#!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py

# Copyright (c) 2008-2015, Christoph Gohlke
# Copyright (c) 2008-2015, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
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# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
#   notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
#   notice, this list of conditions and the following disclaimer in the
#   documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holders nor the names of any
#   contributors may be used to endorse or promote products derived
#   from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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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 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, 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: 2015.08.17

Requirements
------------
* `CPython 2.7 or 3.4 <http://www.python.org>`_ (64 bit recommended)
* `Numpy 1.9.2 <http://www.numpy.org>`_
* `Matplotlib 1.4.3 <http://www.matplotlib.org>`_ (optional for plotting)
* `Tifffile.c 2015.08.17 <http://www.lfd.uci.edu/~gohlke/>`_
  (recommended for faster decoding of PackBits and LZW encoded strings)

Revisions
---------
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 <http://luispedro.org/software/imread>`_
*  `PyLibTiff <http://code.google.com/p/pylibtiff>`_
*  `SimpleITK <http://www.simpleitk.org>`_
*  `PyLSM <https://launchpad.net/pylsm>`_
*  `PyMca.TiffIO.py <http://pymca.sourceforge.net/>`_ (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

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(
        "failed to import the optional _tifffile C extension module.\n"
        "Loading of some compressed images will be very slow.\n"
        "Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/")


__version__ = '2015.08.17'
__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(filename, data, **kwargs):
    """Write image data to TIFF file.

    Refer to the TiffWriter class and member functions for documentation.

    Parameters
    ----------
    filename : str
        Name of file to write.
    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)
    >>> metadata = {'axes': 'TZCYX'}
    >>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})

    """
    tifargs = {}
    for key in ('byteorder', 'bigtiff', 'software', 'imagej'):
        if key in kwargs:
            tifargs[key] = kwargs[key]
            del kwargs[key]

    if 'bigtiff' not in tifargs and 'imagej' not in tifargs and (
            data.size*data.dtype.itemsize > 2000*2**20):
        tifargs['bigtiff'] = True

    with TiffWriter(filename, **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' statement.

    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, 'fill_order': 266,
        '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,
        'image_depth': 32997, 'tile_depth': 32998}

    def __init__(self, filename, bigtiff=False, byteorder=None,
                 software='tifffile.py', imagej=False):
        """Create a new TIFF file for writing.

        Use bigtiff=True when creating files greater than 2 GB.

        Parameters
        ----------
        filename : str
            Name of file to write.
        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 doesn't 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 = open(filename, 'wb')
        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, resolution=None,
             compress=0, colormap=None, tile=None, datetime=None,
             description='', metadata=None, contiguous=True, 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.
            'contig': last dimension contains samples.
            'planar': third last dimension contains samples.
        resolution : (float, float) or ((int, int), (int, int))
            X and Y resolution in dots per inch as float or rational numbers.
        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.
        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.
        datetime : datetime
            Date and time of image creation. Saved with the first page only.
            If None (default), the current date and time is used.
        description : str
            The subject of the image. Saved with the first page only.
            Cannot be used with the ImageJ format. If None (default),
            the data shape and metadata are saved in JSON or ImageJ format.
        metadata : dict
            Additional meta data passed to the image description functions.
        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.
        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')

        # 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
                    data.tofile(fh)
                    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't 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 = shape = data.shape
        data = numpy.atleast_2d(data)

        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:
            if planarconfig:
                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'
            else:
                photometric = 'minisblack'
        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
            if False and (
                    photometric != 'palette' and
                    len(shape) > (3 if volume else 2) and shape[-1] < 5 and
                    all(shape[-1] < i
                        for i in shape[(-4 if volume else -3):-1])):
                # DISABLED: non-standard TIFF, e.g. (220, 320, 2)
                planarconfig = 'contig'
                samplesperpixel = shape[-1]
                data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
            else:
                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 samplesperpixel == 2:
            warnings.warn("writing non-standard TIFF (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 count == 1:
                if isinstance(value, (tuple, list, numpy.ndarray)):
                    value = value[0]
                ifdentry.append(pack(value_format, pack(dtype, value)))
            elif struct.calcsize(dtype) * count <= offset_size:
                ifdentry.append(pack(value_format,
                                     pack(str(count)+dtype, *value)))
            else:
                ifdentry.append(pack(offset_format, 0))
                if isinstance(value, numpy.ndarray):
                    assert value.size == count
                    assert value.dtype.char == dtype
                    ifdvalue = value.tobytes()
                else:
                    ifdvalue = pack(str(count)+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)

