__init__.py

from collections import defaultdict
from pathlib import Path

import numpy
from tifffile import TiffWriter

import event_model
import suitcase.utils

from ._version import get_versions

__version__ = get_versions()['version']
del get_versions


def export(gen, directory, file_prefix='{start[uid]}-', astype='uint16',
           bigtiff=False, byteorder=None, imagej=False, **kwargs):
    """
    Export a stream of documents to TIFF stack(s).

    This creates a file named:
    ``<directory>/<file_prefix>{stream_name}-{field}.tiff``
    for every Event stream and field that contains 2D 'image like' data.

    .. warning::

        This process explicitly ignores all data that is not 2D and does not
        include any metadata in the output file.

    .. note::

        This can alternatively be used to write data to generic buffers rather
        than creating files on disk. See the documentation for the
        ``directory`` parameter below.

    Parameters
    ----------
    gen : generator
        expected to yield ``(name, document)`` pairs

    directory : string, Path or Manager.
        For basic uses, this should be the path to the output directory given
        as a string or Path object. Use an empty string ``''`` to place files
        in the current working directory.

        In advanced applications, this may direct the serialized output to a
        memory buffer, network socket, or other writable buffer. It should be
        an instance of ``suitcase.utils.MemoryBufferManager`` and
        ``suitcase.utils.MultiFileManager`` or any object implementing that
        interface. See the suitcase documentation
        (http://nsls-ii.github.io/suitcase) for details.

    file_prefix : str, optional
        The first part of the filename of the generated output files. This
        string may include templates as in
        ``{start[proposal_id]}-{start[sample_name]}-``,
        which are populated from the RunStart document. The default value is
        ``{start[uid]}-`` which is guaranteed to be present and unique. A more
        descriptive value depends on the application and is therefore left to
        the user.
        Two additional template parameters ``{stream_name}`` and ``{field}``
        are supported. These will be replaced with stream name and detector
        name, respectively.

    astype : numpy dtype
        The image array is converted to this type before being passed to
        tifffile. The default is 16-bit integer (``'uint16'``) since many image
        viewers cannot open higher bit depths. This parameter may be given as a
        numpy dtype object (``numpy.uint32``) or the equivalent string
        (``'uint32'``).

    bigtiff : boolean, optional
        Passed into ``tifffile.TiffWriter``. Default False.

    byteorder : string or None, optional
        Passed into ``tifffile.TiffWriter``. Default None.

    imagej: boolean, optional
        Passed into ``tifffile.TiffWriter``. Default False.

    **kwargs : kwargs
        kwargs to be passed to ``tifffile.TiffWriter.write``.

    Returns
    -------
    artifacts : dict
        Maps 'labels' to lists of artifacts (e.g. filepaths)

    Examples
    --------

    Generate files with unique-identifier names in the current directory.

    >>> export(gen, '')

    Generate files with more readable metadata in the file names.

    >>> export(gen, '', '{start[plan_name]}-{start[motors]}-')

    Include the experiment's start time formatted as YY-MM-DD_HH-MM.

    >>> export(gen, '', '{start[time]:%Y-%m-%d_%H:%M}-')

    Place the files in a different directory, such as on a mounted USB stick.

    >>> export(gen, '/path/to/my_usb_stick')
    """
    with Serializer(directory, file_prefix,
                    astype=astype,
                    bigtiff=bigtiff,
                    byteorder=byteorder,
                    imagej=imagej,
                    **kwargs) as serializer:
        for item in gen:
            serializer(*item)

    return serializer.artifacts


class Serializer(event_model.DocumentRouter):
    """
    Serialize a stream of documents to TIFF stack(s).

    This creates a file named:
    ``<directory>/<file_prefix>{stream_name}-{field}.tiff``
    for every Event stream and field that contains 2D 'image like' data.

    .. warning::

        This process explicitly ignores all data that is not 2D and does not
        include any metadata in the output file.


    .. note::

        This can alternatively be used to write data to generic buffers rather
        than creating files on disk. See the documentation for the
        ``directory`` parameter below.

    Parameters
    ----------
    directory : string, Path or Manager.
        For basic uses, this should be the path to the output directory given
        as a string or Path object. Use an empty string ``''`` to place files
        in the current working directory.

        In advanced applications, this may direct the serialized output to a
        memory buffer, network socket, or other writable buffer. It should be
        an instance of ``suitcase.utils.MemoryBufferManager`` and
        ``suitcase.utils.MultiFileManager`` or any object implementing that
        interface. See the suitcase documentation
        (http://nsls-ii.github.io/suitcase) for details.

    file_prefix : str, optional
        The first part of the filename of the generated output files. This
        string may include templates as in
        ``{start[proposal_id]}-{start[sample_name]}-``,
        which are populated from the RunStart document. The default value is
        ``{start[uid]}-`` which is guaranteed to be present and unique. A more
        descriptive value depends on the application and is therefore left to
        the user.
        Two additional template parameters ``{stream_name}`` and ``{field}``
        are supported. These will be replaced with stream name and detector
        name, respectively.

    astype : numpy dtype
        The image array is converted to this type before being passed to
        tifffile. The default is 16-bit integer (``'uint16'``) since many image
        viewers cannot open higher bit depths. This parameter may be given as a
        numpy dtype object (``numpy.uint32``) or the equivalent string
        (``'uint32'``).

    bigtiff : boolean, optional
        Passed into ``tifffile.TiffWriter``. Default False.

    byteorder : string or None, optional
        Passed into ``tifffile.TiffWriter``. Default None.

    imagej: boolean, optional
        Passed into ``tifffile.TiffWriter``. Default False.

