text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # tifffile.py
  
  # Copyright (c) 2008-2016, Christoph Gohlke
  # Copyright (c) 2008-2016, The Regents of the University of California
  # Produced at the Laboratory for Fluorescence Dynamics
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  #   from this software without specific prior written permission.
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  """Read image and meta data from (bio)TIFF files. Save numpy arrays as TIFF.
  
  Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH,
  SGI, ImageJ, MicroManager, FluoView, SEQ and GEL files.
  Only a subset of the TIFF specification is supported, mainly uncompressed
  and losslessly compressed 2**(0 to 6) bit integer, 16, 32 and 64-bit float,
  grayscale and RGB(A) images, which are commonly used in bio-scientific imaging.
  Specifically, reading JPEG and CCITT compressed image data, chroma subsampling,
  or EXIF, IPTC, GPS, and XMP metadata is not implemented. Only primary info
  records are read for STK, FluoView, MicroManager, and NIH Image formats.
  
  TIFF, the Tagged Image File Format aka Thousands of Incompatible File Formats,
  is under the control of Adobe Systems. BigTIFF allows for files greater than
  4 GB. STK, LSM, FluoView, SGI, SEQ, GEL, and OME-TIFF, are custom extensions
  defined by Molecular Devices (Universal Imaging Corporation), Carl Zeiss
  MicroImaging, Olympus, Silicon Graphics International, Media Cybernetics,
  Molecular Dynamics, and the Open Microscopy Environment consortium
  respectively.
  
  For command line usage run `python tifffile.py --help`
  
  :Author:
    `Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
  
  :Organization:
    Laboratory for Fluorescence Dynamics, University of California, Irvine
  
  :Version: 2016.06.21
  
  Requirements
  ------------
  * `CPython 2.7 or 3.5 <http://www.python.org>`_ (64 bit recommended)
  * `Numpy 1.10 <http://www.numpy.org>`_
  * `Matplotlib 1.5 <http://www.matplotlib.org>`_ (optional for plotting)
  * `Tifffile.c 2016.04.13 <http://www.lfd.uci.edu/~gohlke/>`_
    (recommended for faster decoding of PackBits and LZW encoded strings)
  
  Revisions
  ---------
  2016.06.21
      Do not always memmap contiguous data in page series.
  2016.05.13
      Add option to specify resolution unit.
      Write grayscale images with extra samples when planarconfig is specified.
      Do not write RGB color images with 2 samples.
      Reorder TiffWriter.save keyword arguments (backwards incompatible).
  2016.04.18
      Pass 1932 tests.
      TiffWriter, imread, and imsave accept open binary file streams.
  2016.04.13
      Correctly handle reversed fill order in 2 and 4 bps images (bug fix).
      Implement reverse_bitorder in C.
  2016.03.18
      Fixed saving additional ImageJ metadata.
  2016.02.22
      Pass 1920 tests.
      Write 8 bytes double tag values using offset if necessary (bug fix).
      Add option to disable writing second image description tag.
      Detect tags with incorrect counts.
      Disable color mapping for LSM.
  2015.11.13
      Read LSM 6 mosaics.
      Add option to specify directory of memory-mapped files.
      Add command line options to specify vmin and vmax values for colormapping.
  2015.10.06
      New helper function to apply colormaps.
      Renamed is_palette attributes to is_indexed (backwards incompatible).
      Color-mapped samples are now contiguous (backwards incompatible).
      Do not color-map ImageJ hyperstacks (backwards incompatible).
      Towards supporting Leica SCN.
  2015.09.25
      Read images with reversed bit order (fill_order is lsb2msb).
  2015.09.21
      Read RGB OME-TIFF.
      Warn about malformed OME-XML.
  2015.09.16
      Detect some corrupted ImageJ metadata.
      Better axes labels for 'shaped' files.
      Do not create TiffTags for default values.
      Chroma subsampling is not supported.
      Memory-map data in TiffPageSeries if possible (optional).
  2015.08.17
      Pass 1906 tests.
      Write ImageJ hyperstacks (optional).
      Read and write LZMA compressed data.
      Specify datetime when saving (optional).
      Save tiled and color-mapped images (optional).
      Ignore void byte_counts and offsets if possible.
      Ignore bogus image_depth tag created by ISS Vista software.
      Decode floating point horizontal differencing (not tiled).
      Save image data contiguously if possible.
      Only read first IFD from ImageJ files if possible.
      Read ImageJ 'raw' format (files larger than 4 GB).
      TiffPageSeries class for pages with compatible shape and data type.
      Try to read incomplete tiles.
      Open file dialog if no filename is passed on command line.
      Ignore errors when decoding OME-XML.
      Rename decoder functions (backwards incompatible)
  2014.08.24
      TiffWriter class for incremental writing images.
      Simplified examples.
  2014.08.19
      Add memmap function to FileHandle.
      Add function to determine if image data in TiffPage is memory-mappable.
      Do not close files if multifile_close parameter is False.
  2014.08.10
      Pass 1730 tests.
      Return all extrasamples by default (backwards incompatible).
      Read data from series of pages into memory-mapped array (optional).
      Squeeze OME dimensions (backwards incompatible).
      Workaround missing EOI code in strips.
      Support image and tile depth tags (SGI extension).
      Better handling of STK/UIC tags (backwards incompatible).
      Disable color mapping for STK.
      Julian to datetime converter.
      TIFF ASCII type may be NULL separated.
      Unwrap strip offsets for LSM files greater than 4 GB.
      Correct strip byte counts in compressed LSM files.
      Skip missing files in OME series.
      Read embedded TIFF files.
  2014.02.05
      Save rational numbers as type 5 (bug fix).
  2013.12.20
      Keep other files in OME multi-file series closed.
      FileHandle class to abstract binary file handle.
      Disable color mapping for bad OME-TIFF produced by bio-formats.
      Read bad OME-XML produced by ImageJ when cropping.
  2013.11.03
      Allow zlib compress data in imsave function (optional).
      Memory-map contiguous image data (optional).
  2013.10.28
      Read MicroManager metadata and little endian ImageJ tag.
      Save extra tags in imsave function.
      Save tags in ascending order by code (bug fix).
  2012.10.18
      Accept file like objects (read from OIB files).
  2012.08.21
      Rename TIFFfile to TiffFile and TIFFpage to TiffPage.
      TiffSequence class for reading sequence of TIFF files.
      Read UltraQuant tags.
      Allow float numbers as resolution in imsave function.
  2012.08.03
      Read MD GEL tags and NIH Image header.
  2012.07.25
      Read ImageJ tags.
      ...
  
  Notes
  -----
  The API is not stable yet and might change between revisions.
  
  Tested on little-endian platforms only.
  
  Other Python packages and modules for reading bio-scientific TIFF files:
  
  *  `Imread <https://github.com/luispedro/imread>`_
  *  `PyLibTiff <https://github.com/pearu/pylibtiff>`_
  *  `SimpleITK <http://www.simpleitk.org>`_
  *  `PyLSM <https://launchpad.net/pylsm>`_
  *  `PyMca.TiffIO.py <https://github.com/vasole/pymca>`_ (same as fabio.TiffIO)
  *  `BioImageXD.Readers <http://www.bioimagexd.net/>`_
  *  `Cellcognition.io <http://cellcognition.org/>`_
  *  `CellProfiler.bioformats
     <https://github.com/CellProfiler/python-bioformats>`_
  
  Acknowledgements
  ----------------
  *   Egor Zindy, University of Manchester, for cz_lsm_scan_info specifics.
  *   Wim Lewis for a bug fix and some read_cz_lsm functions.
  *   Hadrien Mary for help on reading MicroManager files.
  *   Christian Kliche for help writing tiled and color-mapped files.
  
  References
  ----------
  (1) TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated.
      http://partners.adobe.com/public/developer/tiff/
  (2) TIFF File Format FAQ. http://www.awaresystems.be/imaging/tiff/faq.html
  (3) MetaMorph Stack (STK) Image File Format.
      http://support.meta.moleculardevices.com/docs/t10243.pdf
  (4) Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010).
      Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011
  (5) File Format Description - LSM 5xx Release 2.0.
      http://ibb.gsf.de/homepage/karsten.rodenacker/IDL/Lsmfile.doc
  (6) The OME-TIFF format.
      http://www.openmicroscopy.org/site/support/file-formats/ome-tiff
  (7) UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
      http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf
  (8) Micro-Manager File Formats.
      http://www.micro-manager.org/wiki/Micro-Manager_File_Formats
  (9) Tags for TIFF and Related Specifications. Digital Preservation.
      http://www.digitalpreservation.gov/formats/content/tiff_tags.shtml
  
  Examples
  --------
  >>> data = numpy.random.rand(5, 301, 219)
  >>> imsave('temp.tif', data)
  
  >>> image = imread('temp.tif')
  >>> numpy.testing.assert_array_equal(image, data)
  
  >>> with TiffFile('temp.tif') as tif:
  ...     images = tif.asarray()
  ...     for page in tif:
  ...         for tag in page.tags.values():
  ...             t = tag.name, tag.value
  ...         image = page.asarray()
  
  """
  
  from __future__ import division, print_function
  
  import sys
  import os
  import re
  import glob
  import math
  import zlib
  import time
  import json
  import struct
  import warnings
  import tempfile
  import datetime
  import collections
  from fractions import Fraction
  from xml.etree import cElementTree as etree
  
  import numpy
  import time
  try:
      import lzma
  except ImportError:
      try:
          import backports.lzma as lzma
      except ImportError:
          lzma = None
  
  try:
      if __package__:
          from . import _tifffile
      else:
          import _tifffile
  except ImportError:
      warnings.warn(
          "ImportError: No module named '_tifffile'. "
          "Loading of some compressed images will be very slow. "
          "Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/")
  
  
  __version__ = '2016.06.21'
  __docformat__ = 'restructuredtext en'
  __all__ = (
      'imsave', 'imread', 'imshow', 'TiffFile', 'TiffWriter', 'TiffSequence',
      # utility functions used in oiffile and czifile
      'FileHandle', 'lazyattr', 'natural_sorted', 'decode_lzw', 'stripnull')
  
  
  def imsave(file, data, **kwargs):
      """Write image data to TIFF file.
  
      Refer to the TiffWriter class and member functions for documentation.
  
      Parameters
      ----------
      file : str or binary stream
          File name or writable binary stream, such as a open file or BytesIO.
      data : array_like
          Input image. The last dimensions are assumed to be image depth,
          height, width, and samples.
      kwargs : dict
          Parameters 'byteorder', 'bigtiff', 'software', and 'imagej', are passed
          to the TiffWriter class.
          Parameters 'photometric', 'planarconfig', 'resolution', 'compress',
          'colormap', 'tile', 'description', 'datetime', 'metadata', 'contiguous'
          and 'extratags' are passed to the TiffWriter.save function.
  
      Examples
      --------
      >>> data = numpy.random.rand(2, 5, 3, 301, 219)
      >>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
  
      """
      tifargs = parse_kwargs(kwargs, 'bigtiff', 'byteorder', 'software',
                             'imagej')
  
      if 'bigtiff' not in tifargs and 'imagej' not in tifargs and (
              data.size*data.dtype.itemsize > 2000*2**20):
          tifargs['bigtiff'] = True
  
      with TiffWriter(file, **tifargs) as tif:
          tif.save(data, **kwargs)
  
  
  class TiffWriter(object):
      """Write image data to TIFF file.
  
      TiffWriter instances must be closed using the 'close' method, which is
      automatically called when using the 'with' context manager.
  
      Examples
      --------
      >>> data = numpy.random.rand(2, 5, 3, 301, 219)
      >>> with TiffWriter('temp.tif', bigtiff=True) as tif:
      ...     for i in range(data.shape[0]):
      ...         tif.save(data[i], compress=6)
  
      """
      TYPES = {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6,
               'h': 8, 'i': 9, 'f': 11, 'd': 12, 'Q': 16, 'q': 17}
      TAGS = {
          'new_subfile_type': 254, 'subfile_type': 255,
          'image_width': 256, 'image_length': 257, 'bits_per_sample': 258,
          'compression': 259, 'photometric': 262, 'document_name': 269,
          'image_description': 270, 'strip_offsets': 273, 'orientation': 274,
          'samples_per_pixel': 277, 'rows_per_strip': 278,
          'strip_byte_counts': 279, 'x_resolution': 282, 'y_resolution': 283,
          'planar_configuration': 284, 'page_name': 285, 'resolution_unit': 296,
          'software': 305, 'datetime': 306, 'predictor': 317, 'color_map': 320,
          'tile_width': 322, 'tile_length': 323, 'tile_offsets': 324,
          'tile_byte_counts': 325, 'extra_samples': 338, 'sample_format': 339,
          'smin_sample_value': 340, 'smax_sample_value': 341,
          'image_depth': 32997, 'tile_depth': 32998}
  
      def __init__(self, file, bigtiff=False, byteorder=None,
                   software='tifffile.py', imagej=False):
          """Open a TIFF file for writing.
  
          Use bigtiff=True when creating files larger than 2 GB.
  
          Parameters
          ----------
          file : str, binary stream, or FileHandle
              File name or writable binary stream, such as a open file
              or BytesIO.
          bigtiff : bool
              If True, the BigTIFF format is used.
          byteorder : {'<', '>'}
              The endianness of the data in the file.
              By default this is the system's native byte order.
          software : str
              Name of the software used to create the file.
              Saved with the first page in the file only.
          imagej : bool
              If True, write an ImageJ hyperstack compatible file.
              This format can handle data types uint8, uint16, or float32 and
              data shapes up to 6 dimensions in TZCYXS order.
              RGB images (S=3 or S=4) must be uint8.
              ImageJ's default byte order is big endian but this implementation
              uses the system's native byte order by default.
              ImageJ does not support BigTIFF format or LZMA compression.
              The ImageJ file format is undocumented.
  
          """
          if byteorder not in (None, '<', '>'):
              raise ValueError("invalid byteorder %s" % byteorder)
          if byteorder is None:
              byteorder = '<' if sys.byteorder == 'little' else '>'
          if imagej and bigtiff:
              warnings.warn("writing incompatible bigtiff ImageJ")
  
          self._byteorder = byteorder
          self._software = software
          self._imagej = bool(imagej)
          self._metadata = None
          self._colormap = None
  
          self._description_offset = 0
          self._description_len_offset = 0
          self._description_len = 0
  
          self._tags = None
          self._shape = None  # normalized shape of data in consecutive pages
          self._data_shape = None  # shape of data in consecutive pages
          self._data_dtype = None  # data type
          self._data_offset = None  # offset to data
          self._data_byte_counts = None  # byte counts per plane
          self._tag_offsets = None  # strip or tile offset tag code
  
          self._fh = FileHandle(file, mode='wb', size=0)
          self._fh.write({'<': b'II', '>': b'MM'}[byteorder])
  
          if bigtiff:
              self._bigtiff = True
              self._offset_size = 8
              self._tag_size = 20
              self._numtag_format = 'Q'
              self._offset_format = 'Q'
              self._value_format = '8s'
              self._fh.write(struct.pack(byteorder+'HHH', 43, 8, 0))
          else:
              self._bigtiff = False
              self._offset_size = 4
              self._tag_size = 12
              self._numtag_format = 'H'
              self._offset_format = 'I'
              self._value_format = '4s'
              self._fh.write(struct.pack(byteorder+'H', 42))
  
          # first IFD
          self._ifd_offset = self._fh.tell()
          self._fh.write(struct.pack(byteorder+self._offset_format, 0))
  
      def save(self, data, photometric=None, planarconfig=None, tile=None,
               contiguous=True, compress=0, colormap=None,
               description=None, datetime=None, resolution=None,
               metadata={}, extratags=()):
          """Write image data and tags to TIFF file.
  
          Image data are written in one stripe per plane by default.
          Dimensions larger than 2 to 4 (depending on photometric mode, planar
          configuration, and SGI mode) are flattened and saved as separate pages.
          The 'sample_format' and 'bits_per_sample' tags are derived from
          the data type.
  
          Parameters
          ----------
          data : numpy.ndarray
              Input image. The last dimensions are assumed to be image depth,
              height (length), width, and samples.
              If a colormap is provided, the dtype must be uint8 or uint16 and
              the data values are indices into the last dimension of the
              colormap.
          photometric : {'minisblack', 'miniswhite', 'rgb', 'palette'}
              The color space of the image data.
              By default this setting is inferred from the data shape and the
              value of colormap.
          planarconfig : {'contig', 'planar'}
              Specifies if samples are stored contiguous or in separate planes.
              By default this setting is inferred from the data shape.
              If this parameter is set, extra samples are used to store grayscale
              images.
              'contig': last dimension contains samples.
              'planar': third last dimension contains samples.
          tile : tuple of int
              The shape (depth, length, width) of image tiles to write.
              If None (default), image data are written in one stripe per plane.
              The tile length and width must be a multiple of 16.
              If the tile depth is provided, the SGI image_depth and tile_depth
              tags are used to save volume data. Few software can read the
              SGI format, e.g. MeVisLab.
          contiguous : bool
              If True (default) and the data and parameters are compatible with
              previous ones, if any, the data are stored contiguously after
              the previous one. Parameters 'photometric' and 'planarconfig' are
              ignored.
          compress : int or 'lzma'
              Values from 0 to 9 controlling the level of zlib compression.
              If 0, data are written uncompressed (default).
              Compression cannot be used to write contiguous files.
              If 'lzma', LZMA compression is used, which is not available on
              all platforms.
          colormap : numpy.ndarray
              RGB color values for the corresponding data value.
              Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16.
          description : str
              The subject of the image. Saved with the first page only.
              Cannot be used with the ImageJ format.
          datetime : datetime
              Date and time of image creation. Saved with the first page only.
              If None (default), the current date and time is used.
          resolution : (float, float[, str]) or ((int, int), (int, int)[, str])
              X and Y resolutions in pixels per resolution unit as float or
              rational numbers.
              A third, optional parameter specifies the resolution unit,
              which must be None (default for ImageJ), 'inch' (default), or 'cm'.
          metadata : dict
              Additional meta data to be saved along with shape information
              in JSON or ImageJ formats in an image_description tag.
              If None, do not write a second image_description tag.
          extratags : sequence of tuples
              Additional tags as [(code, dtype, count, value, writeonce)].
  
              code : int
                  The TIFF tag Id.
              dtype : str
                  Data type of items in 'value' in Python struct format.
                  One of B, s, H, I, 2I, b, h, i, f, d, Q, or q.
              count : int
                  Number of data values. Not used for string values.
              value : sequence
                  'Count' values compatible with 'dtype'.
              writeonce : bool
                  If True, the tag is written to the first page only.
  
          """
          # TODO: refactor this function
          fh = self._fh
          byteorder = self._byteorder
          numtag_format = self._numtag_format
          value_format = self._value_format
          offset_format = self._offset_format
          offset_size = self._offset_size
          tag_size = self._tag_size
  
          data = numpy.asarray(data, dtype=byteorder+data.dtype.char, order='C')
          if data.size == 0:
              raise ValueError("can not save empty array")
  
          # just append contiguous data if possible
          if self._data_shape:
              if (not contiguous or
                      self._data_shape[1:] != data.shape or
                      self._data_dtype != data.dtype or
                      (compress and self._tags) or
                      tile or
                      not numpy.array_equal(colormap, self._colormap)):
                  # incompatible shape, dtype, compression mode, or colormap
                  self._write_remaining_pages()
                  self._write_image_description()
                  self._description_offset = 0
                  self._description_len_offset = 0
                  self._data_shape = None
                  self._colormap = None
                  if self._imagej:
                      raise ValueError(
                          "ImageJ does not support non-contiguous data")
              else:
                  # consecutive mode
                  self._data_shape = (self._data_shape[0] + 1,) + data.shape
                  if not compress:
                      # write contiguous data, write ifds/tags later
                      fh.write_array(data)
                      return
  
          if photometric not in (None, 'minisblack', 'miniswhite',
                                 'rgb', 'palette'):
              raise ValueError("invalid photometric %s" % photometric)
          if planarconfig not in (None, 'contig', 'planar'):
              raise ValueError("invalid planarconfig %s" % planarconfig)
  
          # prepare compression
          if not compress:
              compress = False
              compress_tag = 1
          elif compress == 'lzma':
              compress = lzma.compress
              compress_tag = 34925
              if self._imagej:
                  raise ValueError("ImageJ can not handle LZMA compression")
          elif not 0 <= compress <= 9:
              raise ValueError("invalid compression level %s" % compress)
          elif compress:
              def compress(data, level=compress):
                  return zlib.compress(data, level)
              compress_tag = 32946
  
          # prepare ImageJ format
          if self._imagej:
              if description:
                  warnings.warn("not writing description to ImageJ file")
                  description = None
              volume = False
              if data.dtype.char not in 'BHhf':
                  raise ValueError("ImageJ does not support data type '%s'"
                                   % data.dtype.char)
              ijrgb = photometric == 'rgb' if photometric else None
              if data.dtype.char not in 'B':
                  ijrgb = False
              ijshape = imagej_shape(data.shape, ijrgb)
              if ijshape[-1] in (3, 4):
                  photometric = 'rgb'
                  if data.dtype.char not in 'B':
                      raise ValueError("ImageJ does not support data type '%s' "
                                       "for RGB" % data.dtype.char)
              elif photometric is None:
                  photometric = 'minisblack'
                  planarconfig = None
              if planarconfig == 'planar':
                  raise ValueError("ImageJ does not support planar images")
              else:
                  planarconfig = 'contig' if ijrgb else None
  
          # verify colormap and indices
          if colormap is not None:
              if data.dtype.char not in 'BH':
                  raise ValueError("invalid data dtype for palette mode")
              colormap = numpy.asarray(colormap, dtype=byteorder+'H')
              if colormap.shape != (3, 2**(data.itemsize * 8)):
                  raise ValueError("invalid color map shape")
              self._colormap = colormap
  
          # verify tile shape
          if tile:
              tile = tuple(int(i) for i in tile[:3])
              volume = len(tile) == 3
              if (len(tile) < 2 or tile[-1] % 16 or tile[-2] % 16 or
                      any(i < 1 for i in tile)):
                  raise ValueError("invalid tile shape")
          else:
              tile = ()
              volume = False
  
          # normalize data shape to 5D or 6D, depending on volume:
          #   (pages, planar_samples, [depth,] height, width, contig_samples)
          data_shape = data.shape
  
          if photometric == 'rgb':
              data = reshape_nd(data, 3)
          else:
              data = reshape_nd(data, 2)
  
          shape = data.shape
  
          samplesperpixel = 1
          extrasamples = 0
          if volume and data.ndim < 3:
              volume = False
          if colormap is not None:
              photometric = 'palette'
              planarconfig = None
          if photometric is None:
              photometric = 'minisblack'
              if planarconfig == 'contig':
                  if data.ndim > 2 and shape[-1] in (3, 4):
                      photometric = 'rgb'
              elif planarconfig == 'planar':
                  if volume and data.ndim > 3 and shape[-4] in (3, 4):
                      photometric = 'rgb'
                  elif data.ndim > 2 and shape[-3] in (3, 4):
                      photometric = 'rgb'
              elif data.ndim > 2 and shape[-1] in (3, 4):
                  photometric = 'rgb'
              elif self._imagej:
                  photometric = 'minisblack'
              elif volume and data.ndim > 3 and shape[-4] in (3, 4):
                  photometric = 'rgb'
              elif data.ndim > 2 and shape[-3] in (3, 4):
                  photometric = 'rgb'
          if planarconfig and len(shape) <= (3 if volume else 2):
              planarconfig = None
              photometric = 'minisblack'
          if photometric == 'rgb':
              if len(shape) < 3:
                  raise ValueError("not a RGB(A) image")
              if len(shape) < 4:
                  volume = False
              if planarconfig is None:
                  if shape[-1] in (3, 4):
                      planarconfig = 'contig'
                  elif shape[-4 if volume else -3] in (3, 4):
                      planarconfig = 'planar'
                  elif shape[-1] > shape[-4 if volume else -3]:
                      planarconfig = 'planar'
                  else:
                      planarconfig = 'contig'
              if planarconfig == 'contig':
                  data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
                  samplesperpixel = data.shape[-1]
              else:
                  data = data.reshape(
                      (-1,) + shape[(-4 if volume else -3):] + (1,))
                  samplesperpixel = data.shape[1]
              if samplesperpixel > 3:
                  extrasamples = samplesperpixel - 3
          elif planarconfig and len(shape) > (3 if volume else 2):
              if planarconfig == 'contig':
                  data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
                  samplesperpixel = data.shape[-1]
              else:
                  data = data.reshape(
                      (-1,) + shape[(-4 if volume else -3):] + (1,))
                  samplesperpixel = data.shape[1]
              extrasamples = samplesperpixel - 1
          else:
              planarconfig = None
              # remove trailing 1s
              while len(shape) > 2 and shape[-1] == 1:
                  shape = shape[:-1]
              if len(shape) < 3:
                  volume = False
              data = data.reshape(
                  (-1, 1) + shape[(-3 if volume else -2):] + (1,))
  
