tifffile.py

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

# Copyright (c) 2008-2013, Christoph Gohlke
# Copyright (c) 2008-2013, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# 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"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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"""Read and write image data from and to TIFF files.

Image and meta-data can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH,
ImageJ, 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/CCITT compressed image data or EXIF/IPTC/GPS/XMP
meta-data is not implemented. Only primary info records are read for STK,
FluoView, 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, SEQ, GEL,
and OME-TIFF, are custom extensions defined by MetaMorph, Carl Zeiss
MicroImaging, Olympus, 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: 2013.02.17

Requirements
------------
* `CPython 2.7, 3.2 or 3.3 <http://www.python.org>`_
* `Numpy 1.7 <http://www.numpy.org>`_
* `Matplotlib 1.2 <http://www.matplotlib.org>`_  (optional for plotting)
* `Tifffile.c 2013.01.18 <http://www.lfd.uci.edu/~gohlke/>`_
  (recommended for faster decoding of PackBits and LZW encoded strings)

Notes
-----
The API is not stable yet and might change between revisions.

Tested on little-endian platforms only.

Acknowledgements
----------------
*  Egor Zindy, University of Manchester, for cz_lsm_scan_info specifics.
*  Wim Lewis, for a bug fix and some read_cz_lsm functions.

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) File Format Description - LSM 5xx Release 2.0.
    http://ibb.gsf.de/homepage/karsten.rodenacker/IDL/Lsmfile.doc
(5) BioFormats. http://www.loci.wisc.edu/ome/formats.html
(6) The OME-TIFF format.
    http://www.openmicroscopy.org/site/support/file-formats/ome-tiff
(7) TiffDecoder.java
    http://rsbweb.nih.gov/ij/developer/source/ij/io/TiffDecoder.java.html
(8) UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
    http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf

Alternatives
------------
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/>`_
* `BioImageXD.Readers <http://www.bioimagexd.net/>`_
* `CellProfiler.bioformats <http://www.cellprofiler.org/>`_

Examples
--------
>>> data = numpy.random.rand(301, 219)
>>> imsave('temp.tif', data)
>>> image = imread('temp.tif')
>>> assert numpy.all(image == data)

>>> tif = TiffFile('test.tif')
>>> images = tif.asarray()
>>> image0 = tif[0].asarray()
>>> for page in tif:
...     for tag in page.tags.values():
...         t = tag.name, tag.value
...     image = page.asarray()
...     if page.is_rgb: pass
...     if page.is_palette:
...         t = page.color_map
...     if page.is_stk:
...         t = page.mm_uic_tags.number_planes
...     if page.is_lsm:
...         t = page.cz_lsm_info
>>> tif.close()

"""

from __future__ import division, print_function

import sys
import os
import re
import glob
import math
import zlib
import time
import struct
import warnings
import datetime
import collections
from fractions import Fraction
from xml.etree import cElementTree as ElementTree

import numpy

__version__ = '2013.02.17'
__docformat__ = 'restructuredtext en'
__all__ = ['imsave', 'imread', 'imshow', 'TiffFile', 'TiffSequence']


def imsave(filename, data, photometric=None, planarconfig=None,
           resolution=None, description=None, software='tifffile.py',
           byteorder=None, bigtiff=False):
    """Write image data to TIFF file.

    Image data are written uncompressed in one stripe per plane.
    Dimensions larger than 2 or 3 (depending on photometric mode and
    planar configuration) are flattened and saved as separate pages.

    Parameters
    ----------
    filename : str
        Name of file to write.
    data : array_like
        Input image. The last dimensions are assumed to be image height,
        width, and samples.
    photometric : {'minisblack', 'miniswhite', 'rgb'}
        The color space of the image data.
        By default this setting is inferred from the data shape.
    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.
    description : str
        The subject of the image. Saved with the first page only.
    software : str
        Name of the software used to create the image.
        Saved with the first page only.
    byteorder : {'<', '>'}
        The endianness of the data in the file.
        By default this is the system's native byte order.
    bigtiff : bool
        If True the BigTIFF format is used.
        By default the standard TIFF format is used for data less than 2040 MB.

    Examples
    --------
    >>> data = numpy.random.rand(10, 3, 301, 219)
    >>> imsave('temp.tif', data)

    """
    assert(photometric in (None, 'minisblack', 'miniswhite', 'rgb'))
    assert(planarconfig in (None, 'contig', 'planar'))
    assert(byteorder in (None, '<', '>'))

    if byteorder is None:
        byteorder = '<' if sys.byteorder == 'little' else '>'

    data = numpy.asarray(data, dtype=byteorder+data.dtype.char, order='C')
    data_shape = shape = data.shape
    data = numpy.atleast_2d(data)

    if not bigtiff and data.size * data.dtype.itemsize < 2040*2**20:
        bigtiff = False
        offset_size = 4
        tag_size = 12
        numtag_format = 'H'
        offset_format = 'I'
        val_format = '4s'
    else:
        bigtiff = True
        offset_size = 8
        tag_size = 20
        numtag_format = 'Q'
        offset_format = 'Q'
        val_format = '8s'

    # unify shape of data
    samplesperpixel = 1
    extrasamples = 0
    if photometric is None:
        if data.ndim > 2 and (shape[-3] in (3, 4) or shape[-1] in (3, 4)):
            photometric = 'rgb'
        else:
            photometric = 'minisblack'
    if photometric == 'rgb':
        if len(shape) < 3:
            raise ValueError("not a RGB(A) image")
        if planarconfig is None:
            planarconfig = 'planar' if shape[-3] in (3, 4) else 'contig'
        if planarconfig == 'contig':
            if shape[-1] not in (3, 4):
                raise ValueError("not a contiguous RGB(A) image")
            data = data.reshape((-1, 1) + shape[-3:])
            samplesperpixel = shape[-1]
        else:
            if shape[-3] not in (3, 4):
                raise ValueError("not a planar RGB(A) image")
            data = data.reshape((-1, ) + shape[-3:] + (1, ))
            samplesperpixel = shape[-3]
        if samplesperpixel == 4:
            extrasamples = 1
    elif planarconfig and len(shape) > 2:
        if planarconfig == 'contig':
            data = data.reshape((-1, 1) + shape[-3:])
            samplesperpixel = shape[-1]
        else:
            data = data.reshape((-1, ) + shape[-3:] + (1, ))
            samplesperpixel = shape[-3]
        extrasamples = samplesperpixel - 1
    else:
        planarconfig = None
        data = data.reshape((-1, 1) + shape[-2:] + (1, ))

    shape = data.shape  # (pages, planes, height, width, contig samples)

    bytestr = bytes if sys.version[0] == '2' else lambda x: bytes(x, 'ascii')
    tifftypes = {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6,
                 'h': 8, 'i': 9, 'f': 11, 'd': 12, 'Q': 16, 'q': 17}
    tifftags = {
        '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,
        'extra_samples': 338, 'sample_format': 339}