        # always write shape and metadata to image_description
        self._metadata = {} if metadata is None else metadata
        if self._imagej:
            description = imagej_description(
                data_shape, shape[-1] in (3, 4), self._colormap is not None,
                **self._metadata)
        else:
            description = image_description(
                data_shape, self._colormap is not None, **self._metadata)
        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]))
            addtag('resolution_unit', 'H', 1, 2)
        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:
                                    chunk.tofile(fh)
                                    fh.flush()
            elif compress:
                for plane in data[pageindex]:
                    plane = compress(plane)
                    strip_byte_counts.append(len(plane))
                    fh.write(plane)
            else:
                data.tofile(fh)  # if this fails try update Python and numpy

            # 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 or list
        File name, glob pattern, or list 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 = {}
    if 'multifile' in kwargs:
        kwargs_file['multifile'] = kwargs['multifile']
        del kwargs['multifile']
    else:
        kwargs_file['multifile'] = True
    kwargs_seq = {}
    if 'pattern' in kwargs:
        kwargs_seq['pattern'] = kwargs['pattern']
        del 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 len(files) == 1:
        files = files[0]

    if isinstance(files, basestring):
        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' statement.

    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, 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")

        # TODO? sort pages by page_number value

        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."""
        for series in self.series:
            wrap = 0
            previous_offset = 0
            for page in series.pages:
                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):
        """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 array stored in a binary file on disk
            if possible.

        """
        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_palette:
                result = stack_pages(pages, colormapped=False, squeeze=False)
                result = numpy.take(pages[0].color_map, result, axis=1)
                result = numpy.swapaxes(result, 0, 1)
            else:
                result = stack_pages(pages, memmap=memmap,
                                     colormapped=False, squeeze=False)
        elif len(pages) == 1:
            result = pages[0].asarray(memmap=memmap)
        elif self.is_ome:
            assert not self.is_palette, "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()
        else:
            result = stack_pages(pages, memmap=memmap)

        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 series of TiffPage with compatible shape and properties."""
        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:
                    description = page0.is_shaped
                    metadata = image_description_dict(description)
                    if product(metadata.get('shape', shape)) == product(shape):
                        shape = metadata.get('shape', shape)
                    else:
                        warnings.warn(
                            "metadata shape doesn't match data shape")
                    if 'axes' in metadata:
                        axes = metadata['axes']
                        if len(axes) != len(shape):
                            warnings.warn("axes don't match shape")
                    axes = 'Q'*(len(shape)-len(axes)) + axes[-len(shape):]
                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')
        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
        omexml = omexml.decode('UTF-8', 'ignore')
        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
                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_palette(self):
        """File contains only color-mapped images."""
        return all(p.is_palette 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


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 not (name in tags or default is None):
                tags[name] = TiffTag(code, dtype=dtype, count=count,
                                     value=default, name=name)
            if name in tags and 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))

        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]

        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_depth' not in tags:
            self.image_depth = 1

