#! /usr/bin/python3
# -*- coding: utf-8 -*-
# tifffile.py

# Copyright (c) 2008-2017, Christoph Gohlke
# Copyright (c) 2008-2017, 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.

"""Read image and meta data from (bio) TIFF® files. Save numpy arrays as TIFF.

Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH,
SGI, ImageJ, MicroManager, FluoView, SEQ and GEL files.

Tifffile is not a general purpose TIFF library. 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 image
trees defined via SubIFDs, JPEG and CCITT compression, chroma subsampling,
or IPTC and XMP metadata are not implemented.

TIFF®, the tagged Image File Format, is a trademark and under control of
Adobe Systems Incorporated. BigTIFF allows for files greater than 4 GB.
STK, LSM, FluoView, SGI, SEQ, GEL, and OME-TIFF, are custom extensions
defined by Molecular Devices (Universal Imaging Corporation), Carl Zeiss
MicroImaging, Olympus, Silicon Graphics International, Media Cybernetics,
Molecular Dynamics, and the Open Microscopy Environment consortium

For command line usage run C{python -m tifffile --help}

  `Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_

  Laboratory for Fluorescence Dynamics, University of California, Irvine

:Version: 2017.09.29

* `CPython 3.6 64-bit <http://www.python.org>`_
* `Numpy 1.13 <http://www.numpy.org>`_
* `Matplotlib 2.0 <http://www.matplotlib.org>`_ (optional for plotting)
* `Tifffile.c 2017.01.10 <http://www.lfd.uci.edu/~gohlke/>`_
  (recommended for faster decoding of PackBits and LZW encoded strings)

2017.09.29 (tentative)
    Many backwards incompatible changes improving speed and resource usage:
    Pass 2268 tests.
    Add detail argument to __str__ function. Remove info functions.
    Fix potential issue correcting offsets of large LSM files with positions.
    Remove TiffFile iterator interface; use TiffFile.pages instead.
    Do not make tag values available as TiffPage attributes.
    Use str (not bytes) type for tag and metadata strings (WIP).
    Use documented standard tag and value names (WIP).
    Use enums for some documented TIFF tag values.
    Remove 'memmap' and 'tmpfile' options; use out='memmap' instead.
    Add option to specify output in asarray functions.
    Add option to concurrently decode image strips or tiles using threads.
    Add TiffPage.asrgb function (WIP).
    Do not apply colormap in asarray.
    Remove 'colormapped', 'rgbonly', and 'scale_mdgel' options from asarray.
    Consolidate metadata in TiffFile _metadata functions.
    Remove non-tag metadata properties from TiffPage.
    Add function to convert LSM to tiled BIN files.
    Align image data in file.
    Make TiffPage.dtype a numpy.dtype.
    Add 'ndim' and 'size' properties to TiffPage and TiffPageSeries.
    Allow imsave to write non-BigTIFF files up to ~4 GB.
    Only read one page for shaped series if possible.
    Add memmap function to create memory-mapped array stored in TIFF file.
    Add option to save empty arrays to TIFF files.
    Add option to save truncated TIFF files.
    Allow single tile images to be saved contiguously.
    Add optional movie mode for files with uniform pages.
    Lazy load pages.
    Use lightweight TiffFrame for IFDs sharing properties with key TiffPage.
    Move module constants to 'TIFF' namespace (speed up module import).
    Remove 'fastij' option from TiffFile.
    Remove 'pages' parameter from TiffFile.
    Remove TIFFfile alias.
    Deprecate Python 2.
    Require enum34 and futures packages on Python 2.7.
    Remove Record class and return all metadata as dict instead.
    Add functions to parse STK, MetaSeries, ScanImage, SVS, Pilatus metadata.
    Read tags from EXIF and GPS IFDs.
    Use pformat for tag and metadata values.
    Fix reading some UIC tags (bug fix).
    Do not modify input array in imshow (bug fix).
    Fix Python implementation of unpack_ints.
    Pass 1961 tests.
    Write correct number of sample_format values (bug fix).
    Use Adobe deflate code to write ZIP compressed files.
    Add option to pass tag values as packed binary data for writing.
    Defer tag validation to attribute access.
    Use property instead of lazyattr decorator for simple expressions.
    Write IFDs and tag values on word boundaries.
    Read ScanImage metadata.
    Remove is_rgb and is_indexed attributes from TiffFile.
    Create files used by doctests.
    Read Zeiss SEM metadata.
    Read OME-TIFF with invalid references to external files.
    Rewrite C LZW decoder (5x faster).
    Read corrupted LSM files missing EOI code in LZW stream.
    Add option to append images to existing TIFF files.
    Read files without pages.
    Read S-FEG and Helios NanoLab tags created by FEI software.
    Allow saving Color Filter Array (CFA) images.
    Add info functions returning more information about TiffFile and TiffPage.
    Add option to read specific pages only.
    Remove maxpages argument (backwards incompatible).
    Remove test_tifffile function.
    Pass 1944 tests.
    Improve detection of ImageJ hyperstacks.
    Read TVIPS metadata created by EM-MENU (by Marco Oster).
    Add option to disable using OME-XML metadata.
    Allow non-integer range attributes in modulo tags (by Stuart Berg).
    Do not always memmap contiguous data in page series.
    Add option to specify resolution unit.
    Write grayscale images with extra samples when planarconfig is specified.
    Do not write RGB color images with 2 samples.
    Reorder TiffWriter.save keyword arguments (backwards incompatible).
    Pass 1932 tests.
    TiffWriter, imread, and imsave accept open binary file streams.
    Correctly handle reversed fill order in 2 and 4 bps images (bug fix).
    Implement reverse_bitorder in C.
    Fix saving additional ImageJ metadata.
    Pass 1920 tests.
    Write 8 bytes double tag values using offset if necessary (bug fix).
    Add option to disable writing second image description tag.
    Detect tags with incorrect counts.
    Disable color mapping for LSM.
    Read LSM 6 mosaics.
    Add option to specify directory of memory-mapped files.
    Add command line options to specify vmin and vmax values for colormapping.
    New helper function to apply colormaps.
    Renamed is_palette attributes to is_indexed (backwards incompatible).
    Color-mapped samples are now contiguous (backwards incompatible).
    Do not color-map ImageJ hyperstacks (backwards incompatible).
    Towards supporting Leica SCN.
    Read images with reversed bit order (FillOrder is LSB2MSB).
    Read RGB OME-TIFF.
    Warn about malformed OME-XML.
    Detect some corrupted ImageJ metadata.
    Better axes labels for 'shaped' files.
    Do not create TiffTag for default values.
    Chroma subsampling is not supported.
    Memory-map data in TiffPageSeries if possible (optional).
    Pass 1906 tests.
    Write ImageJ hyperstacks (optional).
    Read and write LZMA compressed data.
    Specify datetime when saving (optional).
    Save tiled and color-mapped images (optional).
    Ignore void bytecounts and offsets if possible.
    Ignore bogus image_depth tag created by ISS Vista software.
    Decode floating point horizontal differencing (not tiled).
    Save image data contiguously if possible.
    Only read first IFD from ImageJ files if possible.
    Read ImageJ 'raw' format (files larger than 4 GB).
    TiffPageSeries class for pages with compatible shape and data type.
    Try to read incomplete tiles.
    Open file dialog if no filename is passed on command line.
    Ignore errors when decoding OME-XML.
    Rename decoder functions (backwards incompatible).
    TiffWriter class for incremental writing images.
    Simplify examples.
    Add memmap function to FileHandle.
    Add function to determine if image data in TiffPage is memory-mappable.
    Do not close files if multifile_close parameter is False.
    Pass 1730 tests.
    Return all extrasamples by default (backwards incompatible).
    Read data from series of pages into memory-mapped array (optional).
    Squeeze OME dimensions (backwards incompatible).
    Workaround missing EOI code in strips.
    Support image and tile depth tags (SGI extension).
    Better handling of STK/UIC tags (backwards incompatible).
    Disable color mapping for STK.
    Julian to datetime converter.
    TIFF ASCII type may be NULL separated.
    Unwrap strip offsets for LSM files greater than 4 GB.
    Correct strip byte counts in compressed LSM files.
    Skip missing files in OME series.
    Read embedded TIFF files.
    Save rational numbers as type 5 (bug fix).
    Keep other files in OME multi-file series closed.
    FileHandle class to abstract binary file handle.
    Disable color mapping for bad OME-TIFF produced by bio-formats.
    Read bad OME-XML produced by ImageJ when cropping.
    Allow zlib compress data in imsave function (optional).
    Memory-map contiguous image data (optional).
    Read MicroManager metadata and little endian ImageJ tag.
    Save extra tags in imsave function.
    Save tags in ascending order by code (bug fix).
    Accept file like objects (read from OIB files).
    Rename TIFFfile to TiffFile and TIFFpage to TiffPage.
    TiffSequence class for reading sequence of TIFF files.
    Read UltraQuant tags.
    Allow float numbers as resolution in imsave function.
    Read MD GEL tags and NIH Image header.
    Read ImageJ tags.

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

Tested on little-endian platforms only.

Other Python packages and modules for reading bio-scientific TIFF files:

*  `python-bioformats <https://github.com/CellProfiler/python-bioformats>`_
*  `Imread <https://github.com/luispedro/imread>`_
*  `PyLibTiff <https://github.com/pearu/pylibtiff>`_
*  `SimpleITK <http://www.simpleitk.org>`_
*  `PyLSM <https://launchpad.net/pylsm>`_
*  `PyMca.TiffIO.py <https://github.com/vasole/pymca>`_ (same as fabio.TiffIO)
*  `BioImageXD.Readers <http://www.bioimagexd.net/>`_
*  `Cellcognition.io <http://cellcognition.org/>`_
*  `pymimage <https://github.com/ardoi/pymimage>`_

*   Egor Zindy, University of Manchester, for lsm_scan_info specifics.
*   Wim Lewis for a bug fix and some LSM functions.
*   Hadrien Mary for help on reading MicroManager files.
*   Christian Kliche for help writing tiled and color-mapped files.

1)  TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated.
2)  TIFF File Format FAQ. http://www.awaresystems.be/imaging/tiff/faq.html
3)  MetaMorph Stack (STK) Image File Format.
4)  Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010).
    Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011
5)  The OME-TIFF format.
6)  UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
7)  Micro-Manager File Formats.
8)  Tags for TIFF and Related Specifications. Digital Preservation.
9)  ScanImage BigTiff Specification - ScanImage 2016.
10) CIPA DC-008-2016: Exchangeable image file format for digital still cameras:
    Exif Version 2.31.

>>> # write and read numpy array
>>> data = numpy.random.rand(5, 301, 219)
>>> imsave('temp.tif', data)
>>> image = imread('temp.tif')
>>> numpy.testing.assert_array_equal(image, data)

>>> # iterate over pages and tags
>>> with TiffFile('temp.tif') as tif:
...     images = tif.asarray()
...     for page in tif.pages:
...         for tag in page.tags.values():
...             _ = tag.name, tag.value
...         image = page.asarray()


from __future__ import division, print_function

import sys
import os
import io
import re
import glob
import math
import zlib
import time
import json
import enum
import struct
import warnings
import tempfile
import datetime
import threading
import collections
import multiprocessing
import concurrent.futures
# from fractions import Fraction  # delay import
# from xml.etree import cElementTree as etree  # delay import

import numpy

    import lzma
except ImportError:
        import backports.lzma as lzma
    except ImportError:
        lzma = None

__version__ = '2017.09.29'
__docformat__ = 'restructuredtext en'
__all__ = (
    'imsave', 'imread', 'imshow', 'memmap',
    'TiffFile', 'TiffWriter', 'TiffSequence',
    # utility functions used by oiffile or czifile
    'FileHandle', 'lazyattr', 'natural_sorted', 'decode_lzw', 'stripnull',
    'create_output', 'repeat_nd', 'format_size', 'product')

def imread(files, **kwargs):
    """Return image data from TIFF file(s) as numpy array.

    Refer to the TiffFile class and member functions for documentation.

    files : str, binary stream, or sequence
        File name, seekable binary stream, glob pattern, or sequence of
        file names.
    kwargs : dict
        Parameters 'multifile' and 'is_ome' are passed to the TiffFile class.
        The 'pattern' parameter is passed to the TiffSequence class.
        Other parameters are passed to the asarray functions.
        The first image series is returned if no arguments are provided.

    >>> # get image from first page
    >>> imsave('temp.tif', numpy.random.rand(3, 4, 301, 219))
    >>> im = imread('temp.tif', key=0)
    >>> im.shape
    (4, 301, 219)

    >>> # get images from sequence of files
    >>> ims = imread(['temp.tif', 'temp.tif'])
    >>> ims.shape
    (2, 3, 4, 301, 219)

    kwargs_file = parse_kwargs(kwargs, 'multifile', 'is_ome')
    kwargs_seq = parse_kwargs(kwargs, 'pattern')

    if isinstance(files, basestring) and any(i in files for i in '?*'):
        files = glob.glob(files)
    if not files:
        raise ValueError('no files found')
    if not hasattr(files, 'seek') and len(files) == 1:
        files = files[0]

    if isinstance(files, basestring) or hasattr(files, 'seek'):
        with TiffFile(files, **kwargs_file) as tif:
            return tif.asarray(**kwargs)
        with TiffSequence(files, **kwargs_seq) as imseq:
            return imseq.asarray(**kwargs)

def imsave(file, data=None, shape=None, dtype=None, bigsize=2**32-2**25,
    """Write numpy array to TIFF file.

    Refer to the TiffWriter class and member functions for documentation.

    file : str or binary stream
        File name or writable binary stream, such as a open file or BytesIO.
    data : array_like
        Input image. The last dimensions are assumed to be image depth,
        height, width, and samples.
        If data is None, an empty array of the specified shape and dtype is
        saved to file.
    shape : tuple
        If data is None, shape of an empty array to save to the file.
    dtype : numpy.dtype
        If data is None, data-type of an empty array to save to the file.
    bigsize : int
        Create a BigTIFF file if the size of data in bytes is larger than
        this threshold and 'imagej' or 'truncate' are not enabled.
        By default, the threshold is 4 GB minus 32 MB reserved for metadata.
        Use the 'bigtiff' parameter to explicitly specify the type of
        file created.
    kwargs : dict
        Parameters 'append', 'byteorder', 'bigtiff', 'software', and 'imagej',
        are passed to TiffWriter().
        Other parameters are passed to TiffWriter.save().

    If the image data are written contiguously, return offset and bytecount
    of image data in the file.

    >>> # save a RGB image
    >>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8')
    >>> imsave('temp.tif', data, photometric='rgb')

    >>> # save a random array and metadata, using compression
    >>> data = numpy.random.rand(2, 5, 3, 301, 219)
    >>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})

    tifargs = parse_kwargs(kwargs, 'append', 'bigtiff', 'byteorder',
                           'software', 'imagej')
    if data is None:
        size = product(shape) * numpy.dtype(dtype).itemsize
            size = data.nbytes
        except Exception:
            size = 0
    if size > bigsize and 'bigtiff' not in tifargs and not (
            tifargs.get('imagej', False) or tifargs.get('truncate', False)):
        tifargs['bigtiff'] = True

    with TiffWriter(file, **tifargs) as tif:
        return tif.save(data, shape, dtype, **kwargs)

def memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+',
    """Return memory-mapped numpy array stored in TIFF file.

    Memory-mapping requires data stored in native byte order, without tiling,
    compression, predictors, etc.
    If shape and dtype are provided, existing files will be overwritten or
    appended to depending on the 'append' parameter.
    Otherwise the image data of a specified page or series in an existing
    file will be memory-mapped. By default, the image data of the first page
    series is memory-mapped.
    Call flush() to write any changes in the array to the file.
    Raise ValueError if the image data in the file is not memory-mappable

    filename : str
        Name of the TIFF file which stores the array.
    shape : tuple
        Shape of the empty array.
    dtype : numpy.dtype
        Data-type of the empty array.
    page : int
        Index of the page which image data to memory-map.
    series : int
        Index of the page series which image data to memory-map.
    mode : {'r+', 'r', 'c'}, optional
        The file open mode. Default is to open existing file for reading and
        writing ('r+').
    kwargs : dict
        Additional parameters passed to imsave() or TiffFile().

    >>> # create an empty TIFF file and write to memory-mapped image
    >>> im = memmap('temp.tif', shape=(256, 256), dtype='float32')
    >>> im[255, 255] = 1.0
    >>> im.flush()
    >>> im.shape, im.dtype
    ((256, 256), dtype('float32'))
    >>> del im

    >>> # memory-map image data in a TIFF file
    >>> im = memmap('temp.tif', page=0)
    >>> im[255, 255]

    if shape is not None and dtype is not None:
        # create a new, empty array
        kwargs.update(data=None, shape=shape, dtype=dtype, returnoffset=True,
        result = imsave(filename, **kwargs)
        if result is None:
            # TODO: fail before creating file or writing data
            raise ValueError("image data is not memory-mappable")
        offset = result[0]
        # use existing file
        with TiffFile(filename, **kwargs) as tif:
            if page is not None:
                page = tif.pages[page]
                if not page.is_memmappable:
                    raise ValueError("image data is not memory-mappable")
                offset, _ = page.is_contiguous
                shape = page.shape
                dtype = page.dtype
                series = tif.series[series]
                if series.offset is None:
                    raise ValueError("image data is not memory-mappable")
                shape = series.shape
                dtype = series.dtype
                offset = series.offset
    return numpy.memmap(filename, dtype, mode, offset, shape, 'C')

class lazyattr(object):
    """Attribute whose value is computed on first access."""
    # TODO: help() doesn't work
    __slots__ = ('func',)

    def __init__(self, func):
        self.func = func
        # self.__name__ = func.__name__
        # self.__doc__ = func.__doc__
        # self.lock = threading.RLock()

    def __get__(self, instance, owner):
        # with self.lock:
        if instance is None:
            return self
            value = self.func(instance)
        except AttributeError as e:
            raise RuntimeError(e)
        if value is NotImplemented:
            return getattr(super(owner, instance), self.func.__name__)
        setattr(instance, self.func.__name__, value)
        return value

class TiffWriter(object):
    """Write numpy arrays to TIFF file.

    TiffWriter instances must be closed using the 'close' method, which is
    automatically called when using the 'with' context manager.

    TiffWriter's main purpose is saving nD numpy array's as TIFF,
    not to create any possible TIFF format. Specifically, JPEG compression,
    SubIFDs, ExifIFD, or GPSIFD tags are not supported.