    tags = []
    tag_data = []

    def pack(fmt, *val):
        return struct.pack(byteorder+fmt, *val)

    def tag(name, dtype, number, value, offset=[0]):
        # append tag binary string to tags list
        # append (offset, value as binary string) to tag_data list
        # increment offset by tag_size
        if dtype == 's':
            value = bytestr(value) + b'\0'
            number = len(value)
            value = (value, )
        t = [pack('HH', tifftags[name], tifftypes[dtype]),
             pack(offset_format, number)]
        if len(dtype) > 1:
            number *= int(dtype[:-1])
            dtype = dtype[-1]
        if number == 1:
            if isinstance(value, (tuple, list)):
                value = value[0]
            t.append(pack(val_format, pack(dtype, value)))
        elif struct.calcsize(dtype) * number <= offset_size:
            t.append(pack(val_format, pack(str(number)+dtype, *value)))
        else:
            t.append(pack(offset_format, 0))
            tag_data.append((offset[0] + offset_size + 4,
                             pack(str(number)+dtype, *value)))
        tags.append(b''.join(t))
        offset[0] += tag_size

    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 software:
        tag('software', 's', 0, software)
    if description:
        tag('image_description', 's', 0, description)
    elif shape != data_shape:
        tag('image_description', 's', 0,
            "shape=(%s)" % (",".join('%i' % i for i in data_shape)))
    tag('datetime', 's', 0,
        datetime.datetime.now().strftime("%Y:%m:%d %H:%M:%S"))
    # write previous tags only once
    writeonce = (len(tags), len(tag_data)) if shape[0] > 1 else None
    tag('compression', 'H', 1, 1)
    tag('orientation', 'H', 1, 1)
    tag('image_width', 'I', 1, shape[-2])
    tag('image_length', 'I', 1, shape[-3])
    tag('new_subfile_type', 'I', 1, 0 if shape[0] == 1 else 2)
    tag('sample_format', 'H', 1,
        {'u': 1, 'i': 2, 'f': 3, 'c': 6}[data.dtype.kind])
    tag('photometric', 'H', 1,
        {'miniswhite': 0, 'minisblack': 1, 'rgb': 2}[photometric])
    tag('samples_per_pixel', 'H', 1, samplesperpixel)
    if planarconfig:
        tag('planar_configuration', 'H', 1, 1 if planarconfig=='contig' else 2)
        tag('bits_per_sample', 'H', samplesperpixel,
            (data.dtype.itemsize * 8, ) * samplesperpixel)
    else:
        tag('bits_per_sample', 'H', 1, data.dtype.itemsize * 8)
    if extrasamples:
        if photometric == 'rgb':
            tag('extra_samples', 'H', 1, 1)  # alpha channel
        else:
            tag('extra_samples', 'H', extrasamples, (0, ) * extrasamples)
    if resolution:
        tag('x_resolution', '2I', 1, rational(resolution[0]))
        tag('y_resolution', '2I', 1, rational(resolution[1]))
        tag('resolution_unit', 'H', 1, 2)
    tag('rows_per_strip', 'I', 1, shape[-3])
    # use one strip per plane
    strip_byte_counts = (data[0, 0].size * data.dtype.itemsize, ) * shape[1]
    tag('strip_byte_counts', offset_format, shape[1], strip_byte_counts)
    # strip_offsets must be the last tag; will be updated later
    tag('strip_offsets', offset_format, shape[1], (0, ) * shape[1])

    fh = open(filename, 'wb')
    seek = fh.seek
    tell = fh.tell

    def write(arg, *args):
        fh.write(pack(arg, *args) if args else arg)

    write({'<': b'II', '>': b'MM'}[byteorder])
    if bigtiff:
        write('HHH', 43, 8, 0)
    else:
        write('H', 42)
    ifd_offset = tell()
    write(offset_format, 0)  # first IFD
    for i in range(shape[0]):
        # update pointer at ifd_offset
        pos = tell()
        seek(ifd_offset)
        write(offset_format, pos)
        seek(pos)
        # write tags
        write(numtag_format, len(tags))
        tag_offset = tell()
        write(b''.join(tags))
        ifd_offset = tell()
        write(offset_format, 0)  # offset to next ifd
        # write extra tag data and update pointers
        for off, dat in tag_data:
            pos = tell()
            seek(tag_offset + off)
            write(offset_format, pos)
            seek(pos)
            write(dat)
        # update strip_offsets
        pos = tell()
        if len(strip_byte_counts) == 1:
            seek(ifd_offset - offset_size)
            write(offset_format, pos)
        else:
            seek(pos - offset_size*shape[1])
            strip_offset = pos
            for size in strip_byte_counts:
                write(offset_format, strip_offset)
                strip_offset += size
        seek(pos)
        # write data
        data[i].tofile(fh)  # if this fails, try update Python and numpy
        fh.flush()
        # remove tags that should be written only once
        if writeonce:
            tags = tags[writeonce[0]:]
            d = writeonce[0] * tag_size
            tag_data = [(o-d, v) for (o, v) in tag_data[writeonce[1]:]]
            writeonce = None
    fh.close()


def imread(files, *args, **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.

    Examples
    --------
    >>> im = imread('test.tif', 0)
    >>> im.shape
    (256, 256, 4)
    >>> ims = imread(['test.tif', 'test.tif'])
    >>> ims.shape
    (2, 256, 256, 4)

    """
    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(*args, **kwargs)
    else:
        with TiffSequence(files, **kwargs_seq) as imseq:
            return imseq.asarray(*args, **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 meta-data from TIFF, STK, LSM, and FluoView files.

    TiffFile instances must be closed using the close method.

    Attributes
    ----------
    pages : list
        All TIFF pages in file.
    series : list of Records(shape, dtype, axes, TIFFpages)
        TIFF pages with compatible shapes and types.

    All attributes are read-only.

    Examples
    --------
    >>> tif = TiffFile('test.tif')
    ... try:
    ...     images = tif.asarray()
    ... except Exception as e:
    ...     print(e)
    ... finally:
    ...     tif.close()

    """
    def __init__(self, arg, name=None, multifile=False):
        """Initialize instance from file.

        Parameters
        ----------
        arg : str or open file
            Name of file or open file object.
        name : str
            Human readable label of open file.
        multifile : bool
            If True, series may include pages from multiple files.