    **kwargs : kwargs
        kwargs to be passed to ``tifffile.TiffWriter.write``.
    """

    def __init__(self, directory, file_prefix='{start[uid]}-', astype='uint16',
                 bigtiff=False, byteorder=None, imagej=False, **kwargs):

        if isinstance(directory, (str, Path)):
            self._manager = suitcase.utils.MultiFileManager(directory)
        else:
            self._manager = directory

        # Map stream name to dict that maps field names to TiffWriter objects.
        self._tiff_writers = defaultdict(dict)

        self._file_prefix = file_prefix
        self._astype = astype  # convert numpy array dtype before tifffile
        self._init_kwargs = {'bigtiff': bigtiff, 'byteorder': byteorder,
                             'imagej': imagej}  # passed to TiffWriter()
        self._kwargs = kwargs  # passed to TiffWriter.write()
        self._start = None  # holds the start document information
        self._descriptors = {}  # maps the descriptor uids to descriptor docs.

    @property
    def artifacts(self):
        # The manager's artifacts attribute is itself a property, and we must
        # access it a new each time to be sure to get the latest content.
        return self._manager.artifacts

    def start(self, doc):
        '''Extracts `start` document information for formatting file_prefix.

        This method checks that only one `start` document is seen and formats
        `file_prefix` based on the contents of the `start` document.

        Parameters:
        -----------
        doc : dict
            RunStart document
        '''

        # raise an error if this is the second `start` document seen.
        if self._start:
            raise RuntimeError(
                "The serializer in suitcase.tiff expects documents from one "
                "run only. Two `start` documents where sent to it")
        else:
            self._start = doc  # record the start doc for later use

    def descriptor(self, doc):
        '''Use `descriptor` doc to map stream_names to descriptor uid's.

        This method uses the descriptor document information to map the
        stream_names to descriptor uid's.

        Parameters:
        -----------
        doc : dict
            EventDescriptor document
        '''
        # record the doc for later use
        self._descriptors[doc['uid']] = doc

    def event_page(self, doc):
        '''Add event page document information to a ".tiff" file.

        This method adds event_page document information to a ".tiff" file,
        creating it if nesecary.

        .. warning::

            All non 2D 'image like' data is explicitly ignored.

        .. note::

            The data in Events might be structured as an Event, an EventPage,
            or a "bulk event" (deprecated). The DocumentRouter base class takes
            care of first transforming the other representations into an
            EventPage and then routing them through here, so no further action
            is required in this class. We can assume we will always receive an
            EventPage.

        Parameters:
        -----------
        doc : dict
            EventPage document
        '''
        event_model.verify_filled(doc)
        descriptor = self._descriptors[doc['descriptor']]
        stream_name = descriptor.get('name')
        for field in doc['data']:
            for img in doc['data'][field]:
                # Check that the data is 2D or 3D; if not ignore it.
                data_key = descriptor['data_keys'][field]
                ndim = len(data_key['shape'] or [])
                if data_key['dtype'] == 'array' and 1 < ndim < 4:
                    # there is data to be written so
                    # create a file for this stream and field
                    # if one does not exist yet
                    if not self._tiff_writers.get(stream_name, {}).get(field):
                        filename = get_prefixed_filename(
                            file_prefix=self._file_prefix,
                            start_doc=self._start,
                            stream_name=stream_name,
                            field=field
                        )
                        fname = self._manager.reserve_name('stream_data', filename)
                        Path(fname).parent.mkdir(parents=True, exist_ok=True)
                        tw = TiffWriter(fname, **self._init_kwargs)
                        self._tiff_writers[stream_name][field] = tw

                    # write the data
                    img_asarray = numpy.asarray(img, dtype=self._astype)
                    if ndim == 2:
                        # handle 2D data just like 3D data
                        # by adding a 3rd dimension
                        img_asarray = numpy.expand_dims(img_asarray, axis=0)
                    for i in range(img_asarray.shape[0]):
                        img_asarray_2d = img_asarray[i, :]
                        # append the image to the file
                        tw = self._tiff_writers[stream_name][field]
                        tw.write(img_asarray_2d, contiguous=True, *self._kwargs)

    def stop(self, doc):
        self.close()

    def close(self):
        '''Close all of the files opened by this Serializer.
        '''
        # Close all the TiffWriter instances, which do some work on cleanup.
        for tw_by_stream in self._tiff_writers.values():
            for tw in tw_by_stream.values():
                tw.close()
        # Then let the manager (perhaps redundantly) close the underlying
        # files.
        self._manager.close()

    def __enter__(self):
        return self

    def __exit__(self, *exception_details):
        self.close()


def get_prefixed_filename(file_prefix, start_doc, stream_name, field):
    '''Assemble the prefixed filename.'''
    templated_file_prefix = file_prefix.format(
        start=start_doc, field=field, stream_name=stream_name)
    filename = f'{templated_file_prefix}{stream_name}-{field}.tiff'
    return filename