    >>> # successively append images to BigTIFF file
    >>> data = numpy.random.rand(2, 5, 3, 301, 219)
    >>> with TiffWriter('temp.tif', bigtiff=True) as tif:
    ...     for i in range(data.shape[0]):
    ...         tif.save(data[i], compress=6)

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

        An empty TIFF file is created if the file does not exist, else the
        file is overwritten with an empty empty TIFF file unless 'append'
        is true. Use bigtiff=True when creating files larger than 4 GB.

        file : str, binary stream, or FileHandle
            File name or writable binary stream, such as a open file
            or BytesIO.
        bigtiff : bool
            If True, the BigTIFF format is used.
        byteorder : {'<', '>'}
            The endianness of the data in the file.
            By default, this is the system's native byte order.
        software : str
            Name of the software used to create the file.
            Saved with the first page in the file only.
            Must be 7-bit ASCII.
        append : bool
            If True and 'file' is an existing standard TIFF file, image data
            and tags are appended to the file.
            Appending data may corrupt specifically formatted TIFF files
            such as LSM, STK, ImageJ, NIH, or FluoView.
        imagej : bool
            If True, write an ImageJ hyperstack compatible file.
            This format can handle data types uint8, uint16, or float32 and
            data shapes up to 6 dimensions in TZCYXS order.
            RGB images (S=3 or S=4) must be uint8.
            ImageJ's default byte order is big endian but this implementation
            uses the system's native byte order by default.
            ImageJ does not support BigTIFF format or LZMA compression.
            The ImageJ file format is undocumented.

        if append:
            # determine if file is an existing TIFF file that can be extended
                with FileHandle(file, mode='rb', size=0) as fh:
                    pos = fh.tell()
                        with TiffFile(fh) as tif:
                            if (append != 'force' and
                                    any(getattr(tif, 'is_'+a) for a in (
                                        'lsm', 'stk', 'imagej', 'nih',
                                        'fluoview', 'micromanager'))):
                                raise ValueError("file contains metadata")
                            byteorder = tif.byteorder
                            bigtiff = tif.is_bigtiff
                            self._ifdoffset = tif.pages.next_page_offset
                            if tif.pages:
                                software = None
                    except Exception as e:
                        raise ValueError("can not append to file: %s" % str(e))
            except (IOError, FileNotFoundError):
                append = False

        if byteorder in (None, '='):
            byteorder = '<' if sys.byteorder == 'little' else '>'
        elif byteorder not in ('<', '>'):
            raise ValueError("invalid byteorder %s" % byteorder)
        if imagej and bigtiff:
            warnings.warn("writing incompatible BigTIFF ImageJ")

        self._byteorder = byteorder
        self._software = software
        self._imagej = bool(imagej)
        self._truncate = False
        self._metadata = None
        self._colormap = None

        self._descriptionoffset = 0
        self._descriptionlen = 0
        self._descriptionlenoffset = 0
        self._tags = None
        self._shape = None  # normalized shape of data in consecutive pages
        self._datashape = None  # shape of data in consecutive pages
        self._datadtype = None  # data type
        self._dataoffset = None  # offset to data
        self._databytecounts = None  # byte counts per plane
        self._tagoffsets = None  # strip or tile offset tag code

        if bigtiff:
            self._bigtiff = True
            self._offsetsize = 8
            self._tagsize = 20
            self._tagnoformat = 'Q'
            self._offsetformat = 'Q'
            self._valueformat = '8s'
            self._bigtiff = False
            self._offsetsize = 4
            self._tagsize = 12
            self._tagnoformat = 'H'
            self._offsetformat = 'I'
            self._valueformat = '4s'

        if append:
            self._fh = FileHandle(file, mode='r+b', size=0)
            self._fh.seek(0, 2)
            self._fh = FileHandle(file, mode='wb', size=0)
            self._fh.write({'<': b'II', '>': b'MM'}[byteorder])
            if bigtiff:
                self._fh.write(struct.pack(byteorder+'HHH', 43, 8, 0))
                self._fh.write(struct.pack(byteorder+'H', 42))
            # first IFD
            self._ifdoffset = self._fh.tell()
            self._fh.write(struct.pack(byteorder+self._offsetformat, 0))

    def save(self, data=None, shape=None, dtype=None, returnoffset=False,
             photometric=None, planarconfig=None, tile=None,
             contiguous=True, align=16, truncate=False, compress=0,
             colormap=None, description=None, datetime=None, resolution=None,
             metadata={}, extratags=()):
        """Write numpy array and tags to TIFF file.

        The data shape's last dimensions are assumed to be image depth,
        height (length), width, and samples.
        If a colormap is provided, the data's dtype must be uint8 or uint16
        and the data values are indices into the last dimension of the
        If shape and dtype are specified, an empty array is saved.
        This option can not be used with compression or multiple tiles.
        Image data are written in one stripe per plane by default.
        Dimensions larger than 2 to 4 (depending on photometric mode, planar
        configuration, and SGI mode) are flattened and saved as separate pages.
        The 'SampleFormat' and 'BitsPerSample' tags are derived from
        the data type.

        data : numpy.ndarray or None
            Input image array.
        shape : tuple or None
            Shape of the empty array to save. Used only if data is None.
        dtype : numpy.dtype or None
            Data-type of the empty array to save. Used only if data is None.
        returnoffset : bool
            If True and the image data in the file is memory-mappable, return
            the offset and number of bytes of the image data in the file.
        photometric : {'MINISBLACK', 'MINISWHITE', 'RGB', 'PALETTE', 'CFA'}
            The color space of the image data.
            By default, this setting is inferred from the data shape and the
            value of colormap.
            For CFA images, DNG tags must be specified in extratags.
        planarconfig : {'CONTIG', 'SEPARATE'}
            Specifies if samples are stored contiguous or in separate planes.
            By default, this setting is inferred from the data shape.
            If this parameter is set, extra samples are used to store grayscale
            'CONTIG': last dimension contains samples.
            'SEPARATE': third last dimension contains samples.
        tile : tuple of int
            The shape (depth, length, width) of image tiles to write.
            If None (default), image data are written in one stripe per plane.
            The tile length and width must be a multiple of 16.
            If the tile depth is provided, the SGI ImageDepth and TileDepth
            tags are used to save volume data.
            Unless a single tile is used, tiles cannot be used to write
            contiguous files.
            Few software can read the SGI format, e.g. MeVisLab.
        contiguous : bool
            If True (default) and the data and parameters are compatible with
            previous ones, if any, the image data are stored contiguously after
            the previous one. Parameters 'photometric' and 'planarconfig'
            are ignored. Parameters 'description', datetime', and 'extratags'
            are written to the first page of a contiguous series only.
        align : int
            Byte boundary on which to align the image data in the file.
            Default 16. Use mmap.ALLOCATIONGRANULARITY for memory-mapped data.
            Following contiguous writes are not aligned.
        truncate : bool
            If True, only write the first page including shape metadata if
            possible (uncompressed, contiguous, not tiled).
            Other TIFF readers will only be able to read part of the data.
        compress : int or 'LZMA'
            Values from 0 to 9 controlling the level of zlib compression.
            If 0, data are written uncompressed (default).
            Compression cannot be used to write contiguous files.
            If 'LZMA', LZMA compression is used, which is not available on
            all platforms.
        colormap : numpy.ndarray
            RGB color values for the corresponding data value.
            Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16.
        description : str
            The subject of the image. Must be 7-bit ASCII. Cannot be used with
            the ImageJ format. Saved with the first page only.
        datetime : datetime
            Date and time of image creation. If None (default), the current
            date and time is used. Saved with the first page only.
        resolution : (float, float[, str]) or ((int, int), (int, int)[, str])
            X and Y resolutions in pixels per resolution unit as float or
            rational numbers. A third, optional parameter specifies the
            resolution unit, which must be None (default for ImageJ),
            'INCH' (default), or 'CENTIMETER'.
        metadata : dict
            Additional meta data to be saved along with shape information
            in JSON or ImageJ formats in an ImageDescription tag.
            If None, do not write a second ImageDescription tag.
            Strings must be 7-bit ASCII. Saved with the first page only.
        extratags : sequence of tuples
            Additional tags as [(code, dtype, count, value, writeonce)].

            code : int
                The TIFF tag Id.
            dtype : str
                Data type of items in 'value' in Python struct format.
                One of B, s, H, I, 2I, b, h, i, 2i, f, d, Q, or q.
            count : int
                Number of data values. Not used for string or byte string
            value : sequence
                'Count' values compatible with 'dtype'.
                Byte strings must contain count values of dtype packed as
                binary data.
            writeonce : bool
                If True, the tag is written to the first page only.

        # TODO: refactor this function
        fh = self._fh
        byteorder = self._byteorder

        if data is None:
            if compress:
                raise ValueError("can not save compressed empty file")
            datashape = shape
            datadtype = numpy.dtype(dtype).newbyteorder(byteorder)
            datadtypechar = datadtype.char
            data = None
            data = numpy.asarray(data, byteorder+data.dtype.char, 'C')
            if data.size == 0:
                raise ValueError("can not save empty array")
            datashape = data.shape
            datadtype = data.dtype
            datadtypechar = data.dtype.char

        returnoffset = returnoffset and datadtype.isnative
        datasize = product(datashape) * datadtype.itemsize

        # just append contiguous data if possible
        self._truncate = bool(truncate)
        if self._datashape:
            if (not contiguous
                    or self._datashape[1:] != datashape
                    or self._datadtype != datadtype
                    or (compress and self._tags)
                    or tile
                    or not numpy.array_equal(colormap, self._colormap)):
                # incompatible shape, dtype, compression mode, or colormap
                self._truncate = False
                self._descriptionoffset = 0
                self._descriptionlenoffset = 0
                self._datashape = None
                self._colormap = None
                if self._imagej:
                    raise ValueError(
                        "ImageJ does not support non-contiguous data")
                # consecutive mode
                self._datashape = (self._datashape[0] + 1,) + datashape
                if not compress:
                    # write contiguous data, write ifds/tags later
                    offset = fh.tell()
                    if data is None:
                    if returnoffset:
                        return offset, datasize

        input_shape = datashape
        tagnoformat = self._tagnoformat
        valueformat = self._valueformat
        offsetformat = self._offsetformat
        offsetsize = self._offsetsize
        tagsize = self._tagsize


        if photometric is not None:
            photometric = enumarg(TIFF.PHOTOMETRIC, photometric)
        if planarconfig:
            planarconfig = enumarg(TIFF.PLANARCONFIG, planarconfig)

        # prepare compression
        if not compress:
            compress = False
            compresstag = 1
        elif compress == 'LZMA':
            compress = lzma.compress
            compresstag = 34925
            if self._imagej:
                raise ValueError("ImageJ can not handle LZMA compression")
        elif not 0 <= compress <= 9:
            raise ValueError("invalid compression level %s" % compress)
        elif compress:
            def compress(data, level=compress):
                return zlib.compress(data, level)
            compresstag = 8

        # prepare ImageJ format
        if self._imagej:
            if description:
                warnings.warn("not writing description to ImageJ file")
                description = None
            volume = False
            if datadtypechar not in 'BHhf':
                raise ValueError(
                    "ImageJ does not support data type '%s'" % datadtypechar)
            ijrgb = photometric == RGB if photometric else None
            if datadtypechar not in 'B':
                ijrgb = False
            ijshape = imagej_shape(datashape, ijrgb)
            if ijshape[-1] in (3, 4):
                photometric = RGB
                if datadtypechar not in 'B':
                    raise ValueError("ImageJ does not support data type '%s' "
                                     "for RGB" % datadtypechar)
            elif photometric is None:
                photometric = MINISBLACK
                planarconfig = None
            if planarconfig == SEPARATE:
                raise ValueError("ImageJ does not support planar images")
                planarconfig = CONTIG if ijrgb else None

        # verify colormap and indices
        if colormap is not None:
            if datadtypechar not in 'BH':
                raise ValueError("invalid data dtype for palette mode")
            colormap = numpy.asarray(colormap, dtype=byteorder+'H')
            if colormap.shape != (3, 2**(datadtype.itemsize * 8)):
                raise ValueError("invalid color map shape")
            self._colormap = colormap

        # verify tile shape
        if tile:
            tile = tuple(int(i) for i in tile[:3])
            volume = len(tile) == 3
            if (len(tile) < 2 or tile[-1] % 16 or tile[-2] % 16 or
                    any(i < 1 for i in tile)):
                raise ValueError("invalid tile shape")
            tile = ()
            volume = False

        # normalize data shape to 5D or 6D, depending on volume:
        #   (pages, planar_samples, [depth,] height, width, contig_samples)
        datashape = reshape_nd(datashape, 3 if photometric == RGB else 2)
        shape = datashape
        ndim = len(datashape)

        samplesperpixel = 1
        extrasamples = 0
        if volume and ndim < 3:
            volume = False
        if colormap is not None:
            photometric = PALETTE
            planarconfig = None
        if photometric is None:
            photometric = MINISBLACK
            if planarconfig == CONTIG:
                if ndim > 2 and shape[-1] in (3, 4):
                    photometric = RGB
            elif planarconfig == SEPARATE:
                if volume and ndim > 3 and shape[-4] in (3, 4):
                    photometric = RGB
                elif ndim > 2 and shape[-3] in (3, 4):
                    photometric = RGB
            elif ndim > 2 and shape[-1] in (3, 4):
                photometric = RGB
            elif self._imagej:
                photometric = MINISBLACK
            elif volume and ndim > 3 and shape[-4] in (3, 4):
                photometric = RGB
            elif ndim > 2 and shape[-3] in (3, 4):
                photometric = RGB
        if planarconfig and len(shape) <= (3 if volume else 2):
            planarconfig = None
            photometric = MINISBLACK
        if photometric == RGB:
            if len(shape) < 3:
                raise ValueError("not a RGB(A) image")
            if len(shape) < 4:
                volume = False
            if planarconfig is None:
                if shape[-1] in (3, 4):
                    planarconfig = CONTIG
                elif shape[-4 if volume else -3] in (3, 4):
                    planarconfig = SEPARATE
                elif shape[-1] > shape[-4 if volume else -3]:
                    planarconfig = SEPARATE
                    planarconfig = CONTIG
            if planarconfig == CONTIG:
                datashape = (-1, 1) + shape[(-4 if volume else -3):]
                samplesperpixel = datashape[-1]
                datashape = (-1,) + shape[(-4 if volume else -3):] + (1,)
                samplesperpixel = datashape[1]
            if samplesperpixel > 3:
                extrasamples = samplesperpixel - 3
        elif photometric == CFA:
            if len(shape) != 2:
                raise ValueError("invalid CFA image")
            volume = False
            planarconfig = None
            datashape = (-1, 1) + shape[-2:] + (1,)
            if 50706 not in (et[0] for et in extratags):
                raise ValueError("must specify DNG tags for CFA image")
        elif planarconfig and len(shape) > (3 if volume else 2):
            if planarconfig == CONTIG:
                datashape = (-1, 1) + shape[(-4 if volume else -3):]
                samplesperpixel = datashape[-1]
                datashape = (-1,) + shape[(-4 if volume else -3):] + (1,)
                samplesperpixel = datashape[1]
            extrasamples = samplesperpixel - 1
            planarconfig = None
            # remove trailing 1s
            while len(shape) > 2 and shape[-1] == 1:
                shape = shape[:-1]
            if len(shape) < 3:
                volume = False
            datashape = (-1, 1) + shape[(-3 if volume else -2):] + (1,)

        # normalize shape to 6D
        assert len(datashape) in (5, 6)
        if len(datashape) == 5:
            datashape = datashape[:2] + (1,) + datashape[2:]
        if datashape[0] == -1:
            s0 = product(input_shape) // product(datashape[1:])
            datashape = (s0,) + datashape[1:]
        shape = datashape
        if data is not None:
            data = data.reshape(shape)

        if tile and not volume:
            tile = (1, tile[-2], tile[-1])

        if photometric == PALETTE:
            if (samplesperpixel != 1 or extrasamples or
                    shape[1] != 1 or shape[-1] != 1):
                raise ValueError("invalid data shape for palette mode")

        if photometric == RGB and samplesperpixel == 2:
            raise ValueError("not a RGB image (samplesperpixel=2)")

        bytestr = bytes if sys.version[0] == '2' else (
            lambda x: bytes(x, 'ascii') if isinstance(x, str) else x)
        tags = []  # list of (code, ifdentry, ifdvalue, writeonce)

        strip_or_tile = 'Tile' if tile else 'Strip'
        tagbytecounts = TIFF.TAG_NAMES[strip_or_tile + 'ByteCounts']
        tag_offsets = TIFF.TAG_NAMES[strip_or_tile + 'Offsets']
        self._tagoffsets = tag_offsets

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

        def addtag(code, dtype, count, value, writeonce=False):
            # Compute ifdentry & ifdvalue bytes from code, dtype, count, value
            # Append (code, ifdentry, ifdvalue, writeonce) to tags list
            code = int(TIFF.TAG_NAMES.get(code, code))
                tifftype = TIFF.DATA_DTYPES[dtype]
            except KeyError:
                raise ValueError("unknown dtype %s" % dtype)
            rawcount = count

            if dtype == 's':
                # strings
                value = bytestr(value) + b'\0'
                count = rawcount = len(value)
                rawcount = value.find(b'\0\0')
                if rawcount < 0:
                    rawcount = count
                    rawcount += 1  # length of string without buffer
                value = (value,)
            elif isinstance(value, bytes):
                # packed binary data
                dtsize = struct.calcsize(dtype)
                if len(value) % dtsize:
                    raise ValueError('invalid packed binary data')
                count = len(value) // dtsize
            if len(dtype) > 1:
                count *= int(dtype[:-1])
                dtype = dtype[-1]
            ifdentry = [pack('HH', code, tifftype),
                        pack(offsetformat, rawcount)]
            ifdvalue = None
            if struct.calcsize(dtype) * count <= offsetsize:
                # value(s) can be written directly
                if isinstance(value, bytes):
                    ifdentry.append(pack(valueformat, value))
                elif count == 1:
                    if isinstance(value, (tuple, list, numpy.ndarray)):
                        value = value[0]
                    ifdentry.append(pack(valueformat, pack(dtype, value)))
                                         pack(str(count)+dtype, *value)))
                # use offset to value(s)
                ifdentry.append(pack(offsetformat, 0))
                if isinstance(value, bytes):
                    ifdvalue = value
                elif isinstance(value, numpy.ndarray):
                    assert value.size == count
                    assert value.dtype.char == dtype
                    ifdvalue = value.tostring()
                elif isinstance(value, (tuple, list)):
                    ifdvalue = pack(str(count)+dtype, *value)
                    ifdvalue = pack(dtype, value)
            tags.append((code, b''.join(ifdentry), ifdvalue, writeonce))

        def rational(arg, max_denominator=1000000):
            # return nominator and denominator from float or two integers
            from fractions import Fraction  # delayed import
                f = Fraction.from_float(arg)
            except TypeError:
                f = Fraction(arg[0], arg[1])
            f = f.limit_denominator(max_denominator)
            return f.numerator, f.denominator

        if description:
            # user provided description
            addtag('ImageDescription', 's', 0, description, writeonce=True)

        # write shape and metadata to ImageDescription
        self._metadata = {} if not metadata else metadata.copy()
        if self._imagej:
            description = imagej_description(
                input_shape, shape[-1] in (3, 4), self._colormap is not None,
        elif metadata or metadata == {}:
            if self._truncate:
            description = json_description(input_shape, **self._metadata)
            description = None
        if description:
            # add 64 bytes buffer
            # the image description might be updated later with the final shape
            description = str2bytes(description, 'ascii')
            description += b'\0'*64
            self._descriptionlen = len(description)
            addtag('ImageDescription', 's', 0, description, writeonce=True)

        if self._software:
            addtag('Software', 's', 0, self._software, writeonce=True)
            self._software = None  # only save to first page in file
        if datetime is None:
            datetime = self._now()
        addtag('DateTime', 's', 0, datetime.strftime("%Y:%m:%d %H:%M:%S"),
        addtag('Compression', 'H', 1, compresstag)
        addtag('ImageWidth', 'I', 1, shape[-2])
        addtag('ImageLength', 'I', 1, shape[-3])
        if tile:
            addtag('TileWidth', 'I', 1, tile[-1])
            addtag('TileLength', 'I', 1, tile[-2])
            if tile[0] > 1:
                addtag('ImageDepth', 'I', 1, shape[-4])
                addtag('TileDepth', 'I', 1, tile[0])
        addtag('NewSubfileType', 'I', 1, 0)
        sampleformat = {'u': 1, 'i': 2, 'f': 3, 'c': 6}[datadtype.kind]
        addtag('SampleFormat', 'H', samplesperpixel,
               (sampleformat,) * samplesperpixel)
        addtag('PhotometricInterpretation', 'H', 1, photometric.value)
        if colormap is not None:
            addtag('ColorMap', 'H', colormap.size, colormap)
        addtag('SamplesPerPixel', 'H', 1, samplesperpixel)
        if planarconfig and samplesperpixel > 1:
            addtag('PlanarConfiguration', 'H', 1, planarconfig.value)
            addtag('BitsPerSample', 'H', samplesperpixel,
                   (datadtype.itemsize * 8,) * samplesperpixel)
            addtag('BitsPerSample', 'H', 1, datadtype.itemsize * 8)
        if extrasamples:
            if photometric == RGB and extrasamples == 1:
                addtag('ExtraSamples', 'H', 1, 1)  # associated alpha channel
                addtag('ExtraSamples', 'H', extrasamples, (0,) * extrasamples)
        if resolution:
            addtag('XResolution', '2I', 1, rational(resolution[0]))
            addtag('YResolution', '2I', 1, rational(resolution[1]))
            if len(resolution) > 2:
                unit = resolution[2]
                if unit is not None:
                    unit = unit.upper()
                unit = {None: 1, 'INCH': 2, 'CM': 3, 'CENTIMETER': 3}[unit]
            elif self._imagej:
                unit = 1
                unit = 2
            addtag('ResolutionUnit', 'H', 1, unit)
        if not tile:
            addtag('RowsPerStrip', 'I', 1, shape[-3])  # * shape[-4]

        contiguous = not compress
        if tile:
            # use one chunk per tile per plane
            tiles = ((shape[2] + tile[0] - 1) // tile[0],
                     (shape[3] + tile[1] - 1) // tile[1],
                     (shape[4] + tile[2] - 1) // tile[2])
            numtiles = product(tiles) * shape[1]
            stripbytecounts = [
                product(tile) * shape[-1] * datadtype.itemsize] * numtiles
            addtag(tagbytecounts, offsetformat, numtiles, stripbytecounts)
            addtag(tag_offsets, offsetformat, numtiles, [0] * numtiles)
            contiguous = contiguous and product(tiles) == 1
            if not contiguous:
                # allocate tile buffer
                chunk = numpy.empty(tile + (shape[-1],), dtype=datadtype)
            # use one strip per plane
            stripbytecounts = [
                product(datashape[2:]) * datadtype.itemsize] * shape[1]
            addtag(tagbytecounts, offsetformat, shape[1], stripbytecounts)
            addtag(tag_offsets, offsetformat, shape[1], [0] * shape[1])

        if data is None and not contiguous:
            raise ValueError("can not write non-contiguous empty file")