        """
        if isinstance(arg, basestring):
            filename = os.path.abspath(arg)
            self._fh = open(filename, 'rb')
        else:
            filename = str(name)
            self._fh = arg

        self._fh.seek(0, 2)
        self._fsize = self._fh.tell()
        self._fh.seek(0)
        self.fname = os.path.basename(filename)
        self.fpath = os.path.dirname(filename)
        self._tiffs = {self.fname: self}  # cache of TIFFfiles
        self.offset_size = None
        self.pages = []
        self._multifile = bool(multifile)
        try:
            self._fromfile()
        except Exception:
            self._fh.close()
            raise

    def close(self):
        """Close open file handle(s)."""
        if not hasattr(self, 'tiffs'):
            return
        for tif in self._tiffs.values():
            if tif._fh:
                tif._fh.close()
                tif._fh = None

    def _fromfile(self):
        """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")
        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 not self.pages:
            raise ValueError("empty TIFF file")

    @lazyattr
    def series(self):
        """Return series of TiffPage with compatible shape and properties."""
        series = []
        if self.is_ome:
            series = self._omeseries()
        elif self.is_fluoview:
            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(self.pages[0].mm_header.dimensions))
            series = [Record(
                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),
                pages=self.pages, dtype=numpy.dtype(self.pages[0].dtype))]
        elif self.is_lsm:
            lsmi = self.pages[0].cz_lsm_info
            axes = CZ_SCAN_TYPES[lsmi.scan_type]
            if self.pages[0].is_rgb:
                axes = axes.replace('C', '').replace('XY', 'XYC')
            axes = axes[::-1]
            shape = [getattr(lsmi, CZ_DIMENSIONS[i]) for i in axes]
            pages = [p for p in self.pages if not p.is_reduced]
            series = [Record(axes=axes, shape=shape, pages=pages,
                             dtype=numpy.dtype(pages[0].dtype))]
            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] + list(pages[0].shape)
                axes = axes[:i] + 'CYX'
                series.append(Record(axes=axes, shape=shape, pages=pages,
                                     dtype=numpy.dtype(pages[0].dtype)))
        elif self.is_imagej:
            shape = []
            axes = []
            ij = self.pages[0].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:
                shape.append(ij['channels'])
                axes.append('C')
            remain = len(self.pages) // (numpy.prod(shape) if shape else 1)
            if remain > 1:
                shape.append(remain)
                axes.append('I')
            shape.extend(self.pages[0].shape)
            axes.extend(self.pages[0].axes)
            axes = ''.join(axes)
            series = [Record(pages=self.pages, shape=shape, axes=axes,
                             dtype=numpy.dtype(self.pages[0].dtype))]
        elif self.is_nih:
            series = [Record(pages=self.pages,
                             shape=(len(self.pages),) + self.pages[0].shape,
                             axes='I' + self.pages[0].axes,
                             dtype=numpy.dtype(self.pages[0].dtype))]
        elif self.pages[0].is_shaped:
            shape = self.pages[0].tags['image_description'].value[7:-1]
            shape = tuple(int(i) for i in shape.split(b','))
            series = [Record(pages=self.pages, shape=shape,
                             axes='Q' * len(shape),
                             dtype=numpy.dtype(self.pages[0].dtype))]

        if not series:
            shapes = []
            pages = {}
            for page in self.pages:
                if not page.shape:
                    continue
                shape = page.shape + (page.axes,
                                      page.compression in TIFF_DECOMPESSORS)
                if not shape in pages:
                    shapes.append(shape)
                    pages[shape] = [page]
                else:
                    pages[shape].append(page)
            series = [Record(pages=pages[s],
                             axes=(('I' + s[-2])
                                   if len(pages[s]) > 1 else s[-2]),
                             dtype=numpy.dtype(pages[s][0].dtype),
                             shape=((len(pages[s]), ) + s[:-2]
                                    if len(pages[s]) > 1 else s[:-2]))
                      for s in shapes]
        return series

    def asarray(self, key=None, series=None):
        """Return image data of 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
            Defines which series of pages to return as array.

        """
        if key is None and series is None:
            series = 0
        if series is not None:
            pages = self.series[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 len(pages) == 1:
            return pages[0].asarray()
        elif self.is_nih:
            result = numpy.vstack(p.asarray(colormapped=False,
                                            squeeze=False) for p in pages)
            if pages[0].is_palette:
                result = numpy.take(pages[0].color_map, result, axis=1)
                result = numpy.swapaxes(result, 0, 1)
        else:
            if self.is_ome and any(p is None for p in pages):
                firstpage = next(p for p in pages if p)
                nopage = numpy.zeros_like(
                    firstpage.asarray())
            result = numpy.vstack((p.asarray() if p else nopage)
                                  for p in pages)
        if key is None:
            try:
                result.shape = self.series[series].shape
            except ValueError:
                warnings.warn("failed to reshape %s to %s" % (
                              result.shape, self.series[series].shape))
                result.shape = (-1,) + pages[0].shape
        else:
            result.shape = (-1,) + pages[0].shape
        return result

    def _omeseries(self):
        """Return image series in OME-TIFF file(s)."""
        root = ElementTree.XML(self.pages[0].tags['image_description'].value)
        uuid = root.attrib.get('UUID', None)
        self._tiffs = {uuid: self}
        modulo = {}
        result = []
        for element in root:
            if element.tag.endswith('BinaryOnly'):
                warnings.warn("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
                axes = "".join(reversed(atr['DimensionOrder']))
                shape = list(int(atr['Size'+ax]) for ax in axes)
                size = numpy.prod(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]]
                    idx = numpy.ravel_multi_index(idx, shape[:-2])
                    for uuid in data:
                        if uuid.tag.endswith('UUID'):
                            if uuid.text not in self._tiffs:
                                if not self._multifile:
                                    # abort reading multi file OME series
                                    return []
                                fn = uuid.attrib['FileName']
                                try:
                                    tf = TiffFile(os.path.join(self.fpath, fn))
                                except (IOError, ValueError):
                                    warnings.warn("failed to read %s" % fn)
                                    break
                                self._tiffs[uuid.text] = tf
                            pages = self._tiffs[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")
                            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")
                result.append(Record(axes=axes, shape=shape, pages=ifds,
                                     dtype=numpy.dtype(ifds[0].dtype)))

        for record in result:
            for axis, (newaxis, labels) in modulo.items():
                i = record.axes.index(axis)
                size = len(labels)
                if record.shape[i] == size:
                    record.axes = record.axes.replace(axis, newaxis, 1)
                else:
                    record.shape[i] //= size
                    record.shape.insert(i+1, size)
                    record.axes = record.axes.replace(axis, axis+newaxis, 1)

        return result

    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.fname.capitalize(),
            format_size(self._fsize),
            {'<': '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._tiffs) > 1:
            result.append("%i files" % (len(self._tiffs)))
        return ", ".join(result)

    def __enter__(self):
        return self

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

    @lazyattr
    def fstat(self):
        try:
            return os.fstat(self._fh.fileno())
        except Exception:  # io.UnsupportedOperation
            return None

    @lazyattr
    def is_bigtiff(self):
        return self.offset_size != 4

    @lazyattr
    def is_rgb(self):
        return all(p.is_rgb for p in self.pages)

    @lazyattr
    def is_palette(self):
        return all(p.is_palette for p in self.pages)

