.. _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: <Group header attributes from feo_rt1.dat>
      data: array<shape=(3, 412), type=dtype('float64')>
      energy: array<shape=(412,), type=dtype('float64')>
      filename: 'feo_rt1.dat'
      header: ['# room temperature FeO', '# data from 20-BM, 2001, as part of NXS school', ... '#------------------------', '#   energy        xmu               i0']
      i0: array<shape=(412,), type=dtype('float64')>
      xmu: array<shape=(412,), type=dtype('float64')>


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<shape=(412,), type=dtype('float64')>
      array_labels: ['a', 'b', 'c']
      attrs: <Group header attributes from feo_rt1.dat>
      b: array<shape=(412,), type=dtype('float64')>
      c: array<shape=(412,), type=dtype('float64')>
      data: array<shape=(3, 412), type=dtype('float64')>
      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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. warning::

  Larch can read Athena Project Files from Athena version 0.9.21 or later.
  If you have a project file older than that, you should be able to open it
  with Athena and re-save it into a newer version.

.. 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: <Group 0x1c2e6f48d0>
      HgS_black: <Group 0x1c2e6f49d0>
      HgS_red: <Group 0x1c2e6f4ad0>
      _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 project 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

.. module:: _io.AthenaProject

.. 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()


Using HDF5 Files
========================

.. module:: _io

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: <Group test.h5/data>
      h5_file: <HDF5 file "test.h5" (mode r)>
    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: <Group test.h5/data/environ>
      full_xrf: <Group test.h5/data/full_xrf>
      merged_xrf: <Group test.h5/data/merged_xrf>
      scan: <Group test.h5/data/scan>


    larch> g.data.scan.sums
    <HDF5 dataset "det": shape (15, 26, 26), type "<f8">

    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.