        # add extra tags from user
        for t in extratags:

        # TODO: check TIFFReadDirectoryCheckOrder warning in files containing
        #   multiple tags of same code
        # the entries in an IFD must be sorted in ascending order by tag code
        tags = sorted(tags, key=lambda x: x[0])

        if not (self._bigtiff or self._imagej) and (
                fh.tell() + datasize > 2**31-1):
            raise ValueError("data too large for standard TIFF file")

        # if not compressed or multi-tiled, write the first ifd and then
        # all data contiguously; else, write all ifds and data interleaved
        for pageindex in range(1 if contiguous else shape[0]):
            # update pointer at ifd_offset
            pos = fh.tell()
            if pos % 2:
                # location of IFD must begin on a word boundary
                pos += 1
            fh.write(pack(offsetformat, pos))

            # write ifdentries
            fh.write(pack(tagnoformat, len(tags)))
            tag_offset = fh.tell()
            fh.write(b''.join(t[1] for t in tags))
            self._ifdoffset = fh.tell()
            fh.write(pack(offsetformat, 0))  # offset to next IFD

            # write tag values and patch offsets in ifdentries, if necessary
            for tagindex, tag in enumerate(tags):
                if tag[2]:
                    pos = fh.tell()
                    if pos % 2:
                        # tag value is expected to begin on word boundary
                        pos += 1
                    fh.seek(tag_offset + tagindex*tagsize + offsetsize + 4)
                    fh.write(pack(offsetformat, pos))
                    if tag[0] == tag_offsets:
                        stripoffsetsoffset = pos
                    elif tag[0] == tagbytecounts:
                        strip_bytecounts_offset = pos
                    elif tag[0] == 270 and tag[2].endswith(b'\0\0\0\0'):
                        # image description buffer
                        self._descriptionoffset = pos
                        self._descriptionlenoffset = (
                            tag_offset + tagindex * tagsize + 4)

            # write image data
            data_offset = fh.tell()
            skip = align - data_offset % align
            fh.seek(skip, 1)
            data_offset += skip
            if compress:
                stripbytecounts = []
            if contiguous:
                if data is None:
            elif tile:
                for plane in data[pageindex]:
                    for tz in range(tiles[0]):
                        for ty in range(tiles[1]):
                            for tx in range(tiles[2]):
                                c0 = min(tile[0], shape[2] - tz*tile[0])
                                c1 = min(tile[1], shape[3] - ty*tile[1])
                                c2 = min(tile[2], shape[4] - tx*tile[2])
                                chunk[c0:, c1:, c2:] = 0
                                chunk[:c0, :c1, :c2] = plane[
                                if compress:
                                    t = compress(chunk)
            elif compress:
                for plane in data[pageindex]:
                    plane = compress(plane)

            # update strip/tile offsets and bytecounts if necessary
            pos = fh.tell()
            for tagindex, tag in enumerate(tags):
                if tag[0] == tag_offsets:  # strip/tile offsets
                    if tag[2]:
                        strip_offset = data_offset
                        for size in stripbytecounts:
                            fh.write(pack(offsetformat, strip_offset))
                            strip_offset += size
                        fh.seek(tag_offset + tagindex*tagsize + offsetsize + 4)
                        fh.write(pack(offsetformat, data_offset))
                elif tag[0] == tagbytecounts:  # strip/tile bytecounts
                    if compress:
                        if tag[2]:
                            for size in stripbytecounts:
                                fh.write(pack(offsetformat, size))
                            fh.seek(tag_offset + tagindex*tagsize +
                                    offsetsize + 4)
                            fh.write(pack(offsetformat, stripbytecounts[0]))

            # remove tags that should be written only once
            if pageindex == 0:
                tags = [tag for tag in tags if not tag[-1]]

        self._shape = shape
        self._datashape = (1,) + input_shape
        self._datadtype = datadtype
        self._dataoffset = data_offset
        self._databytecounts = stripbytecounts

        if contiguous:
            # write remaining ifds/tags later
            self._tags = tags
            # return offset and size of image data
            if returnoffset:
                return data_offset, sum(stripbytecounts)

    def _write_remaining_pages(self):
        """Write outstanding IFDs and tags to file."""
        if not self._tags or self._truncate:

        fh = self._fh
        byteorder = self._byteorder
        offsetformat = self._offsetformat
        offsetsize = self._offsetsize
        tagnoformat = self._tagnoformat
        tagsize = self._tagsize
        dataoffset = self._dataoffset
        pagedatasize = sum(self._databytecounts)
        pageno = self._shape[0] * self._datashape[0] - 1

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

        # construct template IFD in memory
        # need to patch offsets to next IFD and data before writing to disk
        ifd = io.BytesIO()
        ifd.write(pack(tagnoformat, len(self._tags)))
        tagoffset = ifd.tell()
        ifd.write(b''.join(t[1] for t in self._tags))
        ifdoffset = ifd.tell()
        ifd.write(pack(offsetformat, 0))  # offset to next IFD
        # tag values
        for tagindex, tag in enumerate(self._tags):
            offset2value = tagoffset + tagindex*tagsize + offsetsize + 4
            if tag[2]:
                pos = ifd.tell()
                if pos % 2:  # tag value is expected to begin on word boundary
                    pos += 1
                ifd.write(pack(offsetformat, pos + fh.tell()))
                if tag[0] == self._tagoffsets:
                    # save strip/tile offsets for later updates
                    stripoffset2offset = offset2value
                    stripoffset2value = pos
            elif tag[0] == self._tagoffsets:
                # save strip/tile offsets for later updates
                stripoffset2offset = None
                stripoffset2value = offset2value
        # size to word boundary
        if ifd.tell() % 2:

        # check if all IFDs fit in file
        pos = fh.tell()
        if not self._bigtiff and pos + ifd.tell() * pageno > 2**32 - 256:
            if self._imagej:
                warnings.warn("truncating ImageJ file")
            raise ValueError("data too large for non-BigTIFF file")

        for _ in range(pageno):
            # update pointer at IFD offset
            pos = fh.tell()
            fh.write(pack(offsetformat, pos))
            self._ifdoffset = pos + ifdoffset
            # update strip/tile offsets in IFD
            dataoffset += pagedatasize  # offset to image data
            if stripoffset2offset is None:
                ifd.write(pack(offsetformat, dataoffset))
                ifd.write(pack(offsetformat, pos + stripoffset2value))
                stripoffset = dataoffset
                for size in self._databytecounts:
                    ifd.write(pack(offsetformat, stripoffset))
                    stripoffset += size
            # write ifd entry

        self._tags = None
        self._datadtype = None
        self._dataoffset = None
        self._databytecounts = None
        # do not reset _shape or _data_shape

    def _write_image_description(self):
        """Write meta data to ImageDescription tag."""
        if (not self._datashape or self._datashape[0] == 1 or
                self._descriptionoffset <= 0):

        colormapped = self._colormap is not None
        if self._imagej:
            isrgb = self._shape[-1] in (3, 4)
            description = imagej_description(
                self._datashape, isrgb, colormapped, **self._metadata)
            description = json_description(self._datashape, **self._metadata)

        # rewrite description and its length to file
        description = description.encode('utf-8')
        description = description[:self._descriptionlen-1]
        pos = self._fh.tell()

        self._descriptionoffset = 0
        self._descriptionlenoffset = 0
        self._descriptionlen = 0

    def _now(self):
        """Return current date and time."""
        return datetime.datetime.now()

    def close(self):
        """Write remaining pages and close file handle."""
        if not self._truncate:

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):

class TiffFile(object):
    """Read image and metadata from TIFF file.

    TiffFile instances must be closed using the 'close' method, which is
    automatically called when using the 'with' context manager.

    pages : TiffPages
        Sequence of TIFF pages in file.
    series : list of TiffPageSeries
        Sequences of closely related TIFF pages. These are computed
        from OME, LSM, ImageJ, etc. metadata or based on similarity
        of page properties such as shape, dtype, compression, etc.
    byteorder : '>', '<'
        The endianness of data in the file.
        '>': big-endian (Motorola).
        '>': little-endian (Intel).
    is_flag : bool
        If True, file is of a certain format.
        Flags are: bigtiff, movie, shaped, ome, imagej, stk, lsm, fluoview,
        nih, vista, 'micromanager, metaseries, mdgel, mediacy, tvips, fei,
        sem, scn, svs, scanimage, andor, epics, pilatus.

    All attributes are read-only.

    >>> # read image array from TIFF file
    >>> imsave('temp.tif', numpy.random.rand(5, 301, 219))
    >>> with TiffFile('temp.tif') as tif:
    ...     data = tif.asarray()
    >>> data.shape
    (5, 301, 219)

    def __init__(self, arg, name=None, offset=None, size=None,
                 multifile=True, movie=None, **kwargs):
        """Initialize instance from file.

        arg : str or open file
            Name of file or open file object.
            The file objects are closed in TiffFile.close().
        name : str
            Optional name of file in case 'arg' is a file handle.
        offset : int
            Optional start position of embedded file. By default, this is
            the current file position.
        size : int
            Optional size of embedded file. By default, this is the number
            of bytes from the 'offset' to the end of the file.
        multifile : bool
            If True (default), series may include pages from multiple files.
            Currently applies to OME-TIFF only.
        movie : bool
            If True, assume that later pages differ from first page only by
            data offsets and bytecounts. Significantly increases speed and
            reduces memory usage when reading movies with thousands of pages.
            Enabling this for non-movie files will result in data corruption
            or crashes. Python 3 only.
        kwargs : bool
            'is_ome': If False, disable processing of OME-XML metadata.

        if 'fastij' in kwargs:
            del kwargs['fastij']
            raise DeprecationWarning("The fastij option will be removed.")
        for key, value in kwargs.items():
            if key[:3] == 'is_' and key[3:] in TIFF.FILE_FLAGS:
                if value is not None and not value:
                    setattr(self, key, bool(value))
                raise TypeError(
                    "got an unexpected keyword argument '%s'" % key)

        fh = FileHandle(arg, mode='rb', name=name, offset=offset, size=size)
        self._fh = fh
        self._multifile = bool(multifile)
        self._files = {fh.name: self}  # cache of TiffFiles
                byteorder = {b'II': '<', b'MM': '>'}[fh.read(2)]
            except KeyError:
                raise ValueError("invalid TIFF file")
            sys_byteorder = {'big': '>', 'little': '<'}[sys.byteorder]
            self.is_native = byteorder == sys_byteorder

            version = struct.unpack(byteorder+'H', fh.read(2))[0]
            if version == 43:
                # BigTiff
                self.is_bigtiff = True
                offsetsize, zero = struct.unpack(byteorder+'HH', fh.read(4))
                if zero or offsetsize != 8:
                    raise ValueError("invalid BigTIFF file")
                self.byteorder = byteorder
                self.offsetsize = 8
                self.offsetformat = byteorder+'Q'
                self.tagnosize = 8
                self.tagnoformat = byteorder+'Q'
                self.tagsize = 20
                self.tagformat1 = byteorder+'HH'
                self.tagformat2 = byteorder+'Q8s'
            elif version == 42:
                self.is_bigtiff = False
                self.byteorder = byteorder
                self.offsetsize = 4
                self.offsetformat = byteorder+'I'
                self.tagnosize = 2
                self.tagnoformat = byteorder+'H'
                self.tagsize = 12
                self.tagformat1 = byteorder+'HH'
                self.tagformat2 = byteorder+'I4s'
                raise ValueError("not a TIFF file")

            # file handle is at offset to offset to first page
            self.pages = TiffPages(self)

            if self.is_lsm and (self.filehandle.size >= 2**32 or
                                self.pages[0].compression != 1 or
                                self.pages[1].compression != 1):
            elif movie:
                self.pages.useframes = True

        except Exception:

    def filehandle(self):
        """Return file handle."""
        return self._fh

    def filename(self):
        """Return name of file handle."""
        return self._fh.name

    def fstat(self):
        """Return status of file handle as stat_result object."""
            return os.fstat(self._fh.fileno())
        except Exception:  # io.UnsupportedOperation
            return None

    def close(self):
        """Close open file handle(s)."""
        for tif in self._files.values():
        self._files = {}

    def asarray(self, key=None, series=None, out=None, maxworkers=1):
        """Return image data from multiple TIFF pages as numpy array.

        By default, the data from the first series is returned.

        key : int, slice, or sequence of page indices
            Defines which pages to return as array.
        series : int or TiffPageSeries
            Defines which series of pages to return as array.
        out : numpy.ndarray, str, or file-like object; optional
            Buffer where image data will be saved.
            If numpy.ndarray, a writable array of compatible dtype and shape.
            If str or open file, the file name or file object used to
            create a memory-map to an array stored in a binary file on disk.
        maxworkers : int
            Maximum number of threads to concurrently get data from pages.
            Default is 1. If None, up to half the CPU cores are used.
            Reading data from file is limited to a single thread.
            Using multiple threads can significantly speed up this function
            if the bottleneck is decoding compressed data.
            If the bottleneck is I/O or pure Python code, using multiple
            threads might be detrimental.

        if not self.pages:
            return numpy.array([])
        if key is None and series is None:
            series = 0
        if series is not None:
                series = self.series[series]
            except (KeyError, TypeError):
            pages = series.pages
            pages = self.pages

        if key is None:
        elif isinstance(key, inttypes):
            pages = [pages[key]]
        elif isinstance(key, slice):
            pages = pages[key]
        elif isinstance(key, collections.Iterable):
            pages = [pages[k] for k in key]
            raise TypeError("key must be an int, slice, or sequence")

        if not pages:
            raise ValueError("no pages selected")

        if self.is_nih:
            result = stack_pages(pages, out=out, maxworkers=maxworkers,
        elif key is None and series and series.offset:
            if out == 'memmap' and pages[0].is_memmappable:
                result = self.filehandle.memmap_array(
                    series.dtype, series.shape, series.offset)
                if out is not None:
                    out = create_output(out, series.shape, series.dtype)
                i = product(series.shape)
                result = self.filehandle.read_array(series.dtype, i, out=out)
                if not self.is_native:
        elif len(pages) == 1:
            result = pages[0].asarray(out=out)
            result = stack_pages(pages, out=out, maxworkers=maxworkers)

        if result is None:

        if key is None:
                result.shape = series.shape
            except ValueError:
                    warnings.warn("failed to reshape %s to %s" % (
                        result.shape, series.shape))
                    # try series of expected shapes
                    result.shape = (-1,) + series.shape
                except ValueError:
                    # revert to generic shape
                    result.shape = (-1,) + pages[0].shape
        elif len(pages) == 1:
            result.shape = pages[0].shape
            result.shape = (-1,) + pages[0].shape
        return result

    def series(self):
        """Return related pages as TiffPageSeries.

        Side effect: after calling this function, TiffFile.pages might contain
        TiffPage and TiffFrame instances.

        if not self.pages:
            return []

        useframes = self.pages.useframes
        keyframe = self.pages.keyframe
        series = []
        for name in 'ome imagej lsm fluoview nih mdgel shaped'.split():
            if getattr(self, 'is_' + name, False):
                series = getattr(self, '_%s_series' % name)()
        if not series:
            self.pages.useframes = useframes
            self.pages.keyframe = keyframe
            series = self._generic_series()

        # remove empty series, e.g. in MD Gel files
        series = [s for s in series if sum(s.shape) > 0]

        for i, s in enumerate(series):
            s.index = i
        return series

    def _generic_series(self):
        """Return image series in file."""
        if self.pages.useframes:
            # movie mode
            page = self.pages[0]
            shape = page.shape
            axes = page.axes
            if len(self.pages) > 1:
                shape = (len(self.pages),) + shape
                axes = 'I' + axes
            return [TiffPageSeries(self.pages[:], shape, page.dtype, axes,

        result = []
        keys = []
        series = {}
        compressions = TIFF.DECOMPESSORS
        for page in self.pages:
            if not page.shape:
            key = page.shape + (page.axes, page.compression in compressions)
            if key in series:
                series[key] = [page]
        for key in keys:
            pages = series[key]
            page = pages[0]
            shape = page.shape
            axes = page.axes
            if len(pages) > 1:
                shape = (len(pages),) + shape
                axes = 'I' + axes
            result.append(TiffPageSeries(pages, shape, page.dtype, axes,

        return result

    def _shaped_series(self):
        """Return image series in "shaped" file."""
        pages = self.pages
        pages.useframes = True
        lenpages = len(pages)

        def append_series(series, pages, axes, shape, reshape, name):
            page = pages[0]
            if not axes:
                shape = page.shape
                axes = page.axes
                if len(pages) > 1:
                    shape = (len(pages),) + shape
                    axes = 'Q' + axes
            size = product(shape)
            resize = product(reshape)
            if page.is_contiguous and resize > size and resize % size == 0:
                # truncated file
                axes = 'Q' + axes
                shape = (resize // size,) + shape
                axes = reshape_axes(axes, shape, reshape)
                shape = reshape
            except ValueError as e:
            series.append(TiffPageSeries(pages, shape, page.dtype, axes,
                                         name=name, stype='Shaped'))

        keyframe = axes = shape = reshape = name = None
        series = []
        index = 0
        while True:
            if index >= lenpages:
            # new keyframe; start of new series
            pages.keyframe = index
            keyframe = pages[index]
            if not keyframe.is_shaped:
                warnings.warn("invalid shape metadata or corrupted file")
            # read metadata
            axes = None
            shape = None
            metadata = json_description_metadata(keyframe.is_shaped)
            name = metadata.get('name', '')
            reshape = metadata['shape']
            truncated = metadata.get('truncated', False)
            if 'axes' in metadata:
                axes = metadata['axes']
                if len(axes) == len(reshape):
                    shape = reshape
                    axes = ''
                    warnings.warn("axes do not match shape")
            # skip pages if possible
            spages = [keyframe]
            size = product(reshape)
            npages, mod = divmod(size, product(keyframe.shape))
            if mod:
                warnings.warn("series shape not matching page shape")
            if 1 < npages <= lenpages - index:
                size *= keyframe._dtype.itemsize
                if truncated:
                    npages = 1
                elif not (keyframe.is_final and
                          keyframe.offset + size < pages[index+1].offset):
                    # need to read all pages for series
                    for j in range(index+1, index+npages):
                        page = pages[j]
                        page.keyframe = keyframe
            append_series(series, spages, axes, shape, reshape, name)
            index += npages

        return series

    def _imagej_series(self):
        """Return image series in ImageJ file."""
        # ImageJ's dimension order is always TZCYXS
        # TODO: fix loading of color, composite or palette images
        self.pages.useframes = True
        self.pages.keyframe = 0

        ij = self.imagej_metadata
        pages = self.pages
        page = pages[0]

        def is_hyperstack():
            # ImageJ hyperstack store all image metadata in the first page and
            # image data is stored contiguously before the second page, if any.
            if not page.is_final:
                return False
            images = ij.get('images', 0)
            if images <= 1:
                return False
            offset, count = page.is_contiguous
            if (count != product(page.shape) * page.bitspersample // 8
                    or offset + count*images > self.filehandle.size):
                raise ValueError()
            # check that next page is stored after data
            if len(pages) > 1 and offset + count*images > pages[1].offset:
                return False
            return True

            hyperstack = is_hyperstack()
        except ValueError:
            warnings.warn("invalid ImageJ metadata or corrupted file")
        if hyperstack:
            # no need to read other pages
            pages = [page]

        shape = []
        axes = []
        if 'frames' in ij:
        if 'slices' in ij:
        if 'channels' in ij and not (page.photometric == 2 and not
                                     ij.get('hyperstack', False)):
        remain = ij.get('images', len(pages))//(product(shape) if shape else 1)
        if remain > 1:
        if page.axes[0] == 'I':
            # contiguous multiple images
        elif page.axes[:2] == 'SI':
            # color-mapped contiguous multiple images
            shape = page.shape[0:1] + tuple(shape) + page.shape[2:]
            axes = list(page.axes[0]) + axes + list(page.axes[2:])
        return [TiffPageSeries(pages, shape, page.dtype, axes, stype='ImageJ')]