    @lazyattr
    def is_mdgel(self):
        return any(p.is_mdgel for p in self.pages)

    @lazyattr
    def is_mediacy(self):
        return any(p.is_mediacy for p in self.pages)

    @lazyattr
    def is_stk(self):
        return all(p.is_stk for p in self.pages)

    @lazyattr
    def is_lsm(self):
        return self.pages[0].is_lsm

    @lazyattr
    def is_imagej(self):
        return self.pages[0].is_imagej

    @lazyattr
    def is_nih(self):
        return self.pages[0].is_nih

    @lazyattr
    def is_fluoview(self):
        return self.pages[0].is_fluoview

    @lazyattr
    def is_ome(self):
        return 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, colormapped if applicable.
    shape : tuple
        Dimensions of the image array in TIFF page,
        colormapped and with one alpha channel if applicable.
    axes : str
        Axes label codes:
        'X' width, 'Y' height, 'S' sample, 'P' plane, 'I' image series,
        'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
        'T' time, 'R' region|tile, 'A' angle, 'F' 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 array
        Color look up table if exists.
    mm_uic_tags: Record(dict)
        Consolidated MetaMorph mm_uic# tags, if exists.
    cz_lsm_scan_info: Record(dict)
        LSM scan info attributes, if exists.
    imagej_tags: Record(dict)
        Consolidated ImageJ description and meta_data tags, if exists.

    All attributes are read-only.

    """
    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._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.

        """
        fh = self.parent._fh
        byteorder = self.parent.byteorder
        offset_size = self.parent.offset_size

        fmt = {4: 'I', 8: 'Q'}[offset_size]
        offset = struct.unpack(byteorder + fmt, fh.read(offset_size))[0]
        if not offset:
            raise StopIteration()

        # 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]
        except Exception:
            warnings.warn("corrupted page list")
            raise StopIteration()

        for _ in range(numtags):
            tag = TiffTag(self.parent)
            tags[tag.name] = tag

        # read LSM info subrecords
        if self.is_lsm:
            pos = fh.tell()
            for name, reader in CZ_LSM_INFO_READERS.items():
                try:
                    offset = self.cz_lsm_info['offset_'+name]
                except KeyError:
                    continue
                if not offset:
                    continue
                fh.seek(offset)
                try:
                    setattr(self, 'cz_lsm_'+name, reader(fh, byteorder))
                except ValueError:
                    pass
            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:
            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 not 'photometric' in tags:
            self.photometric = None

        if 'image_length' in tags:
            self.strips_per_image = int(math.floor(
                float(self.image_length + self.rows_per_strip - 1) /
                self.rows_per_strip))
        else:
            self.strips_per_image = 0

        key = (self.sample_format, self.bits_per_sample)
        self.dtype = self._dtype = TIFF_SAMPLE_DTYPES.get(key, None)

        if self.is_imagej:
            # consolidate imagej meta data
            adict = imagej_description(tags['image_description'].value)
            try:
                adict.update(imagej_meta_data(
                    tags['imagej_meta_data'].value,
                    tags['imagej_byte_counts'].value,
                    self.parent.byteorder))
            except Exception:
                pass
            self.imagej_tags = Record(adict)

        if not 'image_length' in self.tags or not 'image_width' in self.tags:
            # some GEL file pages are missing image data
            self.image_length = 0
            self.image_width = 0
            self.strip_offsets = 0
            self._shape = ()
            self.shape = ()
            self.axes = ''

        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)

        if self.is_stk:
            # consolidate mm_uci tags
            planes = tags['mm_uic2'].count
            self.mm_uic_tags = Record(tags['mm_uic2'].value)
            for key in ('mm_uic3', 'mm_uic4', 'mm_uic1'):
                if key in tags:
                    self.mm_uic_tags.update(tags[key].value)
            if self.planar_configuration == 'contig':
                self._shape = (planes, 1, self.image_length, self.image_width,
                               self.samples_per_pixel)
                self.shape = tuple(self._shape[i] for i in (0, 2, 3, 4))
                self.axes = 'PYXS'
            else:
                self._shape = (planes, self.samples_per_pixel,
                               self.image_length, self.image_width, 1)
                self.shape = self._shape[:4]
                self.axes = 'PSYX'
            if self.is_palette and (self.color_map.shape[1]
                                    >= 2**self.bits_per_sample):
                self.shape = (3, planes, self.image_length, self.image_width)
                self.axes = 'CPYX'
            else:
                warnings.warn("palette cannot be applied")
                self.is_palette = False
        elif self.is_palette:
            self._shape = (1, 1, self.image_length, self.image_width, 1)
            if self.color_map.shape[1] >= 2**self.bits_per_sample:
                self.shape = (3, self.image_length, self.image_width)
                self.axes = 'CYX'
            else:
                warnings.warn("palette cannot be applied")
                self.is_palette = False
                self.shape = (self.image_length, self.image_width)
                self.axes = 'YX'
        elif self.is_rgb or self.samples_per_pixel > 1:
            if self.planar_configuration == 'contig':
                self._shape = (1, 1, self.image_length, self.image_width,
                               self.samples_per_pixel)
                self.shape = (self.image_length, self.image_width,
                              self.samples_per_pixel)
                self.axes = 'YXS'
            else:
                self._shape = (1, self.samples_per_pixel, self.image_length,
                               self.image_width, 1)
                self.shape = self._shape[1:-1]
                self.axes = 'SYX'
            if self.is_rgb and 'extra_samples' in self.tags:
                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.planar_configuration == 'contig':
                            self.shape = self.shape[:2] + (4,)
                        else:
                            self.shape = (4,) + self.shape[1:]
                        break
        else:
            self._shape = (1, 1, self.image_length, self.image_width, 1)
            self.shape = self._shape[2:4]
            self.axes = 'YX'

        if not self.compression and not 'strip_byte_counts' in tags:
            self.strip_byte_counts = numpy.prod(self.shape) * (
                self.bits_per_sample // 8)

    def asarray(self, squeeze=True, colormapped=True, rgbonly=True):
        """Read image data from file and return as numpy array.

        Raise ValueError if format is unsupported.
        If any argument is False, 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.