        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_palette:
            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)
            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_palette:
                assert False, "color mapping disabled for stk"
                if self.color_map.shape[1] >= 2**self.bits_per_sample:
                    if image_depth == 1:
                        self.shape = (3, planes, image_length, image_width)
                    else:
                        self.shape = (3, planes, image_depth, image_length,
                                      image_width)
                    self.axes = 'S' + self.axes
                else:
                    warnings.warn("palette cannot be applied")
                    self.is_palette = False
        elif self.is_palette:
            samples = 1
            if 'extra_samples' in self.tags:
                samples += len(self.extra_samples)
            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 = (3, image_length, image_width)
                    self.axes = 'SYX'
                else:
                    self.shape = (3, image_depth, image_length, image_width)
                    self.axes = 'SZYX'
            else:
                warnings.warn("palette cannot be applied")
                self.is_palette = 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:
            return
        images = self.imagej_tags.get('images', 0)
        if images <= 1:
            return

        pre = 'tile' if self.is_tiled else 'strip'
        self.tags[pre+'_offsets'].value = (self.is_contiguous[0],)
        self.tags[pre+'_byte_counts'].value = (self.is_contiguous[1] * images,)
        self.shape = (images,) + self.shape
        self._shape = (images,) + self._shape[1:]
        self.axes = 'I' + self.axes
        if self.is_palette:
            # swap first two dimensions
            self.axes = self.axes[1::-1] + self.axes[2:]
            self.shape = self.shape[1::-1] + self.shape[2:]
        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)
        tag = self.tags['sample_format']
        if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
            raise ValueError("sample formats don't match %s" % str(tag.value))

        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

        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)
        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 = unpack(decompress(fh.read(bytecount)))
                    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)
                    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_palette:
            if self.color_map.shape[1] >= 2**bits_per_sample:
                # FluoView and LSM might fail here
                result = numpy.take(self.color_map,
                                    result[:, 0:1, :, :, :, 0:1], axis=1)
        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
                not self.predictor and
                not (rgbonly and 'extra_samples' in self.tags) and
                not (colormapped and self.is_palette))

    @lazyattr
    def is_contiguous(self):
        """Return offset and size of contiguous data, else None.

        Excludes prediction 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_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."""
        return ('planar_configuration' in self.tags and
                self.tags['planar_configuration'].value == 1)

    @lazyattr
    def is_palette(self):
        """Page contains palette-colored image and is not OME or STK."""
        # turn off color mapping for OME-TIFF and STK
        if self.is_stk or self.is_ome or self.parent.is_ome:
            return False
        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 bool(self.tags['new_subfile_type'].value & 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_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 = TIFF_TAGS[code][0]
        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):
            # scalar value if not strip/tile offsets/byte_counts
            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.

    """
    __slots__ = 'pages', 'shape', 'dtype', 'axes', 'parent'

    def __init__(self, pages, shape, dtype, axes, parent=None):
        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)

    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 "* pages: %i\n* dtype: %s\n* shape: %s\n* axes: %s" % (
            len(self.pages), str(self.dtype), str(self.shape), self.axes)


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.
        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 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, *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 don't match.

        """
        im = self.imread(self.files[0], *args, **kwargs)
        shape = self.shape + im.shape
        if memmap:
            with tempfile.NamedTemporaryFile() 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 doesn't match file names")
        matches = matches[-1]
        if len(matches) % 2:
            raise self.ParseError("pattern doesn't match axis name and index")
        axes = ''.join(m for m in matches[::2] if m)
        if not axes:
            raise self.ParseError("pattern doesn't 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 don't 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.

    * Handle embedded files (for CZI within CZI files).
    * Allow to re-open closed files (for multi file formats such as OME-TIFF).
    * Read numpy arrays and records from file like objects.

    Only binary read, seek, tell, and close are supported on embedded files.
    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', '_arg', '_mode', '_name', '_dir',
                 '_offset', '_size', '_close', 'is_file')

    def __init__(self, arg, mode='rb', name=None, offset=None, size=None):
        """Initialize file handle from file name or another file handle.

        Parameters
        ----------
        arg : str, File, or FileHandle
            File name or open file handle.
        mode : str
            File open mode in case 'arg' is a file name.
        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.