    def _fluoview_series(self):
        """Return image series in FluoView file."""
        self.pages.useframes = True
        self.pages.keyframe = 0
        mm = self.fluoview_metadata
        mmhd = list(reversed(mm['Dimensions']))
        axes = ''.join(TIFF.MM_DIMENSIONS.get(i[0].upper(), 'Q')
                       for i in mmhd if i[1] > 1)
        shape = tuple(int(i[1]) for i in mmhd if i[1] > 1)
        return [TiffPageSeries(self.pages, shape, self.pages[0].dtype, axes,
                               name=mm['ImageName'], stype='FluoView')]

    def _mdgel_series(self):
        """Return image series in MD Gel file."""
        # only a single page, scaled according to metadata in second page
        self.pages.useframes = False
        self.pages.keyframe = 0
        md = self.mdgel_metadata
        if md['FileTag'] in (2, 128):
            dtype = numpy.dtype('float32')
            scale = md['ScalePixel']
            scale = scale[0] / scale[1]  # rational
            if md['FileTag'] == 2:
                # squary root data format
                def transform(a):
                    return a.astype('float32')**2 * scale
                def transform(a):
                    return a.astype('float32') * scale
            transform = None
        page = self.pages[0]
        return [TiffPageSeries([page], page.shape, dtype, page.axes,
                               transform=transform, stype='MDGel')]

    def _nih_series(self):
        """Return image series in NIH file."""
        self.pages.useframes = True
        self.pages.keyframe = 0
        page0 = self.pages[0]
        if len(self.pages) == 1:
            shape = page0.shape
            axes = page0.axes
            shape = (len(self.pages),) + page0.shape
            axes = 'I' + page0.axes
        return [
            TiffPageSeries(self.pages, shape, page0.dtype, axes, stype='NIH')]

    def _ome_series(self):
        """Return image series in OME-TIFF file(s)."""
        from xml.etree import cElementTree as etree  # delayed import
        omexml = self.pages[0].description
            root = etree.fromstring(omexml)
        except etree.ParseError as e:
            # TODO: test badly encoded ome-xml
            warnings.warn("ome-xml: %s" % e)
                # might work on Python 2
                omexml = omexml.decode('utf-8', 'ignore').encode('utf-8')
                root = etree.fromstring(omexml)
            except Exception:

        self.pages.useframes = True
        self.pages.keyframe = 0

        uuid = root.attrib.get('UUID', None)
        self._files = {uuid: self}
        dirname = self._fh.dirname
        modulo = {}
        series = []
        for element in root:
            if element.tag.endswith('BinaryOnly'):
                warnings.warn("ome-xml: not an ome-tiff master file")
            if element.tag.endswith('StructuredAnnotations'):
                for annot in element:
                    if not annot.attrib.get('Namespace',
                    for value in annot:
                        for modul in value:
                            for along in modul:
                                if not along.tag[:-1].endswith('Along'):
                                axis = along.tag[-1]
                                newaxis = along.attrib.get('Type', 'other')
                                newaxis = TIFF.AXES_LABELS[newaxis]
                                if 'Start' in along.attrib:
                                    step = float(along.attrib.get('Step', 1))
                                    start = float(along.attrib['Start'])
                                    stop = float(along.attrib['End']) + step
                                    labels = numpy.arange(start, stop, step)
                                    labels = [label.text for label in along
                                              if label.tag.endswith('Label')]
                                modulo[axis] = (newaxis, labels)

            if not element.tag.endswith('Image'):

            attr = element.attrib
            name = attr.get('Name', None)

            for pixels in element:
                if not pixels.tag.endswith('Pixels'):
                attr = pixels.attrib
                dtype = attr.get('PixelType', None)
                axes = ''.join(reversed(attr['DimensionOrder']))
                shape = list(int(attr['Size'+ax]) for ax in axes)
                size = product(shape[:-2])
                ifds = None
                spp = 1  # samples per pixel
                for data in pixels:
                    if data.tag.endswith('Channel'):
                        attr = data.attrib
                        if ifds is None:
                            spp = int(attr.get('SamplesPerPixel', spp))
                            ifds = [None] * (size // spp)
                        elif int(attr.get('SamplesPerPixel', 1)) != spp:
                            raise ValueError(
                                "Can't handle differing SamplesPerPixel")
                    if ifds is None:
                        ifds = [None] * (size // spp)
                    if not data.tag.endswith('TiffData'):
                    attr = data.attrib
                    ifd = int(attr.get('IFD', 0))
                    num = int(attr.get('NumPlanes', 1 if 'IFD' in attr else 0))
                    num = int(attr.get('PlaneCount', num))
                    idx = [int(attr.get('First'+ax, 0)) for ax in axes[:-2]]
                        idx = numpy.ravel_multi_index(idx, shape[:-2])
                    except ValueError:
                        # ImageJ produces invalid ome-xml when cropping
                        warnings.warn("ome-xml: invalid TiffData index")
                    for uuid in data:
                        if not uuid.tag.endswith('UUID'):
                        if uuid.text not in self._files:
                            if not self._multifile:
                                # abort reading multifile OME series
                                # and fall back to generic series
                                return []
                            fname = uuid.attrib['FileName']
                                tif = TiffFile(os.path.join(dirname, fname))
                                tif.pages.useframes = True
                                tif.pages.keyframe = 0
                            except (IOError, FileNotFoundError, ValueError):
                                    "ome-xml: failed to read '%s'" % fname)
                            self._files[uuid.text] = tif
                        pages = self._files[uuid.text].pages
                            for i in range(num if num else len(pages)):
                                ifds[idx + i] = pages[ifd + i]
                        except IndexError:
                            warnings.warn("ome-xml: index out of range")
                        # only process first uuid
                        pages = self.pages
                            for i in range(num if num else len(pages)):
                                ifds[idx + i] = pages[ifd + i]
                        except IndexError:
                            warnings.warn("ome-xml: index out of range")

                if all(i is None for i in ifds):
                    # skip images without data

                # set a keyframe on all ifds
                keyframe = None
                for i in ifds:
                    # try find a TiffPage
                    if i and i == i.keyframe:
                        keyframe = i
                if not keyframe:
                    # reload a TiffPage from file
                    for i, keyframe in enumerate(ifds):
                        if keyframe:
                            keyframe.parent.pages.keyframe = keyframe.index
                            keyframe = keyframe.parent.pages[keyframe.index]
                            ifds[i] = keyframe
                for i in ifds:
                    if i is not None:
                        i.keyframe = keyframe

                dtype = keyframe.dtype
                    TiffPageSeries(ifds, shape, dtype, axes, parent=self,
                                   name=name, stype='OME'))
        for serie in series:
            shape = list(serie.shape)
            for axis, (newaxis, labels) in modulo.items():
                i = serie.axes.index(axis)
                size = len(labels)
                if shape[i] == size:
                    serie.axes = serie.axes.replace(axis, newaxis, 1)
                    shape[i] //= size
                    shape.insert(i+1, size)
                    serie.axes = serie.axes.replace(axis, axis+newaxis, 1)
            serie.shape = tuple(shape)
        # squeeze dimensions
        for serie in series:
            serie.shape, serie.axes = squeeze_axes(serie.shape, serie.axes)
        return series

    def _lsm_series(self):
        """Return main image series in LSM file. Skip thumbnails."""
        lsmi = self.lsm_metadata
        axes = TIFF.CZ_LSMINFO_SCANTYPE[lsmi['ScanType']]
        if self.pages[0].photometric == 2:  # RGB; more than one channel
            axes = axes.replace('C', '').replace('XY', 'XYC')
        if lsmi.get('DimensionP', 0) > 1:
            axes += 'P'
        if lsmi.get('DimensionM', 0) > 1:
            axes += 'M'
        axes = axes[::-1]
        shape = tuple(int(lsmi[TIFF.CZ_LSMINFO_DIMENSIONS[i]]) for i in axes)
        name = lsmi.get('Name', '')
        self.pages.keyframe = 0
        pages = self.pages[::2]
        dtype = pages[0].dtype
        series = [TiffPageSeries(pages, shape, dtype, axes, name=name,

        if self.pages[1].is_reduced:
            self.pages.keyframe = 1
            pages = self.pages[1::2]
            dtype = pages[0].dtype
            cp, i = 1, 0
            while cp < len(pages) and i < len(shape)-2:
                cp *= shape[i]
                i += 1
            shape = shape[:i] + pages[0].shape
            axes = axes[:i] + 'CYX'
            series.append(TiffPageSeries(pages, shape, dtype, axes, name=name,

        return series

    def _lsm_load_pages(self):
        """Load all pages from LSM file."""
        self.pages.cache = True
        self.pages.useframes = True
        # second series: thumbnails
        self.pages.keyframe = 1
        keyframe = self.pages[1]
        for page in self.pages[1::2]:
            page.keyframe = keyframe
        # first series: data
        self.pages.keyframe = 0
        keyframe = self.pages[0]
        for page in self.pages[::2]:
            page.keyframe = keyframe

    def _lsm_fix_strip_offsets(self):
        """Unwrap strip offsets for LSM files greater than 4 GB.

        Each series and position require separate unwrapping (undocumented).

        if self.filehandle.size < 2**32:

        pages = self.pages
        npages = len(pages)
        series = self.series[0]
        axes = series.axes

        # find positions
        positions = 1
        for i in 0, 1:
            if series.axes[i] in 'PM':
                positions *= series.shape[i]

        # make time axis first
        if positions > 1:
            ntimes = 0
            for i in 1, 2:
                if axes[i] == 'T':
                    ntimes = series.shape[i]
            if ntimes:
                div, mod = divmod(npages, 2*positions*ntimes)
                assert mod == 0
                shape = (positions, ntimes, div, 2)
                indices = numpy.arange(product(shape)).reshape(shape)
                indices = numpy.moveaxis(indices, 1, 0)
            indices = numpy.arange(npages).reshape(-1, 2)

        # images of reduced page might be stored first
        if pages[0].dataoffsets[0] > pages[1].dataoffsets[0]:
            indices = indices[..., ::-1]

        # unwrap offsets
        wrap = 0
        previousoffset = 0
        for i in indices.flat:
            page = pages[i]
            dataoffsets = []
            for currentoffset in page.dataoffsets:
                if currentoffset < previousoffset:
                    wrap += 2**32
                dataoffsets.append(currentoffset + wrap)
                previousoffset = currentoffset
            page.dataoffsets = tuple(dataoffsets)

    def _lsm_fix_strip_bytecounts(self):
        """Set databytecounts to size of compressed data.

        The StripByteCounts tag in LSM files contains the number of bytes
        for the uncompressed data.

        pages = self.pages
        if pages[0].compression == 1:
        # sort pages by first strip offset
        pages = sorted(pages, key=lambda p: p.dataoffsets[0])
        npages = len(pages) - 1
        for i, page in enumerate(pages):
            if page.index % 2:
            offsets = page.dataoffsets
            bytecounts = page.databytecounts
            if i < npages:
                lastoffset = pages[i+1].dataoffsets[0]
                # LZW compressed strips might be longer than uncompressed
                lastoffset = min(offsets[-1] + 2*bytecounts[-1], self._fh.size)
            offsets = offsets + (lastoffset,)
            page.databytecounts = tuple(offsets[j+1] - offsets[j]
                                        for j in range(len(bytecounts)))

    def __getattr__(self, name):
        """Return 'is_flag' attributes from first page."""
        if name[3:] in TIFF.FILE_FLAGS:
            if not self.pages:
                return False
            value = bool(getattr(self.pages[0], name))
            setattr(self, name, value)
            return value
        raise AttributeError("'%s' object has no attribute '%s'" %
                             (self.__class__.__name__, name))

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):

    def __str__(self, detail=0):
        """Return string containing information about file.

        The detail parameter specifies the level of detail returned:

        0: file only.
        1: all series, first page of series and its tags.
        2: large tag values and file metadata.
        3: all pages.

        info = [
            "TiffFile '%s'" % snipstr(self._fh.name, 32),
            {'<': 'LittleEndian', '>': 'BigEndian'}[self.byteorder]]
        if self.is_bigtiff:
        info.append('|'.join(f.upper() for f in self.flags))
        if len(self.pages) > 1:
            info.append('%i Pages' % len(self.pages))
        if len(self.series) > 1:
            info.append('%i Series' % len(self.series))
        if len(self._files) > 1:
            info.append('%i Files' % (len(self._files)))
        info = '  '.join(info)
        if detail <= 0:
            return info
        info = [info]
        info.append('\n'.join(str(s) for s in self.series))
        if detail >= 3:
            info.extend((TiffPage.__str__(p, detail=detail)
                         for p in self.pages
                         if p is not None))
            info.extend((TiffPage.__str__(s.pages[0], detail=detail)
                         for s in self.series
                         if s.pages[0] is not None))
        if detail >= 2:
            for name in sorted(self.flags):
                if hasattr(self, name + '_metadata'):
                    m = getattr(self, name + '_metadata')
                    if m:
                            "%s_METADATA\n%s" % (name.upper(), pformat(m)))
        return '\n\n'.join(info).replace('\n\n\n', '\n\n')

    def flags(self):
        """Return set of file flags."""
        return set(name.lower() for name in sorted(TIFF.FILE_FLAGS)
                   if getattr(self, 'is_' + name))

    def is_mdgel(self):
        """File has MD Gel format."""
            return self.pages[0].is_mdgel or self.pages[1].is_mdgel
        except IndexError:
            return False

    def is_movie(self):
        """Return if file is a movie."""
        return self.pages.useframes

    def shaped_metadata(self):
        """Return Tifffile metadata from JSON descriptions as dicts."""
        if not self.is_shaped:
        return tuple(json_description_metadata(s.pages[0].is_shaped)
                     for s in self.series if s.stype.lower() == 'shaped')

    def ome_metadata(self):
        """Return OME XML as dict."""
        if not self.is_ome:
        return xml2dict(self.pages[0].description)

    def lsm_metadata(self):
        """Return LSM metadata from CZ_LSMINFO tag as dict."""
        if not self.is_lsm:
        return self.pages[0].tags['CZ_LSMINFO'].value

    def stk_metadata(self):
        """Return STK metadata from UIC tags as dict."""
        if not self.is_stk:
        page = self.pages[0]
        tags = page.tags
        result = {}
        result['NumberPlanes'] = tags['UIC2tag'].count
        if page.description:
            result['PlaneDescriptions'] = page.description.split('\0')
            # result['plane_descriptions'] = stk_description_metadata(
            #    page.image_description)
        if 'UIC1tag' in tags:
        if 'UIC3tag' in tags:
            result.update(tags['UIC3tag'].value)  # wavelengths
        if 'UIC4tag' in tags:
            result.update(tags['UIC4tag'].value)  # override uic1 tags
        uic2tag = tags['UIC2tag'].value
        result['ZDistance'] = uic2tag['ZDistance']
        result['TimeCreated'] = uic2tag['TimeCreated']
        result['TimeModified'] = uic2tag['TimeModified']
            result['DatetimeCreated'] = numpy.array(
                [julian_datetime(*dt) for dt in
                 zip(uic2tag['DateCreated'], uic2tag['TimeCreated'])],
            result['DatetimeModified'] = numpy.array(
                [julian_datetime(*dt) for dt in
                 zip(uic2tag['DateModified'], uic2tag['TimeModified'])],
        except ValueError as e:
            warnings.warn("stk_metadata: %s" % e)
        return result

    def imagej_metadata(self):
        """Return consolidated ImageJ metadata as dict."""
        if not self.is_imagej:
        page = self.pages[0]
        result = imagej_description_metadata(page.is_imagej)
        if 'IJMetadata' in page.tags:
            except Exception:
        return result

    def fluoview_metadata(self):
        """Return consolidated FluoView metadata as dict."""
        if not self.is_fluoview:
        result = {}
        page = self.pages[0]
        # TODO: read stamps from all pages
        result['Stamp'] = page.tags['MM_Stamp'].value
        # skip parsing image description; not reliable
        # try:
        #     t = fluoview_description_metadata(page.image_description)
        #     if t is not None:
        #         result['ImageDescription'] = t
        # except Exception as e:
        #     warnings.warn(
        #         "failed to read FluoView image description: %s" % e)
        return result

    def nih_metadata(self):
        """Return NIH Image metadata from NIHImageHeader tag as dict."""
        if not self.is_nih:
        return self.pages[0].tags['NIHImageHeader'].value

    def fei_metadata(self):
        """Return FEI metadata from SFEG or HELIOS tags as dict."""
        if not self.is_fei:
        tags = self.pages[0].tags
        if 'FEI_SFEG' in tags:
            return tags['FEI_SFEG'].value
        if 'FEI_HELIOS' in tags:
            return tags['FEI_HELIOS'].value

    def sem_metadata(self):
        """Return SEM metadata from CZ_SEM tag as dict."""
        if not self.is_sem:
        return self.pages[0].tags['CZ_SEM'].value

    def mdgel_metadata(self):
        """Return consolidated metadata from MD GEL tags as dict."""
        for page in self.pages[:2]:
            if 'MDFileTag' in page.tags:
                tags = page.tags
        result = {}
        for code in range(33445, 33453):
            name = TIFF.TAGS[code]
            if name not in tags:
            result[name[2:]] = tags[name].value
        return result

    def andor_metadata(self):
        """Return Andor tags as dict."""
        return self.pages[0].andor_tags

    def epics_metadata(self):
        """Return EPICS areaDetector tags as dict."""
        return self.pages[0].epics_tags

    def tvips_metadata(self):
        """Return TVIPS tag as dict."""
        if not self.is_tvips:
        return self.pages[0].tags['TVIPS'].value

    def metaseries_metadata(self):
        """Return MetaSeries metadata from image description as dict."""
        if not self.is_metaseries:
        return metaseries_description_metadata(self.pages[0].description)

    def pilatus_metadata(self):
        """Return Pilatus metadata from image description as dict."""
        if not self.is_pilatus:
        return pilatus_description_metadata(self.pages[0].description)

    def micromanager_metadata(self):
        """Return consolidated MicroManager metadata as dict."""
        if not self.is_micromanager:
        # from file header
        result = read_micromanager_metadata(self._fh)
        # from tag
        return result

    def scanimage_metadata(self):
        """Return ScanImage non-varying frame and ROI metadata as dict."""
        if not self.is_scanimage:
        result = {}
            framedata, roidata = read_scanimage_metadata(self._fh)
            result['FrameData'] = framedata
        except ValueError:
        # TODO: scanimage_artist_metadata
            result['Description'] = scanimage_description_metadata(
        except Exception as e:
            warnings.warn("scanimage_description_metadata failed: %s" % e)
        return result

class TiffPages(object):
    """Sequence of TIFF image file directories."""
    def __init__(self, parent):
        """Initialize instance from file. Read first TiffPage from file.