        """
        fh = self.parent._fh
        if not fh:
            raise IOError("TIFF file is not open")
        if self.dtype is None:
            raise ValueError("data type not supported: %s%i" % (
                self.sample_format, self.bits_per_sample))
        if self.compression not in TIFF_DECOMPESSORS:
            raise ValueError("cannot decompress %s" % self.compression)
        if ('ycbcr_subsampling' in self.tags
                and self.tags['ycbcr_subsampling'].value not in (1, (1, 1))):
            raise ValueError("YCbCr subsampling not supported")
        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))

        dtype = self._dtype
        shape = self._shape

        if not shape:
            return None

        image_width = self.image_width
        image_length = self.image_length
        typecode = self.parent.byteorder + dtype
        bits_per_sample = self.bits_per_sample

        if self.is_tiled:
            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
            tile_width = self.tile_width
            tile_length = self.tile_length
            tw = (image_width + tile_width - 1) // tile_width
            tl = (image_length + tile_length - 1) // tile_length
            shape = shape[:-3] + (tl*tile_length, tw*tile_width, shape[-1])
            tile_shape = (tile_length, tile_width, shape[-1])
            runlen = tile_width
        else:
            byte_counts = self.strip_byte_counts
            offsets = self.strip_offsets
            runlen = image_width

        try:
            offsets[0]
        except TypeError:
            offsets = (offsets, )
            byte_counts = (byte_counts, )
        if any(o < 2 for o in offsets):
            raise ValueError("corrupted page")

        if (not self.is_tiled and (self.is_stk or (not self.compression
            and bits_per_sample in (8, 16, 32, 64)
            and all(offsets[i] == offsets[i+1] - byte_counts[i]
                    for i in range(len(offsets)-1))))):
            # contiguous data
            fh.seek(offsets[0])
            result = numpy_fromfile(fh, typecode, numpy.prod(shape))
            result = result.astype('=' + dtype)
        else:
            if self.planar_configuration == '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")
                unpack = lambda x: numpy.fromstring(x, typecode)
            elif isinstance(bits_per_sample, tuple):
                unpack = lambda x: unpackrgb(x, typecode, bits_per_sample)
            else:
                unpack = lambda x: unpackints(x, typecode, bits_per_sample,
                                              runlen)
            decompress = TIFF_DECOMPESSORS[self.compression]
            if self.is_tiled:
                result = numpy.empty(shape, dtype)
                tw, tl, pl = 0, 0, 0
                for offset, bytecount in zip(offsets, byte_counts):
                    fh.seek(offset)
                    tile = unpack(decompress(fh.read(bytecount)))
                    tile.shape = tile_shape
                    if self.predictor == 'horizontal':
                        numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile)
                    result[0, pl, tl:tl+tile_length,
                           tw:tw+tile_width, :] = tile
                    del tile
                    tw += tile_width
                    if tw >= shape[-2]:
                        tw, tl = 0, tl + tile_length
                        if tl >= shape[-3]:
                            tl, pl = 0, pl + 1
                result = result[..., :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 = unpack(decompress(fh.read(bytecount)))
                    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 == 'horizontal' and not self.is_tiled:
            # workaround bug in LSM510 software
            if not (self.parent.is_lsm and not self.compression):
                numpy.cumsum(result, axis=-2, dtype=dtype, out=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, 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.planar_configuration == 'contig':
                        result = result[..., [0, 1, 2, 3+i]]
                    else:
                        result = result[:, [0, 1, 2, 3+i]]
                    break
            else:
                if self.planar_configuration == '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)))

        return result

    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_fluoview', 'is_mdgel',
                                     'is_mediacy', 'is_reduced', 'is_tiled')
                     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 is_rgb(self):
        """True if page contains a RGB image."""
        return ('photometric' in self.tags and
                self.tags['photometric'].value == 2)

    @lazyattr
    def is_palette(self):
        """True if page contains a palette-colored image."""
        return ('photometric' in self.tags and
                self.tags['photometric'].value == 3)

    @lazyattr
    def is_tiled(self):
        """True if page contains tiled image."""
        return 'tile_width' in self.tags

    @lazyattr
    def is_reduced(self):
        """True if page is a reduced image of another image."""
        return bool(self.tags['new_subfile_type'].value & 1)

    @lazyattr
    def is_mdgel(self):
        """True if page contains md_file_tag tag."""
        return 'md_file_tag' in self.tags

    @lazyattr
    def is_mediacy(self):
        """True if 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):
        """True if page contains MM_UIC2 tag."""
        return 'mm_uic2' in self.tags

    @lazyattr
    def is_lsm(self):
        """True if page contains LSM CZ_LSM_INFO tag."""
        return 'cz_lsm_info' in self.tags

    @lazyattr
    def is_fluoview(self):
        """True if page contains FluoView MM_STAMP tag."""
        return 'mm_stamp' in self.tags

    @lazyattr
    def is_nih(self):
        """True if page contains NIH image header."""
        return 'nih_image_header' in self.tags

    @lazyattr
    def is_ome(self):
        """True if page contains OME-XML in image_description tag."""
        return ('image_description' in self.tags and self.tags[
            'image_description'].value.startswith(b'<?xml version='))

    @lazyattr
    def is_shaped(self):
        """True if page contains shape in image_description tag."""
        return ('image_description' in self.tags and self.tags[
            'image_description'].value.startswith(b'shape=('))

    @lazyattr
    def is_imagej(self):
        """True if page contains ImageJ description."""
        return ('image_description' in self.tags and
                self.tags['image_description'].value.startswith(b'ImageJ='))


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. For codes in CUSTOM_TAGS the 4 bytes file content.
    value_offset : int
        Location of value in file, if any.

    All attributes are read-only.

    """
    __slots__ = ('code', 'name', 'count', 'dtype', 'value', 'value_offset',
                 '_offset')

    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

    def _fromfile(self, parent):
        """Read tag structure from open file. Advance file cursor."""
        fh = parent._fh
        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:])

        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[dtype]
        except KeyError:
            raise ValueError("unknown TIFF tag data type %i" % dtype)

        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._fsize:
                raise ValueError("corrupt file - invalid tag value offset")
            elif offset < 4:
                warnings.warn("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)
                fh.seek(0, 2)  # bug in numpy/Python 3.x ?
                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(0, 2)  # bug in numpy/Python 3.x ?
            fh.seek(pos)
        else:
            value = struct.unpack(fmt, value[:size])

        if not code in CUSTOM_TAGS:
            if len(value) == 1:
                value = value[0]

        if dtype.endswith('s'):
            value = stripnull(value)

        self.code = code
        self.name = name
        self.dtype = dtype
        self.count = count
        self.value = value

    def __str__(self):
        """Return string containing information about tag."""
        return ' '.join(str(getattr(self, s)) for s in self.__slots__)


class TiffSequence(object):
    """Sequence of image files.

    Properties
    ----------
    files : list
        List of file names.
    shape : tuple
        Shape of image sequence.
    axes : str
        Labels of axes in shape.

    Examples
    --------
    >>> ims = TiffSequence("test.oif.files/*.tif")
    >>> ims = ims.asarray()
    >>> ims.shape
    (2, 100, 256, 256)

    """
    _axes_pattern = """
        # 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'):
        """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.

        """
        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'):
            _imread = imread

            def imread(fname, *args, **kwargs):
                with _imread(fname) as im:
                    return im.asarray(*args, **kwargs)

        self.imread = imread

        self.pattern = self._axes_pattern if pattern == 'axes' else 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 = ((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, *args):
        self.close()

    def close(self):
        pass

    def asarray(self, *args, **kwargs):
        """Read image data from all files and return as single numpy array.