          # normalize shape to 6D
          assert len(data.shape) in (5, 6)
          if len(data.shape) == 5:
              data = data.reshape(data.shape[:2] + (1,) + data.shape[2:])
          shape = data.shape
  
          if tile and not volume:
              tile = (1, tile[-2], tile[-1])
  
          if photometric == 'palette':
              if (samplesperpixel != 1 or extrasamples or
                      shape[1] != 1 or shape[-1] != 1):
                  raise ValueError("invalid data shape for palette mode")
  
          if photometric == 'rgb' and samplesperpixel == 2:
              raise ValueError("not a RGB image (samplesperpixel=2)")
  
          bytestr = bytes if sys.version[0] == '2' else (
              lambda x: bytes(x, 'utf-8') if isinstance(x, str) else x)
          tags = []  # list of (code, ifdentry, ifdvalue, writeonce)
  
          strip_or_tile = 'tile' if tile else 'strip'
          tag_byte_counts = TiffWriter.TAGS[strip_or_tile + '_byte_counts']
          tag_offsets = TiffWriter.TAGS[strip_or_tile + '_offsets']
          self._tag_offsets = tag_offsets
  
          def pack(fmt, *val):
              return struct.pack(byteorder+fmt, *val)
  
          def addtag(code, dtype, count, value, writeonce=False):
              # Compute ifdentry & ifdvalue bytes from code, dtype, count, value
              # Append (code, ifdentry, ifdvalue, writeonce) to tags list
              code = int(TiffWriter.TAGS.get(code, code))
              try:
                  tifftype = TiffWriter.TYPES[dtype]
              except KeyError:
                  raise ValueError("unknown dtype %s" % dtype)
              rawcount = count
              if dtype == 's':
                  value = bytestr(value) + b'\0'
                  count = rawcount = len(value)
                  rawcount = value.find(b'\0\0')
                  if rawcount < 0:
                      rawcount = count
                  else:
                      rawcount += 1  # length of string without buffer
                  value = (value,)
              if len(dtype) > 1:
                  count *= int(dtype[:-1])
                  dtype = dtype[-1]
              ifdentry = [pack('HH', code, tifftype),
                          pack(offset_format, rawcount)]
              ifdvalue = None
              if struct.calcsize(dtype) * count <= offset_size:
                  # value(s) can be written directly
                  if count == 1:
                      if isinstance(value, (tuple, list, numpy.ndarray)):
                          value = value[0]
                      ifdentry.append(pack(value_format, pack(dtype, value)))
                  else:
                      ifdentry.append(pack(value_format,
                                           pack(str(count)+dtype, *value)))
              else:
                  # use offset to value(s)
                  ifdentry.append(pack(offset_format, 0))
                  if isinstance(value, numpy.ndarray):
                      assert value.size == count
                      assert value.dtype.char == dtype
                      ifdvalue = value.tobytes()
                  elif isinstance(value, (tuple, list)):
                      ifdvalue = pack(str(count)+dtype, *value)
                  else:
                      ifdvalue = pack(dtype, value)
              tags.append((code, b''.join(ifdentry), ifdvalue, writeonce))
  
          def rational(arg, max_denominator=1000000):
              # return nominator and denominator from float or two integers
              try:
                  f = Fraction.from_float(arg)
              except TypeError:
                  f = Fraction(arg[0], arg[1])
              f = f.limit_denominator(max_denominator)
              return f.numerator, f.denominator
  
          if description:
              # user provided description
              addtag('image_description', 's', 0, description, writeonce=True)
  
          # write shape and metadata to image_description
          self._metadata = {} if not metadata else metadata
          if self._imagej:
              description = imagej_description(
                  data_shape, shape[-1] in (3, 4), self._colormap is not None,
                  **self._metadata)
          elif metadata or metadata == {}:
              description = image_description(
                  data_shape, self._colormap is not None, **self._metadata)
          else:
              description = None
          if description:
              # add 32 bytes buffer
              # the image description might be updated later with the final shape
              description += b'\0'*32
              self._description_len = len(description)
              addtag('image_description', 's', 0, description, writeonce=True)
  
          if self._software:
              addtag('software', 's', 0, self._software, writeonce=True)
              self._software = None  # only save to first page in file
          if datetime is None:
              datetime = self._now()
          addtag('datetime', 's', 0, datetime.strftime("%Y:%m:%d %H:%M:%S"),
                 writeonce=True)
          addtag('compression', 'H', 1, compress_tag)
          addtag('image_width', 'I', 1, shape[-2])
          addtag('image_length', 'I', 1, shape[-3])
          if tile:
              addtag('tile_width', 'I', 1, tile[-1])
              addtag('tile_length', 'I', 1, tile[-2])
              if tile[0] > 1:
                  addtag('image_depth', 'I', 1, shape[-4])
                  addtag('tile_depth', 'I', 1, tile[0])
          addtag('new_subfile_type', 'I', 1, 0)
          addtag('sample_format', 'H', 1,
                 {'u': 1, 'i': 2, 'f': 3, 'c': 6}[data.dtype.kind])
          addtag('photometric', 'H', 1, {'miniswhite': 0, 'minisblack': 1,
                                         'rgb': 2, 'palette': 3}[photometric])
          if colormap is not None:
              addtag('color_map', 'H', colormap.size, colormap)
          addtag('samples_per_pixel', 'H', 1, samplesperpixel)
          if planarconfig and samplesperpixel > 1:
              addtag('planar_configuration', 'H', 1, 1
                     if planarconfig == 'contig' else 2)
              addtag('bits_per_sample', 'H', samplesperpixel,
                     (data.dtype.itemsize * 8,) * samplesperpixel)
          else:
              addtag('bits_per_sample', 'H', 1, data.dtype.itemsize * 8)
          if extrasamples:
              if photometric == 'rgb' and extrasamples == 1:
                  addtag('extra_samples', 'H', 1, 1)  # associated alpha channel
              else:
                  addtag('extra_samples', 'H', extrasamples, (0,) * extrasamples)
          if resolution:
              addtag('x_resolution', '2I', 1, rational(resolution[0]))
              addtag('y_resolution', '2I', 1, rational(resolution[1]))
              if len(resolution) > 2:
                  resolution_unit = {None: 1, 'inch': 2, 'cm': 3}[resolution[2]]
              elif self._imagej:
                  resolution_unit = 1
              else:
                  resolution_unit = 2
              addtag('resolution_unit', 'H', 1, resolution_unit)
          if not tile:
              addtag('rows_per_strip', 'I', 1, shape[-3])  # * shape[-4]
  
          if tile:
              # use one chunk per tile per plane
              tiles = ((shape[2] + tile[0] - 1) // tile[0],
                       (shape[3] + tile[1] - 1) // tile[1],
                       (shape[4] + tile[2] - 1) // tile[2])
              numtiles = product(tiles) * shape[1]
              strip_byte_counts = [
                  product(tile) * shape[-1] * data.dtype.itemsize] * numtiles
              addtag(tag_byte_counts, offset_format, numtiles, strip_byte_counts)
              addtag(tag_offsets, offset_format, numtiles, [0] * numtiles)
              # allocate tile buffer
              chunk = numpy.empty(tile + (shape[-1],), dtype=data.dtype)
          else:
              # use one strip per plane
              strip_byte_counts = [
                  data[0, 0].size * data.dtype.itemsize] * shape[1]
              addtag(tag_byte_counts, offset_format, shape[1], strip_byte_counts)
              addtag(tag_offsets, offset_format, shape[1], [0] * shape[1])
  
          # add extra tags from user
          for t in extratags:
              addtag(*t)
  
          # TODO: check TIFFReadDirectoryCheckOrder warning in files containing
          #   multiple tags of same code
          # the entries in an IFD must be sorted in ascending order by tag code
          tags = sorted(tags, key=lambda x: x[0])
  
          if not (self._bigtiff or self._imagej) and (
                  fh.tell() + data.size*data.dtype.itemsize > 2**31-1):
              raise ValueError("data too large for standard TIFF file")
  
          # if not compressed or tiled, write the first ifd and then all data
          # contiguously; else, write all ifds and data interleaved
          for pageindex in range(shape[0] if (compress or tile) else 1):
              # update pointer at ifd_offset
              pos = fh.tell()
              fh.seek(self._ifd_offset)
              fh.write(pack(offset_format, pos))
              fh.seek(pos)
  
              # write ifdentries
              fh.write(pack(numtag_format, len(tags)))
              tag_offset = fh.tell()
              fh.write(b''.join(t[1] for t in tags))
              self._ifd_offset = fh.tell()
              fh.write(pack(offset_format, 0))  # offset to next IFD
  
              # write tag values and patch offsets in ifdentries, if necessary
              for tagindex, tag in enumerate(tags):
                  if tag[2]:
                      pos = fh.tell()
                      fh.seek(tag_offset + tagindex*tag_size + offset_size + 4)
                      fh.write(pack(offset_format, pos))
                      fh.seek(pos)
                      if tag[0] == tag_offsets:
                          strip_offsets_offset = pos
                      elif tag[0] == tag_byte_counts:
                          strip_byte_counts_offset = pos
                      elif tag[0] == 270 and tag[2].endswith(b'\0\0\0\0'):
                          # image description buffer
                          self._description_offset = pos
                          self._description_len_offset = (
                              tag_offset + tagindex * tag_size + 4)
                      fh.write(tag[2])
  
              # write image data
              data_offset = fh.tell()
              if compress:
                  strip_byte_counts = []
              if tile:
                  for plane in data[pageindex]:
                      for tz in range(tiles[0]):
                          for ty in range(tiles[1]):
                              for tx in range(tiles[2]):
                                  c0 = min(tile[0], shape[2] - tz*tile[0])
                                  c1 = min(tile[1], shape[3] - ty*tile[1])
                                  c2 = min(tile[2], shape[4] - tx*tile[2])
                                  chunk[c0:, c1:, c2:] = 0
                                  chunk[:c0, :c1, :c2] = plane[
                                      tz*tile[0]:tz*tile[0]+c0,
                                      ty*tile[1]:ty*tile[1]+c1,
                                      tx*tile[2]:tx*tile[2]+c2]
                                  if compress:
                                      t = compress(chunk)
                                      strip_byte_counts.append(len(t))
                                      fh.write(t)
                                  else:
                                      fh.write_array(chunk)
                                      fh.flush()
              elif compress:
                  for plane in data[pageindex]:
                      plane = compress(plane)
                      strip_byte_counts.append(len(plane))
                      fh.write(plane)
              else:
                  fh.write_array(data)
  
              # update strip/tile offsets and byte_counts if necessary
              pos = fh.tell()
              for tagindex, tag in enumerate(tags):
                  if tag[0] == tag_offsets:  # strip/tile offsets
                      if tag[2]:
                          fh.seek(strip_offsets_offset)
                          strip_offset = data_offset
                          for size in strip_byte_counts:
                              fh.write(pack(offset_format, strip_offset))
                              strip_offset += size
                      else:
                          fh.seek(tag_offset + tagindex*tag_size +
                                  offset_size + 4)
                          fh.write(pack(offset_format, data_offset))
                  elif tag[0] == tag_byte_counts:  # strip/tile byte_counts
                      if compress:
                          if tag[2]:
                              fh.seek(strip_byte_counts_offset)
                              for size in strip_byte_counts:
                                  fh.write(pack(offset_format, size))
                          else:
                              fh.seek(tag_offset + tagindex*tag_size +
                                      offset_size + 4)
                              fh.write(pack(offset_format, strip_byte_counts[0]))
                      break
              fh.seek(pos)
              fh.flush()
  
              # remove tags that should be written only once
              if pageindex == 0:
                  tags = [tag for tag in tags if not tag[-1]]
  
          # if uncompressed, write remaining ifds/tags later
          if not (compress or tile):
              self._tags = tags
  
          self._shape = shape
          self._data_shape = (1,) + data_shape
          self._data_dtype = data.dtype
          self._data_offset = data_offset
          self._data_byte_counts = strip_byte_counts
  
      def _write_remaining_pages(self):
          """Write outstanding IFDs and tags to file."""
          if not self._tags:
              return
  
          fh = self._fh
          byteorder = self._byteorder
          numtag_format = self._numtag_format
          offset_format = self._offset_format
          offset_size = self._offset_size
          tag_size = self._tag_size
          data_offset = self._data_offset
          page_data_size = sum(self._data_byte_counts)
          tag_bytes = b''.join(t[1] for t in self._tags)
          numpages = self._shape[0] * self._data_shape[0] - 1
  
          pos = fh.tell()
          if not self._bigtiff and pos + len(tag_bytes) * numpages > 2**32 - 256:
              if self._imagej:
                  warnings.warn("truncating ImageJ file")
                  return
              raise ValueError("data too large for non-bigtiff file")
  
          def pack(fmt, *val):
              return struct.pack(byteorder+fmt, *val)
  
          for _ in range(numpages):
              # update pointer at ifd_offset
              pos = fh.tell()
              fh.seek(self._ifd_offset)
              fh.write(pack(offset_format, pos))
              fh.seek(pos)
  
              # write ifd entries
              fh.write(pack(numtag_format, len(self._tags)))
              tag_offset = fh.tell()
              fh.write(tag_bytes)
              self._ifd_offset = fh.tell()
              fh.write(pack(offset_format, 0))  # offset to next IFD
  
              # offset to image data
              data_offset += page_data_size
  
              # write tag values and patch offsets in ifdentries, if necessary
              for tagindex, tag in enumerate(self._tags):
                  if tag[2]:
                      pos = fh.tell()
                      fh.seek(tag_offset + tagindex*tag_size + offset_size + 4)
                      fh.write(pack(offset_format, pos))
                      fh.seek(pos)
                      if tag[0] == self._tag_offsets:
                          strip_offsets_offset = pos
                      fh.write(tag[2])
  
              # update strip/tile offsets if necessary
              pos = fh.tell()
              for tagindex, tag in enumerate(self._tags):
                  if tag[0] == self._tag_offsets:  # strip/tile offsets
                      if tag[2]:
                          fh.seek(strip_offsets_offset)
                          strip_offset = data_offset
                          for size in self._data_byte_counts:
                              fh.write(pack(offset_format, strip_offset))
                              strip_offset += size
                      else:
                          fh.seek(tag_offset + tagindex*tag_size +
                                  offset_size + 4)
                          fh.write(pack(offset_format, data_offset))
                      break
              fh.seek(pos)
  
          self._tags = None
          self._data_dtype = None
          self._data_offset = None
          self._data_byte_counts = None
          # do not reset _shape or _data_shape
  
      def _write_image_description(self):
          """Write meta data to image_description tag."""
          if (not self._data_shape or self._data_shape[0] == 1 or
                  self._description_offset <= 0):
              return
  
          colormapped = self._colormap is not None
          if self._imagej:
              isrgb = self._shape[-1] in (3, 4)
              description = imagej_description(
                  self._data_shape, isrgb, colormapped, **self._metadata)
          else:
              description = image_description(
                  self._data_shape, colormapped, **self._metadata)
  
          # rewrite description and its length to file
          description = description[:self._description_len-1]
          pos = self._fh.tell()
          self._fh.seek(self._description_offset)
          self._fh.write(description)
          self._fh.seek(self._description_len_offset)
          self._fh.write(struct.pack(self._byteorder+self._offset_format,
                                     len(description)+1))
          self._fh.seek(pos)
  
          self._description_offset = 0
          self._description_len_offset = 0
          self._description_len = 0
  
      def _now(self):
          """Return current date and time."""
          return datetime.datetime.now()
  
      def close(self, truncate=False):
          """Write remaining pages (if not truncate) and close file handle."""
          if not truncate:
              self._write_remaining_pages()
          self._write_image_description()
          self._fh.close()
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
  
  def imread(files, **kwargs):
      """Return image data from TIFF file(s) as numpy array.
  
      The first image series is returned if no arguments are provided.
  
      Parameters
      ----------
      files : str, binary stream, or sequence
          File name, seekable binary stream, glob pattern, or sequence of
          file names.
      key : int, slice, or sequence of page indices
          Defines which pages to return as array.
      series : int
          Defines which series of pages in file to return as array.
      multifile : bool
          If True (default), OME-TIFF data may include pages from multiple files.
      pattern : str
          Regular expression pattern that matches axes names and indices in
          file names.
      kwargs : dict
          Additional parameters passed to the TiffFile or TiffSequence asarray
          function.
  
      Examples
      --------
      >>> imsave('temp.tif', numpy.random.rand(3, 4, 301, 219))
      >>> im = imread('temp.tif', key=0)
      >>> im.shape
      (4, 301, 219)
      >>> ims = imread(['temp.tif', 'temp.tif'])
      >>> ims.shape
      (2, 3, 4, 301, 219)
  
      """
      kwargs_file = parse_kwargs(kwargs, multifile=True)
      kwargs_seq = parse_kwargs(kwargs, 'pattern')
  
      if isinstance(files, basestring) and any(i in files for i in '?*'):
          files = glob.glob(files)
      if not files:
          raise ValueError('no files found')
      if not hasattr(files, 'seek') and len(files) == 1:
          files = files[0]
  
      if isinstance(files, basestring) or hasattr(files, 'seek'):
          with TiffFile(files, **kwargs_file) as tif:
              return tif.asarray(**kwargs)
      else:
          with TiffSequence(files, **kwargs_seq) as imseq:
              return imseq.asarray(**kwargs)
  
  
  class lazyattr(object):
      """Lazy object attribute whose value is computed on first access."""
      __slots__ = ('func',)
  
      def __init__(self, func):
          self.func = func
  
      def __get__(self, instance, owner):
          if instance is None:
              return self
          value = self.func(instance)
          if value is NotImplemented:
              return getattr(super(owner, instance), self.func.__name__)
          setattr(instance, self.func.__name__, value)
          return value
  
  
  class TiffFile(object):
      """Read image and metadata from TIFF, STK, LSM, and FluoView files.
  
      TiffFile instances must be closed using the 'close' method, which is
      automatically called when using the 'with' context manager.
  
      Attributes
      ----------
      pages : list of TiffPage
          All TIFF pages in file.
      series : list of TiffPageSeries
          TIFF pages with compatible shapes and types.
      micromanager_metadata: dict
          Extra MicroManager non-TIFF metadata in the file, if exists.
  
      All attributes are read-only.
  
      Examples
      --------
      >>> with TiffFile('temp.tif') as tif:
      ...     data = tif.asarray()
      ...     data.shape
      (5, 301, 219)
  
      """
      def __init__(self, arg, name=None, offset=None, size=None,
                   multifile=True, multifile_close=True, maxpages=None,
                   fastij=True):
          """Initialize instance from file.
  
          Parameters
          ----------
          arg : str or open file
              Name of file or open file object.
              The file objects are closed in TiffFile.close().
          name : str
              Optional name of file in case 'arg' is a file handle.
          offset : int
              Optional start position of embedded file. By default this is
              the current file position.
          size : int
              Optional size of embedded file. By default this is the number
              of bytes from the 'offset' to the end of the file.
          multifile : bool
              If True (default), series may include pages from multiple files.
              Currently applies to OME-TIFF only.
          multifile_close : bool
              If True (default), keep the handles of other files in multifile
              series closed. This is inefficient when few files refer to
              many pages. If False, the C runtime may run out of resources.
          maxpages : int
              Number of pages to read (default: no limit).
          fastij : bool
              If True (default), try to use only the metadata from the first page
              of ImageJ files. Significantly speeds up loading movies with
              thousands of pages.
  
          """
          self._fh = FileHandle(arg, mode='rb',
                                name=name, offset=offset, size=size)
          self.offset_size = None
          self.pages = []
          self._multifile = bool(multifile)
          self._multifile_close = bool(multifile_close)
          self._files = {self._fh.name: self}  # cache of TiffFiles
          try:
              self._fromfile(maxpages, fastij)
          except Exception:
              self._fh.close()
              raise
  
      @property
      def filehandle(self):
          """Return file handle."""
          return self._fh
  
      @property
      def filename(self):
          """Return name of file handle."""
          return self._fh.name
  
      def close(self):
          """Close open file handle(s)."""
          for tif in self._files.values():
              tif._fh.close()
          self._files = {}
  
      def _fromfile(self, maxpages=None, fastij=True):
          """Read TIFF header and all page records from file."""
          self._fh.seek(0)
          try:
              self.byteorder = {b'II': '<', b'MM': '>'}[self._fh.read(2)]
          except KeyError:
              raise ValueError("not a valid TIFF file")
          self._is_native = self.byteorder == {'big': '>',
                                               'little': '<'}[sys.byteorder]
          version = struct.unpack(self.byteorder+'H', self._fh.read(2))[0]
          if version == 43:
              # BigTiff
              self.offset_size, zero = struct.unpack(self.byteorder+'HH',
                                                     self._fh.read(4))
              if zero or self.offset_size != 8:
                  raise ValueError("not a valid BigTIFF file")
          elif version == 42:
              self.offset_size = 4
          else:
              raise ValueError("not a TIFF file")
          self.pages = []
          while True:
              try:
                  page = TiffPage(self)
                  self.pages.append(page)
              except StopIteration:
                  break
              if maxpages and len(self.pages) > maxpages:
                  break
              if fastij and page.is_imagej:
                  if page._patch_imagej():
                      break  # only read the first page of ImageJ files
                  fastij = False
  
          if not self.pages:
              raise ValueError("empty TIFF file")
  
          if self.is_micromanager:
              # MicroManager files contain metadata not stored in TIFF tags.
              self.micromanager_metadata = read_micromanager_metadata(self._fh)
  
          if self.is_lsm:
              self._fix_lsm_strip_offsets()
              self._fix_lsm_strip_byte_counts()
  
      def _fix_lsm_strip_offsets(self):
          """Unwrap strip offsets for LSM files greater than 4 GB."""
          # each series and position require separate unwrappig (undocumented)
          for series in self.series:
              positions = 1
              for i in 0, 1:
                  if series.axes[i] in 'PM':
                      positions *= series.shape[i]
              positions = len(series.pages) // positions
              for i, page in enumerate(series.pages):
                  if not i % positions:
                      wrap = 0
                      previous_offset = 0
                  strip_offsets = []
                  for current_offset in page.strip_offsets:
                      if current_offset < previous_offset:
                          wrap += 2**32
                      strip_offsets.append(current_offset + wrap)
                      previous_offset = current_offset
                  page.strip_offsets = tuple(strip_offsets)
  
      def _fix_lsm_strip_byte_counts(self):
          """Set strip_byte_counts to size of compressed data.
  
          The strip_byte_counts tag in LSM files contains the number of bytes
          for the uncompressed data.
  
          """
          if not self.pages:
              return
          strips = {}
          for page in self.pages:
              assert len(page.strip_offsets) == len(page.strip_byte_counts)
              for offset, bytecount in zip(page.strip_offsets,
                                           page.strip_byte_counts):
                  strips[offset] = bytecount
          offsets = sorted(strips.keys())
          offsets.append(min(offsets[-1] + strips[offsets[-1]], self._fh.size))
          for i, offset in enumerate(offsets[:-1]):
              strips[offset] = min(strips[offset], offsets[i+1] - offset)
          for page in self.pages:
              if page.compression:
                  page.strip_byte_counts = tuple(
                      strips[offset] for offset in page.strip_offsets)
  
      def asarray(self, key=None, series=None, memmap=False, tempdir=None):
          """Return image data from multiple TIFF pages as numpy array.
  
          By default the first image series is returned.
  
          Parameters
          ----------
          key : int, slice, or sequence of page indices
              Defines which pages to return as array.
          series : int or TiffPageSeries
              Defines which series of pages to return as array.
          memmap : bool
              If True, return an read-only array stored in a binary file on disk
              if possible. The TIFF file is used if possible, else a temporary
              file is created.
          tempdir : str
              The directory where the memory-mapped file will be created.
  