        The file position must be at an offset to an offset to a TiffPage.

        self.parent = parent
        self.pages = []  # cache of TiffPages, TiffFrames, or their offsets
        self.complete = False  # True if offsets to all pages were read
        self._tiffpage = TiffPage  # class for reading tiff pages
        self._keyframe = None
        self._cache = True

        # read offset to first page
        fh = parent.filehandle
        self._nextpageoffset = fh.tell()
        offset = struct.unpack(parent.offsetformat,

        if offset == 0:
            # warnings.warn("file contains no pages")
            self.complete = True
        if offset >= fh.size:
            warnings.warn("invalid page offset (%i)" % offset)
            self.complete = True

        # always read and cache first page
        page = TiffPage(parent, index=0)
        self._keyframe = page

    def cache(self):
        """Return if pages/frames are currenly being cached."""
        return self._cache

    def cache(self, value):
        """Enable or disable caching of pages/frames. Clear cache if False."""
        value = bool(value)
        if self._cache and not value:
        self._cache = value

    def useframes(self):
        """Return if currently using TiffFrame (True) or TiffPage (False)."""
        return self._tiffpage == TiffFrame and TiffFrame is not TiffPage

    def useframes(self, value):
        """Set to use TiffFrame (True) or TiffPage (False)."""
        self._tiffpage = TiffFrame if value else TiffPage

    def keyframe(self):
        """Return index of current keyframe."""
        return self._keyframe.index

    def keyframe(self, index):
        """Set current keyframe. Load TiffPage from file if necessary."""
        if self.complete or 0 <= index < len(self.pages):
            page = self.pages[index]
            if isinstance(page, TiffPage):
                self._keyframe = page
            elif isinstance(page, TiffFrame):
                # remove existing frame
                self.pages[index] = page.offset
        # load TiffPage from file
        useframes = self.useframes
        self._tiffpage = TiffPage
        self._keyframe = self[index]
        self.useframes = useframes

    def next_page_offset(self):
        """Return offset where offset to a new page can be stored."""
        if not self.complete:
        return self._nextpageoffset

    def load(self):
        """Read all remaining pages from file."""
        fh = self.parent.filehandle
        keyframe = self._keyframe
        pages = self.pages
        if not self.complete:
        for i, page in enumerate(pages):
            if isinstance(page, inttypes):
                page = self._tiffpage(self.parent, index=i, keyframe=keyframe)
                pages[i] = page

    def clear(self, fully=True):
        """Delete all but first page from cache. Set keyframe to first page."""
        pages = self.pages
        if not self._cache or len(pages) < 1:
        self._keyframe = pages[0]
        if fully:
            # delete all but first TiffPage/TiffFrame
            for i, page in enumerate(pages[1:]):
                if not isinstance(page, inttypes):
                    pages[i+1] = page.offset
        elif TiffFrame is not TiffPage:
            # delete only TiffFrames
            for i, page in enumerate(pages):
                if isinstance(page, TiffFrame):
                    pages[i] = page.offset

    def _seek(self, index):
        """Seek file to offset of specified page."""
        pages = self.pages
        if not pages:

        fh = self.parent.filehandle
        if fh.closed:
            raise RuntimeError("FileHandle is closed")

        if self.complete or 0 <= index < len(pages):
            page = pages[index]
            offset = page if isinstance(page, inttypes) else page.offset

        offsetformat = self.parent.offsetformat
        offsetsize = self.parent.offsetsize
        tagnoformat = self.parent.tagnoformat
        tagnosize = self.parent.tagnosize
        tagsize = self.parent.tagsize
        unpack = struct.unpack

        page = pages[-1]
        offset = page if isinstance(page, inttypes) else page.offset

        while True:
            # read offsets to pages from file until index is reached
            # skip tags
                tagno = unpack(tagnoformat, fh.read(tagnosize))[0]
                if tagno > 4096:
                    raise ValueError("suspicious number of tags")
            except Exception:
                warnings.warn("corrupted tag list at offset %i" % offset)
                del pages[-1]
                self.complete = True
            self._nextpageoffset = offset + tagnosize + tagno * tagsize

            # read offset to next page
            offset = unpack(offsetformat, fh.read(offsetsize))[0]
            if offset == 0:
                self.complete = True
            if offset >= fh.size:
                warnings.warn("invalid page offset (%i)" % offset)
                self.complete = True

            if 0 <= index < len(pages):

        if index >= len(pages):
            raise IndexError('list index out of range')

        page = pages[index]
        fh.seek(page if isinstance(page, inttypes) else page.offset)

    def __bool__(self):
        """Return True if file contains any pages."""
        return len(self.pages) > 0

    def __len__(self):
        """Return number of pages in file."""
        if not self.complete:
        return len(self.pages)

    def __getitem__(self, key):
        """Return specified page(s) from cache or file."""
        pages = self.pages
        if not pages:
            raise IndexError('list index out of range')
        if key is 0:
            return pages[key]

        if isinstance(key, slice):
            start, stop, _ = key.indices(2**31)
            if not self.complete and max(stop, start) > len(pages):
            return [self[i] for i in range(*key.indices(len(pages)))]

        if self.complete and key >= len(pages):
            raise IndexError('list index out of range')

            page = pages[key]
        except IndexError:
            page = 0
        if not isinstance(page, inttypes):
            return page

        page = self._tiffpage(self.parent, index=key, keyframe=self._keyframe)
        if self._cache:
            pages[key] = page
        return page

    def __iter__(self):
        """Return iterator over all pages."""
        i = 0
        while True:
                yield self[i]
                i += 1
            except IndexError:

class TiffPage(object):
    """TIFF image file directory (IFD).

    index : int
        Index of page in file.
    dtype : numpy.dtype or None
        Data type of the image in IFD.
    shape : tuple
        Dimensions of the image in IFD.
    axes : str
        Axes label codes:
        'X' width, 'Y' height, 'S' sample, 'I' image series|page|plane,
        'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
        'T' time, 'R' region|tile, 'A' angle, 'P' phase, 'H' lifetime,
        'L' exposure, 'V' event, 'Q' unknown, '_' missing
    tags : dict
        Dictionary of tags in IFD. {tag.name: TiffTag}
    colormap : numpy.ndarray
        Color look up table, if exists.

    All attributes are read-only.

    The internal, normalized '_shape' attribute is 6 dimensional:

    0 : number planes/images  (stk, ij).
    1 : planar samplesperpixel.
    2 : imagedepth Z  (sgi).
    3 : imagelength Y.
    4 : imagewidth X.
    5 : contig samplesperpixel.

    # default properties; will be updated from tags
    imagewidth = 0
    imagelength = 0
    imagedepth = 1
    tilewidth = 0
    tilelength = 0
    tiledepth = 1
    bitspersample = 1
    samplesperpixel = 1
    sampleformat = 1
    rowsperstrip = 2**32-1
    compression = 1
    planarconfig = 1
    fillorder = 1
    photometric = 0
    predictor = 1
    extrasamples = 1
    colormap = None
    software = ''
    description = ''
    description1 = ''

    def __init__(self, parent, index, keyframe=None):
        """Initialize instance from file.

        The file handle position must be at offset to a valid IFD.

        self.parent = parent
        self.index = index
        self.shape = ()
        self._shape = ()
        self.dtype = None
        self._dtype = None
        self.axes = ""
        self.tags = {}

        self.dataoffsets = ()
        self.databytecounts = ()

        # read TIFF IFD structure and its tags from file
        fh = parent.filehandle
        self.offset = fh.tell()  # offset to this IDF
            tagno = struct.unpack(parent.tagnoformat,
            if tagno > 4096:
                raise ValueError("suspicious number of tags")
        except Exception:
            raise ValueError("corrupted tag list at offset %i" % self.offset)

        tagsize = parent.tagsize
        data = fh.read(tagsize * tagno)
        tags = self.tags
        index = -tagsize
        for _ in range(tagno):
            index += tagsize
                tag = TiffTag(self.parent, data[index:index+tagsize])
            except TiffTag.Error as e:
            tagname = tag.name
            if tagname not in tags:
                name = tagname
                tags[name] = tag
                # some files contain multiple tags with same code
                # e.g. MicroManager files contain two ImageDescription tags
                i = 1
                while True:
                    name = "%s%i" % (tagname, i)
                    if name not in tags:
                        tags[name] = tag
            name = TIFF.TAG_ATTRIBUTES.get(name, '')
            if name:
                setattr(self, name, tag.value)

        if not tags:
            return  # found in FIBICS

        # consolidate private tags; remove them from self.tags
        if self.is_andor:
        elif self.is_epics:

        if self.is_lsm or (self.index and self.parent.is_lsm):
            # correct non standard LSM bitspersample tags

        if self.is_vista or (self.index and self.parent.is_vista):
            # ISS Vista writes wrong ImageDepth tag
            self.imagedepth = 1

        if self.is_stk and 'UIC1tag' in tags and not tags['UIC1tag'].value:
            # read UIC1tag now that plane count is known
            uic1tag = tags['UIC1tag']
            tags['UIC1tag'].value = read_uic1tag(
                fh, self.parent.byteorder, uic1tag.dtype,
                uic1tag.count, None, tags['UIC2tag'].count)

        if 'IJMetadata' in tags:
            # decode IJMetadata tag
                tags['IJMetadata'].value = imagej_metadata(
            except Exception as e:

        if 'BitsPerSample' in tags:
            tag = tags['BitsPerSample']
            if tag.count == 1:
                self.bitspersample = tag.value
                # LSM might list more items than samples_per_pixel
                value = tag.value[:self.samplesperpixel]
                if any((v-value[0] for v in value)):
                    self.bitspersample = value
                    self.bitspersample = value[0]

        if 'SampleFormat' in tags:
            tag = tags['SampleFormat']
            if tag.count == 1:
                self.sampleformat = tag.value
                value = tag.value[:self.samplesperpixel]
                if any((v-value[0] for v in value)):
                    self.sampleformat = value
                    self.sampleformat = value[0]

        if 'ImageLength' in tags:
            if 'RowsPerStrip' not in tags or tags['RowsPerStrip'].count > 1:
                self.rowsperstrip = self.imagelength
            # self.stripsperimage = int(math.floor(
            #    float(self.imagelength + self.rowsperstrip - 1) /
            #    self.rowsperstrip))

        # determine dtype
        dtype = self.sampleformat, self.bitspersample
        dtype = TIFF.SAMPLE_DTYPES.get(dtype, None)
        if dtype is not None:
            dtype = numpy.dtype(dtype)
        self.dtype = self._dtype = dtype

        # determine shape of data
        imagelength = self.imagelength
        imagewidth = self.imagewidth
        imagedepth = self.imagedepth
        samplesperpixel = self.samplesperpixel

        if self.is_stk:
            assert self.imagedepth == 1
            uictag = tags['UIC2tag'].value
            planes = tags['UIC2tag'].count
            if self.planarconfig == 1:
                self._shape = (
                    planes, 1, 1, imagelength, imagewidth, samplesperpixel)
                if samplesperpixel == 1:
                    self.shape = (planes, imagelength, imagewidth)
                    self.axes = 'YX'
                    self.shape = (
                        planes, imagelength, imagewidth, samplesperpixel)
                    self.axes = 'YXS'
                self._shape = (
                    planes, samplesperpixel, 1, imagelength, imagewidth, 1)
                if samplesperpixel == 1:
                    self.shape = (planes, imagelength, imagewidth)
                    self.axes = 'YX'
                    self.shape = (
                        planes, samplesperpixel, imagelength, imagewidth)
                    self.axes = 'SYX'
            # detect type of series
            if planes == 1:
                self.shape = self.shape[1:]
            elif numpy.all(uictag['ZDistance'] != 0):
                self.axes = 'Z' + self.axes
            elif numpy.all(numpy.diff(uictag['TimeCreated']) != 0):
                self.axes = 'T' + self.axes
                self.axes = 'I' + self.axes
        elif self.photometric == 2 or samplesperpixel > 1:  # PHOTOMETRIC.RGB
            if self.planarconfig == 1:
                self._shape = (
                    1, 1, imagedepth, imagelength, imagewidth, samplesperpixel)
                if imagedepth == 1:
                    self.shape = (imagelength, imagewidth, samplesperpixel)
                    self.axes = 'YXS'
                    self.shape = (
                        imagedepth, imagelength, imagewidth, samplesperpixel)
                    self.axes = 'ZYXS'
                self._shape = (1, samplesperpixel, imagedepth,
                               imagelength, imagewidth, 1)
                if imagedepth == 1:
                    self.shape = (samplesperpixel, imagelength, imagewidth)
                    self.axes = 'SYX'
                    self.shape = (
                        samplesperpixel, imagedepth, imagelength, imagewidth)
                    self.axes = 'SZYX'
            self._shape = (1, 1, imagedepth, imagelength, imagewidth, 1)
            if imagedepth == 1:
                self.shape = (imagelength, imagewidth)
                self.axes = 'YX'
                self.shape = (imagedepth, imagelength, imagewidth)
                self.axes = 'ZYX'

        # dataoffsets and databytecounts
        if 'TileOffsets' in tags:
            self.dataoffsets = tags['TileOffsets'].value
        elif 'StripOffsets' in tags:
            self.dataoffsets = tags['StripOffsets'].value
            self.dataoffsets = (0,)

        if 'TileByteCounts' in tags:
            self.databytecounts = tags['TileByteCounts'].value
        elif 'StripByteCounts' in tags:
            self.databytecounts = tags['StripByteCounts'].value
        elif self.compression == 1:
            self.databytecounts = (
                product(self.shape) * (self.bitspersample // 8),)
            raise ValueError("ByteCounts not found")
        assert len(self.shape) == len(self.axes)

    def asarray(self, out=None, squeeze=True, lock=None, reopen=True,
                maxsize=64*2**30, validate=True):
        """Read image data from file and return as numpy array.

        Raise ValueError if format is unsupported.

        out : numpy.ndarray, str, or file-like object; optional
            Buffer where image data will be saved.
            If numpy.ndarray, a writable array of compatible dtype and shape.
            If str or open file, the file name or file object used to
            create a memory-map to an array stored in a binary file on disk.
        squeeze : bool
            If True, all length-1 dimensions (except X and Y) are
            squeezed out from the array.
            If False, the shape of the returned array might be different from
            the page.shape.
        lock : {RLock, NullContext}
            A reentrant lock used to syncronize reads from file.
            If None (default), the lock of the parent's filehandle is used.
        reopen : bool
            If True (default) and the parent file handle is closed, the file
            is temporarily re-opened and closed if no exception occurs.
        maxsize: int or None
            Maximum size of data before a ValueError is raised.
            Can be used to catch DOS. Default: 64 GB.
        validate : bool
            If True (default), validate various parameters.
            If None, only validate parameters and return None.

        self_ = self
        self = self.keyframe  # self or keyframe

        if not self._shape or product(self._shape) == 0:

        tags = self.tags

        if validate or validate is None:
            if maxsize and product(self._shape) > maxsize:
                raise ValueError("data is too large %s" % str(self._shape))
            if self.dtype is None:
                raise ValueError("data type not supported: %s%i" % (
                    self.sampleformat, self.bitspersample))
            if self.compression not in TIFF.DECOMPESSORS:
                raise ValueError(
                    "can not decompress %s" % self.compression.name)
            if 'SampleFormat' in tags:
                tag = tags['SampleFormat']
                if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
                    raise ValueError(
                        "sample formats do not match %s" % tag.value)
            if self.is_chroma_subsampled:
                # TODO: implement chroma subsampling
                raise NotImplementedError("chroma subsampling not supported")
            if validate is None:

        fh = self_.parent.filehandle
        lock = fh.lock if lock is None else lock
        with lock:
            closed = fh.closed
            if closed:
                if reopen:
                    raise IOError("file handle is closed")

        dtype = self._dtype
        shape = self._shape
        imagewidth = self.imagewidth
        imagelength = self.imagelength
        imagedepth = self.imagedepth
        bitspersample = self.bitspersample
        typecode = self.parent.byteorder + dtype.char
        lsb2msb = self.fillorder == 2
        offsets, bytecounts = self_.offsets_bytecounts
        istiled = self.is_tiled

        if istiled:
            tilewidth = self.tilewidth
            tilelength = self.tilelength
            tiledepth = self.tiledepth
            tw = (imagewidth + tilewidth - 1) // tilewidth
            tl = (imagelength + tilelength - 1) // tilelength
            td = (imagedepth + tiledepth - 1) // tiledepth
            shape = (shape[0], shape[1],
                     td*tiledepth, tl*tilelength, tw*tilewidth, shape[-1])
            tileshape = (tiledepth, tilelength, tilewidth, shape[-1])
            runlen = tilewidth
            runlen = imagewidth

        if out == 'memmap' and self.is_memmappable:
            with lock:
                result = fh.memmap_array(typecode, shape, offset=offsets[0])
        elif self.is_contiguous:
            isnative = self.parent.is_native
            if out is not None:
                isnative = True
                out = create_output(out, shape, dtype)
            with lock:
                result = fh.read_array(typecode, product(shape), out=out)
            if not isnative:
                result = result.astype('=' + dtype.char)
            if lsb2msb:
            result = create_output(out, shape, dtype)
            if self.planarconfig == 1:
                runlen *= self.samplesperpixel
            if bitspersample in (8, 16, 32, 64, 128):
                if (bitspersample * runlen) % 8:
                    raise ValueError("data and sample size mismatch")

                def unpack(x, typecode=typecode):
                    if self.predictor == 3:  # PREDICTOR.FLOATINGPOINT
                        # the floating point horizontal differencing decoder
                        # needs the raw byte order
                        typecode = dtype.char
                        return numpy.fromstring(x, typecode)
                    except ValueError as e:
                        # strips may be missing EOI
                        # warnings.warn("unpack: %s" % e)
                        xlen = ((len(x) // (bitspersample // 8)) *
                                (bitspersample // 8))
                        return numpy.fromstring(x[:xlen], typecode)

            elif isinstance(bitspersample, tuple):
                def unpack(x):
                    return unpack_rgb(x, typecode, bitspersample)
                def unpack(x):
                    return unpack_ints(x, typecode, bitspersample, runlen)

            decompress = TIFF.DECOMPESSORS[self.compression]
            if self.compression == 7:  # COMPRESSION.JPEG
                if 'JPEGTables' in tags:
                    table = tags['JPEGTables'].value
                    table = b''

                def decompress(x):
                    return decode_jpeg(x, table, self.photometric)

            if istiled:
                tw, tl, td, pl = 0, 0, 0, 0
                for tile in buffered_read(fh, lock, offsets, bytecounts):
                    if lsb2msb:
                        tile = reverse_bitorder(tile)
                    tile = decompress(tile)
                    tile = unpack(tile)
                        tile.shape = tileshape
                    except ValueError:
                        # incomplete tiles; see gdal issue #1179
                        warnings.warn("invalid tile data")
                        t = numpy.zeros(tileshape, dtype).reshape(-1)
                        s = min(tile.size, t.size)
                        t[:s] = tile[:s]
                        tile = t.reshape(tileshape)
                    if self.predictor == 2:  # PREDICTOR.HORIZONTAL
                        numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile)
                    elif self.predictor == 3:  # PREDICTOR.FLOATINGPOINT
                        raise NotImplementedError()
                    result[0, pl, td:td+tiledepth,
                           tl:tl+tilelength, tw:tw+tilewidth, :] = tile
                    del tile
                    tw += tilewidth
                    if tw >= shape[4]:
                        tw, tl = 0, tl + tilelength
                        if tl >= shape[3]:
                            tl, td = 0, td + tiledepth
                            if td >= shape[2]:
                                td, pl = 0, pl + 1
                result = result[...,
                                :imagedepth, :imagelength, :imagewidth, :]
                strip_size = self.rowsperstrip * self.imagewidth
                if self.planarconfig == 1:
                    strip_size *= self.samplesperpixel
                result = result.reshape(-1)
                index = 0
                for strip in buffered_read(fh, lock, offsets, bytecounts):
                    if lsb2msb:
                        strip = reverse_bitorder(strip)
                    strip = decompress(strip)
                    strip = unpack(strip)
                    size = min(result.size, strip.size, strip_size,
                               result.size - index)
                    result[index:index+size] = strip[:size]
                    del strip
                    index += size

        result.shape = self._shape

        if self.predictor != 1 and not (istiled and not self.is_contiguous):
            if self.parent.is_lsm and self.compression == 1:
                pass  # work around bug in LSM510 software
            elif self.predictor == 2:  # PREDICTOR.HORIZONTAL
                numpy.cumsum(result, axis=-2, dtype=dtype, out=result)
            elif self.predictor == 3:  # PREDICTOR.FLOATINGPOINT
                result = decode_floats(result)

        if squeeze:
                result.shape = self.shape
            except ValueError:
                warnings.warn("failed to reshape from %s to %s" % (
                    str(result.shape), str(self.shape)))

        if closed:
            # TODO: file should remain open if an exception occurred above
        return result

    def asrgb(self, uint8=False, alpha=None, colormap=None,
              dmin=None, dmax=None, *args, **kwargs):
        """Return image data as RGB(A).