        """
        self._fh = None
        self._arg = arg
        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._arg, basestring):
            # file name
            self._arg = os.path.abspath(self._arg)
            self._dir, self._name = os.path.split(self._arg)
            self._fh = open(self._arg, self._mode)
            self._close = True
            if self._offset is None:
                self._offset = 0
        elif isinstance(self._arg, FileHandle):
            # FileHandle
            self._fh = self._arg._fh
            if self._offset is None:
                self._offset = 0
            self._offset += self._arg._offset
            self._close = False
            if not self._name:
                if self._offset:
                    name, ext = os.path.splitext(self._arg._name)
                    self._name = "%s@%i%s" % (name, self._offset, ext)
                else:
                    self._name = self._arg._name
            self._dir = self._arg._dir
        else:
            # open file object
            self._fh = self._arg
            if self._offset is None:
                self._offset = self._arg.tell()
            self._close = False
            if not self._name:
                try:
                    self._dir, self._name = os.path.split(self._fh.name)
                except AttributeError:
                    self._name = "Unnamed stream"

        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 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 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:
                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
            self.is_file = False

    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")

    results = {}
    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")
    results['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))
    results['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")
    results['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")
    results['comments'] = read_json(fh, byteorder, None, count)

    return results


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 = (3,) + shape
    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)


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 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 don't 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(data, axes, asaxes='CTZYX'):
    """Return data 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 data
    shape = data.shape
    for ax in reversed(asaxes):
        if ax not in axes:
            axes = ax + axes
            shape = (1,) + shape
    data = data.reshape(shape)
    # transpose axes
    data = data.transpose([axes.index(ax) for ax in asaxes])
    return data


def stack_pages(pages, memmap=False, *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)

    result = pages[0].asarray(*args, **kwargs)
    shape = (len(pages),) + result.shape
    if memmap:
        with tempfile.NamedTemporaryFile() as fh:
            result = numpy.memmap(fh, dtype=result.dtype, shape=shape)
    else:
        result = numpy.empty(shape, dtype=result.dtype)

    for i, page in enumerate(pages):
        result[i] = page.asarray(*args, **kwargs)

    return result


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', 'KB', 'MB', 'GB', 'TB'):
        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 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',
}

# 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, 1, 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),
    338: ('extra_samples', None, 3, None,
          {0: 'unspecified', 1: 'assocalpha', 2: 'unassalpha'}),
    339: ('sample_format', 1, 3, 1, TIFF_SAMPLE_FORMATS),
    340: ('smin_sample_value', None, None, None, None),
    341: ('smax_sample_value', None, None, None, None),
    347: ('jpeg_tables', None, 7, None, None),
    530: ('ycbcr_subsampling', 1, 3, 2, None),
    531: ('ycbcr_positioning', 1, 3, 1, None),
    32996: ('sgi_matteing', None, None, 1, None),  # use extra_samples
    32996: ('sgi_datatype', None, None, 1, None),  # use sample_format
    32997: ('image_depth', None, 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 imshow(data, title=None, vmin=0, vmax=None, cmap=None,
           bitspersample=None, photometric='rgb', interpolation='nearest',
           dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs):
    """Plot n-dimensional images using matplotlib.pyplot.

    Return figure, subplot and plot axis.
    Requires pyplot already imported `from matplotlib import pyplot`.

    Parameters
    ----------
    bitspersample : int or None
        Number of bits per channel in integer RGB images.
    photometric : {'miniswhite', 'minisblack', 'rgb', or 'palette'}
        The color space of the image data.
    title : str
        Window and subplot title.
    figure : matplotlib.figure.Figure (optional).
        Matplotlib to use for plotting.
    subplot : int
        A matplotlib.pyplot.subplot axis.
    maxdim : int
        maximum image width and length.
    kwargs : optional
        Arguments for matplotlib.pyplot.imshow.