          """
          if key is None and series is None:
              series = 0
          if series is not None:
              try:
                  series = self.series[series]
              except (KeyError, TypeError):
                  pass
              pages = series.pages
          else:
              pages = self.pages
  
          if key is None:
              pass
          elif isinstance(key, int):
              pages = [pages[key]]
          elif isinstance(key, slice):
              pages = pages[key]
          elif isinstance(key, collections.Iterable):
              pages = [pages[k] for k in key]
          else:
              raise TypeError("key must be an int, slice, or sequence")
  
          if not len(pages):
              raise ValueError("no pages selected")
  
          if self.is_nih:
              if pages[0].is_indexed:
                  result = stack_pages(pages, colormapped=False, squeeze=False)
                  result = apply_colormap(result, pages[0].color_map)
              else:
                  result = stack_pages(pages, memmap=memmap, tempdir=tempdir,
                                       colormapped=False, squeeze=False)
          elif len(pages) == 1:
              result = pages[0].asarray(memmap=memmap)
          elif self.is_ome:
              assert not self.is_indexed, "color mapping disabled for ome-tiff"
              if any(p is None for p in pages):
                  # zero out missing pages
                  firstpage = next(p for p in pages if p)
                  nopage = numpy.zeros_like(
                      firstpage.asarray(memmap=False))
              if memmap:
                  with tempfile.NamedTemporaryFile() as fh:
                      result = numpy.memmap(fh, series.dtype, shape=series.shape)
                      result = result.reshape(-1)
              else:
                  result = numpy.empty(series.shape, series.dtype).reshape(-1)
              index = 0
  
              class KeepOpen:
                  # keep Tiff files open between consecutive pages
                  def __init__(self, parent, close):
                      self.master = parent
                      self.parent = parent
                      self._close = close
  
                  def open(self, page):
                      if self._close and page and page.parent != self.parent:
                          if self.parent != self.master:
                              self.parent.filehandle.close()
                          self.parent = page.parent
                          self.parent.filehandle.open()
  
                  def close(self):
                      if self._close and self.parent != self.master:
                          self.parent.filehandle.close()
  
              keep = KeepOpen(self, self._multifile_close)
              for page in pages:
                  keep.open(page)
                  if page:
                      a = page.asarray(memmap=False, colormapped=False,
                                       reopen=False)
                  else:
                      a = nopage
                  try:
                      result[index:index + a.size] = a.reshape(-1)
                  except ValueError as e:
                      warnings.warn("ome-tiff: %s" % e)
                      break
                  index += a.size
              keep.close()
          elif key is None and series and series.offset:
              if memmap:
                  result = self.filehandle.memmap_array(
                      series.dtype, series.shape, series.offset)
              else:
                  self.filehandle.seek(series.offset)
                  result = self.filehandle.read_array(
                      series.dtype, product(series.shape))
          else:
              result = stack_pages(pages, memmap=memmap, tempdir=tempdir)
  
          if key is None:
              try:
                  result.shape = series.shape
              except ValueError:
                  try:
                      warnings.warn("failed to reshape %s to %s" % (
                          result.shape, series.shape))
                      # try series of expected shapes
                      result.shape = (-1,) + series.shape
                  except ValueError:
                      # revert to generic shape
                      result.shape = (-1,) + pages[0].shape
          elif len(pages) == 1:
              result.shape = pages[0].shape
          else:
              result.shape = (-1,) + pages[0].shape
          return result
  
      @lazyattr
      def series(self):
          """Return pages with compatible properties as TiffPageSeries."""
          if not self.pages:
              return []
  
          series = []
          if self.is_ome:
              series = self._ome_series()
          elif self.is_fluoview:
              series = self._fluoview_series()
          elif self.is_lsm:
              series = self._lsm_series()
          elif self.is_imagej:
              series = self._imagej_series()
          elif self.is_nih:
              series = self._nih_series()
  
          if not series:
              # generic detection of series
              shapes = []
              pages = {}
              index = 0
              for page in self.pages:
                  if not page.shape:
                      continue
                  if page.is_shaped:
                      index += 1  # shape starts a new series
                  shape = page.shape + (index, page.axes,
                                        page.compression in TIFF_DECOMPESSORS)
                  if shape in pages:
                      pages[shape].append(page)
                  else:
                      shapes.append(shape)
                      pages[shape] = [page]
              series = []
              for s in shapes:
                  shape = ((len(pages[s]),) + s[:-3] if len(pages[s]) > 1
                           else s[:-3])
                  axes = (('I' + s[-2]) if len(pages[s]) > 1 else s[-2])
                  page0 = pages[s][0]
                  if page0.is_shaped:
                      metadata = image_description_dict(page0.is_shaped)
                      reshape = metadata['shape']
                      if 'axes' in metadata:
                          reaxes = metadata['axes']
                          if len(reaxes) == len(reshape):
                              axes = reaxes
                              shape = reshape
                          else:
                              warnings.warn("axes do not match shape")
                      try:
                          axes = reshape_axes(axes, shape, reshape)
                          shape = reshape
                      except ValueError as e:
                          warnings.warn(e.message)
                  series.append(
                      TiffPageSeries(pages[s], shape, page0.dtype, axes))
  
          # remove empty series, e.g. in MD Gel files
          series = [s for s in series if sum(s.shape) > 0]
          return series
  
      def _fluoview_series(self):
          """Return image series in FluoView file."""
          page0 = self.pages[0]
          dims = {
              b'X': 'X', b'Y': 'Y', b'Z': 'Z', b'T': 'T',
              b'WAVELENGTH': 'C', b'TIME': 'T', b'XY': 'R',
              b'EVENT': 'V', b'EXPOSURE': 'L'}
          mmhd = list(reversed(page0.mm_header.dimensions))
          axes = ''.join(dims.get(i[0].strip().upper(), 'Q')
                         for i in mmhd if i[1] > 1)
          shape = tuple(int(i[1]) for i in mmhd if i[1] > 1)
          return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
  
      def _lsm_series(self):
          """Return image series in LSM file."""
          page0 = self.pages[0]
          lsmi = page0.cz_lsm_info
          axes = CZ_SCAN_TYPES[lsmi.scan_type]
          if page0.is_rgb:
              axes = axes.replace('C', '').replace('XY', 'XYC')
          if hasattr(lsmi, 'dimension_p') and lsmi.dimension_p > 1:
              axes += 'P'
          if hasattr(lsmi, 'dimension_m') and lsmi.dimension_m > 1:
              axes += 'M'
          axes = axes[::-1]
          shape = tuple(getattr(lsmi, CZ_DIMENSIONS[i]) for i in axes)
          pages = [p for p in self.pages if not p.is_reduced]
          dtype = pages[0].dtype
          series = [TiffPageSeries(pages, shape, dtype, axes)]
          if len(pages) != len(self.pages):  # reduced RGB pages
              pages = [p for p in self.pages if p.is_reduced]
              cp = 1
              i = 0
              while cp < len(pages) and i < len(shape)-2:
                  cp *= shape[i]
                  i += 1
              shape = shape[:i] + pages[0].shape
              axes = axes[:i] + 'CYX'
              dtype = pages[0].dtype
              series.append(TiffPageSeries(pages, shape, dtype, axes))
          return series
  
      def _imagej_series(self):
          """Return image series in ImageJ file."""
          # ImageJ's dimension order is always TZCYXS
          # TODO: fix loading of color, composite or palette images
          shape = []
          axes = []
          page0 = self.pages[0]
          ij = page0.imagej_tags
          if 'frames' in ij:
              shape.append(ij['frames'])
              axes.append('T')
          if 'slices' in ij:
              shape.append(ij['slices'])
              axes.append('Z')
          if 'channels' in ij and not (self.is_rgb and not
                                       ij.get('hyperstack', False)):
              shape.append(ij['channels'])
              axes.append('C')
          remain = ij.get('images', len(self.pages)) // (product(shape)
                                                         if shape else 1)
          if remain > 1:
              shape.append(remain)
              axes.append('I')
          if page0.axes[0] == 'I':
              # contiguous multiple images
              shape.extend(page0.shape[1:])
              axes.extend(page0.axes[1:])
          elif page0.axes[:2] == 'SI':
              # color-mapped contiguous multiple images
              shape = page0.shape[0:1] + tuple(shape) + page0.shape[2:]
              axes = list(page0.axes[0]) + axes + list(page0.axes[2:])
          else:
              shape.extend(page0.shape)
              axes.extend(page0.axes)
          return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
  
      def _nih_series(self):
          """Return image series in NIH file."""
          page0 = self.pages[0]
          if len(self.pages) == 1:
              shape = page0.shape
              axes = page0.axes
          else:
              shape = (len(self.pages),) + page0.shape
              axes = 'I' + page0.axes
          return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
  
      def _ome_series(self):
          """Return image series in OME-TIFF file(s)."""
          omexml = self.pages[0].tags['image_description'].value
          try:
              root = etree.fromstring(omexml)
          except etree.ParseError as e:
              # TODO: test this
              warnings.warn("ome-xml: %s" % e)
              omexml = omexml.decode('utf-8', 'ignore').encode('utf-8')
              root = etree.fromstring(omexml)
          uuid = root.attrib.get('UUID', None)
          self._files = {uuid: self}
          dirname = self._fh.dirname
          modulo = {}
          series = []
          for element in root:
              if element.tag.endswith('BinaryOnly'):
                  warnings.warn("ome-xml: not an ome-tiff master file")
                  break
              if element.tag.endswith('StructuredAnnotations'):
                  for annot in element:
                      if not annot.attrib.get('Namespace',
                                              '').endswith('modulo'):
                          continue
                      for value in annot:
                          for modul in value:
                              for along in modul:
                                  if not along.tag[:-1].endswith('Along'):
                                      continue
                                  axis = along.tag[-1]
                                  newaxis = along.attrib.get('Type', 'other')
                                  newaxis = AXES_LABELS[newaxis]
                                  if 'Start' in along.attrib:
                                      labels = range(
                                          int(along.attrib['Start']),
                                          int(along.attrib['End']) + 1,
                                          int(along.attrib.get('Step', 1)))
                                  else:
                                      labels = [label.text for label in along
                                                if label.tag.endswith('Label')]
                                  modulo[axis] = (newaxis, labels)
              if not element.tag.endswith('Image'):
                  continue
              for pixels in element:
                  if not pixels.tag.endswith('Pixels'):
                      continue
                  atr = pixels.attrib
                  dtype = atr.get('Type', None)
                  axes = ''.join(reversed(atr['DimensionOrder']))
                  shape = list(int(atr['Size'+ax]) for ax in axes)
                  size = product(shape[:-2])
                  ifds = [None] * (size // self.pages[0].samples_per_pixel)
                  for data in pixels:
                      if not data.tag.endswith('TiffData'):
                          continue
                      atr = data.attrib
                      ifd = int(atr.get('IFD', 0))
                      num = int(atr.get('NumPlanes', 1 if 'IFD' in atr else 0))
                      num = int(atr.get('PlaneCount', num))
                      idx = [int(atr.get('First'+ax, 0)) for ax in axes[:-2]]
                      try:
                          idx = numpy.ravel_multi_index(idx, shape[:-2])
                      except ValueError:
                          # ImageJ produces invalid ome-xml when cropping
                          warnings.warn("ome-xml: invalid TiffData index")
                          continue
                      for uuid in data:
                          if not uuid.tag.endswith('UUID'):
                              continue
                          if uuid.text not in self._files:
                              if not self._multifile:
                                  # abort reading multifile OME series
                                  # and fall back to generic series
                                  return []
                              fname = uuid.attrib['FileName']
                              try:
                                  tif = TiffFile(os.path.join(dirname, fname))
                              except (IOError, ValueError):
                                  tif.close()
                                  warnings.warn(
                                      "ome-xml: failed to read '%s'" % fname)
                                  break
                              self._files[uuid.text] = tif
                              if self._multifile_close:
                                  tif.close()
                          pages = self._files[uuid.text].pages
                          try:
                              for i in range(num if num else len(pages)):
                                  ifds[idx + i] = pages[ifd + i]
                          except IndexError:
                              warnings.warn("ome-xml: index out of range")
                          # only process first uuid
                          break
                      else:
                          pages = self.pages
                          try:
                              for i in range(num if num else len(pages)):
                                  ifds[idx + i] = pages[ifd + i]
                          except IndexError:
                              warnings.warn("ome-xml: index out of range")
                  if all(i is None for i in ifds):
                      # skip images without data
                      continue
                  dtype = next(i for i in ifds if i).dtype
                  series.append(TiffPageSeries(ifds, shape, dtype, axes, self))
          for serie in series:
              shape = list(serie.shape)
              for axis, (newaxis, labels) in modulo.items():
                  i = serie.axes.index(axis)
                  size = len(labels)
                  if shape[i] == size:
                      serie.axes = serie.axes.replace(axis, newaxis, 1)
                  else:
                      shape[i] //= size
                      shape.insert(i+1, size)
                      serie.axes = serie.axes.replace(axis, axis+newaxis, 1)
              serie.shape = tuple(shape)
          # squeeze dimensions
          for serie in series:
              serie.shape, serie.axes = squeeze_axes(serie.shape, serie.axes)
          return series
  
      def __len__(self):
          """Return number of image pages in file."""
          return len(self.pages)
  
      def __getitem__(self, key):
          """Return specified page."""
          return self.pages[key]
  
      def __iter__(self):
          """Return iterator over pages."""
          return iter(self.pages)
  
      def __str__(self):
          """Return string containing information about file."""
          result = [
              self._fh.name.capitalize(),
              format_size(self._fh.size),
              {'<': 'little endian', '>': 'big endian'}[self.byteorder]]
          if self.is_bigtiff:
              result.append("bigtiff")
          if len(self.pages) > 1:
              result.append("%i pages" % len(self.pages))
          if len(self.series) > 1:
              result.append("%i series" % len(self.series))
          if len(self._files) > 1:
              result.append("%i files" % (len(self._files)))
          return ", ".join(result)
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      @lazyattr
      def fstat(self):
          try:
              return os.fstat(self._fh.fileno())
          except Exception:  # io.UnsupportedOperation
              return None
  
      @lazyattr
      def is_bigtiff(self):
          """File has BigTIFF format."""
          return self.offset_size != 4
  
      @lazyattr
      def is_rgb(self):
          """File contains only RGB images."""
          return all(p.is_rgb for p in self.pages)
  
      @lazyattr
      def is_indexed(self):
          """File contains only indexed images."""
          return all(p.is_indexed for p in self.pages)
  
      @lazyattr
      def is_mdgel(self):
          """File has MD Gel format."""
          return any(p.is_mdgel for p in self.pages)
  
      @lazyattr
      def is_mediacy(self):
          """File was created by Media Cybernetics software."""
          return any(p.is_mediacy for p in self.pages)
  
      @lazyattr
      def is_stk(self):
          """File has MetaMorph STK format."""
          return all(p.is_stk for p in self.pages)
  
      @lazyattr
      def is_lsm(self):
          """File was created by Carl Zeiss software."""
          return len(self.pages) and self.pages[0].is_lsm
  
      @lazyattr
      def is_vista(self):
          """File was created by ISS Vista."""
          return len(self.pages) and self.pages[0].is_vista
  
      @lazyattr
      def is_imagej(self):
          """File has ImageJ format."""
          return len(self.pages) and self.pages[0].is_imagej
  
      @lazyattr
      def is_micromanager(self):
          """File was created by MicroManager."""
          return len(self.pages) and self.pages[0].is_micromanager
  
      @lazyattr
      def is_nih(self):
          """File has NIH Image format."""
          return len(self.pages) and self.pages[0].is_nih
  
      @lazyattr
      def is_fluoview(self):
          """File was created by Olympus FluoView."""
          return len(self.pages) and self.pages[0].is_fluoview
  
      @lazyattr
      def is_ome(self):
          """File has OME-TIFF format."""
          return len(self.pages) and self.pages[0].is_ome
  
      @lazyattr
      def is_scn(self):
          """File has Leica SCN format."""
          return len(self.pages) and self.pages[0].is_scn
  
  
  class TiffPage(object):
      """A TIFF image file directory (IFD).
  
      Attributes
      ----------
      index : int
          Index of page in file.
      dtype : str {TIFF_SAMPLE_DTYPES}
          Data type of image, color-mapped if applicable.
      shape : tuple
          Dimensions of the image array in TIFF page,
          color-mapped and with extra samples if applicable.
      axes : str
          Axes label codes:
          'X' width, 'Y' height, 'S' sample, 'I' image series|page|plane,
          'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
          'T' time, 'R' region|tile, 'A' angle, 'P' phase, 'H' lifetime,
          'L' exposure, 'V' event, 'Q' unknown, '_' missing
      tags : TiffTags
          Dictionary of tags in page.
          Tag values are also directly accessible as attributes.
      color_map : numpy.ndarray
          Color look up table, if exists.
      cz_lsm_scan_info: Record(dict)
          LSM scan info attributes, if exists.
      imagej_tags: Record(dict)
          Consolidated ImageJ description and metadata tags, if exists.
      uic_tags: Record(dict)
          Consolidated MetaMorph STK/UIC tags, if exists.
  
      All attributes are read-only.
  
      Notes
      -----
      The internal, normalized '_shape' attribute is 6 dimensional:
  
      0. number planes/images  (stk, ij).
      1. planar samples_per_pixel.
      2. image_depth Z  (sgi).
      3. image_length Y.
      4. image_width X.
      5. contig samples_per_pixel.
  
      """
      def __init__(self, parent):
          """Initialize instance from file."""
          self.parent = parent
          self.index = len(parent.pages)
          self.shape = self._shape = ()
          self.dtype = self._dtype = None
          self.axes = ""
          self.tags = TiffTags()
          self._offset = 0
  
          self._fromfile()
          self._process_tags()
  
      def _fromfile(self):
          """Read TIFF IFD structure and its tags from file.
  
          File cursor must be at storage position of IFD offset and is left at
          offset to next IFD.
  
          Raises StopIteration if offset (first bytes read) is 0
          or a corrupted page list is encountered.
  
          """
          fh = self.parent.filehandle
          byteorder = self.parent.byteorder
          offset_size = self.parent.offset_size
  
          # read offset to this IFD
          fmt = {4: 'I', 8: 'Q'}[offset_size]
          offset = struct.unpack(byteorder + fmt, fh.read(offset_size))[0]
          if not offset:
              raise StopIteration()
          if offset >= fh.size:
              warnings.warn("invalid page offset > file size")
              raise StopIteration()
          self._offset = offset
  
          # read standard tags
          tags = self.tags
          fh.seek(offset)
          fmt, size = {4: ('H', 2), 8: ('Q', 8)}[offset_size]
          try:
              numtags = struct.unpack(byteorder + fmt, fh.read(size))[0]
              if numtags > 4096:
                  raise ValueError("suspicious number of tags")
          except Exception:
              warnings.warn("corrupted page list at offset %i" % offset)
              raise StopIteration()
  
          tagcode = 0
          for _ in range(numtags):
              try:
                  tag = TiffTag(self.parent)
              except TiffTag.Error as e:
                  warnings.warn(str(e))
                  continue
              if tagcode > tag.code:
                  # expected for early LSM and tifffile versions
                  warnings.warn("tags are not ordered by code")
              tagcode = tag.code
              if tag.name not in tags:
                  tags[tag.name] = tag
              else:
                  # some files contain multiple IFD with same code
                  # e.g. MicroManager files contain two image_description
                  i = 1
                  while True:
                      name = "%s_%i" % (tag.name, i)
                      if name not in tags:
                          tags[name] = tag
                          break
  
          pos = fh.tell()  # where offset to next IFD can be found
  
          if self.is_lsm or (self.index and self.parent.is_lsm):
              # correct non standard LSM bitspersample tags
              self.tags['bits_per_sample']._fix_lsm_bitspersample(self)
  
          if self.is_lsm:
              # read LSM info subrecords
              for name, reader in CZ_LSM_INFO_READERS.items():
                  try:
                      offset = self.cz_lsm_info['offset_'+name]
                  except KeyError:
                      continue
                  if offset < 8:
                      # older LSM revision
                      continue
                  fh.seek(offset)
                  try:
                      setattr(self, 'cz_lsm_'+name, reader(fh))
                  except ValueError:
                      pass
          elif self.is_stk and 'uic1tag' in tags and not tags['uic1tag'].value:
              # read uic1tag now that plane count is known
              uic1tag = tags['uic1tag']
              fh.seek(uic1tag.value_offset)
              tags['uic1tag'].value = Record(
                  read_uic1tag(fh, byteorder, uic1tag.dtype, uic1tag.count,
                               tags['uic2tag'].count))
          fh.seek(pos)
  
      def _process_tags(self):
          """Validate standard tags and initialize attributes.
  
          Raise ValueError if tag values are not supported.
  
          """
          tags = self.tags
          for code, (name, default, dtype, count, validate) in TIFF_TAGS.items():
              if name in tags:
                  #tags[name] = TiffTag(code, dtype=dtype, count=count,
                  #                     value=default, name=name)
                  if validate:
                      try:
                          if tags[name].count == 1:
                              setattr(self, name, validate[tags[name].value])
                          else:
                              setattr(self, name, tuple(
                                  validate[value] for value in tags[name].value))
                      except KeyError:
                          raise ValueError("%s.value (%s) not supported" %
                                           (name, tags[name].value))
              elif default is not None:
                  setattr(self, name, validate[default] if validate else default)
  
          if 'bits_per_sample' in tags:
              tag = tags['bits_per_sample']
              if tag.count == 1:
                  self.bits_per_sample = tag.value
              else:
                  # LSM might list more items than samples_per_pixel
                  value = tag.value[:self.samples_per_pixel]
                  if any((v-value[0] for v in value)):
                      self.bits_per_sample = value
                  else:
                      self.bits_per_sample = value[0]
  
          if 'sample_format' in tags:
              tag = tags['sample_format']
              if tag.count == 1:
                  self.sample_format = TIFF_SAMPLE_FORMATS[tag.value]
              else:
                  value = tag.value[:self.samples_per_pixel]
                  if any((v-value[0] for v in value)):
                      self.sample_format = [TIFF_SAMPLE_FORMATS[v]
                                            for v in value]
                  else:
                      self.sample_format = TIFF_SAMPLE_FORMATS[value[0]]
  
          if 'photometric' not in tags:
              self.photometric = None
  
          if 'image_length' in tags:
              self.strips_per_image = int(math.floor(
                  float(self.image_length + self.rows_per_strip - 1) /
                  self.rows_per_strip))
          else:
              self.strips_per_image = 0
  
          key = (self.sample_format, self.bits_per_sample)
          self.dtype = self._dtype = TIFF_SAMPLE_DTYPES.get(key, None)
  
          if 'image_length' not in self.tags or 'image_width' not in self.tags:
              # some GEL file pages are missing image data
              self.image_length = 0
              self.image_width = 0
              self.image_depth = 0
              self.strip_offsets = 0
              self._shape = ()
              self.shape = ()
              self.axes = ''
  
          if self.is_vista or self.parent.is_vista:
              # ISS Vista writes wrong image_depth tag
              self.image_depth = 1
  
          if self.is_indexed:
              self.dtype = self.tags['color_map'].dtype[1]
              self.color_map = numpy.array(self.color_map, self.dtype)
              dmax = self.color_map.max()
              if dmax < 256:
                  self.dtype = numpy.uint8
                  self.color_map = self.color_map.astype(self.dtype)
              #else:
              #    self.dtype = numpy.uint8
              #    self.color_map >>= 8
              #    self.color_map = self.color_map.astype(self.dtype)
              # TODO: support other photometric modes than RGB
              self.color_map.shape = (3, -1)
  
          # determine shape of data
          image_length = self.image_length
          image_width = self.image_width
          image_depth = self.image_depth
          samples_per_pixel = self.samples_per_pixel
  
          if self.is_stk:
              assert self.image_depth == 1
              planes = self.tags['uic2tag'].count
              if self.is_contig:
                  self._shape = (planes, 1, 1, image_length, image_width,
                                 samples_per_pixel)
                  if samples_per_pixel == 1:
                      self.shape = (planes, image_length, image_width)
                      self.axes = 'YX'
                  else:
                      self.shape = (planes, image_length, image_width,
                                    samples_per_pixel)
                      self.axes = 'YXS'
              else:
                  self._shape = (planes, samples_per_pixel, 1, image_length,
                                 image_width, 1)
                  if samples_per_pixel == 1:
                      self.shape = (planes, image_length, image_width)
                      self.axes = 'YX'
                  else:
                      self.shape = (planes, samples_per_pixel, image_length,
                                    image_width)
                      self.axes = 'SYX'
              # detect type of series
              if planes == 1:
                  self.shape = self.shape[1:]
              elif numpy.all(self.uic2tag.z_distance != 0):
                  self.axes = 'Z' + self.axes
              elif numpy.all(numpy.diff(self.uic2tag.time_created) != 0):
                  self.axes = 'T' + self.axes
              else:
                  self.axes = 'I' + self.axes
              # DISABLED
              if self.is_indexed:
                  assert False, "color mapping disabled for stk"
                  if self.color_map.shape[1] >= 2**self.bits_per_sample:
                      if image_depth == 1:
                          self.shape = (planes, image_length, image_width,
                                        self.color_map.shape[0])
                      else:
                          self.shape = (planes, image_depth, image_length,
                                        image_width, self.color_map.shape[0])
                      self.axes = self.axes + 'S'
                  else:
                      warnings.warn("palette cannot be applied")
                      self.is_indexed = False
          elif self.is_indexed:
              samples = 1
              if 'extra_samples' in self.tags:
                  samples += self.tags['extra_samples'].count
              if self.is_contig:
                  self._shape = (1, 1, image_depth, image_length, image_width,
                                 samples)
              else:
                  self._shape = (1, samples, image_depth, image_length,
                                 image_width, 1)
              if self.color_map.shape[1] >= 2**self.bits_per_sample:
                  if image_depth == 1:
                      self.shape = (image_length, image_width,
                                    self.color_map.shape[0])
                      self.axes = 'YXS'
                  else:
                      self.shape = (image_depth, image_length, image_width,
                                    self.color_map.shape[0])
                      self.axes = 'ZYXS'
              else:
                  warnings.warn("palette cannot be applied")
                  self.is_indexed = False
                  if image_depth == 1:
                      self.shape = (image_length, image_width)
                      self.axes = 'YX'
                  else:
                      self.shape = (image_depth, image_length, image_width)
                      self.axes = 'ZYX'
          elif self.is_rgb or samples_per_pixel > 1:
              if self.is_contig:
                  self._shape = (1, 1, image_depth, image_length, image_width,
                                 samples_per_pixel)
                  if image_depth == 1:
                      self.shape = (image_length, image_width, samples_per_pixel)
                      self.axes = 'YXS'
                  else:
                      self.shape = (image_depth, image_length, image_width,
                                    samples_per_pixel)
                      self.axes = 'ZYXS'
              else:
                  self._shape = (1, samples_per_pixel, image_depth,
                                 image_length, image_width, 1)
                  if image_depth == 1:
                      self.shape = (samples_per_pixel, image_length, image_width)
                      self.axes = 'SYX'
                  else:
                      self.shape = (samples_per_pixel, image_depth,
                                    image_length, image_width)
                      self.axes = 'SZYX'
              if False and self.is_rgb and 'extra_samples' in self.tags:
                  # DISABLED: only use RGB and first alpha channel if exists
                  extra_samples = self.extra_samples
                  if self.tags['extra_samples'].count == 1:
                      extra_samples = (extra_samples,)
                  for exs in extra_samples:
                      if exs in ('unassalpha', 'assocalpha', 'unspecified'):
                          if self.is_contig:
                              self.shape = self.shape[:-1] + (4,)
                          else:
                              self.shape = (4,) + self.shape[1:]
                          break
          else:
              self._shape = (1, 1, image_depth, image_length, image_width, 1)
              if image_depth == 1:
                  self.shape = (image_length, image_width)
                  self.axes = 'YX'
              else:
                  self.shape = (image_depth, image_length, image_width)
                  self.axes = 'ZYX'
          if not self.compression and 'strip_byte_counts' not in tags:
              self.strip_byte_counts = (
                  product(self.shape) * (self.bits_per_sample // 8),)
  
          assert len(self.shape) == len(self.axes)
  
      def _patch_imagej(self):
          """Return if ImageJ data are contiguous and adjust page attributes.
  