        Work in progress.

        data = self.asarray(*args, **kwargs)
        self = self.keyframe  # self or keyframe
        photometric = self.photometric

        if photometric == PHOTOMETRIC.PALETTE:
            colormap = self.colormap
            if (colormap.shape[1] < 2**self.bitspersample or
                    self.dtype.char not in 'BH'):
                raise ValueError("can not apply colormap")
            if uint8:
                if colormap.max() > 255:
                    colormap >>= 8
                colormap = colormap.astype('uint8')
            if 'S' in self.axes:
                data = data[..., 0] if self.planarconfig == 1 else data[0]
            data = apply_colormap(data, colormap)

        elif photometric == PHOTOMETRIC.RGB:
            if 'ExtraSamples' in self.tags:
                if alpha is None:
                    alpha = TIFF.EXTRASAMPLE
                extrasamples = self.extrasamples
                if self.tags['ExtraSamples'].count == 1:
                    extrasamples = (extrasamples,)
                for i, exs in enumerate(extrasamples):
                    if exs in alpha:
                        if self.planarconfig == 1:
                            data = data[..., [0, 1, 2, 3+i]]
                            data = data[:, [0, 1, 2, 3+i]]
                if self.planarconfig == 1:
                    data = data[..., :3]
                    data = data[:, :3]
            # TODO: convert to uint8

        elif photometric == PHOTOMETRIC.MINISBLACK:
            raise NotImplementedError()
        elif photometric == PHOTOMETRIC.MINISWHITE:
            raise NotImplementedError()
        elif photometric == PHOTOMETRIC.SEPARATED:
            raise NotImplementedError()
            raise NotImplementedError()
        return data

    def aspage(self):
        return self

    def keyframe(self):
        return self

    def keyframe(self, index):

    def offsets_bytecounts(self):
        """Return simplified offsets and bytecounts."""
        if self.is_contiguous:
            offset, byte_count = self.is_contiguous
            return [offset], [byte_count]
        return clean_offsets_counts(self.dataoffsets, self.databytecounts)

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

        Excludes prediction and fill_order.

        if (self.compression != 1
                or self.bitspersample not in (8, 16, 32, 64)):
        if 'TileWidth' in self.tags:
            if (self.imagewidth != self.tilewidth or
                    self.imagelength % self.tilelength or
                    self.tilewidth % 16 or self.tilelength % 16):
            if ('ImageDepth' in self.tags and 'TileDepth' in self.tags and
                    (self.imagelength != self.tilelength or
                     self.imagedepth % self.tiledepth)):

        offsets = self.dataoffsets
        bytecounts = self.databytecounts
        if len(offsets) == 1:
            return offsets[0], bytecounts[0]
        if self.is_stk or all((offsets[i] + bytecounts[i] == offsets[i+1] or
                               bytecounts[i+1] == 0)  # no data/ignore offset
                              for i in range(len(offsets)-1)):
            return offsets[0], sum(bytecounts)

    def is_final(self):
        """Return if page's image data is stored in final form.

        Excludes byte-swapping.

        return (self.is_contiguous and self.fillorder == 1 and
                self.predictor == 1 and not self.is_chroma_subsampled)

    def is_memmappable(self):
        """Return if page's image data in file can be memory-mapped."""
        return (self.parent.filehandle.is_file and self.is_final and
                (self.bitspersample == 8 or self.parent.is_native) and
                self.is_contiguous[0] % self.dtype.itemsize == 0)

    def __str__(self, detail=0):
        """Return string containing information about page."""
        if self.keyframe != self:
            return TiffFrame.__str__(self, detail)
        attr = ''
        for name in ('memmappable', 'final', 'contiguous'):
            attr = getattr(self, 'is_'+name)
            if attr:
                attr = name.upper()
        info = '  '.join(s for s in (
            'x'.join(str(i) for i in self.shape),
            '%s%s' % (TIFF.SAMPLEFORMAT(self.sampleformat).name,
            '|'.join(i for i in (
                'TILED' if self.is_tiled else '',
                self.compression.name if self.compression != 1 else '',
                self.planarconfig.name if self.planarconfig != 1 else '',
                self.predictor.name if self.predictor != 1 else '',
                self.fillorder.name if self.fillorder != 1 else '')
                     if i),
            '|'.join((f.upper() for f in self.flags))
            ) if s)
        info = "TiffPage %i @%i  %s" % (self.index, self.offset, info)
        if detail <= 0:
            return info
        info = [info]
        tags = self.tags
        tlines = []
        vlines = []
        for tag in sorted(tags.values(), key=lambda x: x.code):
            value = tag.__str__()
            if detail > 1 and len(value) > TIFF.PRINT_LINE_WIDTH:
                vlines.append("%s\n%s" % (tag.name.upper(),
        if detail > 1:
        return '\n\n'.join(info)

    def flags(self):
        """Return set of flags."""
        return set((name.lower() for name in sorted(TIFF.FILE_FLAGS)
                    if getattr(self, 'is_' + name)))

    def ndim(self):
        """Return number of array dimensions."""
        return len(self.shape)

    def size(self):
        """Return number of elements in array."""
        return product(self.shape)

    def andor_tags(self):
        """Return consolidated metadata from Andor tags as dict.

        Remove Andor tags from self.tags.

        if not self.is_andor:
        tags = self.tags
        result = {'Id': tags['AndorId'].value}
        for tag in list(self.tags.values()):
            code = tag.code
            if not 4864 < code < 5031:
            value = tag.value
            name = tag.name[5:] if len(tag.name) > 5 else tag.name
            result[name] = value
            del tags[tag.name]
        return result

    def epics_tags(self):
        """Return consolidated metadata from EPICS areaDetector tags as dict.

        Remove areaDetector tags from self.tags.

        # TODO: obtain test file
        if not self.is_epics:
        result = {}
        tags = self.tags
        for tag in list(self.tags.values()):
            code = tag.code
            if not 65000 < code < 65500:
            value = tag.value
            if code == 65000:
                result['timeStamp'] = float(value)
            elif code == 65001:
                result['uniqueID'] = int(value)
            elif code == 65002:
                result['epicsTS'] = int(value)
            elif code == 65003:
                result['epicsTS'] = int(value)
                key, value = value.split(':')
                result[key] = astype(value)
            del tags[tag.name]
        return result

    def is_tiled(self):
        """Page contains tiled image."""
        return 'TileWidth' in self.tags

    def is_reduced(self):
        """Page is reduced image of another image."""
        return ('NewSubfileType' in self.tags and
                self.tags['NewSubfileType'].value & 1)

    def is_chroma_subsampled(self):
        """Page contains chroma subsampled image."""
        return ('YCbCrSubSampling' in self.tags and
                self.tags['YCbCrSubSampling'].value != (1, 1))

    def is_imagej(self):
        """Return ImageJ description if exists, else None."""
        for description in (self.description, self.description1):
            if not description:
            if description[:7] == 'ImageJ=':
                return description

    def is_shaped(self):
        """Return description containing array shape if exists, else None."""
        for description in (self.description, self.description1):
            if not description:
            if description[:1] == '{' and '"shape":' in description:
                return description
            if description[:6] == 'shape=':
                return description

    def is_mdgel(self):
        """Page contains MDFileTag tag."""
        return 'MDFileTag' in self.tags

    def is_mediacy(self):
        """Page contains Media Cybernetics Id tag."""
        return ('MC_Id' in self.tags and
                self.tags['MC_Id'].value[:7] == b'MC TIFF')

    def is_stk(self):
        """Page contains UIC2Tag tag."""
        return 'UIC2tag' in self.tags

    def is_lsm(self):
        """Page contains CZ_LSMINFO tag."""
        return 'CZ_LSMINFO' in self.tags

    def is_fluoview(self):
        """Page contains FluoView MM_STAMP tag."""
        return 'MM_Stamp' in self.tags

    def is_nih(self):
        """Page contains NIH image header."""
        return 'NIHImageHeader' in self.tags

    def is_sgi(self):
        """Page contains SGI image and tile depth tags."""
        return 'ImageDepth' in self.tags and 'TileDepth' in self.tags

    def is_vista(self):
        """Software tag is 'ISS Vista'."""
        return self.software == 'ISS Vista'

    def is_metaseries(self):
        """Page contains MDS MetaSeries metadata in ImageDescription tag."""
        if self.index > 1 or self.software != 'MetaSeries':
            return False
        d = self.description
        return d.startswith('<MetaData>') and d.endswith('</MetaData>')

    def is_ome(self):
        """Page contains OME-XML in ImageDescription tag."""
        if self.index > 1 or not self.description:
            return False
        d = self.description
        return d[:14] == '<?xml version=' and d[-6:] == '</OME>'

    def is_scn(self):
        """Page contains Leica SCN XML in ImageDescription tag."""
        if self.index > 1 or not self.description:
            return False
        d = self.description
        return d[:14] == '<?xml version=' and d[-6:] == '</scn>'

    def is_micromanager(self):
        """Page contains Micro-Manager metadata."""
        return 'MicroManagerMetadata' in self.tags

    def is_andor(self):
        """Page contains Andor Technology tags."""
        return 'AndorId' in self.tags

    def is_pilatus(self):
        """Page contains Pilatus tags."""
        return (self.software[:8] == 'TVX TIFF' and
                self.description[:2] == '# ')

    def is_epics(self):
        """Page contains EPICS areaDetector tags."""
        return self.description == 'EPICS areaDetector'

    def is_tvips(self):
        """Page contains TVIPS metadata."""
        return 'TVIPS' in self.tags

    def is_fei(self):
        """Page contains SFEG or HELIOS metadata."""
        return 'FEI_SFEG' in self.tags or 'FEI_HELIOS' in self.tags

    def is_sem(self):
        """Page contains Zeiss SEM metadata."""
        return 'CZ_SEM' in self.tags

    def is_svs(self):
        """Page contains Aperio metadata."""
        return self.description[:20] == 'Aperio Image Library'

    def is_scanimage(self):
        """Page contains ScanImage metadata."""
        return (self.description[:12] == 'state.config' or
                self.software[:22] == 'SI.LINE_FORMAT_VERSION')

class TiffFrame(object):
    """Lightweight TIFF image file directory (IFD).

    Only a limited number of tag values are read from file, e.g. StripOffsets,
    and StripByteCounts. Other tag values are assumed to be identical with a
    specified TiffPage instance, the keyframe.

    This is intended to reduce resource usage and speed up reading data from
    file, not for introspection of metadata.

    Not compatible with Python 2.

    __slots__ = ('keyframe', 'parent', 'index', 'offset',
                 'dataoffsets', 'databytecounts')

    is_mdgel = False
    tags = {}

    def __init__(self, parent, index, keyframe):
        """Read specified tags from file.

        The file handle position must be at the offset to a valid IFD.

        self.keyframe = keyframe
        self.parent = parent
        self.index = index

        unpack = struct.unpack
        fh = parent.filehandle
        self.offset = fh.tell()
            tagno = unpack(parent.tagnoformat, fh.read(parent.tagnosize))[0]
            if tagno > 4096:
                raise ValueError("suspicious number of tags")
        except Exception:
            raise ValueError("corrupted page list at offset %i" % self.offset)

        # tags = {}
        tagcodes = {273, 279, 324, 325}  # TIFF.FRAME_TAGS
        tagsize = parent.tagsize
        codeformat = parent.tagformat1[:2]

        data = fh.read(tagsize * tagno)
        index = -tagsize
        for _ in range(tagno):
            index += tagsize
            code = unpack(codeformat, data[index:index+2])[0]
            if code not in tagcodes:
                tag = TiffTag(parent, data[index:index+tagsize])
            except TiffTag.Error as e:
            if code == 273 or code == 324:
                setattr(self, 'dataoffsets', tag.value)
            elif code == 279 or code == 325:
                setattr(self, 'databytecounts', tag.value)
            # elif code == 270:
            #     tagname = tag.name
            #     if tagname not in tags:
            #         tags[tagname] = bytes2str(tag.value)
            #     elif 'ImageDescription1' not in tags:
            #         tags['ImageDescription1'] = bytes2str(tag.value)
            # else:
            #     tags[tag.name] = tag.value

    def aspage(self):
        """Return TiffPage from file."""
        return TiffPage(self.parent, index=self.index, keyframe=None)

    def asarray(self, *args, **kwargs):
        """Read image data from file and return as numpy array."""
        # TODO: fix TypeError on Python 2
        #   "TypeError: unbound method asarray() must be called with TiffPage
        #   instance as first argument (got TiffFrame instance instead)"
        kwargs['validate'] = False
        return TiffPage.asarray(self, *args, **kwargs)

    def asrgb(self, *args, **kwargs):
        """Read image data from file and return RGB image as numpy array."""
        kwargs['validate'] = False
        return TiffPage.asrgb(self, *args, **kwargs)

    def offsets_bytecounts(self):
        """Return simplified offsets and bytecounts."""
        if self.keyframe.is_contiguous:
            return self.dataoffsets[:1], self.keyframe.is_contiguous[1:]
        return clean_offsets_counts(self.dataoffsets, self.databytecounts)

    def is_contiguous(self):
        """Return offset and size of contiguous data, else None."""
        if self.keyframe.is_contiguous:
            return self.dataoffsets[0], self.keyframe.is_contiguous[1]

    def is_memmappable(self):
        """Return if page's image data in file can be memory-mapped."""
        return self.keyframe.is_memmappable

    def __getattr__(self, name):
        """Return attribute from keyframe."""
        if name in TIFF.FRAME_ATTRS:
            return getattr(self.keyframe, name)
        raise AttributeError("'%s' object has no attribute '%s'" %
                             (self.__class__.__name__, name))

    def __str__(self, detail=0):
        """Return string containing information about frame."""
        info = '  '.join(s for s in (
            'x'.join(str(i) for i in self.shape),
        return "TiffFrame %i @%i  %s" % (self.index, self.offset, info)

class TiffTag(object):
    """TIFF tag structure.

    name : string
        Name of tag.
    code : int
        Decimal code of tag.
    dtype : str
        Datatype of tag data. One of TIFF DATA_FORMATS.
    count : int
        Number of values.
    value : various types
        Tag data as Python object.
    valueoffset : int
        Location of value in file.

    All attributes are read-only.

    __slots__ = ('code', 'count', 'dtype', 'value', 'valueoffset')

    class Error(Exception):

    def __init__(self, parent, tagheader, **kwargs):
        """Initialize instance from tag header."""
        fh = parent.filehandle
        byteorder = parent.byteorder
        unpack = struct.unpack
        offsetsize = parent.offsetsize

        self.valueoffset = fh.tell() + offsetsize + 4
        code, dtype = unpack(parent.tagformat1, tagheader[:4])
        count, value = unpack(parent.tagformat2, tagheader[4:])

            dtype = TIFF.DATA_FORMATS[dtype]
        except KeyError:
            raise TiffTag.Error("unknown tag data type %i" % dtype)

        fmt = '%s%i%s' % (byteorder, count * int(dtype[0]), dtype[1])
        size = struct.calcsize(fmt)
        if size > offsetsize or code in TIFF.TAG_READERS:
            self.valueoffset = offset = unpack(parent.offsetformat, value)[0]
            if offset < 8 or offset > fh.size - size:
                raise TiffTag.Error("invalid tag value offset")
            # if offset % 2:
            #     warnings.warn("tag value does not begin on word boundary")
            if code in TIFF.TAG_READERS:
                readfunc = TIFF.TAG_READERS[code]
                value = readfunc(fh, byteorder, dtype, count, offsetsize)
            elif code in TIFF.TAGS or dtype[-1] == 's':
                value = unpack(fmt, fh.read(size))
                value = read_numpy(fh, byteorder, dtype, count, offsetsize)
            value = unpack(fmt, value[:size])

        process = code not in TIFF.TAG_READERS and code not in TIFF.TAG_TUPLE
        if process and dtype[-1] == 's' and isinstance(value[0], bytes):
            # TIFF ASCII fields can contain multiple strings,
            #   each terminated with a NUL
            value = bytes2str(stripascii(value[0]).strip())
            if code in TIFF.TAG_ENUM:
                t = TIFF.TAG_ENUM[code]
                    value = tuple(t(v) for v in value)
                except ValueError as e:
            if process:
                if len(value) == 1:
                    value = value[0]

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

    def name(self):
        return TIFF.TAGS.get(self.code, str(self.code))

    def _fix_lsm_bitspersample(self, parent):
        """Correct LSM bitspersample tag.

        Old LSM writers may use a separate region for two 16-bit values,
        although they fit into the tag value element of the tag.

        if self.code == 258 and self.count == 2:
            # TODO: test this case; need example file
            warnings.warn("correcting LSM bitspersample tag")
            tof = parent.offsetformat[parent.offsetsize]
            self.valueoffset = struct.unpack(tof, self._value)[0]
            self.value = struct.unpack("<HH", parent.filehandle.read(4))

    def __str__(self):
        """Return string containing information about tag."""
        if self.code in TIFF.TAG_ENUM:
            if self.count == 1:
                value = TIFF.TAG_ENUM[self.code](self.value).name
                value = tuple(v.name for v in self.value)
        elif isinstance(self.value, unicode):
                value = pformat(self.value)
                value = value.replace(u'\n', u'\\n').replace(u'\r', u'')
                value = u'"%s"' % value
            value = pformat(self.value, linewidth=False, maxlines=2)
            value = str(value).split('\n', 1)[0]

        tcode = "%i%s" % (self.count * int(self.dtype[0]), self.dtype[1])
        line = "TiffTag %i %s  %s @%i  %s" % (
            self.code, self.name, tcode, self.valueoffset, value)
        return line

class TiffPageSeries(object):
    """Series of TIFF pages with compatible shape and data type.

    pages : list of TiffPage
        Sequence of TiffPages in series.
    dtype : numpy.dtype or str
        Data type of the image array in series.
    shape : tuple
        Dimensions of the image array in series.
    axes : str
        Labels of axes in shape. See TiffPage.axes.
    offset : int or None
        Position of image data in file if memory-mappable, else None.

    def __init__(self, pages, shape, dtype, axes,
                 parent=None, name=None, transform=None, stype=None):
        """Initialize instance."""
        self.index = 0
        self.pages = pages
        self.shape = tuple(shape)
        self.axes = ''.join(axes)
        self.dtype = numpy.dtype(dtype)
        self.stype = stype if stype else ''
        self.name = name if name else ''
        self.transform = transform
        if parent:
            self.parent = parent
        elif pages:
            self.parent = pages[0].parent
            self.parent = None

    def asarray(self, out=None):
        """Return image data from series of TIFF pages as numpy array."""
        if self.parent:
            result = self.parent.asarray(series=self, out=out)
            if self.transform is not None:
                result = self.transform(result)
            return result

    def offset(self):
        """Return offset to series data in file, if any."""
        if not self.pages:

        pos = 0
        for page in self.pages:
            if page is None:
            if not page.is_final:
            if not pos:
                pos = page.is_contiguous[0] + page.is_contiguous[1]
            if pos != page.is_contiguous[0]:
            pos += page.is_contiguous[1]

        page = self.pages[0]
        offset = page.is_contiguous[0]
        if (page.is_imagej or page.is_shaped) and len(self.pages) == 1:
            # truncated files
            return offset
        if pos == offset + product(self.shape) * self.dtype.itemsize:
            return offset

    def ndim(self):
        """Return number of array dimensions."""
        return len(self.shape)

    def size(self):
        """Return number of elements in array."""
        return int(product(self.shape))

    def __len__(self):
        """Return number of TiffPages in series."""
        return len(self.pages)

    def __getitem__(self, key):
        """Return specified TiffPage."""
        return self.pages[key]

    def __iter__(self):
        """Return iterator over TiffPages in series."""
        return iter(self.pages)

    def __str__(self):
        """Return string with information about series."""
        s = '  '.join(s for s in (
            snipstr("'%s'" % self.name, 20) if self.name else '',
            'x'.join(str(i) for i in self.shape),
            '%i Pages' % len(self.pages),
            ('Offset=%i' % self.offset) if self.offset else '') if s)
        return 'TiffPageSeries %i  %s' % (self.index, s)

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

    The image data in all files must match shape, dtype, etc.

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

    >>> # read image stack from sequence of TIFF files
    >>> imsave('temp_C001T001.tif', numpy.random.rand(64, 64))
    >>> imsave('temp_C001T002.tif', numpy.random.rand(64, 64))
    >>> tifs = TiffSequence("temp_C001*.tif")
    >>> tifs.shape
    (1, 2)
    >>> tifs.axes
    >>> data = tifs.asarray()
    >>> data.shape
    (1, 2, 64, 64)

    _patterns = {
        'axes': r"""
            # matches Olympus OIF and Leica TIFF series

    class ParseError(Exception):

    def __init__(self, files, imread=TiffFile, pattern='axes',
                 *args, **kwargs):
        """Initialize instance from multiple files.

        files : str, or sequence of str
            Glob pattern or sequence of file names.
            Binary streams are not supported.
        imread : function or class
            Image read function or class with asarray function returning numpy
            array from single file.
        pattern : str
            Regular expression pattern that matches axes names and sequence
            indices in file names.
            By default, this matches Olympus OIF and Leica TIFF series.

        if isinstance(files, basestring):
            files = natural_sorted(glob.glob(files))
        files = list(files)
        if not files:
            raise ValueError("no files found")
        if not isinstance(files[0], basestring):
            raise ValueError("not a file name")
        self.files = files

        if hasattr(imread, 'asarray'):
            # redefine imread
            _imread = imread

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

        self.imread = imread

        self.pattern = self._patterns.get(pattern, pattern)
            if not self.axes:
                self.axes = 'I'
        except self.ParseError:
            self.axes = 'I'
            self.shape = (len(files),)
            self._startindex = (0,)
            self._indices = tuple((i,) for i in range(len(files)))

    def __str__(self):
        """Return string with information about image sequence."""
        return "\n".join([
            ' size: %i' % len(self.files),
            ' axes: %s' % self.axes,
            ' shape: %s' % str(self.shape)])

    def __len__(self):
        return len(self.files)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):

    def close(self):

    def asarray(self, out=None, *args, **kwargs):
        """Read image data from all files and return as numpy array.