    """
    #if photometric not in ('miniswhite', 'minisblack', 'rgb', 'palette'):
    #    raise ValueError("Can't handle %s photometrics" % photometric)
    # TODO: handle photometric == 'separated' (CMYK)
    isrgb = photometric in ('rgb', 'palette')
    data = numpy.atleast_2d(data.squeeze())

    dims = data.ndim
    if dims < 2:
        raise ValueError("not an image")
    elif dims == 2:
        dims = 0
        isrgb = False
    else:
        if isrgb and data.shape[-3] in (3, 4):
            data = numpy.swapaxes(data, -3, -2)
            data = numpy.swapaxes(data, -2, -1)
        elif not isrgb and (data.shape[-1] < data.shape[-2] // 8 and
                            data.shape[-1] < data.shape[-3] // 8 and
                            data.shape[-1] < 5):
            data = numpy.swapaxes(data, -3, -1)
            data = numpy.swapaxes(data, -2, -1)
        isrgb = isrgb and data.shape[-1] in (3, 4)
        dims -= 3 if isrgb else 2

    if isrgb:
        data = data[..., :maxdim, :maxdim, :maxdim]
    else:
        data = data[..., :maxdim, :maxdim]

    if photometric == 'palette' and isrgb:
        datamax = data.max()
        if datamax > 255:
            data >>= 8  # possible precision loss
        data = data.astype('B')
    elif data.dtype.kind in 'ui':
        if not (isrgb and data.dtype.itemsize <= 1) or bitspersample is None:
            try:
                bitspersample = int(math.ceil(math.log(data.max(), 2)))
            except Exception:
                bitspersample = data.dtype.itemsize * 8
        elif not isinstance(bitspersample, int):
            # bitspersample can be tuple, e.g. (5, 6, 5)
            bitspersample = data.dtype.itemsize * 8
        datamax = 2**bitspersample
        if isrgb:
            if bitspersample < 8:
                data <<= 8 - bitspersample
            elif bitspersample > 8:
                data >>= bitspersample - 8  # precision loss
            data = data.astype('B')
    elif data.dtype.kind == 'f':
        datamax = data.max()
        if isrgb and datamax > 1.0:
            if data.dtype.char == 'd':
                data = data.astype('f')
            data /= datamax
    elif data.dtype.kind == 'b':
        datamax = 1
    elif data.dtype.kind == 'c':
        # TODO: handle complex types
        raise NotImplementedError("complex type")

    if not isrgb:
        if vmax is None:
            vmax = datamax
        if vmin is None:
            if data.dtype.kind == 'i':
                dtmin = numpy.iinfo(data.dtype).min
                vmin = numpy.min(data)
                if vmin == dtmin:
                    vmin = numpy.min(data > dtmin)
            if data.dtype.kind == 'f':
                dtmin = numpy.finfo(data.dtype).min
                vmin = numpy.min(data)
                if vmin == dtmin:
                    vmin = numpy.min(data > dtmin)
            else:
                vmin = 0

    pyplot = sys.modules['matplotlib.pyplot']

    if figure is None:
        pyplot.rc('font', family='sans-serif', weight='normal', size=8)
        figure = pyplot.figure(dpi=dpi, figsize=(10.3, 6.3), frameon=True,
                               facecolor='1.0', edgecolor='w')
        try:
            figure.canvas.manager.window.title(title)
        except Exception:
            pass
        pyplot.subplots_adjust(bottom=0.03*(dims+2), top=0.9,
                               left=0.1, right=0.95, hspace=0.05, wspace=0.0)
    subplot = pyplot.subplot(subplot)

    if title:
        try:
            title = unicode(title, 'Windows-1252')
        except TypeError:
            pass
        pyplot.title(title, size=11)

    if cmap is None:
        if data.dtype.kind in 'ubf' or vmin == 0:
            cmap = 'cubehelix'
        else:
            cmap = 'coolwarm'
        if photometric == 'miniswhite':
            cmap += '_r'

    image = pyplot.imshow(data[(0,) * dims].squeeze(), vmin=vmin, vmax=vmax,
                          cmap=cmap, interpolation=interpolation, **kwargs)

    if not isrgb:
        pyplot.colorbar()  # panchor=(0.55, 0.5), fraction=0.05

    def format_coord(x, y):
        # callback function to format coordinate display in toolbar
        x = int(x + 0.5)
        y = int(y + 0.5)
        try:
            if dims:
                return "%s @ %s [%4i, %4i]" % (cur_ax_dat[1][y, x],
                                               current, x, y)
            else:
                return "%s @ [%4i, %4i]" % (data[y, x], x, y)
        except IndexError:
            return ""