        Raise IndexError if image shapes don't match.

        """
        im = self.imread(self.files[0])
        result_shape = self.shape + im.shape
        result = numpy.zeros(result_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 = result_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 numpy.prod(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):
            if k.startswith('_'):  # does not work with byte
                continue
            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 TIFFtags with attribute access."""
    def __str__(self):
        """Return string with information about all tags."""
        s = []
        #sortbycode = lambda a, b: cmp(a.code, b.code)
        #for tag in sorted(self.values(), sortbycode):
        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,
                                        str(tag.value).split('\n', 1)[0])
            s.append(line[:PRINT_LINE_LEN].lstrip())
        return '\n'.join(s)


def read_bytes(fh, byteorder, dtype, count):
    """Read tag data from file and return as byte string."""
    return numpy_fromfile(fh, byteorder+dtype[-1], count).tostring()


def read_numpy(fh, byteorder, dtype, count):
    """Read tag data from file and return as numpy array."""
    return numpy_fromfile(fh, byteorder+dtype[-1], count)


def read_mm_header(fh, byteorder, dtype, count):
    """Read MM_HEADER tag from file and return as numpy.rec.array."""
    return numpy.rec.fromfile(fh, MM_HEADER, 1, byteorder=byteorder)[0]


def read_mm_stamp(fh, byteorder, dtype, count):
    """Read MM_STAMP tag from file and return as numpy.array."""
    return numpy_fromfile(fh, byteorder+'8f8', 1)[0]


def read_mm_uic1(fh, byteorder, dtype, count):
    """Read MM_UIC1 tag from file and return as dictionary."""
    t = fh.read(8*count)
    t = struct.unpack('%s%iI' % (byteorder, 2*count), t)
    return dict((MM_TAG_IDS[k], v) for k, v in zip(t[::2], t[1::2])
                if k in MM_TAG_IDS)


def read_mm_uic2(fh, byteorder, dtype, count):
    """Read MM_UIC2 tag from file and return as dictionary."""
    result = {'number_planes': count}
    values = numpy_fromfile(fh, byteorder+'I', 6*count)
    result['z_distance'] = values[0::6] // values[1::6]
    #result['date_created'] = tuple(values[2::6])
    #result['time_created'] = tuple(values[3::6])
    #result['date_modified'] = tuple(values[4::6])
    #result['time_modified'] = tuple(values[5::6])
    return result


def read_mm_uic3(fh, byteorder, dtype, count):
    """Read MM_UIC3 tag from file and return as dictionary."""
    t = numpy_fromfile(fh, byteorder+'I', 2*count)
    return {'wavelengths': t[0::2] // t[1::2]}


def read_mm_uic4(fh, byteorder, dtype, count):
    """Read MM_UIC4 tag from file and return as dictionary."""
    t = struct.unpack(byteorder + 'hI'*count, fh.read(6*count))
    return dict((MM_TAG_IDS[k], v) for k, v in zip(t[::2], t[1::2])
                if k in MM_TAG_IDS)


def read_cz_lsm_info(fh, byteorder, dtype, count):
    """Read CS_LSM_INFO tag from file and return as numpy.rec.array."""
    result = numpy.rec.fromfile(fh, CZ_LSM_INFO, 1,
                                byteorder=byteorder)[0]
    {50350412: '1.3', 67127628: '2.0'}[result.magic_number]  # validation
    return result


def read_cz_lsm_time_stamps(fh, byteorder):
    """Read LSM time stamps from file and return as list."""
    size, count = struct.unpack(byteorder+'II', fh.read(8))
    if size != (8 + 8 * count):
        raise ValueError("lsm_time_stamps block is too short")
    return struct.unpack(('%s%dd' % (byteorder, count)),
                         fh.read(8*count))


def read_cz_lsm_event_list(fh, byteorder):
    """Read LSM events from file and return as list of (time, type, text)."""
    count = struct.unpack(byteorder+'II', fh.read(8))[1]
    events = []
    while count > 0:
        esize, etime, etype = struct.unpack(byteorder+'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, byteorder):
    """Read LSM scan information from file and return as Record."""
    block = Record()
    blocks = [block]
    unpack = struct.unpack
    if 0x10000000 != struct.unpack(byteorder+"I", fh.read(4))[0]:
        raise ValueError("not a lsm_scan_info structure")
    fh.read(8)
    while True:
        entry, dtype, size = unpack(byteorder+"III", fh.read(12))
        if dtype == 2:
            value = stripnull(fh.read(size))
        elif dtype == 4:
            value = unpack(byteorder+"i", fh.read(4))[0]
        elif dtype == 5:
            value = unpack(byteorder+"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:
            block = blocks.pop()
        else:
            setattr(block, "unknown_%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 = numpy.rec.fromfile(fh, NIH_IMAGE_HEADER, 1, byteorder=byteorder)[0]
    a = a.newbyteorder(byteorder)
    a.xunit = a.xunit[:a._xunit_len]
    a.um = a.um[:a._um_len]
    return a


def imagej_meta_data(data, bytecounts, byteorder):
    """Return dict from ImageJ meta data tag value."""

    if sys.version_info[0] > 2:
        _str = lambda x: str(x, 'cp1252')
    else:
        _str = str

    def read_string(data, byteorder):
        return _str(data[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 = {
        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)}

    if not bytecounts:
        raise ValueError("no ImageJ meta data")

    if not data.startswith(b'IJIJ'):
        raise ValueError("invalid ImageJ meta data")

    header_size = bytecounts[0]
    if header_size < 12 or header_size > 804:
        raise ValueError("invalid ImageJ meta data 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(description):
    """Return dict from ImageJ image_description tag."""

    def _bool(val):
        return {b'true': True, b'false': False}[val.lower()]

    if sys.version_info[0] > 2:
        _str = lambda x: str(x, 'cp1252')
    else:
        _str = str

    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
    return result


def _replace_by(module_function, package=None, warn=True):
    """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 not package:
                module = import_module(module)
            else:
                module = import_module('.' + module, package=package)
            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


@_replace_by('_tifffile.decodepackbits')
def decodepackbits(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.decodelzw')
def decodelzw(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 = code << (bitcount % 8)
        code = 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(
            "decodelzw encountered unexpected end of stream (code %i)" % code)

    return b''.join(result)


@_replace_by('_tifffile.unpackints')
def unpackints(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 = code << (bitcount % 8)
        code = code & bitmask
        result[i] = code >> shrbits
        bitcount += itemsize
        if (i+1) % runlen == 0:
            bitcount += skipbits
    return result


def unpackrgb(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(unpackrgb(data, '<B', (5, 6, 5), False))
    [ 1  1  1 31 63 31]
    >>> print(unpackrgb(data, '<B', (5, 6, 5)))
    [  8   4   8 255 255 255]
    >>> print(unpackrgb(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 array
        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 numpy_fromfile(arg, dtype=float, count=-1, sep=''):
    """Return array from data in binary file.