        The args and kwargs parameters are passed to the imread function.

        Raise IndexError or ValueError if image shapes do not match.

        im = self.imread(self.files[0], *args, **kwargs)
        shape = self.shape + im.shape
        result = create_output(out, 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._startindex)]
            index = numpy.ravel_multi_index(index, self.shape)
            im = self.imread(fname, *args, **kwargs)
            result[index] = im
        result.shape = shape
        return result

    def _parse(self):
        """Get axes and shape from file names."""
        if not self.pattern:
            raise self.ParseError("invalid pattern")
        pattern = re.compile(self.pattern, re.IGNORECASE | re.VERBOSE)
        matches = pattern.findall(self.files[0])
        if not matches:
            raise self.ParseError("pattern does not match file names")
        matches = matches[-1]
        if len(matches) % 2:
            raise self.ParseError("pattern does not match axis name and index")
        axes = ''.join(m for m in matches[::2] if m)
        if not axes:
            raise self.ParseError("pattern does not match file names")

        indices = []
        for fname in self.files:
            matches = pattern.findall(fname)[-1]
            if axes != ''.join(m for m in matches[::2] if m):
                raise ValueError("axes do not match within the image sequence")
            indices.append([int(m) for m in matches[1::2] if m])
        shape = tuple(numpy.max(indices, axis=0))
        startindex = tuple(numpy.min(indices, axis=0))
        shape = tuple(i-j+1 for i, j in zip(shape, startindex))
        if product(shape) != len(self.files):
            warnings.warn("files are missing. Missing data are zeroed")

        self.axes = axes.upper()
        self.shape = shape
        self._indices = indices
        self._startindex = startindex

class FileHandle(object):
    """Binary file handle.

    A limited, special purpose file handler that can:

    * handle embedded files (for CZI within CZI files)
    * re-open closed files (for multi file formats, such as OME-TIFF)
    * read and write numpy arrays and records from file like objects

    Only 'rb' and 'wb' modes are supported. Concurrently reading and writing
    of the same stream is untested.

    When initialized from another file handle, do not use it unless this
    FileHandle is closed.

    name : str
        Name of the file.
    path : str
        Absolute path to file.
    size : int
        Size of file in bytes.
    is_file : bool
        If True, file has a filno and can be memory-mapped.

    All attributes are read-only.

    __slots__ = ('_fh', '_file', '_mode', '_name', '_dir', '_lock',
                 '_offset', '_size', '_close', 'is_file')

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

        file : str, binary stream, or FileHandle
            File name or seekable binary stream, such as a open file
            or BytesIO.
        mode : str
            File open mode in case 'file' is a file name. Must be 'rb' or 'wb'.
        name : str
            Optional name of file in case 'file' is a binary stream.
        offset : int
            Optional start position of embedded file. By default, this is
            the current file position.
        size : int
            Optional size of embedded file. By default, this is the number
            of bytes from the 'offset' to the end of the file.

        self._fh = None
        self._file = file
        self._mode = mode
        self._name = name
        self._dir = ''
        self._offset = offset
        self._size = size
        self._close = True
        self.is_file = False
        self._lock = NullContext()

    def open(self):
        """Open or re-open file."""
        if self._fh:
            return  # file is open

        if isinstance(self._file, basestring):
            # file name
            self._file = os.path.realpath(self._file)
            self._dir, self._name = os.path.split(self._file)
            self._fh = open(self._file, self._mode)
            self._close = True
            if self._offset is None:
                self._offset = 0
        elif isinstance(self._file, FileHandle):
            # FileHandle
            self._fh = self._file._fh
            if self._offset is None:
                self._offset = 0
            self._offset += self._file._offset
            self._close = False
            if not self._name:
                if self._offset:
                    name, ext = os.path.splitext(self._file._name)
                    self._name = "%s@%i%s" % (name, self._offset, ext)
                    self._name = self._file._name
            if self._mode and self._mode != self._file._mode:
                raise ValueError('FileHandle has wrong mode')
            self._mode = self._file._mode
            self._dir = self._file._dir
        elif hasattr(self._file, 'seek'):
            # binary stream: open file, BytesIO
            except Exception:
                raise ValueError("binary stream is not seekable")
            self._fh = self._file
            if self._offset is None:
                self._offset = self._file.tell()
            self._close = False
            if not self._name:
                    self._dir, self._name = os.path.split(self._fh.name)
                except AttributeError:
                    self._name = "Unnamed binary stream"
                self._mode = self._fh.mode
            except AttributeError:
            raise ValueError("The first parameter must be a file name, "
                             "seekable binary stream, or FileHandle")

        if self._offset:

        if self._size is None:
            pos = self._fh.tell()
            self._fh.seek(self._offset, 2)
            self._size = self._fh.tell()

            self.is_file = True
        except Exception:
            self.is_file = False

    def read(self, size=-1):
        """Read 'size' bytes from file, or until EOF is reached."""
        if size < 0 and self._offset:
            size = self._size
        return self._fh.read(size)

    def write(self, bytestring):
        """Write bytestring to file."""
        return self._fh.write(bytestring)

    def flush(self):
        """Flush write buffers if applicable."""
        return self._fh.flush()

    def memmap_array(self, dtype, shape, offset=0, mode='r', order='C'):
        """Return numpy.memmap of data stored in file."""
        if not self.is_file:
            raise ValueError("Can not memory-map file without fileno")
        return numpy.memmap(self._fh, dtype=dtype, mode=mode,
                            offset=self._offset + offset,
                            shape=shape, order=order)

    def read_array(self, dtype, count=-1, sep="", chunksize=2**25, out=None):
        """Return numpy array from file.

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

        fh = self._fh
        dtype = numpy.dtype(dtype)
        size = self._size if count < 0 else count * dtype.itemsize

        if out is None:
                return numpy.fromfile(fh, dtype, count, sep)
            except IOError:
                # ByteIO
                data = fh.read(size)
                return numpy.fromstring(data, dtype, count, sep)

        # Read data from file in chunks and copy to output array
        shape = out.shape
        size = min(out.nbytes, size)
        out = out.reshape(-1)
        index = 0
        while size > 0:
            data = fh.read(min(chunksize, size))
            datasize = len(data)
            if datasize == 0:
            size -= datasize
            data = numpy.fromstring(data, dtype)
            out[index:index+data.size] = data
            index += data.size

        if hasattr(out, 'flush'):
        return out.reshape(shape)

    def read_record(self, dtype, shape=1, byteorder=None):
        """Return numpy record from file."""
        rec = numpy.rec
            record = rec.fromfile(self._fh, dtype, shape, byteorder=byteorder)
        except Exception:
            dtype = numpy.dtype(dtype)
            if shape is None:
                shape = self._size // dtype.itemsize
            size = product(sequence(shape)) * dtype.itemsize
            data = self._fh.read(size)
            record = rec.fromstring(data, dtype, shape, byteorder=byteorder)
        return record[0] if shape == 1 else record

    def write_empty(self, size):
        """Append size bytes to file. Position must be at end of file."""
        if size < 1:
        self._fh.seek(size-1, 1)

    def write_array(self, data):
        """Write numpy array to binary file."""
        except Exception:
            # BytesIO

    def tell(self):
        """Return file's current position."""
        return self._fh.tell() - self._offset

    def seek(self, offset, whence=0):
        """Set file's current position."""
        if self._offset:
            if whence == 0:
                self._fh.seek(self._offset + offset, whence)
            elif whence == 2 and self._size > 0:
                self._fh.seek(self._offset + self._size + offset, 0)
        self._fh.seek(offset, whence)

    def close(self):
        """Close file."""
        if self._close and self._fh:
            self._fh = None

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):

    def __getattr__(self, name):
        """Return attribute from underlying file object."""
        if self._offset:
                "FileHandle: '%s' not implemented for embedded files" % name)
        return getattr(self._fh, name)

    def name(self):
        return self._name

    def dirname(self):
        return self._dir

    def path(self):
        return os.path.join(self._dir, self._name)

    def size(self):
        return self._size

    def closed(self):
        return self._fh is None

    def lock(self):
        return self._lock

    def lock(self, value):
        self._lock = threading.RLock() if value else NullContext()

class NullContext(object):
    """Null context manager.

    >>> with NullContext():
    ...     pass

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):

class OpenFileCache(object):
    """Keep files open."""

    __slots__ = ('files', 'past', 'lock', 'size')

    def __init__(self, size, lock=None):
        """Initialize open file cache."""
        self.past = []  # FIFO of opened files
        self.files = {}  # refcounts of opened files
        self.lock = NullContext() if lock is None else lock
        self.size = int(size)

    def open(self, filehandle):
        """Re-open file if necessary."""
        with self.lock:
            if filehandle in self.files:
                self.files[filehandle] += 1
            elif filehandle.closed:
                self.files[filehandle] = 1

    def close(self, filehandle):
        """Close openend file if no longer used."""
        with self.lock:
            if filehandle in self.files:
                self.files[filehandle] -= 1
                # trim the file cache
                index = 0
                size = len(self.past)
                while size > self.size and index < size:
                    filehandle = self.past[index]
                    if self.files[filehandle] == 0:
                        del self.files[filehandle]
                        del self.past[index]
                        size -= 1
                        index += 1

    def clear(self):
        """Close all opened files if not in use."""
        with self.lock:
            for filehandle, refcount in list(self.files.items()):
                if refcount == 0:
                    del self.files[filehandle]
                    del self.past[self.past.index(filehandle)]

class LazyConst(object):
    """Class whose attributes are computed on first access from its methods."""
    def __init__(self, cls):
        self._cls = cls
        self.__doc__ = getattr(cls, '__doc__')

    def __getattr__(self, name):
        func = getattr(self._cls, name)
        if not callable(func):
            return func
            value = func()
        except TypeError:
            # Python 2 unbound method
            value = func.__func__()
        setattr(self, name, value)
        return value

class TIFF(object):
    """Namespace for module constants."""

    def TAGS():
        # TIFF tag codes and names
        return {
            254: 'NewSubfileType',
            255: 'SubfileType',
            256: 'ImageWidth',
            257: 'ImageLength',
            258: 'BitsPerSample',
            259: 'Compression',
            262: 'PhotometricInterpretation',
            263: 'Threshholding',
            264: 'CellWidth',
            265: 'CellLength',
            266: 'FillOrder',
            269: 'DocumentName',
            270: 'ImageDescription',
            271: 'Make',
            272: 'Model',
            273: 'StripOffsets',
            274: 'Orientation',
            277: 'SamplesPerPixel',
            278: 'RowsPerStrip',
            279: 'StripByteCounts',
            280: 'MinSampleValue',
            281: 'MaxSampleValue',
            282: 'XResolution',
            283: 'YResolution',
            284: 'PlanarConfiguration',
            285: 'PageName',
            286: 'XPosition',
            287: 'YPosition',
            288: 'FreeOffsets',
            289: 'FreeByteCounts',
            290: 'GrayResponseUnit',
            291: 'GrayResponseCurve',
            292: 'T4Options',
            293: 'T6Options',
            296: 'ResolutionUnit',
            297: 'PageNumber',
            300: 'ColorResponseUnit',
            301: 'TransferFunction',
            305: 'Software',
            306: 'DateTime',
            315: 'Artist',
            316: 'HostComputer',
            317: 'Predictor',
            318: 'WhitePoint',
            319: 'PrimaryChromaticities',
            320: 'ColorMap',
            321: 'HalftoneHints',
            322: 'TileWidth',
            323: 'TileLength',
            324: 'TileOffsets',
            325: 'TileByteCounts',
            326: 'BadFaxLines',
            327: 'CleanFaxData',
            328: 'ConsecutiveBadFaxLines',
            330: 'SubIFDs',
            332: 'InkSet',
            333: 'InkNames',
            334: 'NumberOfInks',
            336: 'DotRange',
            337: 'TargetPrinter',
            338: 'ExtraSamples',
            339: 'SampleFormat',
            340: 'SMinSampleValue',
            341: 'SMaxSampleValue',
            342: 'TransferRange',
            343: 'ClipPath',
            344: 'XClipPathUnits',
            345: 'YClipPathUnits',
            346: 'Indexed',
            347: 'JPEGTables',
            351: 'OPIProxy',
            400: 'GlobalParametersIFD',
            401: 'ProfileType',
            402: 'FaxProfile',
            403: 'CodingMethods',
            404: 'VersionYear',
            405: 'ModeNumber',
            433: 'Decode',
            434: 'DefaultImageColor',
            435: 'T82Options',
            512: 'JPEGProc',
            513: 'JPEGInterchangeFormat',
            514: 'JPEGInterchangeFormatLength',
            515: 'JPEGRestartInterval',
            517: 'JPEGLosslessPredictors',
            518: 'JPEGPointTransforms',
            519: 'JPEGQTables',
            520: 'JPEGDCTables',
            521: 'JPEGACTables',
            529: 'YCbCrCoefficients',
            530: 'YCbCrSubSampling',
            531: 'YCbCrPositioning',
            532: 'ReferenceBlackWhite',
            559: 'StripRowCounts',
            700: 'XMP',
            4864: 'AndorId',  # TODO: Andor Technology 4864 - 5030
            4869: 'AndorTemperature',
            4876: 'AndorExposureTime',
            4878: 'AndorKineticCycleTime',
            4879: 'AndorAccumulations',
            4881: 'AndorAcquisitionCycleTime',
            4882: 'AndorReadoutTime',
            4884: 'AndorPhotonCounting',
            4885: 'AndorEmDacLevel',
            4890: 'AndorFrames',
            4896: 'AndorHorizontalFlip',
            4897: 'AndorVerticalFlip',
            4898: 'AndorClockwise',
            4899: 'AndorCounterClockwise',
            4904: 'AndorVerticalClockVoltage',
            4905: 'AndorVerticalShiftSpeed',
            4907: 'AndorPreAmpSetting',
            4908: 'AndorCameraSerial',
            4911: 'AndorActualTemperature',
            4912: 'AndorBaselineClamp',
            4913: 'AndorPrescans',
            4914: 'AndorModel',
            4915: 'AndorChipSizeX',
            4916: 'AndorChipSizeY',
            4944: 'AndorBaselineOffset',
            4966: 'AndorSoftwareVersion',
            # Private tags
            32781: 'ImageID',
            32932: 'WangAnnotation',
            32995: 'Matteing',
            32996: 'DataType',
            32997: 'ImageDepth',
            32998: 'TileDepth',
            33300: 'ImageFullWidth',
            33301: 'ImageFullLength',
            33302: 'TextureFormat',
            33303: 'TextureWrapModes',
            33304: 'FieldOfViewCotangent',
            33305: 'MatrixWorldToScreen',
            33306: 'MatrixWorldToCamera',
            33421: 'CFARepeatPatternDim',
            33422: 'CFAPattern',
            33432: 'Copyright',
            33445: 'MDFileTag',
            33446: 'MDScalePixel',
            33447: 'MDColorTable',
            33448: 'MDLabName',
            33449: 'MDSampleInfo',
            33450: 'MDPrepDate',
            33451: 'MDPrepTime',
            33452: 'MDFileUnits',
            33550: 'ModelPixelScaleTag',
            33628: 'UIC1tag',  # Metamorph  Universal Imaging Corp STK
            33629: 'UIC2tag',
            33630: 'UIC3tag',
            33631: 'UIC4tag',
            33723: 'IPTC',
            33918: 'INGRPacketDataTag',
            33919: 'INGRFlagRegisters',
            33920: 'IrasBTransformationMatrix',
            33922: 'ModelTiepointTag',
            34118: 'CZ_SEM',  # Zeiss SEM
            34122: 'IPLAB',  # number of images
            34264: 'ModelTransformationTag',
            34361: 'MM_Header',
            34362: 'MM_Stamp',
            34363: 'MM_Unknown',
            34377: 'Photoshop',
            34386: 'MM_UserBlock',
            34412: 'CZ_LSMINFO',
            34665: 'ExifIFD',
            34675: 'ICCProfile',
            34680: 'FEI_SFEG',  #
            34682: 'FEI_HELIOS',  #
            34683: 'FEI_TITAN',  #
            34732: 'ImageLayer',
            34735: 'GeoKeyDirectoryTag',
            34736: 'GeoDoubleParamsTag',
            34737: 'GeoAsciiParamsTag',
            34853: 'GPSIFD',
            34908: 'HylaFAXFaxRecvParams',
            34909: 'HylaFAXFaxSubAddress',
            34910: 'HylaFAXFaxRecvTime',
            34911: 'FaxDcs',
            37439: 'StoNits',
            37679: 'MODI_TXT',  # Microsoft Office Document Imaging
            37681: 'MODI_POS',
            37680: 'MODI_OLE',
            37706: 'TVIPS',  # offset to TemData structure
            37707: 'TVIPS1',
            37708: 'TVIPS2',  # same TemData structure as undefined
            37724: 'ImageSourceData',
            40001: 'MC_IpWinScal',  # Media Cybernetics
            40100: 'MC_IdOld',
            40965: 'InteroperabilityIFD',
            42112: 'GDAL_METADATA',
            42113: 'GDAL_NODATA',
            43314: 'NIHImageHeader',
            50215: 'OceScanjobDescription',
            50216: 'OceApplicationSelector',
            50217: 'OceIdentificationNumber',
            50218: 'OceImageLogicCharacteristics',
            50288: 'MC_Id',  # Media Cybernetics
            50289: 'MC_XYPosition',
            50290: 'MC_ZPosition',
            50291: 'MC_XYCalibration',
            50292: 'MC_LensCharacteristics',
            50293: 'MC_ChannelName',
            50294: 'MC_ExcitationWavelength',
            50295: 'MC_TimeStamp',
            50296: 'MC_FrameProperties',
            50706: 'DNGVersion',
            50707: 'DNGBackwardVersion',
            50708: 'UniqueCameraModel',
            50709: 'LocalizedCameraModel',
            50710: 'CFAPlaneColor',
            50711: 'CFALayout',
            50712: 'LinearizationTable',
            50713: 'BlackLevelRepeatDim',
            50714: 'BlackLevel',
            50715: 'BlackLevelDeltaH',
            50716: 'BlackLevelDeltaV',
            50717: 'WhiteLevel',
            50718: 'DefaultScale',
            50719: 'DefaultCropOrigin',
            50720: 'DefaultCropSize',
            50721: 'ColorMatrix1',
            50722: 'ColorMatrix2',
            50723: 'CameraCalibration1',
            50724: 'CameraCalibration2',
            50725: 'ReductionMatrix1',
            50726: 'ReductionMatrix2',
            50727: 'AnalogBalance',
            50728: 'AsShotNeutral',
            50729: 'AsShotWhiteXY',
            50730: 'BaselineExposure',
            50731: 'BaselineNoise',
            50732: 'BaselineSharpness',
            50733: 'BayerGreenSplit',
            50734: 'LinearResponseLimit',
            50735: 'CameraSerialNumber',
            50736: 'LensInfo',
            50737: 'ChromaBlurRadius',
            50738: 'AntiAliasStrength',
            50739: 'ShadowScale',
            50740: 'DNGPrivateData',
            50741: 'MakerNoteSafety',
            50778: 'CalibrationIlluminant1',
            50779: 'CalibrationIlluminant2',
            50780: 'BestQualityScale',
            50781: 'RawDataUniqueID',
            50784: 'AliasLayerMetadata',
            50827: 'OriginalRawFileName',
            50828: 'OriginalRawFileData',
            50829: 'ActiveArea',
            50830: 'MaskedAreas',
            50831: 'AsShotICCProfile',
            50832: 'AsShotPreProfileMatrix',
            50833: 'CurrentICCProfile',
            50834: 'CurrentPreProfileMatrix',
            50838: 'IJMetadataByteCounts',
            50839: 'IJMetadata',
            51023: 'FibicsXML',  #
            51123: 'MicroManagerMetadata',
            65200: 'FlexXML',  #
            65563: 'PerSample',

    def TAG_NAMES():
        return {v: c for c, v in TIFF.TAGS.items()}

    def TAG_READERS():
        # Map TIFF tag codes to import functions
        return {
            320: read_colormap,
            700: read_bytes,  # read_utf8,
            34377: read_numpy,
            33723: read_bytes,
            34675: read_bytes,
            33628: read_uic1tag,  # Universal Imaging Corp STK
            33629: read_uic2tag,
            33630: read_uic3tag,
            33631: read_uic4tag,
            34118: read_cz_sem,  # Carl Zeiss SEM
            34361: read_mm_header,  # Olympus FluoView
            34362: read_mm_stamp,
            34363: read_numpy,  # MM_Unknown
            34386: read_numpy,  # MM_UserBlock
            34412: read_cz_lsminfo,  # Carl Zeiss LSM
            34680: read_fei_metadata,  # S-FEG
            34682: read_fei_metadata,  # Helios NanoLab
            37706: read_tvips_header,  # TVIPS EMMENU
            43314: read_nih_image_header,
            # 40001: read_bytes,
            40100: read_bytes,
            50288: read_bytes,
            50296: read_bytes,
            50839: read_bytes,
            51123: read_json,
            34665: read_exif_ifd,
            34853: read_gps_ifd,
            40965: read_interoperability_ifd

    def TAG_TUPLE():
        # Tags whose values must be stored as tuples
        return frozenset((273, 279, 324, 325, 530, 531))

