group, and will not be returned.