          Patch 'strip_offsets' and 'strip_byte_counts' tags to span the
          complete contiguous data.
  
          ImageJ stores all image metadata in the first page and image data is
          stored contiguously before the second page, if any. No need to
          read other pages.
  
          """
          if not self.is_imagej or not self.is_contiguous or self.parent.is_ome:
              return
          images = self.imagej_tags.get('images', 0)
          if images <= 1:
              return
          offset, count = self.is_contiguous
          shape = self.shape
          if self.is_indexed:
              shape = shape[:-1]
          if (count != product(shape) * self.bits_per_sample // 8 or
                  offset + count*images > self.parent.filehandle.size):
              self.is_imagej = False
              warnings.warn("corrupted ImageJ metadata or file")
              return
  
          pre = 'tile' if self.is_tiled else 'strip'
          self.tags[pre+'_offsets'].value = (offset,)
          self.tags[pre+'_byte_counts'].value = (count * images,)
          self.shape = (images,) + self.shape
          self._shape = (images,) + self._shape[1:]
          self.axes = 'I' + self.axes
          return True
  
      def asarray(self, squeeze=True, colormapped=True, rgbonly=False,
                  scale_mdgel=False, memmap=False, reopen=True,
                  maxsize=64*1024*1024*1024):
          """Read image data from file and return as numpy array.
  
          Raise ValueError if format is unsupported.
          If any of 'squeeze', 'colormapped', or 'rgbonly' are not the default,
          the shape of the returned array might be different from the page shape.
  
          Parameters
          ----------
          squeeze : bool
              If True, all length-1 dimensions (except X and Y) are
              squeezed out from result.
          colormapped : bool
              If True, color mapping is applied for palette-indexed images.
          rgbonly : bool
              If True, return RGB(A) image without additional extra samples.
          memmap : bool
              If True, use numpy.memmap to read arrays from file if possible.
              For use on 64 bit systems and files with few huge contiguous data.
          reopen : bool
              If True and the parent file handle is closed, the file is
              temporarily re-opened (and closed if no exception occurs).
          scale_mdgel : bool
              If True, MD Gel data will be scaled according to the private
              metadata in the second TIFF page. The dtype will be float32.
          maxsize: int or None
              Maximum size of data before a ValueError is raised.
              Can be used to catch DOS. Default: 64 GB.
  
          """
          if not self._shape:
              return
          if maxsize and product(self._shape) > maxsize:
              raise ValueError("data is too large %s" % str(self._shape))
  
          if self.dtype is None:
              raise ValueError("data type not supported: %s%i" % (
                  self.sample_format, self.bits_per_sample))
          if self.compression not in TIFF_DECOMPESSORS:
              raise ValueError("cannot decompress %s" % self.compression)
          if 'sample_format' in self.tags:
              tag = self.tags['sample_format']
              if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
                  raise ValueError("sample formats do not match %s" % tag.value)
  
          if self.is_chroma_subsampled:
              # TODO: implement chroma subsampling
              raise NotImplementedError("chroma subsampling not supported")
  
          fh = self.parent.filehandle
          closed = fh.closed
          if closed:
              if reopen:
                  fh.open()
              else:
                  raise IOError("file handle is closed")
  
          dtype = self._dtype
          shape = self._shape
          image_width = self.image_width
          image_length = self.image_length
          image_depth = self.image_depth
          typecode = self.parent.byteorder + dtype
          bits_per_sample = self.bits_per_sample
          lsb2msb = self.fill_order == 'lsb2msb'
  
          byte_counts, offsets = self._byte_counts_offsets
  
          if self.is_tiled:
              tile_width = self.tile_width
              tile_length = self.tile_length
              tile_depth = self.tile_depth if 'tile_depth' in self.tags else 1
              tw = (image_width + tile_width - 1) // tile_width
              tl = (image_length + tile_length - 1) // tile_length
              td = (image_depth + tile_depth - 1) // tile_depth
              shape = (shape[0], shape[1],
                       td*tile_depth, tl*tile_length, tw*tile_width, shape[-1])
              tile_shape = (tile_depth, tile_length, tile_width, shape[-1])
              runlen = tile_width
          else:
              runlen = image_width
  
          if memmap and self._is_memmappable(rgbonly, colormapped):
              result = fh.memmap_array(typecode, shape, offset=offsets[0])
          elif self.is_contiguous:
              fh.seek(offsets[0])
              result = fh.read_array(typecode, product(shape))
              result = result.astype('=' + dtype)
              if lsb2msb:
                  reverse_bitorder(result)
          else:
              if self.is_contig:
                  runlen *= self.samples_per_pixel
              if bits_per_sample in (8, 16, 32, 64, 128):
                  if (bits_per_sample * runlen) % 8:
                      raise ValueError("data and sample size mismatch")
  
                  def unpack(x, typecode=typecode):
                      if self.predictor == 'float':
                          # the floating point horizontal differencing decoder
                          # needs the raw byte order
                          typecode = dtype
                      try:
                          return numpy.fromstring(x, typecode)
                      except ValueError as e:
                          # strips may be missing EOI
                          warnings.warn("unpack: %s" % e)
                          xlen = ((len(x) // (bits_per_sample // 8)) *
                                  (bits_per_sample // 8))
                          return numpy.fromstring(x[:xlen], typecode)
  
              elif isinstance(bits_per_sample, tuple):
                  def unpack(x):
                      return unpack_rgb(x, typecode, bits_per_sample)
              else:
                  def unpack(x):
                      return unpack_ints(x, typecode, bits_per_sample, runlen)
  
              decompress = TIFF_DECOMPESSORS[self.compression]
              if self.compression == 'jpeg':
                  table = self.jpeg_tables if 'jpeg_tables' in self.tags else b''
  
                  def decompress(x):
                      return decode_jpeg(x, table, self.photometric)
  
              if self.is_tiled:
                  result = numpy.empty(shape, dtype)
                  tw, tl, td, pl = 0, 0, 0, 0
                  for offset, bytecount in zip(offsets, byte_counts):
                      fh.seek(offset)
                      tile = fh.read(bytecount)
                      if lsb2msb:
                          tile = reverse_bitorder(tile)
                      tile = decompress(tile)
                      tile = unpack(tile)
                      try:
                          tile.shape = tile_shape
                      except ValueError:
                          # incomplete tiles; see gdal issue #1179
                          warnings.warn("invalid tile data")
                          t = numpy.zeros(tile_shape, dtype).reshape(-1)
                          s = min(tile.size, t.size)
                          t[:s] = tile[:s]
                          tile = t.reshape(tile_shape)
                      if self.predictor == 'horizontal':
                          numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile)
                      elif self.predictor == 'float':
                          raise NotImplementedError()
                      result[0, pl, td:td+tile_depth,
                             tl:tl+tile_length, tw:tw+tile_width, :] = tile
                      del tile
                      tw += tile_width
                      if tw >= shape[4]:
                          tw, tl = 0, tl + tile_length
                          if tl >= shape[3]:
                              tl, td = 0, td + tile_depth
                              if td >= shape[2]:
                                  td, pl = 0, pl + 1
                  result = result[...,
                                  :image_depth, :image_length, :image_width, :]
              else:
                  strip_size = (self.rows_per_strip * self.image_width *
                                self.samples_per_pixel)
                  result = numpy.empty(shape, dtype).reshape(-1)
                  index = 0
                  for offset, bytecount in zip(offsets, byte_counts):
                      fh.seek(offset)
                      strip = fh.read(bytecount)
                      if lsb2msb:
                          strip = reverse_bitorder(strip)
                      strip = decompress(strip)
                      strip = unpack(strip)
                      size = min(result.size, strip.size, strip_size,
                                 result.size - index)
                      result[index:index+size] = strip[:size]
                      del strip
                      index += size
  
          result.shape = self._shape
  
          if self.predictor and not (self.is_tiled and not self.is_contiguous):
              if self.parent.is_lsm and not self.compression:
                  pass  # work around bug in LSM510 software
              elif self.predictor == 'horizontal':
                  numpy.cumsum(result, axis=-2, dtype=dtype, out=result)
              elif self.predictor == 'float':
                  result = decode_floats(result)
          if colormapped and self.is_indexed:
              if self.color_map.shape[1] >= 2**bits_per_sample:
                  # FluoView and LSM might fail here
                  result = apply_colormap(result[:, 0:1, :, :, :, 0:1],
                                          self.color_map)
          elif rgbonly and self.is_rgb and 'extra_samples' in self.tags:
              # return only RGB and first alpha channel if exists
              extra_samples = self.extra_samples
              if self.tags['extra_samples'].count == 1:
                  extra_samples = (extra_samples,)
              for i, exs in enumerate(extra_samples):
                  if exs in ('unassalpha', 'assocalpha', 'unspecified'):
                      if self.is_contig:
                          result = result[..., [0, 1, 2, 3+i]]
                      else:
                          result = result[:, [0, 1, 2, 3+i]]
                      break
              else:
                  if self.is_contig:
                      result = result[..., :3]
                  else:
                      result = result[:, :3]
  
          if squeeze:
              try:
                  result.shape = self.shape
              except ValueError:
                  warnings.warn("failed to reshape from %s to %s" % (
                      str(result.shape), str(self.shape)))
  
          if scale_mdgel and self.parent.is_mdgel:
              # MD Gel stores private metadata in the second page
              tags = self.parent.pages[1]
              if tags.md_file_tag in (2, 128):
                  scale = tags.md_scale_pixel
                  scale = scale[0] / scale[1]  # rational
                  result = result.astype('float32')
                  if tags.md_file_tag == 2:
                      result **= 2  # squary root data format
                  result *= scale
  
          if closed:
              # TODO: file should remain open if an exception occurred above
              fh.close()
          return result
  
      @lazyattr
      def _byte_counts_offsets(self):
          """Return simplified byte_counts and offsets."""
          if 'tile_offsets' in self.tags:
              byte_counts = self.tile_byte_counts
              offsets = self.tile_offsets
          else:
              byte_counts = self.strip_byte_counts
              offsets = self.strip_offsets
  
          j = 0
          for i, (b, o) in enumerate(zip(byte_counts, offsets)):
              if b > 0 and o > 0:
                  if i > j:
                      byte_counts[j] = b
                      offsets[j] = o
                  j += 1
              elif b > 0 and o <= 0:
                  raise ValueError("invalid offset")
              else:
                  warnings.warn("empty byte count")
          if j == 0:
              j = 1
  
          return byte_counts[:j], offsets[:j]
  
      def _is_memmappable(self, rgbonly, colormapped):
          """Return if page's image data in file can be memory-mapped."""
          return (self.parent.filehandle.is_file and
                  self.is_contiguous and
                  (self.bits_per_sample == 8 or self.parent._is_native) and
                  self.fill_order == 'msb2lsb' and
                  not self.predictor and
                  not self.is_chroma_subsampled and
                  not (rgbonly and 'extra_samples' in self.tags) and
                  not (colormapped and self.is_indexed))
  
      @lazyattr
      def is_contiguous(self):
          """Return offset and size of contiguous data, else None.
  
          Excludes prediction, fill_order, and colormapping.
  
          """
          if self.compression or self.bits_per_sample not in (8, 16, 32, 64):
              return
          if self.is_tiled:
              if (self.image_width != self.tile_width or
                      self.image_length % self.tile_length or
                      self.tile_width % 16 or self.tile_length % 16):
                  return
              if ('image_depth' in self.tags and 'tile_depth' in self.tags and
                      (self.image_length != self.tile_length or
                       self.image_depth % self.tile_depth)):
                  return
              offsets = self.tile_offsets
              byte_counts = self.tile_byte_counts
          else:
              offsets = self.strip_offsets
              byte_counts = self.strip_byte_counts
          if len(offsets) == 1:
              return offsets[0], byte_counts[0]
          if self.is_stk or all(offsets[i] + byte_counts[i] == offsets[i+1] or
                                byte_counts[i+1] == 0  # no data/ignore offset
                                for i in range(len(offsets)-1)):
              return offsets[0], sum(byte_counts)
  
      def __str__(self):
          """Return string containing information about page."""
          s = ', '.join(s for s in (
              ' x '.join(str(i) for i in self.shape),
              str(numpy.dtype(self.dtype)),
              '%s bit' % str(self.bits_per_sample),
              self.photometric if 'photometric' in self.tags else '',
              self.compression if self.compression else 'raw',
              '|'.join(t[3:] for t in (
                  'is_stk', 'is_lsm', 'is_nih', 'is_ome', 'is_imagej',
                  'is_micromanager', 'is_fluoview', 'is_mdgel', 'is_mediacy',
                  'is_scn', 'is_sgi', 'is_reduced', 'is_tiled',
                  'is_contiguous') if getattr(self, t))) if s)
          return "Page %i: %s" % (self.index, s)
  
      def __getattr__(self, name):
          """Return tag value."""
          if name in self.tags:
              value = self.tags[name].value
              setattr(self, name, value)
              return value
          raise AttributeError(name)
  
      @lazyattr
      def uic_tags(self):
          """Consolidate UIC tags."""
          if not self.is_stk:
              raise AttributeError("uic_tags")
          tags = self.tags
          result = Record()
          result.number_planes = tags['uic2tag'].count
          if 'image_description' in tags:
              result.plane_descriptions = self.image_description.split(b'\x00')
          if 'uic1tag' in tags:
              result.update(tags['uic1tag'].value)
          if 'uic3tag' in tags:
              result.update(tags['uic3tag'].value)  # wavelengths
          if 'uic4tag' in tags:
              result.update(tags['uic4tag'].value)  # override uic1 tags
          uic2tag = tags['uic2tag'].value
          result.z_distance = uic2tag.z_distance
          result.time_created = uic2tag.time_created
          result.time_modified = uic2tag.time_modified
          try:
              result.datetime_created = [
                  julian_datetime(*dt) for dt in
                  zip(uic2tag.date_created, uic2tag.time_created)]
              result.datetime_modified = [
                  julian_datetime(*dt) for dt in
                  zip(uic2tag.date_modified, uic2tag.time_modified)]
          except ValueError as e:
              warnings.warn("uic_tags: %s" % e)
          return result
  
      @lazyattr
      def imagej_tags(self):
          """Consolidate ImageJ metadata."""
          if not self.is_imagej:
              raise AttributeError("imagej_tags")
          result = imagej_description_dict(self.is_imagej)
          if 'imagej_metadata' in self.tags:
              try:
                  result.update(imagej_metadata(
                      self.tags['imagej_metadata'].value,
                      self.tags['imagej_byte_counts'].value,
                      self.parent.byteorder))
              except Exception as e:
                  warnings.warn(str(e))
          return Record(result)
  
      @lazyattr
      def is_rgb(self):
          """Page contains a RGB image."""
          return ('photometric' in self.tags and
                  self.tags['photometric'].value == 2)
  
      @lazyattr
      def is_contig(self):
          """Page contains contiguous image."""
          if 'planar_configuration' in self.tags:
              return self.tags['planar_configuration'].value == 1
          return True
  
      @lazyattr
      def is_indexed(self):
          """Page contains indexed, palette-colored image.
  
          Disable color-mapping for OME, LSM, STK, and ImageJ hyperstacks.
  
          """
          if (self.is_stk or self.is_lsm or self.parent.is_lsm or
                  self.is_ome or self.parent.is_ome):
              return False
          if self.is_imagej:
              if b'mode' in self.is_imagej:
                  return False
          elif self.parent.is_imagej:
              return self.parent.is_indexed
          return ('photometric' in self.tags and
                  self.tags['photometric'].value == 3)
  
      @lazyattr
      def is_tiled(self):
          """Page contains tiled image."""
          return 'tile_width' in self.tags
  
      @lazyattr
      def is_reduced(self):
          """Page is reduced image of another image."""
          return ('new_subfile_type' in self.tags and
                  self.tags['new_subfile_type'].value & 1)
  
      @lazyattr
      def is_chroma_subsampled(self):
          """Page contains chroma subsampled image."""
          return ('ycbcr_subsampling' in self.tags and
                  self.tags['ycbcr_subsampling'].value != (1, 1))
  
      @lazyattr
      def is_mdgel(self):
          """Page contains md_file_tag tag."""
          return 'md_file_tag' in self.tags
  
      @lazyattr
      def is_mediacy(self):
          """Page contains Media Cybernetics Id tag."""
          return ('mc_id' in self.tags and
                  self.tags['mc_id'].value.startswith(b'MC TIFF'))
  
      @lazyattr
      def is_stk(self):
          """Page contains UIC2Tag tag."""
          return 'uic2tag' in self.tags
  
      @lazyattr
      def is_lsm(self):
          """Page contains LSM CZ_LSM_INFO tag."""
          return 'cz_lsm_info' in self.tags
  
      @lazyattr
      def is_fluoview(self):
          """Page contains FluoView MM_STAMP tag."""
          return 'mm_stamp' in self.tags
  
      @lazyattr
      def is_nih(self):
          """Page contains NIH image header."""
          return 'nih_image_header' in self.tags
  
      @lazyattr
      def is_sgi(self):
          """Page contains SGI image and tile depth tags."""
          return 'image_depth' in self.tags and 'tile_depth' in self.tags
  
      @lazyattr
      def is_vista(self):
          """Software tag is 'ISS Vista'."""
          return ('software' in self.tags and
                  self.tags['software'].value == b'ISS Vista')
  
      @lazyattr
      def is_ome(self):
          """Page contains OME-XML in image_description tag."""
          if 'image_description' not in self.tags:
              return False
          d = self.tags['image_description'].value.strip()
          return d.startswith(b'<?xml version=') and d.endswith(b'</OME>')
  
      @lazyattr
      def is_scn(self):
          """Page contains Leica SCN XML in image_description tag."""
          if 'image_description' not in self.tags:
              return False
          d = self.tags['image_description'].value.strip()
          return d.startswith(b'<?xml version=') and d.endswith(b'</scn>')
  
      @lazyattr
      def is_shaped(self):
          """Return description containing shape if exists, else None."""
          if 'image_description' in self.tags:
              description = self.tags['image_description'].value
              if b'"shape":' in description or b'shape=(' in description:
                  return description
          if 'image_description_1' in self.tags:
              description = self.tags['image_description_1'].value
              if b'"shape":' in description or b'shape=(' in description:
                  return description
  
      @lazyattr
      def is_imagej(self):
          """Return ImageJ description if exists, else None."""
          if 'image_description' in self.tags:
              description = self.tags['image_description'].value
              if description.startswith(b'ImageJ='):
                  return description
          if 'image_description_1' in self.tags:
              # Micromanager
              description = self.tags['image_description_1'].value
              if description.startswith(b'ImageJ='):
                  return description
  
      @lazyattr
      def is_micromanager(self):
          """Page contains Micro-Manager metadata."""
          return 'micromanager_metadata' in self.tags
  
  
  class TiffTag(object):
      """A TIFF tag structure.
  
      Attributes
      ----------
      name : string
          Attribute name of tag.
      code : int
          Decimal code of tag.
      dtype : str
          Datatype of tag data. One of TIFF_DATA_TYPES.
      count : int
          Number of values.
      value : various types
          Tag data as Python object.
      value_offset : int
          Location of value in file, if any.
  
      All attributes are read-only.
  
      """
      __slots__ = ('code', 'name', 'count', 'dtype', 'value', 'value_offset',
                   '_offset', '_value', '_type')
  
      class Error(Exception):
          pass
  
      def __init__(self, arg, **kwargs):
          """Initialize instance from file or arguments."""
          self._offset = None
          if hasattr(arg, '_fh'):
              self._fromfile(arg, **kwargs)
          else:
              self._fromdata(arg, **kwargs)
  
      def _fromdata(self, code, dtype, count, value, name=None):
          """Initialize instance from arguments."""
          self.code = int(code)
          self.name = name if name else str(code)
          self.dtype = TIFF_DATA_TYPES[dtype]
          self.count = int(count)
          self.value = value
          self._value = value
          self._type = dtype
  
      def _fromfile(self, parent):
          """Read tag structure from open file. Advance file cursor."""
          fh = parent.filehandle
          byteorder = parent.byteorder
          self._offset = fh.tell()
          self.value_offset = self._offset + parent.offset_size + 4
  
          fmt, size = {4: ('HHI4s', 12), 8: ('HHQ8s', 20)}[parent.offset_size]
          data = fh.read(size)
          code, dtype = struct.unpack(byteorder + fmt[:2], data[:4])
          count, value = struct.unpack(byteorder + fmt[2:], data[4:])
          self._value = value
          self._type = dtype
  
          if code in TIFF_TAGS:
              name, _, _, cout_, _ = TIFF_TAGS[code]
              if cout_ and cout_ != count:
                  count = cout_
                  warnings.warn("incorrect count for tag '%s'" % name)
          elif code in CUSTOM_TAGS:
              name = CUSTOM_TAGS[code][0]
          else:
              name = str(code)
  
          try:
              dtype = TIFF_DATA_TYPES[self._type]
          except KeyError:
              raise TiffTag.Error("unknown tag data type %i" % self._type)
  
          fmt = '%s%i%s' % (byteorder, count*int(dtype[0]), dtype[1])
          size = struct.calcsize(fmt)
          if size > parent.offset_size or code in CUSTOM_TAGS:
              pos = fh.tell()
              tof = {4: 'I', 8: 'Q'}[parent.offset_size]
              self.value_offset = offset = struct.unpack(byteorder+tof, value)[0]
              if offset < 0 or offset > parent.filehandle.size:
                  raise TiffTag.Error("corrupt file - invalid tag value offset")
              elif offset < 4:
                  raise TiffTag.Error("corrupt value offset for tag %i" % code)
              fh.seek(offset)
              if code in CUSTOM_TAGS:
                  readfunc = CUSTOM_TAGS[code][1]
                  value = readfunc(fh, byteorder, dtype, count)
                  if isinstance(value, dict):  # numpy.core.records.record
                      value = Record(value)
              elif code in TIFF_TAGS or dtype[-1] == 's':
                  value = struct.unpack(fmt, fh.read(size))
              else:
                  value = read_numpy(fh, byteorder, dtype, count)
              fh.seek(pos)
          else:
              value = struct.unpack(fmt, value[:size])
  
          if code not in CUSTOM_TAGS and code not in (
                  273, 279, 324, 325, 530, 531):
              # scalar value if not strip/tile offsets/byte_counts or subsampling
              if len(value) == 1:
                  value = value[0]
  
          if (dtype.endswith('s') and isinstance(value, bytes) and
                  self._type != 7):
              # TIFF ASCII fields can contain multiple strings,
              #   each terminated with a NUL
              value = stripascii(value)
  
          self.code = code
          self.name = name
          self.dtype = dtype
          self.count = count
          self.value = value
  
      def _fix_lsm_bitspersample(self, parent):
          """Correct LSM bitspersample tag.
  