    Work around numpy issue #2230, "numpy.fromfile does not accept StringIO
    object" https://github.com/numpy/numpy/issues/2230.

    """
    try:
        return numpy.fromfile(arg, dtype, count, sep)
    except IOError:
        if count < 0:
            size = 2**30
        else:
            size = count * numpy.dtype(dtype).itemsize
        data = arg.read(int(size))
        return numpy.fromstring(data, dtype, count, sep)


def stripnull(string):
    """Return string truncated at first null character."""
    i = string.find(b'\x00')
    return string if (i < 0) else string[:i]


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 natural_sorted(iterable):
    """Return human sorted list of strings.

    Examples
    --------
    >>> natural_sorted(['f1', 'f2', 'f10'])
    ['f1', 'f2', 'f10']

    """
    numbers = re.compile('(\d+)')
    sortkey = lambda x: [(int(c) if c.isdigit() else c)
                         for c in re.split(numbers, x)]
    return sorted(iterable, key=sortkey)


def datetime_from_timestamp(n, epoch=datetime.datetime.fromordinal(693594)):
    """Return datetime object from timestamp in Excel serial format.

    Examples
    --------
    >>> datetime_from_timestamp(40237.029999999795)
    datetime.datetime(2010, 2, 28, 0, 43, 11, 999982)

    """
    return epoch + datetime.timedelta(n)


def test_tifffile(directory='testimages', verbose=True):
    """Read all images in directory. Print error message on failure.

    Examples
    --------
    >>> 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',
    6: 'cielab',
    7: 'icclab',
    8: 'itulab',
    32844: 'logl',
    32845: 'logluv',
}

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',
}

TIFF_DECOMPESSORS = {
    None: lambda x: x,
    'adobe_deflate': zlib.decompress,
    'deflate': zlib.decompress,
    'packbits': decodepackbits,
    'lzw': decodelzw,
}

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: '1B',   # 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',
}

AXES_LABELS = {
    'X': 'width',
    'Y': 'height',
    'Z': 'depth',
    'S': 'sample',  # rgb(a)
    'P': 'plane',  # page
    'T': 'time',
    'C': 'channel',  # color, emission wavelength
    'A': 'angle',
    'F': 'phase',
    'R': 'tile',  # region, point
    'H': 'lifetime',  # histogram
    'E': 'lambda',  # excitation wavelength
    'L': 'exposure',  # lux
    'V': 'event',
    'Q': 'other',
}

AXES_LABELS.update(dict((v, k) for k, v in AXES_LABELS.items()))

# 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'),
    ('p1', [('x', 'i2'), ('y', 'i2')]),
    ('p2', [('x', 'i2'), ('y', 'i2')]),
    ('curvefit_t', 'i2'),
    ('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'),
    ('lutmode_t', 'u1'),
    ('invertedtable', 'b1'),
    ('zeroclip', 'b1'),
    ('_xunit_len', 'u1'),
    ('xunit', 'a11'),
    ('stacktype_t', 'i2'),
]

#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')

# MetaMorph STK tags
MM_TAG_IDS = {
    0: 'auto_scale',
    1: 'min_scale',
    2: 'max_scale',
    3: 'spatial_calibration',
    #4: 'x_calibration',
    #5: 'y_calibration',
    #6: 'calibration_units',
    #7: 'name',
    8: 'thresh_state',
    9: 'thresh_state_red',
    11: 'thresh_state_green',
    12: 'thresh_state_blue',
    13: 'thresh_state_lo',
    14: 'thresh_state_hi',
    15: 'zoom',
    #16: 'create_time',
    #17: 'last_saved_time',
    18: 'current_buffer',
    19: 'gray_fit',
    20: 'gray_point_count',
    #21: 'gray_x',
    #22: 'gray_y',
    #23: 'gray_min',
    #24: 'gray_max',
    #25: 'gray_unit_name',
    26: 'standard_lut',
    27: 'wavelength',
    #28: 'stage_position',
    #29: 'camera_chip_offset',
    #30: 'overlay_mask',
    #31: 'overlay_compress',
    #32: 'overlay',
    #33: 'special_overlay_mask',
    #34: 'special_overlay_compress',
    #35: 'special_overlay',
    36: 'image_property',
    #37: 'stage_label',
    #38: 'autoscale_lo_info',
    #39: 'autoscale_hi_info',
    #40: 'absolute_z',
    #41: 'absolute_z_valid',
    #42: 'gamma',
    #43: 'gamma_red',
    #44: 'gamma_green',
    #45: 'gamma_blue',
    #46: 'camera_bin',
    47: 'new_lut',
    #48: 'image_property_ex',
    49: 'plane_property',
    #50: 'user_lut_table',
    51: 'red_autoscale_info',
    #52: 'red_autoscale_lo_info',
    #53: 'red_autoscale_hi_info',
    54: 'red_minscale_info',
    55: 'red_maxscale_info',
    56: 'green_autoscale_info',
    #57: 'green_autoscale_lo_info',
    #58: 'green_autoscale_hi_info',
    59: 'green_minscale_info',
    60: 'green_maxscale_info',
    61: 'blue_autoscale_info',
    #62: 'blue_autoscale_lo_info',
    #63: 'blue_autoscale_hi_info',
    64: 'blue_min_scale_info',
    65: 'blue_max_scale_info',
    #66: 'overlay_plane_color'
}

# Olymus 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', 'i4'),
    ('structure_size', 'i4'),
    ('dimension_x', 'i4'),
    ('dimension_y', 'i4'),
    ('dimension_z', 'i4'),
    ('dimension_channels', 'i4'),
    ('dimension_time', 'i4'),
    ('dimension_data_type', 'i4'),
    ('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'),
    ('data_type', 'u4'),
    ('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_information', 'u4'),
    ('offset_ks_data', 'u4'),
    ('offset_time_stamps', 'u4'),
    ('offset_event_list', 'u4'),
    ('offset_roi', 'u4'),
    ('offset_bleach_roi', 'u4'),
    ('offset_next_recording', 'u4'),
    ('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'),
]

# Import functions for LSM_INFO subrecords
CZ_LSM_INFO_READERS = {
    'scan_information': read_cz_lsm_scan_info,
    'time_stamps': read_cz_lsm_time_stamps,
    'event_list': read_cz_lsm_event_list,
}

# 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',
}

# Descriptions of cz_lsm_info.data_type
CZ_DATA_TYPES = {
    0: 'varying data types',
    2: '12 bit unsigned integer',
    5: '32 bit float',
}

CZ_LSM_SCAN_INFO_ARRAYS = {
    0x20000000: "tracks",
    0x30000000: "lasers",
    0x60000000: "detectionchannels",
    0x80000000: "illuminationchannels",
    0xa0000000: "beamsplitters",
    0xc0000000: "datachannels",
    0x13000000: "markers",
    0x11000000: "timers",
}