    pyplot.gca().format_coord = format_coord

    if dims:
        current = list((0,) * dims)
        cur_ax_dat = [0, data[tuple(current)].squeeze()]
        sliders = [pyplot.Slider(
            pyplot.axes([0.125, 0.03*(axis+1), 0.725, 0.025]),
            'Dimension %i' % axis, 0, data.shape[axis]-1, 0, facecolor='0.5',
            valfmt='%%.0f [%i]' % data.shape[axis]) for axis in range(dims)]
        for slider in sliders:
            slider.drawon = False

        def set_image(current, sliders=sliders, data=data):
            # change image and redraw canvas
            cur_ax_dat[1] = data[tuple(current)].squeeze()
            image.set_data(cur_ax_dat[1])
            for ctrl, index in zip(sliders, current):
                ctrl.eventson = False
                ctrl.set_val(index)
                ctrl.eventson = True
            figure.canvas.draw()

        def on_changed(index, axis, data=data, current=current):
            # callback function for slider change event
            index = int(round(index))
            cur_ax_dat[0] = axis
            if index == current[axis]:
                return
            if index >= data.shape[axis]:
                index = 0
            elif index < 0:
                index = data.shape[axis] - 1
            current[axis] = index
            set_image(current)

        def on_keypressed(event, data=data, current=current):
            # callback function for key press event
            key = event.key
            axis = cur_ax_dat[0]
            if str(key) in '0123456789':
                on_changed(key, axis)
            elif key == 'right':
                on_changed(current[axis] + 1, axis)
            elif key == 'left':
                on_changed(current[axis] - 1, axis)
            elif key == 'up':
                cur_ax_dat[0] = 0 if axis == len(data.shape)-1 else axis + 1
            elif key == 'down':
                cur_ax_dat[0] = len(data.shape)-1 if axis == 0 else axis - 1
            elif key == 'end':
                on_changed(data.shape[axis] - 1, axis)
            elif key == 'home':
                on_changed(0, axis)

        figure.canvas.mpl_connect('key_press_event', on_keypressed)
        for axis, ctrl in enumerate(sliders):
            ctrl.on_changed(lambda k, a=axis: on_changed(k, a))

    return figure, subplot, image


def _app_show():
    """Block the GUI. For use as skimage plugin."""
    pyplot = sys.modules['matplotlib.pyplot']
    pyplot.show()


def main(argv=None):
    """Command line usage main function."""
    if float(sys.version[0:3]) < 2.6:
        print("This script requires Python version 2.6 or better.")
        print("This is Python version %s" % sys.version)
        return 0
    if argv is None:
        argv = sys.argv

    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="don't read OME series from multiple files")
    opt('--noplot', dest='noplot', action='store_true', default=False,
        help="don't 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('--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)

    if settings.doctest:
        import doctest
        doctest.testmod()
        return 0
    if not path:
        try:
            import tkFileDialog as filedialog
        except ImportError:
            from tkinter import filedialog
        path = filedialog.askopenfilename(filetypes=[
            ("TIF files", "*.tif"), ("LSM files", "*.lsm"),
            ("STK files", "*.stk"), ("allfiles", "*")])
        #parser.error("No file specified")
    if settings.test:
        test_tifffile(path, settings.verbose)
        return 0

    if any(i in path for i in '?*'):
        path = glob.glob(path)
        if not path:
            print('no files match the pattern')
            return 0
        # TODO: handle image sequences
        #if len(path) == 1:
        path = path[0]

    print("Reading file structure...", end=' ')
    start = time.time()
    try:
        tif = TiffFile(path, multifile=not settings.nomultifile)
    except Exception as e:
        if settings.debug:
            raise
        else:
            print("\n", e)
            sys.exit(0)
    print("%.3f ms" % ((time.time()-start) * 1e3))

    if tif.is_ome:
        settings.norgb = True

    images = [(None, tif[0 if settings.page < 0 else settings.page])]
    if not settings.noplot:
        print("Reading image data... ", end=' ')

        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