          Old LSM writers may use a separate region for two 16-bit values,
          although they fit into the tag value element of the tag.
  
          """
          if self.code == 258 and self.count == 2:
              # TODO: test this case; need example file
              warnings.warn("correcting LSM bitspersample tag")
              fh = parent.filehandle
              tof = {4: '<I', 8: '<Q'}[parent.offset_size]
              self.value_offset = struct.unpack(tof, self._value)[0]
              fh.seek(self.value_offset)
              self.value = struct.unpack("<HH", fh.read(4))
  
      def as_str(self):
          """Return value as human readable string."""
          return ((str(self.value).split('\n', 1)[0]) if (self._type != 7)
                  else '<undefined>')
  
      def __str__(self):
          """Return string containing information about tag."""
          return ' '.join(str(getattr(self, s)) for s in self.__slots__)
  
  
  class TiffPageSeries(object):
      """Series of TIFF pages with compatible shape and data type.
  
      Attributes
      ----------
      pages : list of TiffPage
          Sequence of TiffPages in series.
      dtype : numpy.dtype or str
          Data type of the image array in series.
      shape : tuple
          Dimensions of the image array in series.
      axes : str
          Labels of axes in shape. See TiffPage.axes.
      offset : int or None
          Position of image data in file if memory-mappable, else None.
  
      """
      #__slots__ = 'pages', 'shape', 'dtype', 'axes', 'parent'
  
      def __init__(self, pages, shape, dtype, axes, parent=None):
          # TODO? sort pages by page number?
          self.pages = pages
          self.shape = tuple(shape)
          self.axes = ''.join(axes)
          self.dtype = numpy.dtype(dtype)
          if parent:
              self.parent = parent
          elif len(pages):
              self.parent = pages[0].parent
          else:
              self.parent = None
  
      def asarray(self, memmap=False):
          """Return image data from series of TIFF pages as numpy array.
  
          Parameters
          ----------
          memmap : bool
              If True, return an array stored in a binary file on disk
              if possible.
  
          """
          if self.parent:
              return self.parent.asarray(series=self, memmap=memmap)
  
      @lazyattr
      def offset(self):
          """Return offset to memory-mappable data in page series."""
          if len(self.pages) == 0:
              return
  
          rgbonly = False
          colormapped = self.pages[0].is_indexed
          pos = 0
          for page in self.pages:
              if page is None:
                  return
              if not page._is_memmappable(rgbonly, colormapped):
                  return
              if not pos:
                  pos = page.is_contiguous[0] + page.is_contiguous[1]
                  continue
              if pos != page.is_contiguous[0]:
                  return
              pos += page.is_contiguous[1]
  
          offset = self.pages[0].is_contiguous[0]
          if (pos != offset + product(self.shape) * self.dtype.itemsize and
                  not self.pages[0].is_imagej):
              return
          return offset
  
      def __len__(self):
          """Return number of TiffPages in series."""
          return len(self.pages)
  
      def __getitem__(self, key):
          """Return specified TiffPage."""
          return self.pages[key]
  
      def __iter__(self):
          """Return iterator over TiffPages in series."""
          return iter(self.pages)
  
      def __str__(self):
          """Return string with information about series."""
          return "\n".join("* %s: %s" % kv for kv in (
              ("pages", len(self.pages)),
              ("dtype", str(self.dtype)),
              ("shape", str(self.shape)),
              ("axes", self.axes),
              ("offset", self.offset)
          ))
  
  
  class TiffSequence(object):
      """Sequence of image files.
  
      The data shape and dtype of all files must match.
  
      Attributes
      ----------
      files : list
          List of file names.
      shape : tuple
          Shape of image sequence.
      axes : str
          Labels of axes in shape.
  
      Examples
      --------
      >>> tifs = TiffSequence("test.oif.files/*.tif")
      >>> tifs.shape, tifs.axes
      ((2, 100), 'CT')
      >>> data = tifs.asarray()
      >>> data.shape
      (2, 100, 256, 256)
  
      """
      _patterns = {
          'axes': r"""
              # matches Olympus OIF and Leica TIFF series
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
              """}
  
      class ParseError(Exception):
          pass
  
      def __init__(self, files, imread=TiffFile, pattern='axes',
                   *args, **kwargs):
          """Initialize instance from multiple files.
  
          Parameters
          ----------
          files : str, or sequence of str
              Glob pattern or sequence of file names.
              Binary streams are not supported.
          imread : function or class
              Image read function or class with asarray function returning numpy
              array from single file.
          pattern : str
              Regular expression pattern that matches axes names and sequence
              indices in file names.
              By default this matches Olympus OIF and Leica TIFF series.
  
          """
          if isinstance(files, basestring):
              files = natural_sorted(glob.glob(files))
          files = list(files)
          if not files:
              raise ValueError("no files found")
          if not isinstance(files[0], basestring):
              raise ValueError("not a file name")
          #if not os.path.isfile(files[0]):
          #    raise ValueError("file not found")
          self.files = files
  
          if hasattr(imread, 'asarray'):
              # redefine imread
              _imread = imread
  
              def imread(fname, *args, **kwargs):
                  with _imread(fname) as im:
                      return im.asarray(*args, **kwargs)
  
          self.imread = imread
  
          self.pattern = self._patterns.get(pattern, pattern)
          try:
              self._parse()
              if not self.axes:
                  self.axes = 'I'
          except self.ParseError:
              self.axes = 'I'
              self.shape = (len(files),)
              self._start_index = (0,)
              self._indices = tuple((i,) for i in range(len(files)))
  
      def __str__(self):
          """Return string with information about image sequence."""
          return "\n".join([
              self.files[0],
              '* files: %i' % len(self.files),
              '* axes: %s' % self.axes,
              '* shape: %s' % str(self.shape)])
  
      def __len__(self):
          return len(self.files)
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def close(self):
          pass
  
      def asarray(self, memmap=False, tempdir=None, *args, **kwargs):
          """Read image data from all files and return as single numpy array.
  
          If memmap is True, return an array stored in a binary file on disk.
          The args and kwargs parameters are passed to the imread function.
  
          Raise IndexError or ValueError if image shapes do not match.
  
          """
          im = self.imread(self.files[0], *args, **kwargs)
          shape = self.shape + im.shape
          if memmap:
              with tempfile.NamedTemporaryFile(dir=tempdir) as fh:
                  result = numpy.memmap(fh, dtype=im.dtype, shape=shape)
          else:
              result = numpy.zeros(shape, dtype=im.dtype)
          result = result.reshape(-1, *im.shape)
          for index, fname in zip(self._indices, self.files):
              index = [i-j for i, j in zip(index, self._start_index)]
              index = numpy.ravel_multi_index(index, self.shape)
              im = self.imread(fname, *args, **kwargs)
              result[index] = im
          result.shape = shape
          return result
  
      def _parse(self):
          """Get axes and shape from file names."""
          if not self.pattern:
              raise self.ParseError("invalid pattern")
          pattern = re.compile(self.pattern, re.IGNORECASE | re.VERBOSE)
          matches = pattern.findall(self.files[0])
          if not matches:
              raise self.ParseError("pattern does not match file names")
          matches = matches[-1]
          if len(matches) % 2:
              raise self.ParseError("pattern does not match axis name and index")
          axes = ''.join(m for m in matches[::2] if m)
          if not axes:
              raise self.ParseError("pattern does not match file names")
  
          indices = []
          for fname in self.files:
              matches = pattern.findall(fname)[-1]
              if axes != ''.join(m for m in matches[::2] if m):
                  raise ValueError("axes do not match within the image sequence")
              indices.append([int(m) for m in matches[1::2] if m])
          shape = tuple(numpy.max(indices, axis=0))
          start_index = tuple(numpy.min(indices, axis=0))
          shape = tuple(i-j+1 for i, j in zip(shape, start_index))
          if product(shape) != len(self.files):
              warnings.warn("files are missing. Missing data are zeroed")
  
          self.axes = axes.upper()
          self.shape = shape
          self._indices = indices
          self._start_index = start_index
  
  
  class Record(dict):
      """Dictionary with attribute access.
  
      Can also be initialized with numpy.core.records.record.
  
      """
      __slots__ = ()
  
      def __init__(self, arg=None, **kwargs):
          if kwargs:
              arg = kwargs
          elif arg is None:
              arg = {}
          try:
              dict.__init__(self, arg)
          except (TypeError, ValueError):
              for i, name in enumerate(arg.dtype.names):
                  v = arg[i]
                  self[name] = v if v.dtype.char != 'S' else stripnull(v)
  
      def __getattr__(self, name):
          return self[name]
  
      def __setattr__(self, name, value):
          self.__setitem__(name, value)
  
      def __str__(self):
          """Pretty print Record."""
          s = []
          lists = []
          for k in sorted(self):
              try:
                  if k.startswith('_'):  # does not work with byte
                      continue
              except AttributeError:
                  pass
              v = self[k]
              if isinstance(v, (list, tuple)) and len(v):
                  if isinstance(v[0], Record):
                      lists.append((k, v))
                      continue
                  elif isinstance(v[0], TiffPage):
                      v = [i.index for i in v if i]
              s.append(
                  ("* %s: %s" % (k, str(v))).split("\n", 1)[0]
                  [:PRINT_LINE_LEN].rstrip())
          for k, v in lists:
              l = []
              for i, w in enumerate(v):
                  l.append("* %s[%i]\n  %s" % (k, i,
                                               str(w).replace("\n", "\n  ")))
              s.append('\n'.join(l))
          return '\n'.join(s)
  
  
  class TiffTags(Record):
      """Dictionary of TiffTag with attribute access."""
  
      def __str__(self):
          """Return string with information about all tags."""
          s = []
          for tag in sorted(self.values(), key=lambda x: x.code):
              typecode = "%i%s" % (tag.count * int(tag.dtype[0]), tag.dtype[1])
              line = "* %i %s (%s) %s" % (
                  tag.code, tag.name, typecode, tag.as_str())
              s.append(line[:PRINT_LINE_LEN].lstrip())
          return '\n'.join(s)
  
  
  class FileHandle(object):
      """Binary file handle.
  
      A limited, special purpose file handler that:
  
      * handles embedded files (for CZI within CZI files)
      * allows to re-open closed files (for multi file formats, such as OME-TIFF)
      * reads and writes numpy arrays and records from file like objects
  
      Only 'rb' and 'wb' modes are supported. Concurrently reading and writing
      of the same stream is untested.
  
      When initialized from another file handle, do not use it unless this
      FileHandle is closed.
  
      Attributes
      ----------
      name : str
          Name of the file.
      path : str
          Absolute path to file.
      size : int
          Size of file in bytes.
      is_file : bool
          If True, file has a filno and can be memory-mapped.
  
      All attributes are read-only.
  
      """
      __slots__ = ('_fh', '_file', '_mode', '_name', '_dir',
                   '_offset', '_size', '_close', 'is_file')
  
      def __init__(self, file, mode='rb', name=None, offset=None, size=None):
          """Initialize file handle from file name or another file handle.
  
          Parameters
          ----------
          file : str, binary stream, or FileHandle
              File name or seekable binary stream, such as a open file
              or BytesIO.
          mode : str
              File open mode in case 'file' is a file name. Must be 'rb' or 'wb'.
          name : str
              Optional name of file in case 'file' is a binary stream.
          offset : int
              Optional start position of embedded file. By default this is
              the current file position.
          size : int
              Optional size of embedded file. By default this is the number
              of bytes from the 'offset' to the end of the file.
  
          """
          self._fh = None
          self._file = file
          self._mode = mode
          self._name = name
          self._dir = ''
          self._offset = offset
          self._size = size
          self._close = True
          self.is_file = False
          self.open()
  
      def open(self):
          """Open or re-open file."""
          if self._fh:
              return  # file is open
  
          if isinstance(self._file, basestring):
              # file name
              self._file = os.path.abspath(self._file)
              self._dir, self._name = os.path.split(self._file)
              self._fh = open(self._file, self._mode)
              self._close = True
              if self._offset is None:
                  self._offset = 0
          elif isinstance(self._file, FileHandle):
              # FileHandle
              self._fh = self._file._fh
              if self._offset is None:
                  self._offset = 0
              self._offset += self._file._offset
              self._close = False
              if not self._name:
                  if self._offset:
                      name, ext = os.path.splitext(self._file._name)
                      self._name = "%s@%i%s" % (name, self._offset, ext)
                  else:
                      self._name = self._file._name
              if self._mode and self._mode != self._file._mode:
                  raise ValueError('FileHandle has wrong mode')
              self._mode = self._file._mode
              self._dir = self._file._dir
          elif hasattr(self._file, 'seek'):
              # binary stream: open file, BytesIO
              try:
                  self._file.tell()
              except Exception:
                  raise ValueError("binary stream is not seekable")
              self._fh = self._file
              if self._offset is None:
                  self._offset = self._file.tell()
              self._close = False
              if not self._name:
                  try:
                      self._dir, self._name = os.path.split(self._fh.name)
                  except AttributeError:
                      self._name = "Unnamed binary stream"
              try:
                  self._mode = self._fh.mode
              except AttributeError:
                  pass
          else:
              raise ValueError("The first parameter must be a file name, "
                               "seekable binary stream, or FileHandle")
  
          # if self._mode not in (None, 'rb', 'wb', 'r+b', 'rb+', 'w+b', 'wb+'):
          #    raise ValueError('file mode not supported: %s' % self._mode)
  
          if self._offset:
              self._fh.seek(self._offset)
  
          if self._size is None:
              pos = self._fh.tell()
              self._fh.seek(self._offset, 2)
              self._size = self._fh.tell()
              self._fh.seek(pos)
  
          try:
              self._fh.fileno()
              self.is_file = True
          except Exception:
              self.is_file = False
  
      def read(self, size=-1):
          """Read 'size' bytes from file, or until EOF is reached."""
          if size < 0 and self._offset:
              size = self._size
          return self._fh.read(size)
  
      def write(self, bytestring):
          """Write bytestring to file."""
          return self._fh.write(bytestring)
  
      def flush(self):
          """Flush write buffers if applicable."""
          return self._fh.flush()
  
      def memmap_array(self, dtype, shape, offset=0, mode='r', order='C'):
          """Return numpy.memmap of data stored in file."""
          if not self.is_file:
              raise ValueError("Can not memory-map file without fileno.")
          return numpy.memmap(self._fh, dtype=dtype, mode=mode,
                              offset=self._offset + offset,
                              shape=shape, order=order)
  
      def read_array(self, dtype, count=-1, sep=""):
          """Return numpy array from file.
  
          Work around numpy issue #2230, "numpy.fromfile does not accept
          StringIO object" https://github.com/numpy/numpy/issues/2230.
  
          """
          try:
              return numpy.fromfile(self._fh, dtype, count, sep)
          except IOError:
              if count < 0:
                  size = self._size
              else:
                  size = count * numpy.dtype(dtype).itemsize
              data = self._fh.read(size)
              return numpy.fromstring(data, dtype, count, sep)
  
      def read_record(self, dtype, shape=1, byteorder=None):
          """Return numpy record from file."""
          try:
              rec = numpy.rec.fromfile(self._fh, dtype, shape,
                                       byteorder=byteorder)
          except Exception:
              dtype = numpy.dtype(dtype)
              if shape is None:
                  shape = self._size // dtype.itemsize
              size = product(sequence(shape)) * dtype.itemsize
              data = self._fh.read(size)
              return numpy.rec.fromstring(data, dtype, shape,
                                          byteorder=byteorder)
          return rec[0] if shape == 1 else rec
  
      def write_array(self, data):
          """Write numpy array to binary file."""
          try:
              data.tofile(self._fh)
          except Exception:
              # BytesIO
              self._fh.write(data.tostring())
  
      def tell(self):
          """Return file's current position."""
          return self._fh.tell() - self._offset
  
      def seek(self, offset, whence=0):
          """Set file's current position."""
          if self._offset:
              if whence == 0:
                  self._fh.seek(self._offset + offset, whence)
                  return
              elif whence == 2 and self._size > 0:
                  self._fh.seek(self._offset + self._size + offset, 0)
                  return
          self._fh.seek(offset, whence)
  
      def close(self):
          """Close file."""
          if self._close and self._fh:
              self._fh.close()
              self._fh = None
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def __getattr__(self, name):
          """Return attribute from underlying file object."""
          if self._offset:
              warnings.warn(
                  "FileHandle: '%s' not implemented for embedded files" % name)
          return getattr(self._fh, name)
  
      @property
      def name(self):
          return self._name
  
      @property
      def dirname(self):
          return self._dir
  
      @property
      def path(self):
          return os.path.join(self._dir, self._name)
  
      @property
      def size(self):
          return self._size
  
      @property
      def closed(self):
          return self._fh is None
  
  
  def read_bytes(fh, byteorder, dtype, count):
      """Read tag data from file and return as byte string."""
      dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
      return fh.read_array(dtype, count).tostring()
  
  
  def read_numpy(fh, byteorder, dtype, count):
      """Read tag data from file and return as numpy array."""
      dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
      return fh.read_array(dtype, count)
  
  
  def read_json(fh, byteorder, dtype, count):
      """Read JSON tag data from file and return as object."""
      data = fh.read(count)
      try:
          return json.loads(unicode(stripnull(data), 'utf-8'))
      except ValueError:
          warnings.warn("invalid JSON '%s'" % data)
  
  
  def read_mm_header(fh, byteorder, dtype, count):
      """Read MM_HEADER tag from file and return as numpy.rec.array."""
      return fh.read_record(MM_HEADER, byteorder=byteorder)
  
  
  def read_mm_stamp(fh, byteorder, dtype, count):
      """Read MM_STAMP tag from file and return as numpy.ndarray."""
      return fh.read_array(byteorder+'f8', 8)
  
  
  def read_uic1tag(fh, byteorder, dtype, count, plane_count=None):
      """Read MetaMorph STK UIC1Tag from file and return as dictionary.
  
      Return empty dictionary if plane_count is unknown.
  
      """
      assert dtype in ('2I', '1I') and byteorder == '<'
      result = {}
      if dtype == '2I':
          # pre MetaMorph 2.5 (not tested)
          values = fh.read_array('<u4', 2*count).reshape(count, 2)
          result = {'z_distance': values[:, 0] / values[:, 1]}
      elif plane_count:
          for _ in range(count):
              tagid = struct.unpack('<I', fh.read(4))[0]
              if tagid in (28, 29, 37, 40, 41):
                  # silently skip unexpected tags
                  fh.read(4)
                  continue
              name, value = read_uic_tag(fh, tagid, plane_count, offset=True)
              result[name] = value
      return result
  
  
  def read_uic2tag(fh, byteorder, dtype, plane_count):
      """Read MetaMorph STK UIC2Tag from file and return as dictionary."""
      assert dtype == '2I' and byteorder == '<'
      values = fh.read_array('<u4', 6*plane_count).reshape(plane_count, 6)
      return {
          'z_distance': values[:, 0] / values[:, 1],
          'date_created': values[:, 2],  # julian days
          'time_created': values[:, 3],  # milliseconds
          'date_modified': values[:, 4],  # julian days
          'time_modified': values[:, 5],  # milliseconds
      }
  
  
  def read_uic3tag(fh, byteorder, dtype, plane_count):
      """Read MetaMorph STK UIC3Tag from file and return as dictionary."""
      assert dtype == '2I' and byteorder == '<'
      values = fh.read_array('<u4', 2*plane_count).reshape(plane_count, 2)
      return {'wavelengths': values[:, 0] / values[:, 1]}
  
  
  def read_uic4tag(fh, byteorder, dtype, plane_count):
      """Read MetaMorph STK UIC4Tag from file and return as dictionary."""
      assert dtype == '1I' and byteorder == '<'
      result = {}
      while True:
          tagid = struct.unpack('<H', fh.read(2))[0]
          if tagid == 0:
              break
          name, value = read_uic_tag(fh, tagid, plane_count, offset=False)
          result[name] = value
      return result
  
  
  def read_uic_tag(fh, tagid, plane_count, offset):
      """Read a single UIC tag value from file and return tag name and value.
  
      UIC1Tags use an offset.
  