CZ_LSM_SCAN_INFO_STRUCTS = {
    0x40000000: "tracks",
    0x50000000: "lasers",
    0x70000000: "detectionchannels",
    0x90000000: "illuminationchannels",
    0xb0000000: "beamsplitters",
    0xd0000000: "datachannels",
    0x14000000: "markers",
    0x12000000: "timers",
}

CZ_LSM_SCAN_INFO_ATTRIBUTES = {
    0x10000001: "name",
    0x10000002: "description",
    0x10000003: "notes",
    0x10000004: "objective",
    0x10000005: "processing_summary",
    0x10000006: "special_scan_mode",
    0x10000007: "oledb_recording_scan_type",
    0x10000008: "oledb_recording_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_bccorrection",
    0x10000049: "position_bccorrection1",
    0x10000050: "position_bccorrection2",
    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",
    # lasers
    0x50000001: "name",
    0x50000002: "acquire",
    0x50000003: "power",
    # tracks
    0x40000001: "multiplex_type",
    0x40000002: "multiplex_order",
    0x40000003: "sampling_mode",
    0x40000004: "sampling_method",
    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",
    # detection_channels
    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: "detection_channel_name",
    0x70000015: "detection_detector_gain_bc1",
    0x70000016: "detection_detector_gain_bc2",
    0x70000017: "detection_amplifier_gain_bc1",
    0x70000018: "detection_amplifier_gain_bc2",
    0x70000019: "detection_amplifier_offset_bc1",
    0x70000020: "detection_amplifier_offset_bc2",
    0x70000021: "detection_spectral_scan_channels",
    0x70000022: "detection_spi_wavelength_start",
    0x70000023: "detection_spi_wavelength_stop",
    0x70000026: "detection_dye_name",
    0x70000027: "detection_dye_folder",
    # illumination_channels
    0x90000001: "name",
    0x90000002: "power",
    0x90000003: "wavelength",
    0x90000004: "aquire",
    0x90000005: "detchannel_name",
    0x90000006: "power_bc1",
    0x90000007: "power_bc2",
    # beam_splitters
    0xb0000001: "filter_set",
    0xb0000002: "filter",
    0xb0000003: "name",
    # data_channels
    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",
    # markers
    0x14000001: "name",
    0x14000002: "description",
    0x14000003: "trigger_in",
    0x14000004: "trigger_out",
    # timers
    0x12000001: "name",
    0x12000002: "description",
    0x12000003: "interval",
    0x12000004: "trigger_in",
    0x12000005: "trigger_out",
    0x12000006: "activation_time",
    0x12000007: "activation_number",
}

# 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'}),
    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),
    347: ('jpeg_tables', None, None, None, None),
    530: ('ycbcr_subsampling', 1, 3, 2, None),
    531: ('ycbcr_positioning', 1, 3, 1, None),
    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),
    37510: ('user_comment', None, None, 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),
    42112: ('gdal_metadata', None, 2, None, None),
    42113: ('gdal_nodata', None, 2, None, None),
    50838: ('imagej_byte_counts', None, None, 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),
    # 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_numpy),
    33628: ('mm_uic1', read_mm_uic1),
    33629: ('mm_uic2', read_mm_uic2),
    33630: ('mm_uic3', read_mm_uic3),
    33631: ('mm_uic4', read_mm_uic4),
    34361: ('mm_header', read_mm_header),
    34362: ('mm_stamp', read_mm_stamp),
    34386: ('mm_user_block', read_bytes),
    34412: ('cz_lsm_info', read_cz_lsm_info),
    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_meta_data', read_bytes),
}

# 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 size in any dimension.
    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())
    data = data[(slice(0, maxdim), ) * len(data.shape)]

    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] in (3, 4)):
            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 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

    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 'ub' and vmin == 0:
            cmap = 'gray'
        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

    search_doc = lambda r, d: re.search(r, __doc__).group(1) if __doc__ else d
    parser = optparse.OptionParser(
        usage="usage: %prog [options] path",
        description=search_doc("\n\n([^|]*?)\n\n", ''),
        version="%%prog %s" % search_doc(":Version: (.*)", "Unknown"))
    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 internal tests")
    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:
        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=' ')
        notnone = lambda x: next(i for i in x if i is not None)
        start = time.time()
        try:
            if settings.page >= 0:
                images = [(tif.asarray(key=settings.page),
                           tif[settings.page])]
            elif settings.series >= 0:
                images = [(tif.asarray(series=settings.series),
                           notnone(tif.series[settings.series].pages))]
            else:
                images = []
                for i, s in enumerate(tif.series):
                    try:
                        images.append(
                            (tif.asarray(series=i), notnone(s.pages)))
                    except ValueError as e:
                        images.append((None, notnone(s.pages)))
                        if settings.debug:
                            raise
                        else:
                            print("\n* series %i failed: %s... " % (i, e),
                                  end='')
            print("%.3f ms" % ((time.time()-start) * 1e3))
        except Exception as e:
            if settings.debug:
                raise
            else:
                print(e)
    tif.close()

    print("\nTIFF file:", tif)
    print()
    for i, s in enumerate(tif.series):
        print ("Series %i" % i)
        print(s)
        print()
    for i, page in images:
        print(page)
        print(page.tags)
        if page.is_palette:
            print("\nColor Map:", page.color_map.shape, page.color_map.dtype)
        for attr in ('cz_lsm_info', 'cz_lsm_scan_information', 'mm_uic_tags',
                     'mm_header', 'imagej_tags', 'nih_image_header'):
            if hasattr(page, attr):
                print("", attr.upper(), Record(getattr(page, attr)), sep="\n")
        print()

    if images and not settings.noplot:
        try:
            import matplotlib
            matplotlib.use('TkAgg')
            from matplotlib import pyplot
        except ImportError as e:
            warnings.warn("failed to import matplotlib.\n%s" % e)
        else:
            for img, page in images:
                if img is None:
                    continue
                vmin, vmax = None, None
                if 'gdal_nodata' in page.tags:
                    vmin = numpy.min(img[img > float(page.gdal_nodata)])
                if page.is_stk:
                    try:
                        vmin = page.mm_uic_tags['min_scale']
                        vmax = page.mm_uic_tags['max_scale']
                    except KeyError:
                        pass
                    else:
                        if vmax <= vmin:
                            vmin, vmax = None, None
                title = "%s\n %s" % (str(tif), str(page))
                imshow(img, title=title, vmin=vmin, vmax=vmax,
                       bitspersample=page.bits_per_sample,
                       photometric=page.photometric,
                       interpolation=settings.interpol,
                       dpi=settings.dpi)
            pyplot.show()


TIFFfile = TiffFile  # backwards compatibility

if sys.version_info[0] > 2:
    basestring = str
    unicode = str

if __name__ == "__main__":
    sys.exit(main())