      """
      def read_int(count=1):
          value = struct.unpack('<%iI' % count, fh.read(4*count))
          return value[0] if count == 1 else value
  
      try:
          name, dtype = UIC_TAGS[tagid]
      except KeyError:
          # unknown tag
          return '_tagid_%i' % tagid, read_int()
  
      if offset:
          pos = fh.tell()
          if dtype not in (int, None):
              off = read_int()
              if off < 8:
                  warnings.warn("invalid offset for uic tag '%s': %i"
                                % (name, off))
                  return name, off
              fh.seek(off)
  
      if dtype is None:
          # skip
          name = '_' + name
          value = read_int()
      elif dtype is int:
          # int
          value = read_int()
      elif dtype is Fraction:
          # fraction
          value = read_int(2)
          value = value[0] / value[1]
      elif dtype is julian_datetime:
          # datetime
          value = julian_datetime(*read_int(2))
      elif dtype is read_uic_image_property:
          # ImagePropertyEx
          value = read_uic_image_property(fh)
      elif dtype is str:
          # pascal string
          size = read_int()
          if 0 <= size < 2**10:
              value = struct.unpack('%is' % size, fh.read(size))[0][:-1]
              value = stripnull(value)
          elif offset:
              value = ''
              warnings.warn("corrupt string in uic tag '%s'" % name)
          else:
              raise ValueError("invalid string size %i" % size)
      elif dtype == '%ip':
          # sequence of pascal strings
          value = []
          for _ in range(plane_count):
              size = read_int()
              if 0 <= size < 2**10:
                  string = struct.unpack('%is' % size, fh.read(size))[0][:-1]
                  string = stripnull(string)
                  value.append(string)
              elif offset:
                  warnings.warn("corrupt string in uic tag '%s'" % name)
              else:
                  raise ValueError("invalid string size %i" % size)
      else:
          # struct or numpy type
          dtype = '<' + dtype
          if '%i' in dtype:
              dtype = dtype % plane_count
          if '(' in dtype:
              # numpy type
              value = fh.read_array(dtype, 1)[0]
              if value.shape[-1] == 2:
                  # assume fractions
                  value = value[..., 0] / value[..., 1]
          else:
              # struct format
              value = struct.unpack(dtype, fh.read(struct.calcsize(dtype)))
              if len(value) == 1:
                  value = value[0]
  
      if offset:
          fh.seek(pos + 4)
  
      return name, value
  
  
  def read_uic_image_property(fh):
      """Read UIC ImagePropertyEx tag from file and return as dict."""
      # TODO: test this
      size = struct.unpack('B', fh.read(1))[0]
      name = struct.unpack('%is' % size, fh.read(size))[0][:-1]
      flags, prop = struct.unpack('<IB', fh.read(5))
      if prop == 1:
          value = struct.unpack('II', fh.read(8))
          value = value[0] / value[1]
      else:
          size = struct.unpack('B', fh.read(1))[0]
          value = struct.unpack('%is' % size, fh.read(size))[0]
      return dict(name=name, flags=flags, value=value)
  
  
  def read_cz_lsm_info(fh, byteorder, dtype, count):
      """Read CS_LSM_INFO tag from file and return as numpy.rec.array."""
      assert byteorder == '<'
      magic_number, structure_size = struct.unpack('<II', fh.read(8))
      if magic_number not in (50350412, 67127628):
          raise ValueError("not a valid CS_LSM_INFO structure")
      fh.seek(-8, 1)
  
      if structure_size < numpy.dtype(CZ_LSM_INFO).itemsize:
          # adjust structure according to structure_size
          cz_lsm_info = []
          size = 0
          for name, dtype in CZ_LSM_INFO:
              size += numpy.dtype(dtype).itemsize
              if size > structure_size:
                  break
              cz_lsm_info.append((name, dtype))
      else:
          cz_lsm_info = CZ_LSM_INFO
  
      return fh.read_record(cz_lsm_info, byteorder=byteorder)
  
  
  def read_cz_lsm_floatpairs(fh):
      """Read LSM sequence of float pairs from file and return as list."""
      size = struct.unpack('<i', fh.read(4))[0]
      return fh.read_array('<2f8', count=size)
  
  
  def read_cz_lsm_positions(fh):
      """Read LSM positions from file and return as list."""
      size = struct.unpack('<I', fh.read(4))[0]
      return fh.read_array('<2f8', count=size)
  
  
  def read_cz_lsm_time_stamps(fh):
      """Read LSM time stamps from file and return as list."""
      size, count = struct.unpack('<ii', fh.read(8))
      if size != (8 + 8 * count):
          raise ValueError("lsm_time_stamps block is too short")
      # return struct.unpack('<%dd' % count, fh.read(8*count))
      return fh.read_array('<f8', count=count)
  
  
  def read_cz_lsm_event_list(fh):
      """Read LSM events from file and return as list of (time, type, text)."""
      count = struct.unpack('<II', fh.read(8))[1]
      events = []
      while count > 0:
          esize, etime, etype = struct.unpack('<IdI', fh.read(16))
          etext = stripnull(fh.read(esize - 16))
          events.append((etime, etype, etext))
          count -= 1
      return events
  
  
  def read_cz_lsm_scan_info(fh):
      """Read LSM scan information from file and return as Record."""
      block = Record()
      blocks = [block]
      unpack = struct.unpack
      if 0x10000000 != struct.unpack('<I', fh.read(4))[0]:
          # not a Recording sub block
          raise ValueError("not a lsm_scan_info structure")
      fh.read(8)
      while True:
          entry, dtype, size = unpack('<III', fh.read(12))
          if dtype == 2:
              # ascii
              value = stripnull(fh.read(size))
          elif dtype == 4:
              # long
              value = unpack('<i', fh.read(4))[0]
          elif dtype == 5:
              # rational
              value = unpack('<d', fh.read(8))[0]
          else:
              value = 0
          if entry in CZ_LSM_SCAN_INFO_ARRAYS:
              blocks.append(block)
              name = CZ_LSM_SCAN_INFO_ARRAYS[entry]
              newobj = []
              setattr(block, name, newobj)
              block = newobj
          elif entry in CZ_LSM_SCAN_INFO_STRUCTS:
              blocks.append(block)
              newobj = Record()
              block.append(newobj)
              block = newobj
          elif entry in CZ_LSM_SCAN_INFO_ATTRIBUTES:
              name = CZ_LSM_SCAN_INFO_ATTRIBUTES[entry]
              setattr(block, name, value)
          elif entry == 0xffffffff:
              # end sub block
              block = blocks.pop()
          else:
              # unknown entry
              setattr(block, "entry_0x%x" % entry, value)
          if not blocks:
              break
      return block
  
  
  def read_nih_image_header(fh, byteorder, dtype, count):
      """Read NIH_IMAGE_HEADER tag from file and return as numpy.rec.array."""
      a = fh.read_record(NIH_IMAGE_HEADER, byteorder=byteorder)
      a = a.newbyteorder(byteorder)
      a.xunit = a.xunit[:a._xunit_len]
      a.um = a.um[:a._um_len]
      return a
  
  
  def read_micromanager_metadata(fh):
      """Read MicroManager non-TIFF settings from open file and return as dict.
  
      The settings can be used to read image data without parsing the TIFF file.
  
      Raise ValueError if file does not contain valid MicroManager metadata.
  
      """
      fh.seek(0)
      try:
          byteorder = {b'II': '<', b'MM': '>'}[fh.read(2)]
      except IndexError:
          raise ValueError("not a MicroManager TIFF file")
  
      result = {}
      fh.seek(8)
      (index_header, index_offset, display_header, display_offset,
       comments_header, comments_offset, summary_header, summary_length
       ) = struct.unpack(byteorder + "IIIIIIII", fh.read(32))
  
      if summary_header != 2355492:
          raise ValueError("invalid MicroManager summary_header")
      result['summary'] = read_json(fh, byteorder, None, summary_length)
  
      if index_header != 54773648:
          raise ValueError("invalid MicroManager index_header")
      fh.seek(index_offset)
      header, count = struct.unpack(byteorder + "II", fh.read(8))
      if header != 3453623:
          raise ValueError("invalid MicroManager index_header")
      data = struct.unpack(byteorder + "IIIII"*count, fh.read(20*count))
      result['index_map'] = {
          'channel': data[::5], 'slice': data[1::5], 'frame': data[2::5],
          'position': data[3::5], 'offset': data[4::5]}
  
      if display_header != 483765892:
          raise ValueError("invalid MicroManager display_header")
      fh.seek(display_offset)
      header, count = struct.unpack(byteorder + "II", fh.read(8))
      if header != 347834724:
          raise ValueError("invalid MicroManager display_header")
      result['display_settings'] = read_json(fh, byteorder, None, count)
  
      if comments_header != 99384722:
          raise ValueError("invalid MicroManager comments_header")
      fh.seek(comments_offset)
      header, count = struct.unpack(byteorder + "II", fh.read(8))
      if header != 84720485:
          raise ValueError("invalid MicroManager comments_header")
      result['comments'] = read_json(fh, byteorder, None, count)
  
      return result
  
  
  def imagej_metadata(data, bytecounts, byteorder):
      """Return dictionary from ImageJ metadata tag value."""
      _str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252')
  
      def read_string(data, byteorder):
          return _str(stripnull(data[0 if byteorder == '<' else 1::2]))
  
      def read_double(data, byteorder):
          return struct.unpack(byteorder+('d' * (len(data) // 8)), data)
  
      def read_bytes(data, byteorder):
          #return struct.unpack('b' * len(data), data)
          return numpy.fromstring(data, 'uint8')
  
      metadata_types = {  # big endian
          b'info': ('info', read_string),
          b'labl': ('labels', read_string),
          b'rang': ('ranges', read_double),
          b'luts': ('luts', read_bytes),
          b'roi ': ('roi', read_bytes),
          b'over': ('overlays', read_bytes)}
      metadata_types.update(  # little endian
          dict((k[::-1], v) for k, v in metadata_types.items()))
  
      if not bytecounts:
          raise ValueError("no ImageJ metadata")
  
      if not data[:4] in (b'IJIJ', b'JIJI'):
          raise ValueError("invalid ImageJ metadata")
  
      header_size = bytecounts[0]
      if header_size < 12 or header_size > 804:
          raise ValueError("invalid ImageJ metadata header size")
  
      ntypes = (header_size - 4) // 8
      header = struct.unpack(byteorder+'4sI'*ntypes, data[4:4+ntypes*8])
      pos = 4 + ntypes * 8
      counter = 0
      result = {}
      for mtype, count in zip(header[::2], header[1::2]):
          values = []
          name, func = metadata_types.get(mtype, (_str(mtype), read_bytes))
          for _ in range(count):
              counter += 1
              pos1 = pos + bytecounts[counter]
              values.append(func(data[pos:pos1], byteorder))
              pos = pos1
          result[name.strip()] = values[0] if count == 1 else values
      return result
  
  
  def imagej_description_dict(description):
      """Return dictionary from ImageJ image description byte string.
  
      Raise ValueError if not a valid ImageJ description.
  
      >>> description = b'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
      >>> imagej_description_dict(description)  # doctest: +SKIP
      {'ImageJ': '1.11a', 'images': 510, 'hyperstack': True}
  
      """
      def _bool(val):
          return {b'true': True, b'false': False}[val.lower()]
  
      _str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252')
      result = {}
      for line in description.splitlines():
          try:
              key, val = line.split(b'=')
          except Exception:
              continue
          key = key.strip()
          val = val.strip()
          for dtype in (int, float, _bool, _str):
              try:
                  val = dtype(val)
                  break
              except Exception:
                  pass
          result[_str(key)] = val
      if 'ImageJ' not in result:
          raise ValueError("not a ImageJ image description")
      return result
  
  
  def imagej_description(shape, rgb=None, colormaped=False, version='1.11a',
                         hyperstack=None, mode=None, loop=None, **kwargs):
      """Return ImageJ image decription from data shape as byte string.
  
      ImageJ can handle up to 6 dimensions in order TZCYXS.
  
      >>> imagej_description((51, 5, 2, 196, 171))  # doctest: +SKIP
      ImageJ=1.11a
      images=510
      channels=2
      slices=5
      frames=51
      hyperstack=true
      mode=grayscale
      loop=false
  
      """
      if colormaped:
          raise NotImplementedError("ImageJ colormapping not supported")
      shape = imagej_shape(shape, rgb=rgb)
      rgb = shape[-1] in (3, 4)
  
      result = ['ImageJ=%s' % version]
      append = []
      result.append('images=%i' % product(shape[:-3]))
      if hyperstack is None:
          #if product(shape[:-3]) > 1:
          hyperstack = True
          append.append('hyperstack=true')
      else:
          append.append('hyperstack=%s' % bool(hyperstack))
      if shape[2] > 1:
          result.append('channels=%i' % shape[2])
      if mode is None and not rgb:
          mode = 'grayscale'
      if hyperstack and mode:
          append.append('mode=%s' % mode)
      if shape[1] > 1:
          result.append('slices=%i' % shape[1])
      if shape[0] > 1:
          result.append("frames=%i" % shape[0])
          if loop is None:
              append.append('loop=false')
      if loop is not None:
          append.append('loop=%s' % bool(loop))
      for key, value in kwargs.items():
          append.append('%s=%s' % (key.lower(), value))
  
      return str2bytes('\n'.join(result + append + ['']))
  
  
  def imagej_shape(shape, rgb=None):
      """Return shape normalized to 6D ImageJ hyperstack TZCYXS.
  
      Raise ValueError if not a valid ImageJ hyperstack shape.
  
      >>> imagej_shape((2, 3, 4, 5, 3), False)
      (2, 3, 4, 5, 3, 1)
  
      """
      shape = tuple(int(i) for i in shape)
      ndim = len(shape)
      if 1 > ndim > 6:
          raise ValueError("invalid ImageJ hyperstack: not 2 to 6 dimensional")
      if rgb is None:
          rgb = shape[-1] in (3, 4) and ndim > 2
      if rgb and shape[-1] not in (3, 4):
          raise ValueError("invalid ImageJ hyperstack: not a RGB image")
      if not rgb and ndim == 6 and shape[-1] != 1:
          raise ValueError("invalid ImageJ hyperstack: not a non-RGB image")
      if rgb or shape[-1] == 1:
          return (1, ) * (6 - ndim) + shape
      else:
          return (1, ) * (5 - ndim) + shape + (1,)
  
  
  def image_description_dict(description):
      """Return dictionary from image description byte string.
  
      Raise ValuError if description is of unknown format.
  
      >>> image_description_dict(b'shape=(256, 256, 3)')
      {'shape': (256, 256, 3)}
      >>> description = b'{"shape": [256, 256, 3], "axes": "YXS"}'
      >>> image_description_dict(description)  # doctest: +SKIP
      {'shape': [256, 256, 3], 'axes': 'YXS'}
  
      """
      if description.startswith(b'shape='):
          # old style 'shaped' description
          shape = tuple(int(i) for i in description[7:-1].split(b','))
          return dict(shape=shape)
      if description.startswith(b'{') and description.endswith(b'}'):
          # JSON description
          return json.loads(description.decode('utf-8'))
      raise ValueError("unknown image description")
  
  
  def image_description(shape, colormaped=False, **metadata):
      """Return image description from data shape and meta data.
  
      Return UTF-8 encoded JSON.
  
      >>> image_description((256, 256, 3), axes='YXS')  # doctest: +SKIP
      b'{"shape": [256, 256, 3], "axes": "YXS"}'
  
      """
      if colormaped:
          shape = shape + (3,)
      metadata.update({'shape': shape})
      return json.dumps(metadata).encode('utf-8')
  
  
  def _replace_by(module_function, package=__package__, warn=False):
      """Try replace decorated function by module.function."""
      try:
          from importlib import import_module
      except ImportError:
          warnings.warn('could not import module importlib')
          return lambda func: func
  
      def decorate(func, module_function=module_function, warn=warn):
          try:
              module, function = module_function.split('.')
              if package:
                  module = import_module('.' + module, package=package)
              else:
                  module = import_module(module)
              func, oldfunc = getattr(module, function), func
              globals()['__old_' + func.__name__] = oldfunc
          except Exception:
              if warn:
                  warnings.warn("failed to import %s" % module_function)
          return func
  
      return decorate
  
  
  def decode_floats(data):
      """Decode floating point horizontal differencing.
  
      The TIFF predictor type 3 reorders the bytes of the image values and
      applies horizontal byte differencing to improve compression of floating
      point images. The ordering of interleaved color channels is preserved.
  
      Parameters
      ----------
      data : numpy.ndarray
          The image to be decoded. The dtype must be a floating point.
          The shape must include the number of contiguous samples per pixel
          even if 1.
  
      """
      shape = data.shape
      dtype = data.dtype
      if len(shape) < 3:
          raise ValueError('invalid data shape')
      if dtype.char not in 'dfe':
          raise ValueError('not a floating point image')
      littleendian = data.dtype.byteorder == '<' or (
          sys.byteorder == 'little' and data.dtype.byteorder == '=')
      # undo horizontal byte differencing
      data = data.view('uint8')
      data.shape = shape[:-2] + (-1,) + shape[-1:]
      numpy.cumsum(data, axis=-2, dtype='uint8', out=data)
      # reorder bytes
      if littleendian:
          data.shape = shape[:-2] + (-1,) + shape[-2:]
      data = numpy.swapaxes(data, -3, -2)
      data = numpy.swapaxes(data, -2, -1)
      data = data[..., ::-1]
      # back to float
      data = numpy.ascontiguousarray(data)
      data = data.view(dtype)
      data.shape = shape
      return data
  
  
  def decode_jpeg(encoded, tables=b'', photometric=None,
                  ycbcr_subsampling=None, ycbcr_positioning=None):
      """Decode JPEG encoded byte string (using _czifile extension module)."""
      from czifile import _czifile
      image = _czifile.decode_jpeg(encoded, tables)
      if photometric == 'rgb' and ycbcr_subsampling and ycbcr_positioning:
          # TODO: convert YCbCr to RGB
          pass
      return image.tostring()
  
  
  @_replace_by('_tifffile.decode_packbits')
  def decode_packbits(encoded):
      """Decompress PackBits encoded byte string.
  
      PackBits is a simple byte-oriented run-length compression scheme.
  
      """
      func = ord if sys.version[0] == '2' else lambda x: x
      result = []
      result_extend = result.extend
      i = 0
      try:
          while True:
              n = func(encoded[i]) + 1
              i += 1
              if n < 129:
                  result_extend(encoded[i:i+n])
                  i += n
              elif n > 129:
                  result_extend(encoded[i:i+1] * (258-n))
                  i += 1
      except IndexError:
          pass
      return b''.join(result) if sys.version[0] == '2' else bytes(result)
  
  
  @_replace_by('_tifffile.decode_lzw')
  def decode_lzw(encoded):
      """Decompress LZW (Lempel-Ziv-Welch) encoded TIFF strip (byte string).
  
      The strip must begin with a CLEAR code and end with an EOI code.
  
      This is an implementation of the LZW decoding algorithm described in (1).
      It is not compatible with old style LZW compressed files like quad-lzw.tif.
  
      """
      len_encoded = len(encoded)
      bitcount_max = len_encoded * 8
      unpack = struct.unpack
  
      if sys.version[0] == '2':
          newtable = [chr(i) for i in range(256)]
      else:
          newtable = [bytes([i]) for i in range(256)]
      newtable.extend((0, 0))
  
      def next_code():
          """Return integer of 'bitw' bits at 'bitcount' position in encoded."""
          start = bitcount // 8
          s = encoded[start:start+4]
          try:
              code = unpack('>I', s)[0]
          except Exception:
              code = unpack('>I', s + b'\x00'*(4-len(s)))[0]
          code <<= bitcount % 8
          code &= mask
          return code >> shr
  
      switchbitch = {  # code: bit-width, shr-bits, bit-mask
          255: (9, 23, int(9*'1'+'0'*23, 2)),
          511: (10, 22, int(10*'1'+'0'*22, 2)),
          1023: (11, 21, int(11*'1'+'0'*21, 2)),
          2047: (12, 20, int(12*'1'+'0'*20, 2)), }
      bitw, shr, mask = switchbitch[255]
      bitcount = 0
  
      if len_encoded < 4:
          raise ValueError("strip must be at least 4 characters long")
  
      if next_code() != 256:
          raise ValueError("strip must begin with CLEAR code")
  
      code = 0
      oldcode = 0
      result = []
      result_append = result.append
      while True:
          code = next_code()  # ~5% faster when inlining this function
          bitcount += bitw
          if code == 257 or bitcount >= bitcount_max:  # EOI
              break
          if code == 256:  # CLEAR
              table = newtable[:]
              table_append = table.append
              lentable = 258
              bitw, shr, mask = switchbitch[255]
              code = next_code()
              bitcount += bitw
              if code == 257:  # EOI
                  break
              result_append(table[code])
          else:
              if code < lentable:
                  decoded = table[code]
                  newcode = table[oldcode] + decoded[:1]
              else:
                  newcode = table[oldcode]
                  newcode += newcode[:1]
                  decoded = newcode
              result_append(decoded)
              table_append(newcode)
              lentable += 1
          oldcode = code
          if lentable in switchbitch:
              bitw, shr, mask = switchbitch[lentable]
  
      if code != 257:
          warnings.warn("unexpected end of lzw stream (code %i)" % code)
  
      return b''.join(result)
  
  
  @_replace_by('_tifffile.unpack_ints')
  def unpack_ints(data, dtype, itemsize, runlen=0):
      """Decompress byte string to array of integers of any bit size <= 32.
  
      Parameters
      ----------
      data : byte str
          Data to decompress.
      dtype : numpy.dtype or str
          A numpy boolean or integer type.
      itemsize : int
          Number of bits per integer.
      runlen : int
          Number of consecutive integers, after which to start at next byte.
  
      """
      if itemsize == 1:  # bitarray
          data = numpy.fromstring(data, '|B')
          data = numpy.unpackbits(data)
          if runlen % 8:
              data = data.reshape(-1, runlen + (8 - runlen % 8))
              data = data[:, :runlen].reshape(-1)
          return data.astype(dtype)
  
      dtype = numpy.dtype(dtype)
      if itemsize in (8, 16, 32, 64):
          return numpy.fromstring(data, dtype)
      if itemsize < 1 or itemsize > 32:
          raise ValueError("itemsize out of range: %i" % itemsize)
      if dtype.kind not in "biu":
          raise ValueError("invalid dtype")
  
      itembytes = next(i for i in (1, 2, 4, 8) if 8 * i >= itemsize)
      if itembytes != dtype.itemsize:
          raise ValueError("dtype.itemsize too small")
      if runlen == 0:
          runlen = len(data) // itembytes
      skipbits = runlen*itemsize % 8
      if skipbits:
          skipbits = 8 - skipbits
      shrbits = itembytes*8 - itemsize
      bitmask = int(itemsize*'1'+'0'*shrbits, 2)
      dtypestr = '>' + dtype.char  # dtype always big endian?
  
      unpack = struct.unpack
      l = runlen * (len(data)*8 // (runlen*itemsize + skipbits))
      result = numpy.empty((l,), dtype)
      bitcount = 0
      for i in range(len(result)):
          start = bitcount // 8
          s = data[start:start+itembytes]
          try:
              code = unpack(dtypestr, s)[0]
          except Exception:
              code = unpack(dtypestr, s + b'\x00'*(itembytes-len(s)))[0]
          code <<= bitcount % 8
          code &= bitmask
          result[i] = code >> shrbits
          bitcount += itemsize
          if (i+1) % runlen == 0:
              bitcount += skipbits
      return result
  
  
  def unpack_rgb(data, dtype='<B', bitspersample=(5, 6, 5), rescale=True):
      """Return array from byte string containing packed samples.
  
      Use to unpack RGB565 or RGB555 to RGB888 format.
  
      Parameters
      ----------
      data : byte str
          The data to be decoded. Samples in each pixel are stored consecutively.
          Pixels are aligned to 8, 16, or 32 bit boundaries.
      dtype : numpy.dtype
          The sample data type. The byteorder applies also to the data stream.
      bitspersample : tuple
          Number of bits for each sample in a pixel.
      rescale : bool
          Upscale samples to the number of bits in dtype.
  
      Returns
      -------
      result : ndarray
          Flattened array of unpacked samples of native dtype.
  
      Examples
      --------
      >>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff)
      >>> print(unpack_rgb(data, '<B', (5, 6, 5), False))
      [ 1  1  1 31 63 31]
      >>> print(unpack_rgb(data, '<B', (5, 6, 5)))
      [  8   4   8 255 255 255]
      >>> print(unpack_rgb(data, '<B', (5, 5, 5)))
      [ 16   8   8 255 255 255]
  
      """
      dtype = numpy.dtype(dtype)
      bits = int(numpy.sum(bitspersample))
      if not (bits <= 32 and all(i <= dtype.itemsize*8 for i in bitspersample)):
          raise ValueError("sample size not supported %s" % str(bitspersample))
      dt = next(i for i in 'BHI' if numpy.dtype(i).itemsize*8 >= bits)
      data = numpy.fromstring(data, dtype.byteorder+dt)
      result = numpy.empty((data.size, len(bitspersample)), dtype.char)
      for i, bps in enumerate(bitspersample):
          t = data >> int(numpy.sum(bitspersample[i+1:]))
          t &= int('0b'+'1'*bps, 2)
          if rescale:
              o = ((dtype.itemsize * 8) // bps + 1) * bps
              if o > data.dtype.itemsize * 8:
                  t = t.astype('I')
              t *= (2**o - 1) // (2**bps - 1)
              t //= 2**(o - (dtype.itemsize * 8))
          result[:, i] = t
      return result.reshape(-1)
  
  
  @_replace_by('_tifffile.reverse_bitorder')
  def reverse_bitorder(data):
      """Reverse bits in each byte of byte string or numpy array.
  
      Decode data where pixels with lower column values are stored in the
      lower-order bits of the bytes (fill_order == 'lsb2msb').
  
      Parameters
      ----------
      data : byte string or ndarray
          The data to be bit reversed. If byte string, a new bit-reversed byte
          string is returned. Numpy arrays are bit-reversed in-place.
  
      Examples
      --------
      >>> reverse_bitorder(b'\x01\x64')
      b'\x80&'
  
      >>> data = numpy.array([1, 666], dtype='uint16')
      >>> reverse_bitorder(data)
      >>> data
      array([  128, 16473], dtype=uint16)
  
      """
      table = (
          b'\x00\x80@\xc0 \xa0`\xe0\x10\x90P\xd00\xb0p\xf0\x08\x88H\xc8(\xa8h'
          b'\xe8\x18\x98X\xd88\xb8x\xf8\x04\x84D\xc4$\xa4d\xe4\x14\x94T\xd44'
          b'\xb4t\xf4\x0c\x8cL\xcc,\xacl\xec\x1c\x9c\\\xdc<\xbc|\xfc\x02\x82B'
          b'\xc2"\xa2b\xe2\x12\x92R\xd22\xb2r\xf2\n\x8aJ\xca*\xaaj\xea\x1a'
          b'\x9aZ\xda:\xbaz\xfa\x06\x86F\xc6&\xa6f\xe6\x16\x96V\xd66\xb6v\xf6'
          b'\x0e\x8eN\xce.\xaen\xee\x1e\x9e^\xde>\xbe~\xfe\x01\x81A\xc1!\xa1a'
          b'\xe1\x11\x91Q\xd11\xb1q\xf1\t\x89I\xc9)\xa9i\xe9\x19\x99Y\xd99'
          b'\xb9y\xf9\x05\x85E\xc5%\xa5e\xe5\x15\x95U\xd55\xb5u\xf5\r\x8dM'
          b'\xcd-\xadm\xed\x1d\x9d]\xdd=\xbd}\xfd\x03\x83C\xc3#\xa3c\xe3\x13'
          b'\x93S\xd33\xb3s\xf3\x0b\x8bK\xcb+\xabk\xeb\x1b\x9b[\xdb;\xbb{\xfb'
          b'\x07\x87G\xc7\'\xa7g\xe7\x17\x97W\xd77\xb7w\xf7\x0f\x8fO\xcf/\xafo'
          b'\xef\x1f\x9f_\xdf?\xbf\x7f\xff')
      try:
          view = data.view('uint8')
          numpy.take(numpy.fromstring(table, dtype='uint8'), view, out=view)
      except AttributeError:
          return data.translate(table)
      except ValueError:
          raise NotImplementedError("slices of arrays not supported")
  
  
  def apply_colormap(image, colormap, contig=True):
      """Return palette-colored image.
  
      The image values are used to index the colormap on axis 1. The returned
      image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype.
  
      Parameters
      ----------
      image : numpy.ndarray
          Indexes into the colormap.
      colormap : numpy.ndarray
          RGB lookup table aka palette of shape (3, 2**bits_per_sample).
      contig : bool
          If True, return a contiguous array.
  
      Examples
      --------
      >>> image = numpy.arange(256, dtype='uint8')
      >>> colormap = numpy.vstack([image, image, image]).astype('uint16') * 256
      >>> apply_colormap(image, colormap)[-1]
      array([65280, 65280, 65280], dtype=uint16)
  
      """
      image = numpy.take(colormap, image, axis=1)
      image = numpy.rollaxis(image, 0, image.ndim)
      if contig:
          image = numpy.ascontiguousarray(image)
      return image
  
  
  def reorient(image, orientation):
      """Return reoriented view of image array.
  
      Parameters
      ----------
      image : numpy.ndarray
          Non-squeezed output of asarray() functions.
          Axes -3 and -2 must be image length and width respectively.
      orientation : int or str
          One of TIFF_ORIENTATIONS keys or values.
  
      """
      o = TIFF_ORIENTATIONS.get(orientation, orientation)
      if o == 'top_left':
          return image
      elif o == 'top_right':
          return image[..., ::-1, :]
      elif o == 'bottom_left':
          return image[..., ::-1, :, :]
      elif o == 'bottom_right':
          return image[..., ::-1, ::-1, :]
      elif o == 'left_top':
          return numpy.swapaxes(image, -3, -2)
      elif o == 'right_top':
          return numpy.swapaxes(image, -3, -2)[..., ::-1, :]
      elif o == 'left_bottom':
          return numpy.swapaxes(image, -3, -2)[..., ::-1, :, :]
      elif o == 'right_bottom':
          return numpy.swapaxes(image, -3, -2)[..., ::-1, ::-1, :]
  
  
  def reshape_nd(image, ndim):
      """Return image array with at least ndim dimensions.
  
      Prepend 1s to image shape as necessary.
  
      >>> reshape_nd(numpy.empty(0), 1).shape
      (0,)
      >>> reshape_nd(numpy.empty(1), 2).shape
      (1, 1)
      >>> reshape_nd(numpy.empty((2, 3)), 3).shape
      (1, 2, 3)
      >>> reshape_nd(numpy.empty((3, 4, 5)), 3).shape
      (3, 4, 5)
  
      """
      if image.ndim >= ndim:
          return image
      image = image.reshape((1,) * (ndim - image.ndim) + image.shape)
      return image
  
  
  def squeeze_axes(shape, axes, skip='XY'):
      """Return shape and axes with single-dimensional entries removed.
  
      Remove unused dimensions unless their axes are listed in 'skip'.
  
      >>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
      ((5, 2, 1), 'TYX')
  
      """
      if len(shape) != len(axes):
          raise ValueError("dimensions of axes and shape do not match")
      shape, axes = zip(*(i for i in zip(shape, axes)
                          if i[0] > 1 or i[1] in skip))
      return tuple(shape), ''.join(axes)
  
  
  def transpose_axes(image, axes, asaxes='CTZYX'):
      """Return image with its axes permuted to match specified axes.
  
      A view is returned if possible.
  
      >>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape
      (5, 2, 1, 3, 4)
  
      """
      for ax in axes:
          if ax not in asaxes:
              raise ValueError("unknown axis %s" % ax)
      # add missing axes to image
      shape = image.shape
      for ax in reversed(asaxes):
          if ax not in axes:
              axes = ax + axes
              shape = (1,) + shape
      image = image.reshape(shape)
      # transpose axes
      image = image.transpose([axes.index(ax) for ax in asaxes])
      return image
  
  
  def reshape_axes(axes, shape, newshape):
      """Return axes matching new shape.
  
      Unknown dimensions are labelled 'Q'.
  
      >>> reshape_axes('YXS', (219, 301, 1), (219, 301))
      'YX'
      >>> reshape_axes('IYX', (12, 219, 301), (3, 4, 219, 1, 301, 1))
      'QQYQXQ'
  
      """
      shape = tuple(shape)
      newshape = tuple(newshape)
      if len(axes) != len(shape):
          raise ValueError("axes do not match shape")
      if product(shape) != product(newshape):
          raise ValueError("can not reshape %s to %s" % (shape, newshape))
      if not axes or not newshape:
          return ''
  
      lendiff = max(0, len(shape) - len(newshape))
      if lendiff:
          newshape = newshape + (1,) * lendiff
  
      i = len(shape)-1
      prodns = 1
      prods = 1
      result = []
      for ns in newshape[::-1]:
          prodns *= ns
          while i > 0 and shape[i] == 1 and ns != 1:
              i -= 1
          if ns == shape[i] and prodns == prods*shape[i]:
              prods *= shape[i]
              result.append(axes[i])
              i -= 1
          else:
              result.append('Q')
  
      return ''.join(reversed(result[lendiff:]))
  
  
  def stack_pages(pages, memmap=False, tempdir=None, *args, **kwargs):
      """Read data from sequence of TiffPage and stack them vertically.
  
      If memmap is True, return an array stored in a binary file on disk.
      Additional parameters are passsed to the page asarray function.
  
      """
      if len(pages) == 0:
          raise ValueError("no pages")
  
      if len(pages) == 1:
          return pages[0].asarray(memmap=memmap, *args, **kwargs)
  
      data0 = pages[0].asarray(*args, **kwargs)
      shape = (len(pages),) + data0.shape
      if memmap:
          with tempfile.NamedTemporaryFile(dir=tempdir) as fh:
              data = numpy.memmap(fh, dtype=data0.dtype, shape=shape)
      else:
          data = numpy.empty(shape, dtype=data0.dtype)
  
      data[0] = data0
      if memmap:
          data.flush()
      del data0
      for i, page in enumerate(pages[1:]):
          data[i+1] = page.asarray(*args, **kwargs)
          if memmap:
              data.flush()
  
      return data
  
  
  def stripnull(string):
      """Return string truncated at first null character.
  
      Clean NULL terminated C strings.
  
      >>> stripnull(b'string\\x00')
      b'string'
  
      """
      i = string.find(b'\x00')
      return string if (i < 0) else string[:i]
  
  
  def stripascii(string):
      """Return string truncated at last byte that is 7bit ASCII.
  
      Clean NULL separated and terminated TIFF strings.
  
      >>> stripascii(b'string\\x00string\\n\\x01\\x00')
      b'string\\x00string\\n'
      >>> stripascii(b'\\x00')
      b''
  
      """
      # TODO: pythonize this
      ord_ = ord if sys.version_info[0] < 3 else lambda x: x
      i = len(string)
      while i:
          i -= 1
          if 8 < ord_(string[i]) < 127:
              break
      else:
          i = -1
      return string[:i+1]
  
  
  def format_size(size):
      """Return file size as string from byte size."""
      for unit in ('B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'):
          if size < 2048:
              return "%.f %s" % (size, unit)
          size /= 1024.0
  
  
  def sequence(value):
      """Return tuple containing value if value is not a sequence.
  
      >>> sequence(1)
      (1,)
      >>> sequence([1])
      [1]
  
      """
      try:
          len(value)
          return value
      except TypeError:
          return (value,)
  
  
  def product(iterable):
      """Return product of sequence of numbers.
  
      Equivalent of functools.reduce(operator.mul, iterable, 1).
  
      >>> product([2**8, 2**30])
      274877906944
      >>> product([])
      1
  
      """
      prod = 1
      for i in iterable:
          prod *= i
      return prod
  
  
  def natural_sorted(iterable):
      """Return human sorted list of strings.
  
      E.g. for sorting file names.
  
      >>> natural_sorted(['f1', 'f2', 'f10'])
      ['f1', 'f2', 'f10']
  
      """
      def sortkey(x):
          return [(int(c) if c.isdigit() else c) for c in re.split(numbers, x)]
      numbers = re.compile(r'(\d+)')
      return sorted(iterable, key=sortkey)
  
  
  def excel_datetime(timestamp, epoch=datetime.datetime.fromordinal(693594)):
      """Return datetime object from timestamp in Excel serial format.
  
      Convert LSM time stamps.
  
      >>> excel_datetime(40237.029999999795)
      datetime.datetime(2010, 2, 28, 0, 43, 11, 999982)
  
      """
      return epoch + datetime.timedelta(timestamp)
  
  
  def julian_datetime(julianday, milisecond=0):
      """Return datetime from days since 1/1/4713 BC and ms since midnight.
  
      Convert Julian dates according to MetaMorph.
  
      >>> julian_datetime(2451576, 54362783)
      datetime.datetime(2000, 2, 2, 15, 6, 2, 783)
  
      """
      if julianday <= 1721423:
          # no datetime before year 1
          return None
  
      a = julianday + 1
      if a > 2299160:
          alpha = math.trunc((a - 1867216.25) / 36524.25)
          a += 1 + alpha - alpha // 4
      b = a + (1524 if a > 1721423 else 1158)
      c = math.trunc((b - 122.1) / 365.25)
      d = math.trunc(365.25 * c)
      e = math.trunc((b - d) / 30.6001)
  
      day = b - d - math.trunc(30.6001 * e)
      month = e - (1 if e < 13.5 else 13)
      year = c - (4716 if month > 2.5 else 4715)
  
      hour, milisecond = divmod(milisecond, 1000 * 60 * 60)
      minute, milisecond = divmod(milisecond, 1000 * 60)
      second, milisecond = divmod(milisecond, 1000)
  
      return datetime.datetime(year, month, day,
                               hour, minute, second, milisecond)
  
  
  def parse_kwargs(kwargs, *keys, **keyvalues):
      """Return dict with keys from keys|keyvals and values from kwargs|keyvals.
  
      Existing keys are deleted from kwargs.
  
      >>> kwargs = {'one': 1, 'two': 2, 'four': 4}
      >>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5)
      >>> kwargs == {'one': 1}
      True
      >>> kwargs2 == {'two': 2, 'four': 4, 'five': 5}
      True
  
      """
      result = {}
      for key in keys:
          if key in kwargs:
              result[key] = kwargs[key]
              del kwargs[key]
      for key, value in keyvalues.items():
          if key in kwargs:
              result[key] = kwargs[key]
              del kwargs[key]
          else:
              result[key] = value
      return result
  
  
  def update_kwargs(kwargs, **keyvalues):
      """Update dict with keys and values if keys do not already exist.
  
      >>> kwargs = {'one': 1, }
      >>> update_kwargs(kwargs, one=None, two=2)
      >>> kwargs == {'one': 1, 'two': 2}
      True
  
      """
      for key, value in keyvalues.items():
          if key not in kwargs:
              kwargs[key] = value
  
  
  def test_tifffile(directory='testimages', verbose=True):
      """Read all images in directory.
  
      Print error message on failure.
  
      >>> test_tifffile(verbose=False)
  
      """
      successful = 0
      failed = 0
      start = time.time()
      for f in glob.glob(os.path.join(directory, '*.*')):
          if verbose:
              print("\n%s>\n" % f.lower(), end='')
          t0 = time.time()
          try:
              tif = TiffFile(f, multifile=True)
          except Exception as e:
              if not verbose:
                  print(f, end=' ')
              print("ERROR:", e)
              failed += 1
              continue
          try:
              img = tif.asarray()
          except ValueError:
              try:
                  img = tif[0].asarray()
              except Exception as e:
                  if not verbose:
                      print(f, end=' ')
                  print("ERROR:", e)
                  failed += 1
                  continue
          finally:
              tif.close()
          successful += 1
          if verbose:
              print("%s, %s %s, %s, %.0f ms" % (
                  str(tif), str(img.shape), img.dtype, tif[0].compression,
                  (time.time()-t0) * 1e3))
      if verbose:
          print("\nSuccessfully read %i of %i files in %.3f s\n" % (
              successful, successful+failed, time.time()-start))
  
  
  class TIFF_SUBFILE_TYPES(object):
      def __getitem__(self, key):
          result = []
          if key & 1:
              result.append('reduced_image')
          if key & 2:
              result.append('page')
          if key & 4:
              result.append('mask')
          return tuple(result)
  
  
  TIFF_PHOTOMETRICS = {
      0: 'miniswhite',
      1: 'minisblack',
      2: 'rgb',
      3: 'palette',
      4: 'mask',
      5: 'separated',  # CMYK
      6: 'ycbcr',
      8: 'cielab',
      9: 'icclab',
      10: 'itulab',
      32803: 'cfa',  # Color Filter Array
      32844: 'logl',
      32845: 'logluv',
      34892: 'linear_raw'
  }
  
  TIFF_COMPESSIONS = {
      1: None,
      2: 'ccittrle',
      3: 'ccittfax3',
      4: 'ccittfax4',
      5: 'lzw',
      6: 'ojpeg',
      7: 'jpeg',
      8: 'adobe_deflate',
      9: 't85',
      10: 't43',
      32766: 'next',
      32771: 'ccittrlew',
      32773: 'packbits',
      32809: 'thunderscan',
      32895: 'it8ctpad',
      32896: 'it8lw',
      32897: 'it8mp',
      32898: 'it8bl',
      32908: 'pixarfilm',
      32909: 'pixarlog',
      32946: 'deflate',
      32947: 'dcs',
      34661: 'jbig',
      34676: 'sgilog',
      34677: 'sgilog24',
      34712: 'jp2000',
      34713: 'nef',
      34925: 'lzma',
  
  }
  
  TIFF_DECOMPESSORS = {
      None: lambda x: x,
      'adobe_deflate': zlib.decompress,
      'deflate': zlib.decompress,
      'packbits': decode_packbits,
      'lzw': decode_lzw,
      # 'jpeg': decode_jpeg
  }
  
  if lzma:
      TIFF_DECOMPESSORS['lzma'] = lzma.decompress
  
  TIFF_DATA_TYPES = {
      1: '1B',   # BYTE 8-bit unsigned integer.
      2: '1s',   # ASCII 8-bit byte that contains a 7-bit ASCII code;
                 #   the last byte must be NULL (binary zero).
      3: '1H',   # SHORT 16-bit (2-byte) unsigned integer
      4: '1I',   # LONG 32-bit (4-byte) unsigned integer.
      5: '2I',   # RATIONAL Two LONGs: the first represents the numerator of
                 #   a fraction; the second, the denominator.
      6: '1b',   # SBYTE An 8-bit signed (twos-complement) integer.
      7: '1s',   # UNDEFINED An 8-bit byte that may contain anything,
                 #   depending on the definition of the field.
      8: '1h',   # SSHORT A 16-bit (2-byte) signed (twos-complement) integer.
      9: '1i',   # SLONG A 32-bit (4-byte) signed (twos-complement) integer.
      10: '2i',  # SRATIONAL Two SLONGs: the first represents the numerator
                 #   of a fraction, the second the denominator.
      11: '1f',  # FLOAT Single precision (4-byte) IEEE format.
      12: '1d',  # DOUBLE Double precision (8-byte) IEEE format.
      13: '1I',  # IFD unsigned 4 byte IFD offset.
      #14: '',   # UNICODE
      #15: '',   # COMPLEX
      16: '1Q',  # LONG8 unsigned 8 byte integer (BigTiff)
      17: '1q',  # SLONG8 signed 8 byte integer (BigTiff)
      18: '1Q',  # IFD8 unsigned 8 byte IFD offset (BigTiff)
  }
  
  TIFF_SAMPLE_FORMATS = {
      1: 'uint',
      2: 'int',
      3: 'float',
      #4: 'void',
      #5: 'complex_int',
      6: 'complex',
  }
  
  TIFF_SAMPLE_DTYPES = {
      ('uint', 1): '?',  # bitmap
      ('uint', 2): 'B',
      ('uint', 3): 'B',
      ('uint', 4): 'B',
      ('uint', 5): 'B',
      ('uint', 6): 'B',
      ('uint', 7): 'B',
      ('uint', 8): 'B',
      ('uint', 9): 'H',
      ('uint', 10): 'H',
      ('uint', 11): 'H',
      ('uint', 12): 'H',
      ('uint', 13): 'H',
      ('uint', 14): 'H',
      ('uint', 15): 'H',
      ('uint', 16): 'H',
      ('uint', 17): 'I',
      ('uint', 18): 'I',
      ('uint', 19): 'I',
      ('uint', 20): 'I',
      ('uint', 21): 'I',
      ('uint', 22): 'I',
      ('uint', 23): 'I',
      ('uint', 24): 'I',
      ('uint', 25): 'I',
      ('uint', 26): 'I',
      ('uint', 27): 'I',
      ('uint', 28): 'I',
      ('uint', 29): 'I',
      ('uint', 30): 'I',
      ('uint', 31): 'I',
      ('uint', 32): 'I',
      ('uint', 64): 'Q',
      ('int', 8): 'b',
      ('int', 16): 'h',
      ('int', 32): 'i',
      ('int', 64): 'q',
      ('float', 16): 'e',
      ('float', 32): 'f',
      ('float', 64): 'd',
      ('complex', 64): 'F',
      ('complex', 128): 'D',
      ('uint', (5, 6, 5)): 'B',
  }
  
  TIFF_ORIENTATIONS = {
      1: 'top_left',
      2: 'top_right',
      3: 'bottom_right',
      4: 'bottom_left',
      5: 'left_top',
      6: 'right_top',
      7: 'right_bottom',
      8: 'left_bottom',
  }
  
  # TODO: is there a standard for character axes labels?
  AXES_LABELS = {
      'X': 'width',
      'Y': 'height',
      'Z': 'depth',
      'S': 'sample',  # rgb(a)
      'I': 'series',  # general sequence, plane, page, IFD
      'T': 'time',
      'C': 'channel',  # color, emission wavelength
      'A': 'angle',
      'P': 'phase',  # formerly F    # P is Position in LSM!
      'R': 'tile',  # region, point, mosaic
      'H': 'lifetime',  # histogram
      'E': 'lambda',  # excitation wavelength
      'L': 'exposure',  # lux
      'V': 'event',
      'Q': 'other',
      'M': 'mosaic',  # LSM 6
  }
  
  AXES_LABELS.update(dict((v, k) for k, v in AXES_LABELS.items()))
  
  # Map OME pixel types to numpy dtype
  OME_PIXEL_TYPES = {
      'int8': 'i1',
      'int16': 'i2',
      'int32': 'i4',
      'uint8': 'u1',
      'uint16': 'u2',
      'uint32': 'u4',
      'float': 'f4',
      # 'bit': 'bit',
      'double': 'f8',
      'complex': 'c8',
      'double-complex': 'c16',
  }
  
  # NIH Image PicHeader v1.63
  NIH_IMAGE_HEADER = [
      ('fileid', 'a8'),
      ('nlines', 'i2'),
      ('pixelsperline', 'i2'),
      ('version', 'i2'),
      ('oldlutmode', 'i2'),
      ('oldncolors', 'i2'),
      ('colors', 'u1', (3, 32)),
      ('oldcolorstart', 'i2'),
      ('colorwidth', 'i2'),
      ('extracolors', 'u2', (6, 3)),
      ('nextracolors', 'i2'),
      ('foregroundindex', 'i2'),
      ('backgroundindex', 'i2'),
      ('xscale', 'f8'),
      ('_x0', 'i2'),
      ('_x1', 'i2'),
      ('units_t', 'i2'),  # NIH_UNITS_TYPE
      ('p1', [('x', 'i2'), ('y', 'i2')]),
      ('p2', [('x', 'i2'), ('y', 'i2')]),
      ('curvefit_t', 'i2'),  # NIH_CURVEFIT_TYPE
      ('ncoefficients', 'i2'),
      ('coeff', 'f8', 6),
      ('_um_len', 'u1'),
      ('um', 'a15'),
      ('_x2', 'u1'),
      ('binarypic', 'b1'),
      ('slicestart', 'i2'),
      ('sliceend', 'i2'),
      ('scalemagnification', 'f4'),
      ('nslices', 'i2'),
      ('slicespacing', 'f4'),
      ('currentslice', 'i2'),
      ('frameinterval', 'f4'),
      ('pixelaspectratio', 'f4'),
      ('colorstart', 'i2'),
      ('colorend', 'i2'),
      ('ncolors', 'i2'),
      ('fill1', '3u2'),
      ('fill2', '3u2'),
      ('colortable_t', 'u1'),  # NIH_COLORTABLE_TYPE
      ('lutmode_t', 'u1'),  # NIH_LUTMODE_TYPE
      ('invertedtable', 'b1'),
      ('zeroclip', 'b1'),
      ('_xunit_len', 'u1'),
      ('xunit', 'a11'),
      ('stacktype_t', 'i2'),  # NIH_STACKTYPE_TYPE
  ]
  
  NIH_COLORTABLE_TYPE = (
      'CustomTable', 'AppleDefault', 'Pseudo20', 'Pseudo32', 'Rainbow',
      'Fire1', 'Fire2', 'Ice', 'Grays', 'Spectrum')
  
  NIH_LUTMODE_TYPE = (
      'PseudoColor', 'OldAppleDefault', 'OldSpectrum', 'GrayScale',
      'ColorLut', 'CustomGrayscale')
  
  NIH_CURVEFIT_TYPE = (
      'StraightLine', 'Poly2', 'Poly3', 'Poly4', 'Poly5', 'ExpoFit',
      'PowerFit', 'LogFit', 'RodbardFit', 'SpareFit1', 'Uncalibrated',
      'UncalibratedOD')
  
  NIH_UNITS_TYPE = (
      'Nanometers', 'Micrometers', 'Millimeters', 'Centimeters', 'Meters',
      'Kilometers', 'Inches', 'Feet', 'Miles', 'Pixels', 'OtherUnits')
  
  NIH_STACKTYPE_TYPE = (
      'VolumeStack', 'RGBStack', 'MovieStack', 'HSVStack')
  
  # Map Universal Imaging Corporation MetaMorph internal tag ids to name and type
  UIC_TAGS = {
      0: ('auto_scale', int),
      1: ('min_scale', int),
      2: ('max_scale', int),
      3: ('spatial_calibration', int),
      4: ('x_calibration', Fraction),
      5: ('y_calibration', Fraction),
      6: ('calibration_units', str),
      7: ('name', str),
      8: ('thresh_state', int),
      9: ('thresh_state_red', int),
      10: ('tagid_10', None),  # undefined
      11: ('thresh_state_green', int),
      12: ('thresh_state_blue', int),
      13: ('thresh_state_lo', int),
      14: ('thresh_state_hi', int),
      15: ('zoom', int),
      16: ('create_time', julian_datetime),
      17: ('last_saved_time', julian_datetime),
      18: ('current_buffer', int),
      19: ('gray_fit', None),
      20: ('gray_point_count', None),
      21: ('gray_x', Fraction),
      22: ('gray_y', Fraction),
      23: ('gray_min', Fraction),
      24: ('gray_max', Fraction),
      25: ('gray_unit_name', str),
      26: ('standard_lut', int),
      27: ('wavelength', int),
      28: ('stage_position', '(%i,2,2)u4'),  # N xy positions as fractions
      29: ('camera_chip_offset', '(%i,2,2)u4'),  # N xy offsets as fractions
      30: ('overlay_mask', None),
      31: ('overlay_compress', None),
      32: ('overlay', None),
      33: ('special_overlay_mask', None),
      34: ('special_overlay_compress', None),
      35: ('special_overlay', None),
      36: ('image_property', read_uic_image_property),
      37: ('stage_label', '%ip'),  # N str
      38: ('autoscale_lo_info', Fraction),
      39: ('autoscale_hi_info', Fraction),
      40: ('absolute_z', '(%i,2)u4'),  # N fractions
      41: ('absolute_z_valid', '(%i,)u4'),  # N long
      42: ('gamma', int),
      43: ('gamma_red', int),
      44: ('gamma_green', int),
      45: ('gamma_blue', int),
      46: ('camera_bin', int),
      47: ('new_lut', int),
      48: ('image_property_ex', None),
      49: ('plane_property', int),
      50: ('user_lut_table', '(256,3)u1'),
      51: ('red_autoscale_info', int),
      52: ('red_autoscale_lo_info', Fraction),
      53: ('red_autoscale_hi_info', Fraction),
      54: ('red_minscale_info', int),
      55: ('red_maxscale_info', int),
      56: ('green_autoscale_info', int),
      57: ('green_autoscale_lo_info', Fraction),
      58: ('green_autoscale_hi_info', Fraction),
      59: ('green_minscale_info', int),
      60: ('green_maxscale_info', int),
      61: ('blue_autoscale_info', int),
      62: ('blue_autoscale_lo_info', Fraction),
      63: ('blue_autoscale_hi_info', Fraction),
      64: ('blue_min_scale_info', int),
      65: ('blue_max_scale_info', int),
      #66: ('overlay_plane_color', read_uic_overlay_plane_color),
  }
  
  
  # Olympus FluoView
  MM_DIMENSION = [
      ('name', 'a16'),
      ('size', 'i4'),
      ('origin', 'f8'),
      ('resolution', 'f8'),
      ('unit', 'a64'),
  ]
  
  MM_HEADER = [
      ('header_flag', 'i2'),
      ('image_type', 'u1'),
      ('image_name', 'a257'),
      ('offset_data', 'u4'),
      ('palette_size', 'i4'),
      ('offset_palette0', 'u4'),
      ('offset_palette1', 'u4'),
      ('comment_size', 'i4'),
      ('offset_comment', 'u4'),
      ('dimensions', MM_DIMENSION, 10),
      ('offset_position', 'u4'),
      ('map_type', 'i2'),
      ('map_min', 'f8'),
      ('map_max', 'f8'),
      ('min_value', 'f8'),
      ('max_value', 'f8'),
      ('offset_map', 'u4'),
      ('gamma', 'f8'),
      ('offset', 'f8'),
      ('gray_channel', MM_DIMENSION),
      ('offset_thumbnail', 'u4'),
      ('voice_field', 'i4'),
      ('offset_voice_field', 'u4'),
  ]
  
  # Carl Zeiss LSM
  CZ_LSM_INFO = [
      ('magic_number', 'u4'),
      ('structure_size', 'i4'),
      ('dimension_x', 'i4'),
      ('dimension_y', 'i4'),
      ('dimension_z', 'i4'),
      ('dimension_channels', 'i4'),
      ('dimension_time', 'i4'),
      ('data_type', 'i4'),  # CZ_DATA_TYPES
      ('thumbnail_x', 'i4'),
      ('thumbnail_y', 'i4'),
      ('voxel_size_x', 'f8'),
      ('voxel_size_y', 'f8'),
      ('voxel_size_z', 'f8'),
      ('origin_x', 'f8'),
      ('origin_y', 'f8'),
      ('origin_z', 'f8'),
      ('scan_type', 'u2'),
      ('spectral_scan', 'u2'),
      ('type_of_data', 'u4'),  # CZ_TYPE_OF_DATA
      ('offset_vector_overlay', 'u4'),
      ('offset_input_lut', 'u4'),
      ('offset_output_lut', 'u4'),
      ('offset_channel_colors', 'u4'),
      ('time_interval', 'f8'),
      ('offset_channel_data_types', 'u4'),
      ('offset_scan_info', 'u4'),  # CZ_LSM_SCAN_INFO
      ('offset_ks_data', 'u4'),
      ('offset_time_stamps', 'u4'),
      ('offset_event_list', 'u4'),
      ('offset_roi', 'u4'),
      ('offset_bleach_roi', 'u4'),
      ('offset_next_recording', 'u4'),
      # LSM 2.0 ends here
      ('display_aspect_x', 'f8'),
      ('display_aspect_y', 'f8'),
      ('display_aspect_z', 'f8'),
      ('display_aspect_time', 'f8'),
      ('offset_mean_of_roi_overlay', 'u4'),
      ('offset_topo_isoline_overlay', 'u4'),
      ('offset_topo_profile_overlay', 'u4'),
      ('offset_linescan_overlay', 'u4'),
      ('offset_toolbar_flags', 'u4'),
      ('offset_channel_wavelength', 'u4'),
      ('offset_channel_factors', 'u4'),
      ('objective_sphere_correction', 'f8'),
      ('offset_unmix_parameters', 'u4'),
      # LSM 3.2, 4.0 end here
      ('offset_acquisition_parameters', 'u4'),
      ('offset_characteristics', 'u4'),
      ('offset_palette', 'u4'),
      ('time_difference_x', 'f8'),
      ('time_difference_y', 'f8'),
      ('time_difference_z', 'f8'),
      ('internal_use_1', 'u4'),
      ('dimension_p', 'i4'),
      ('dimension_m', 'i4'),
      ('dimensions_reserved', '16i4'),
      ('offset_tile_positions', 'u4'),
      ('reserved_1', '9u4'),
      ('offset_positions', 'u4'),
      ('reserved_2', '21u4'),  # must be 0
  ]
  
  # Import functions for LSM_INFO sub-records
  CZ_LSM_INFO_READERS = {
      'scan_info': read_cz_lsm_scan_info,
      'time_stamps': read_cz_lsm_time_stamps,
      'event_list': read_cz_lsm_event_list,
      'channel_colors': read_cz_lsm_floatpairs,
      'positions': read_cz_lsm_floatpairs,
      'tile_positions': read_cz_lsm_floatpairs,
  }
  
  # Map cz_lsm_info.scan_type to dimension order
  CZ_SCAN_TYPES = {
      0: 'XYZCT',  # x-y-z scan
      1: 'XYZCT',  # z scan (x-z plane)
      2: 'XYZCT',  # line scan
      3: 'XYTCZ',  # time series x-y
      4: 'XYZTC',  # time series x-z
      5: 'XYTCZ',  # time series 'Mean of ROIs'
      6: 'XYZTC',  # time series x-y-z
      7: 'XYCTZ',  # spline scan
      8: 'XYCZT',  # spline scan x-z
      9: 'XYTCZ',  # time series spline plane x-z
      10: 'XYZCT',  # point mode
  }
  
  # Map dimension codes to cz_lsm_info attribute
  CZ_DIMENSIONS = {
      'X': 'dimension_x',
      'Y': 'dimension_y',
      'Z': 'dimension_z',
      'C': 'dimension_channels',
      'T': 'dimension_time',
      'P': 'dimension_p',
      'M': 'dimension_m',
  }
  
  # Description of cz_lsm_info.data_type
  CZ_DATA_TYPES = {
      0: 'varying data types',
      1: '8 bit unsigned integer',
      2: '12 bit unsigned integer',
      5: '32 bit float',
  }
  
  # Description of cz_lsm_info.type_of_data
  CZ_TYPE_OF_DATA = {
      0: 'Original scan data',
      1: 'Calculated data',
      2: '3D reconstruction',
      3: 'Topography height map',
  }
  
  CZ_LSM_SCAN_INFO_ARRAYS = {
      0x20000000: "tracks",
      0x30000000: "lasers",
      0x60000000: "detection_channels",
      0x80000000: "illumination_channels",
      0xa0000000: "beam_splitters",
      0xc0000000: "data_channels",
      0x11000000: "timers",
      0x13000000: "markers",
  }
  
  CZ_LSM_SCAN_INFO_STRUCTS = {
      # 0x10000000: "recording",
      0x40000000: "track",
      0x50000000: "laser",
      0x70000000: "detection_channel",
      0x90000000: "illumination_channel",
      0xb0000000: "beam_splitter",
      0xd0000000: "data_channel",
      0x12000000: "timer",
      0x14000000: "marker",
  }
  
  CZ_LSM_SCAN_INFO_ATTRIBUTES = {
      # recording
      0x10000001: "name",
      0x10000002: "description",
      0x10000003: "notes",
      0x10000004: "objective",
      0x10000005: "processing_summary",
      0x10000006: "special_scan_mode",
      0x10000007: "scan_type",
      0x10000008: "scan_mode",
      0x10000009: "number_of_stacks",
      0x1000000a: "lines_per_plane",
      0x1000000b: "samples_per_line",
      0x1000000c: "planes_per_volume",
      0x1000000d: "images_width",
      0x1000000e: "images_height",
      0x1000000f: "images_number_planes",
      0x10000010: "images_number_stacks",
      0x10000011: "images_number_channels",
      0x10000012: "linscan_xy_size",
      0x10000013: "scan_direction",
      0x10000014: "time_series",
      0x10000015: "original_scan_data",
      0x10000016: "zoom_x",
      0x10000017: "zoom_y",
      0x10000018: "zoom_z",
      0x10000019: "sample_0x",
      0x1000001a: "sample_0y",
      0x1000001b: "sample_0z",
      0x1000001c: "sample_spacing",
      0x1000001d: "line_spacing",
      0x1000001e: "plane_spacing",
      0x1000001f: "plane_width",
      0x10000020: "plane_height",
      0x10000021: "volume_depth",
      0x10000023: "nutation",
      0x10000034: "rotation",
      0x10000035: "precession",
      0x10000036: "sample_0time",
      0x10000037: "start_scan_trigger_in",
      0x10000038: "start_scan_trigger_out",
      0x10000039: "start_scan_event",
      0x10000040: "start_scan_time",
      0x10000041: "stop_scan_trigger_in",
      0x10000042: "stop_scan_trigger_out",
      0x10000043: "stop_scan_event",
      0x10000044: "stop_scan_time",
      0x10000045: "use_rois",
      0x10000046: "use_reduced_memory_rois",
      0x10000047: "user",
      0x10000048: "use_bc_correction",
      0x10000049: "position_bc_correction1",
      0x10000050: "position_bc_correction2",
      0x10000051: "interpolation_y",
      0x10000052: "camera_binning",
      0x10000053: "camera_supersampling",
      0x10000054: "camera_frame_width",
      0x10000055: "camera_frame_height",
      0x10000056: "camera_offset_x",
      0x10000057: "camera_offset_y",
      0x10000059: "rt_binning",
      0x1000005a: "rt_frame_width",
      0x1000005b: "rt_frame_height",
      0x1000005c: "rt_region_width",
      0x1000005d: "rt_region_height",
      0x1000005e: "rt_offset_x",
      0x1000005f: "rt_offset_y",
      0x10000060: "rt_zoom",
      0x10000061: "rt_line_period",
      0x10000062: "prescan",
      0x10000063: "scan_direction_z",
      # track
      0x40000001: "multiplex_type",  # 0 after line; 1 after frame
      0x40000002: "multiplex_order",
      0x40000003: "sampling_mode",  # 0 sample; 1 line average; 2 frame average
      0x40000004: "sampling_method",  # 1 mean; 2 sum
      0x40000005: "sampling_number",
      0x40000006: "acquire",
      0x40000007: "sample_observation_time",
      0x4000000b: "time_between_stacks",
      0x4000000c: "name",
      0x4000000d: "collimator1_name",
      0x4000000e: "collimator1_position",
      0x4000000f: "collimator2_name",
      0x40000010: "collimator2_position",
      0x40000011: "is_bleach_track",
      0x40000012: "is_bleach_after_scan_number",
      0x40000013: "bleach_scan_number",
      0x40000014: "trigger_in",
      0x40000015: "trigger_out",
      0x40000016: "is_ratio_track",
      0x40000017: "bleach_count",
      0x40000018: "spi_center_wavelength",
      0x40000019: "pixel_time",
      0x40000021: "condensor_frontlens",
      0x40000023: "field_stop_value",
      0x40000024: "id_condensor_aperture",
      0x40000025: "condensor_aperture",
      0x40000026: "id_condensor_revolver",
      0x40000027: "condensor_filter",
      0x40000028: "id_transmission_filter1",
      0x40000029: "id_transmission1",
      0x40000030: "id_transmission_filter2",
      0x40000031: "id_transmission2",
      0x40000032: "repeat_bleach",
      0x40000033: "enable_spot_bleach_pos",
      0x40000034: "spot_bleach_posx",
      0x40000035: "spot_bleach_posy",
      0x40000036: "spot_bleach_posz",
      0x40000037: "id_tubelens",
      0x40000038: "id_tubelens_position",
      0x40000039: "transmitted_light",
      0x4000003a: "reflected_light",
      0x4000003b: "simultan_grab_and_bleach",
      0x4000003c: "bleach_pixel_time",
      # laser
      0x50000001: "name",
      0x50000002: "acquire",
      0x50000003: "power",
      # detection_channel
      0x70000001: "integration_mode",
      0x70000002: "special_mode",
      0x70000003: "detector_gain_first",
      0x70000004: "detector_gain_last",
      0x70000005: "amplifier_gain_first",
      0x70000006: "amplifier_gain_last",
      0x70000007: "amplifier_offs_first",
      0x70000008: "amplifier_offs_last",
      0x70000009: "pinhole_diameter",
      0x7000000a: "counting_trigger",
      0x7000000b: "acquire",
      0x7000000c: "point_detector_name",
      0x7000000d: "amplifier_name",
      0x7000000e: "pinhole_name",
      0x7000000f: "filter_set_name",
      0x70000010: "filter_name",
      0x70000013: "integrator_name",
      0x70000014: "channel_name",
      0x70000015: "detector_gain_bc1",
      0x70000016: "detector_gain_bc2",
      0x70000017: "amplifier_gain_bc1",
      0x70000018: "amplifier_gain_bc2",
      0x70000019: "amplifier_offset_bc1",
      0x70000020: "amplifier_offset_bc2",
      0x70000021: "spectral_scan_channels",
      0x70000022: "spi_wavelength_start",
      0x70000023: "spi_wavelength_stop",
      0x70000026: "dye_name",
      0x70000027: "dye_folder",
      # illumination_channel
      0x90000001: "name",
      0x90000002: "power",
      0x90000003: "wavelength",
      0x90000004: "aquire",
      0x90000005: "detchannel_name",
      0x90000006: "power_bc1",
      0x90000007: "power_bc2",
      # beam_splitter
      0xb0000001: "filter_set",
      0xb0000002: "filter",
      0xb0000003: "name",
      # data_channel
      0xd0000001: "name",
      0xd0000003: "acquire",
      0xd0000004: "color",
      0xd0000005: "sample_type",
      0xd0000006: "bits_per_sample",
      0xd0000007: "ratio_type",
      0xd0000008: "ratio_track1",
      0xd0000009: "ratio_track2",
      0xd000000a: "ratio_channel1",
      0xd000000b: "ratio_channel2",
      0xd000000c: "ratio_const1",
      0xd000000d: "ratio_const2",
      0xd000000e: "ratio_const3",
      0xd000000f: "ratio_const4",
      0xd0000010: "ratio_const5",
      0xd0000011: "ratio_const6",
      0xd0000012: "ratio_first_images1",
      0xd0000013: "ratio_first_images2",
      0xd0000014: "dye_name",
      0xd0000015: "dye_folder",
      0xd0000016: "spectrum",
      0xd0000017: "acquire",
      # timer
      0x12000001: "name",
      0x12000002: "description",
      0x12000003: "interval",
      0x12000004: "trigger_in",
      0x12000005: "trigger_out",
      0x12000006: "activation_time",
      0x12000007: "activation_number",
      # marker
      0x14000001: "name",
      0x14000002: "description",
      0x14000003: "trigger_in",
      0x14000004: "trigger_out",
  }
  
  # Map TIFF tag code to attribute name, default value, type, count, validator
  TIFF_TAGS = {
      254: ('new_subfile_type', 0, 4, 1, TIFF_SUBFILE_TYPES()),
      255: ('subfile_type', None, 3, 1,
            {0: 'undefined', 1: 'image', 2: 'reduced_image', 3: 'page'}),
      256: ('image_width', None, 4, 1, None),
      257: ('image_length', None, 4, 1, None),
      258: ('bits_per_sample', 1, 3, None, None),
      259: ('compression', 1, 3, 1, TIFF_COMPESSIONS),
      262: ('photometric', None, 3, 1, TIFF_PHOTOMETRICS),
      266: ('fill_order', 1, 3, 1, {1: 'msb2lsb', 2: 'lsb2msb'}),
      269: ('document_name', None, 2, None, None),
      270: ('image_description', None, 2, None, None),
      271: ('make', None, 2, None, None),
      272: ('model', None, 2, None, None),
      273: ('strip_offsets', None, 4, None, None),
      274: ('orientation', 1, 3, 1, TIFF_ORIENTATIONS),
      277: ('samples_per_pixel', 1, 3, 1, None),
      278: ('rows_per_strip', 2**32-1, 4, 1, None),
      279: ('strip_byte_counts', None, 4, None, None),
      280: ('min_sample_value', None, 3, None, None),
      281: ('max_sample_value', None, 3, None, None),  # 2**bits_per_sample
      282: ('x_resolution', None, 5, 1, None),
      283: ('y_resolution', None, 5, 1, None),
      284: ('planar_configuration', 1, 3, 1, {1: 'contig', 2: 'separate'}),
      285: ('page_name', None, 2, None, None),
      286: ('x_position', None, 5, 1, None),
      287: ('y_position', None, 5, 1, None),
      296: ('resolution_unit', 2, 4, 1, {1: None, 2: 'inch', 3: 'centimeter'}),
      297: ('page_number', None, 3, 2, None),
      305: ('software', None, 2, None, None),
      306: ('datetime', None, 2, None, None),
      315: ('artist', None, 2, None, None),
      316: ('host_computer', None, 2, None, None),
      317: ('predictor', 1, 3, 1, {1: None, 2: 'horizontal', 3: 'float'}),
      318: ('white_point', None, 5, 2, None),
      319: ('primary_chromaticities', None, 5, 6, None),
      320: ('color_map', None, 3, None, None),
      322: ('tile_width', None, 4, 1, None),
      323: ('tile_length', None, 4, 1, None),
      324: ('tile_offsets', None, 4, None, None),
      325: ('tile_byte_counts', None, 4, None, None),
      330: ('sub_ifds', None, 4, None, None),
      338: ('extra_samples', None, 3, None,
            {0: 'unspecified', 1: 'assocalpha', 2: 'unassalpha'}),
      339: ('sample_format', 1, 3, None, TIFF_SAMPLE_FORMATS),
      340: ('smin_sample_value', None, None, None, None),
      341: ('smax_sample_value', None, None, None, None),
      346: ('indexed', 0, 3, 1, None),
      347: ('jpeg_tables', None, 7, None, None),
      530: ('ycbcr_subsampling', (1, 1), 3, 2, None),
      531: ('ycbcr_positioning', (1, 1), 3, 1, None),
      532: ('reference_black_white', None, 5, 1, None),
      32996: ('sgi_matteing', None, None, 1, None),  # use extra_samples
      32996: ('sgi_datatype', None, None, None, None),  # use sample_format
      32997: ('image_depth', 1, 4, 1, None),
      32998: ('tile_depth', None, 4, 1, None),
      33432: ('copyright', None, 1, None, None),
      33445: ('md_file_tag', None, 4, 1, None),
      33446: ('md_scale_pixel', None, 5, 1, None),
      33447: ('md_color_table', None, 3, None, None),
      33448: ('md_lab_name', None, 2, None, None),
      33449: ('md_sample_info', None, 2, None, None),
      33450: ('md_prep_date', None, 2, None, None),
      33451: ('md_prep_time', None, 2, None, None),
      33452: ('md_file_units', None, 2, None, None),
      33550: ('model_pixel_scale', None, 12, 3, None),
      33922: ('model_tie_point', None, 12, None, None),
      34665: ('exif_ifd', None, None, 1, None),
      34735: ('geo_key_directory', None, 3, None, None),
      34736: ('geo_double_params', None, 12, None, None),
      34737: ('geo_ascii_params', None, 2, None, None),
      34853: ('gps_ifd', None, None, 1, None),
      37510: ('user_comment', None, None, None, None),
      42112: ('gdal_metadata', None, 2, None, None),
      42113: ('gdal_nodata', None, 2, None, None),
      50289: ('mc_xy_position', None, 12, 2, None),
      50290: ('mc_z_position', None, 12, 1, None),
      50291: ('mc_xy_calibration', None, 12, 3, None),
      50292: ('mc_lens_lem_na_n', None, 12, 3, None),
      50293: ('mc_channel_name', None, 1, None, None),
      50294: ('mc_ex_wavelength', None, 12, 1, None),
      50295: ('mc_time_stamp', None, 12, 1, None),
      50838: ('imagej_byte_counts', None, None, None, None),
      51023: ('fibics_xml', None, 2, None, None),
      65200: ('flex_xml', None, 2, None, None),
      # code: (attribute name, default value, type, count, validator)
  }
  
  # Map custom TIFF tag codes to attribute names and import functions
  CUSTOM_TAGS = {
      700: ('xmp', read_bytes),
      34377: ('photoshop', read_numpy),
      33723: ('iptc', read_bytes),
      34675: ('icc_profile', read_bytes),
      33628: ('uic1tag', read_uic1tag),  # Universal Imaging Corporation STK
      33629: ('uic2tag', read_uic2tag),
      33630: ('uic3tag', read_uic3tag),
      33631: ('uic4tag', read_uic4tag),
      34361: ('mm_header', read_mm_header),  # Olympus FluoView
      34362: ('mm_stamp', read_mm_stamp),
      34386: ('mm_user_block', read_bytes),
      34412: ('cz_lsm_info', read_cz_lsm_info),  # Carl Zeiss LSM
      43314: ('nih_image_header', read_nih_image_header),
      # 40001: ('mc_ipwinscal', read_bytes),
      40100: ('mc_id_old', read_bytes),
      50288: ('mc_id', read_bytes),
      50296: ('mc_frame_properties', read_bytes),
      50839: ('imagej_metadata', read_bytes),
      51123: ('micromanager_metadata', read_json),
  }
  
  # Max line length of printed output
  PRINT_LINE_LEN = 79
  
  def askopenfilename(**kwargs):
      """Return file name(s) from Tkinter's file open dialog."""
      try:
          from Tkinter import Tk
          import tkFileDialog as filedialog
      except ImportError:
          from tkinter import Tk, filedialog
      root = Tk()
      root.withdraw()
      root.update()
      filenames = filedialog.askopenfilename(**kwargs)
      root.destroy()
      return filenames
  
  
  def tif2resolution(path1):
      import optparse
  
      parser = optparse.OptionParser(
          usage="usage: %prog [options] path",
          description="Display image data in TIFF files.",
          version="%%prog %s" % __version__)
      opt = parser.add_option
      opt('-p', '--page', dest='page', type='int', default=-1,
          help="display single page")
      opt('-s', '--series', dest='series', type='int', default=-1,
          help="display series of pages of same shape")
      opt('--nomultifile', dest='nomultifile', action='store_true',
          default=False, help="do not read OME series from multiple files")
      opt('--noplot', dest='noplot', action='store_true', default=False,
          help="do not display images")
      opt('--interpol', dest='interpol', metavar='INTERPOL', default='bilinear',
          help="image interpolation method")
      opt('--dpi', dest='dpi', type='int', default=96,
          help="set plot resolution")
      opt('--vmin', dest='vmin', type='int', default=None,
          help="set minimum value for colormapping")
      opt('--vmax', dest='vmax', type='int', default=None,
          help="set maximum value for colormapping")
      opt('--debug', dest='debug', action='store_true', default=False,
          help="raise exception on failures")
      opt('--test', dest='test', action='store_true', default=False,
          help="try read all images in path")
      opt('--doctest', dest='doctest', action='store_true', default=False,
          help="runs the docstring examples")
      opt('-v', '--verbose', dest='verbose', action='store_true', default=True)
      opt('-q', '--quiet', dest='verbose', action='store_false')
  
      settings, path = parser.parse_args()
      path = ' '.join(path)
  
      tif = TiffFile(path1)
  
      images = [(None, tif[0 if settings.page < 0 else settings.page])]
      if not settings.noplot:
  
          def notnone(x):
              return next(i for i in x if i is not None)
          start = time.time()
          try:
              if settings.page >= 0:
                  images = [(tif.asarray(key=settings.page),
                             tif[settings.page])]
              elif settings.series >= 0:
                  images = [(tif.asarray(series=settings.series),
                             notnone(tif.series[settings.series].pages))]
              else:
                  images = []
                  for i, s in enumerate(tif.series):
                      try:
                          images.append(
                              (tif.asarray(series=i), notnone(s.pages)))
                      except ValueError as e:
                          images.append((None, notnone(s.pages)))
                          if settings.debug:
                              raise
                          else:
                              print("\n* series %i failed: %s... " % (i, e),
                                    end='')
          except Exception as e:
              if settings.debug:
                  raise
              else:
                  print(e)
  
      tif.close()
  
      for i, page in images:
          type(page)
  
      a0 = page.tags
      a1 = a0.values()
  
      return a1[2].value
  