        #  Map tag codes to TiffPage attribute names
        return {
            'ImageWidth': 'imagewidth',
            'ImageLength': 'imagelength',
            'BitsPerSample': 'bitspersample',
            'Compression': 'compression',
            'PlanarConfiguration': 'planarconfig',
            'FillOrder': 'fillorder',
            'PhotometricInterpretation': 'photometric',
            'ColorMap': 'colormap',
            'ImageDescription': 'description',
            'ImageDescription1': 'description1',
            'SamplesPerPixel': 'samplesperpixel',
            'RowsPerStrip': 'rowsperstrip',
            'Software': 'software',
            'Predictor': 'predictor',
            'TileWidth': 'tilewidth',
            'TileLength': 'tilelength',
            'ExtraSamples': 'extrasamples',
            'SampleFormat': 'sampleformat',
            'ImageDepth': 'imagedepth',
            'TileDepth': 'tiledepth',

    def TAG_ENUM():
        return {
            # 254: TIFF.FILETYPE,
            255: TIFF.OFILETYPE,
            259: TIFF.COMPRESSION,
            262: TIFF.PHOTOMETRIC,
            263: TIFF.THRESHHOLD,
            266: TIFF.FILLORDER,
            274: TIFF.ORIENTATION,
            284: TIFF.PLANARCONFIG,
            # 292: TIFF.GROUP3OPT,
            # 293: TIFF.GROUP4OPT,
            296: TIFF.RESUNIT,
            317: TIFF.PREDICTOR,
            338: TIFF.EXTRASAMPLE,
            339: TIFF.SAMPLEFORMAT,
            # 512: TIFF.JPEGPROC,
            # 531: TIFF.YCBCRPOSITION,

    def FILETYPE():
        class FILETYPE(enum.IntFlag):
            # Python 3.6 only
            UNDEFINED = 0
            REDUCEDIMAGE = 1
            PAGE = 2
            MASK = 4
        return FILETYPE

    def OFILETYPE():
        class OFILETYPE(enum.IntEnum):
            UNDEFINED = 0
            IMAGE = 1
            REDUCEDIMAGE = 2
            PAGE = 3
        return OFILETYPE

    def COMPRESSION():
        class COMPRESSION(enum.IntEnum):
            NONE = 1  # Uncompressed
            CCITTRLE = 2  # CCITT 1D
            CCITT_T4 = 3  # 'T4/Group 3 Fax',
            CCITT_T6 = 4  # 'T6/Group 4 Fax',
            LZW = 5
            OJPEG = 6  # old-style JPEG
            JPEG = 7
            ADOBE_DEFLATE = 8
            JBIG_BW = 9
            JBIG_COLOR = 10
            JPEG_99 = 99
            KODAK_262 = 262
            NEXT = 32766
            SONY_ARW = 32767
            PACKED_RAW = 32769
            SAMSUNG_SRW = 32770
            CCIRLEW = 32771
            SAMSUNG_SRW2 = 32772
            PACKBITS = 32773
            THUNDERSCAN = 32809
            IT8CTPAD = 32895
            IT8LW = 32896
            IT8MP = 32897
            IT8BL = 32898
            PIXARFILM = 32908
            PIXARLOG = 32909
            DEFLATE = 32946
            DCS = 32947
            APERIO_JP2000_YCBC = 33003  # Leica Aperio
            APERIO_JP2000_RGB = 33005  # Leica Aperio
            JBIG = 34661
            SGILOG = 34676
            SGILOG24 = 34677
            JPEG2000 = 34712
            NIKON_NEF = 34713
            JBIG2 = 34715
            MDI_BINARY = 34718  # 'Microsoft Document Imaging
            MDI_PROGRESSIVE = 34719  # 'Microsoft Document Imaging
            MDI_VECTOR = 34720  # 'Microsoft Document Imaging
            JPEG_LOSSY = 34892
            LZMA = 34925
            OPS_PNG = 34933  # Objective Pathology Services
            OPS_JPEGXR = 34934  # Objective Pathology Services
            KODAK_DCR = 65000
            PENTAX_PEF = 65535
            # def __bool__(self): return self != 1  # Python 3.6 only
        return COMPRESSION

    def PHOTOMETRIC():
        class PHOTOMETRIC(enum.IntEnum):
            MINISWHITE = 0
            MINISBLACK = 1
            RGB = 2
            PALETTE = 3
            MASK = 4
            SEPARATED = 5  # CMYK
            YCBCR = 6
            CIELAB = 8
            ICCLAB = 9
            ITULAB = 10
            CFA = 32803  # Color Filter Array
            LOGL = 32844
            LOGLUV = 32845
            LINEAR_RAW = 34892
        return PHOTOMETRIC

    def THRESHHOLD():
        class THRESHHOLD(enum.IntEnum):
            BILEVEL = 1
            HALFTONE = 2
            ERRORDIFFUSE = 3
        return THRESHHOLD

    def FILLORDER():
        class FILLORDER(enum.IntEnum):
            MSB2LSB = 1
            LSB2MSB = 2
        return FILLORDER

    def ORIENTATION():
        class ORIENTATION(enum.IntEnum):
            TOPLEFT = 1
            TOPRIGHT = 2
            BOTRIGHT = 3
            BOTLEFT = 4
            LEFTTOP = 5
            RIGHTTOP = 6
            RIGHTBOT = 7
            LEFTBOT = 8
        return ORIENTATION

        class PLANARCONFIG(enum.IntEnum):
            CONTIG = 1
            SEPARATE = 2
        return PLANARCONFIG

        class GRAYRESPONSEUNIT(enum.IntEnum):
            _10S = 1
            _100S = 2
            _1000S = 3
            _10000S = 4
            _100000S = 5

    def GROUP4OPT():
        class GROUP4OPT(enum.IntEnum):
            UNCOMPRESSED = 2
        return GROUP4OPT

    def RESUNIT():
        class RESUNIT(enum.IntEnum):
            NONE = 1
            INCH = 2
            CENTIMETER = 3
            # def __bool__(self): return self != 1  # Python 3.6 only
        return RESUNIT

        class COLORRESPONSEUNIT(enum.IntEnum):
            _10S = 1
            _100S = 2
            _1000S = 3
            _10000S = 4
            _100000S = 5

    def PREDICTOR():
        class PREDICTOR(enum.IntEnum):
            NONE = 1
            HORIZONTAL = 2
            FLOATINGPOINT = 3
            # def __bool__(self): return self != 1  # Python 3.6 only
        return PREDICTOR

    def EXTRASAMPLE():
        class EXTRASAMPLE(enum.IntEnum):
            UNSPECIFIED = 0
            ASSOCALPHA = 1
            UNASSALPHA = 2
        return EXTRASAMPLE

        class SAMPLEFORMAT(enum.IntEnum):
            UINT = 1
            INT = 2
            IEEEFP = 3
            VOID = 4
            COMPLEXINT = 5
            COMPLEXIEEEFP = 6
        return SAMPLEFORMAT

    def DATATYPES():
        class DATATYPES(enum.IntEnum):
            NOTYPE = 0
            BYTE = 1
            ASCII = 2
            SHORT = 3
            LONG = 4
            RATIONAL = 5
            SBYTE = 6
            UNDEFINED = 7
            SSHORT = 8
            SLONG = 9
            SRATIONAL = 10
            FLOAT = 11
            DOUBLE = 12
            IFD = 13
            UNICODE = 14
            COMPLEX = 15
            LONG8 = 16
            SLONG8 = 17
            IFD8 = 18
        return DATATYPES

    def DATA_FORMATS():
        # Map TIFF DATATYPES to Python struct formats
        return {
            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: '1p',   # 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)

    def DATA_DTYPES():
        # Map numpy dtypes to TIFF DATATYPES
        return {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6,
                'h': 8, 'i': 9, '2i': 10, 'f': 11, 'd': 12, 'Q': 16, 'q': 17}

    def SAMPLE_DTYPES():
        # Map TIFF SampleFormats and BitsPerSample to numpy dtype
        return {
            (1, 1): '?',  # bitmap
            (1, 2): 'B',
            (1, 3): 'B',
            (1, 4): 'B',
            (1, 5): 'B',
            (1, 6): 'B',
            (1, 7): 'B',
            (1, 8): 'B',
            (1, 9): 'H',
            (1, 10): 'H',
            (1, 11): 'H',
            (1, 12): 'H',
            (1, 13): 'H',
            (1, 14): 'H',
            (1, 15): 'H',
            (1, 16): 'H',
            (1, 17): 'I',
            (1, 18): 'I',
            (1, 19): 'I',
            (1, 20): 'I',
            (1, 21): 'I',
            (1, 22): 'I',
            (1, 23): 'I',
            (1, 24): 'I',
            (1, 25): 'I',
            (1, 26): 'I',
            (1, 27): 'I',
            (1, 28): 'I',
            (1, 29): 'I',
            (1, 30): 'I',
            (1, 31): 'I',
            (1, 32): 'I',
            (1, 64): 'Q',
            (2, 8): 'b',
            (2, 16): 'h',
            (2, 32): 'i',
            (2, 64): 'q',
            (3, 16): 'e',
            (3, 32): 'f',
            (3, 64): 'd',
            (6, 64): 'F',
            (6, 128): 'D',
            (1, (5, 6, 5)): 'B',

        decompressors = {
            None: identityfunc,
            1: identityfunc,
            5: decode_lzw,
            # 7: decode_jpeg,
            8: zlib.decompress,
            32946: zlib.decompress,
            32773: decode_packbits,
        if lzma:
            decompressors[34925] = lzma.decompress
        return decompressors

    def FRAME_ATTRS():
        # Attributes that a TiffFrame shares with its keyframe
        return set('shape ndim size dtype axes is_final'.split())

    def FILE_FLAGS():
        # TiffFile and TiffPage 'is_\*' attributes
        exclude = set('reduced final memmappable contiguous '
        return set(a[3:] for a in dir(TiffPage)
                   if a[:3] == 'is_' and a[3:] not in exclude)

        # TIFF file extensions
        return tuple('tif tiff ome.tif lsm stk '
                     'gel seq svs bif tf8 tf2 btf'.split())

        # String for use in Windows File Open box
        return [("%s files" % ext.upper(), "*.%s" % ext)
                for ext in TIFF.FILE_EXTENSIONS] + [("allfiles", "*")]

    def AXES_LABELS():
        # TODO: is there a standard for character axes labels?
        axes = {
            'X': 'width',
            'Y': 'height',
            'Z': 'depth',
            'S': 'sample',  # rgb(a)
            'I': 'series',  # general sequence, plane, page, IFD
            'T': 'time',
            'C': 'channel',  # color, emission wavelength
            'A': 'angle',
            'P': 'phase',  # formerly F    # P is Position in LSM!
            'R': 'tile',  # region, point, mosaic
            'H': 'lifetime',  # histogram
            'E': 'lambda',  # excitation wavelength
            'L': 'exposure',  # lux
            'V': 'event',
            'Q': 'other',
            'M': 'mosaic',  # LSM 6
        axes.update(dict((v, k) for k, v in axes.items()))
        return axes

    def ANDOR_TAGS():
        # Andor Technology tags #4864 - 5030
        return set(range(4864, 5030))

    def EXIF_TAGS():
        return {
            33434: 'ExposureTime',
            33437: 'FNumber',
            34850: 'ExposureProgram',
            34852: 'SpectralSensitivity',
            34855: 'ISOSpeedRatings',
            34856: 'OECF',
            34858: 'TimeZoneOffset',
            34859: 'SelfTimerMode',
            34864: 'SensitivityType',
            34865: 'StandardOutputSensitivity',
            34866: 'RecommendedExposureIndex',
            34867: 'ISOSpeed',
            34868: 'ISOSpeedLatitudeyyy',
            34869: 'ISOSpeedLatitudezzz',
            36864: 'ExifVersion',
            36867: 'DateTimeOriginal',
            36868: 'DateTimeDigitized',
            36873: 'GooglePlusUploadCode',
            36880: 'OffsetTime',
            36881: 'OffsetTimeOriginal',
            36882: 'OffsetTimeDigitized',
            37121: 'ComponentsConfiguration',
            37122: 'CompressedBitsPerPixel',
            37377: 'ShutterSpeedValue',
            37378: 'ApertureValue',
            37379: 'BrightnessValue',
            37380: 'ExposureBiasValue',
            37381: 'MaxApertureValue',
            37382: 'SubjectDistance',
            37383: 'MeteringMode',
            37384: 'LightSource',
            37385: 'Flash',
            37386: 'FocalLength',
            37393: 'ImageNumber',
            37394: 'SecurityClassification',
            37395: 'ImageHistory',
            37396: 'SubjectArea',
            37500: 'MakerNote',
            37510: 'UserComment',
            37520: 'SubsecTime',
            37521: 'SubsecTimeOriginal',
            37522: 'SubsecTimeDigitized',
            37888: 'Temperature',
            37889: 'Humidity',
            37890: 'Pressure',
            37891: 'WaterDepth',
            37892: 'Acceleration',
            37893: 'CameraElevationAngle',
            40960: 'FlashpixVersion',
            40961: 'ColorSpace',
            40962: 'PixelXDimension',
            40963: 'PixelYDimension',
            40964: 'RelatedSoundFile',
            41483: 'FlashEnergy',
            41484: 'SpatialFrequencyResponse',
            41486: 'FocalPlaneXResolution',
            41487: 'FocalPlaneYResolution',
            41488: 'FocalPlaneResolutionUnit',
            41492: 'SubjectLocation',
            41493: 'ExposureIndex',
            41495: 'SensingMethod',
            41728: 'FileSource',
            41729: 'SceneType',
            41730: 'CFAPattern',
            41985: 'CustomRendered',
            41986: 'ExposureMode',
            41987: 'WhiteBalance',
            41988: 'DigitalZoomRatio',
            41989: 'FocalLengthIn35mmFilm',
            41990: 'SceneCaptureType',
            41991: 'GainControl',
            41992: 'Contrast',
            41993: 'Saturation',
            41994: 'Sharpness',
            41995: 'DeviceSettingDescription',
            41996: 'SubjectDistanceRange',
            42016: 'ImageUniqueID',
            42032: 'CameraOwnerName',
            42033: 'BodySerialNumber',
            42034: 'LensSpecification',
            42035: 'LensMake',
            42036: 'LensModel',
            42037: 'LensSerialNumber',
            42240: 'Gamma',
            59932: 'Padding',
            59933: 'OffsetSchema',
            65000: 'OwnerName',
            65001: 'SerialNumber',
            65002: 'Lens',
            65100: 'RawFile',
            65101: 'Converter',
            65102: 'WhiteBalance',
            65105: 'Exposure',
            65106: 'Shadows',
            65107: 'Brightness',
            65108: 'Contrast',
            65109: 'Saturation',
            65110: 'Sharpness',
            65111: 'Smoothness',
            65112: 'MoireFilter',

    def GPS_TAGS():
        return {
            0: 'GPSVersionID',
            1: 'GPSLatitudeRef',
            2: 'GPSLatitude',
            3: 'GPSLongitudeRef',
            4: 'GPSLongitude',
            5: 'GPSAltitudeRef',
            6: 'GPSAltitude',
            7: 'GPSTimeStamp',
            8: 'GPSSatellites',
            9: 'GPSStatus',
            10: 'GPSMeasureMode',
            11: 'GPSDOP',
            12: 'GPSSpeedRef',
            13: 'GPSSpeed',
            14: 'GPSTrackRef',
            15: 'GPSTrack',
            16: 'GPSImgDirectionRef',
            17: 'GPSImgDirection',
            18: 'GPSMapDatum',
            19: 'GPSDestLatitudeRef',
            20: 'GPSDestLatitude',
            21: 'GPSDestLongitudeRef',
            22: 'GPSDestLongitude',
            23: 'GPSDestBearingRef',
            24: 'GPSDestBearing',
            25: 'GPSDestDistanceRef',
            26: 'GPSDestDistance',
            27: 'GPSProcessingMethod',
            28: 'GPSAreaInformation',
            29: 'GPSDateStamp',
            30: 'GPSDifferential',
            31: 'GPSHPositioningError',

    def IOP_TAGS():
        return {
            1: 'InteroperabilityIndex',
            2: 'InteroperabilityVersion',
            4096: 'RelatedImageFileFormat',
            4097: 'RelatedImageWidth',
            4098: 'RelatedImageLength',

    def CZ_LSMINFO():
        return [
            ('MagicNumber', 'u4'),
            ('StructureSize', 'i4'),
            ('DimensionX', 'i4'),
            ('DimensionY', 'i4'),
            ('DimensionZ', 'i4'),
            ('DimensionChannels', 'i4'),
            ('DimensionTime', 'i4'),
            ('DataType', 'i4'),  # DATATYPES
            ('ThumbnailX', 'i4'),
            ('ThumbnailY', 'i4'),
            ('VoxelSizeX', 'f8'),
            ('VoxelSizeY', 'f8'),
            ('VoxelSizeZ', 'f8'),
            ('OriginX', 'f8'),
            ('OriginY', 'f8'),
            ('OriginZ', 'f8'),
            ('ScanType', 'u2'),
            ('SpectralScan', 'u2'),
            ('TypeOfData', 'u4'),  # TYPEOFDATA
            ('OffsetVectorOverlay', 'u4'),
            ('OffsetInputLut', 'u4'),
            ('OffsetOutputLut', 'u4'),
            ('OffsetChannelColors', 'u4'),
            ('TimeIntervall', 'f8'),
            ('OffsetChannelDataTypes', 'u4'),
            ('OffsetScanInformation', 'u4'),  # SCANINFO
            ('OffsetKsData', 'u4'),
            ('OffsetTimeStamps', 'u4'),
            ('OffsetEventList', 'u4'),
            ('OffsetRoi', 'u4'),
            ('OffsetBleachRoi', 'u4'),
            ('OffsetNextRecording', 'u4'),
            # LSM 2.0 ends here
            ('DisplayAspectX', 'f8'),
            ('DisplayAspectY', 'f8'),
            ('DisplayAspectZ', 'f8'),
            ('DisplayAspectTime', 'f8'),
            ('OffsetMeanOfRoisOverlay', 'u4'),
            ('OffsetTopoIsolineOverlay', 'u4'),
            ('OffsetTopoProfileOverlay', 'u4'),
            ('OffsetLinescanOverlay', 'u4'),
            ('ToolbarFlags', 'u4'),
            ('OffsetChannelWavelength', 'u4'),
            ('OffsetChannelFactors', 'u4'),
            ('ObjectiveSphereCorrection', 'f8'),
            ('OffsetUnmixParameters', 'u4'),
            # LSM 3.2, 4.0 end here
            ('OffsetAcquisitionParameters', 'u4'),
            ('OffsetCharacteristics', 'u4'),
            ('OffsetPalette', 'u4'),
            ('TimeDifferenceX', 'f8'),
            ('TimeDifferenceY', 'f8'),
            ('TimeDifferenceZ', 'f8'),
            ('InternalUse1', 'u4'),
            ('DimensionP', 'i4'),
            ('DimensionM', 'i4'),
            ('DimensionsReserved', '16i4'),
            ('OffsetTilePositions', 'u4'),
            ('', '9u4'),  # Reserved
            ('OffsetPositions', 'u4'),
            # ('', '21u4'),  # must be 0

        # Import functions for CZ_LSMINFO sub-records
        # TODO: read more CZ_LSMINFO sub-records
        return {
            'ScanInformation': read_lsm_scaninfo,
            'TimeStamps': read_lsm_timestamps,
            'EventList': read_lsm_eventlist,
            'ChannelColors': read_lsm_channelcolors,
            'Positions': read_lsm_floatpairs,
            'TilePositions': read_lsm_floatpairs,
            'VectorOverlay': None,
            'InputLut': None,
            'OutputLut': None,
            'TimeIntervall': None,
            'ChannelDataTypes': None,
            'KsData': None,
            'Roi': None,
            'BleachRoi': None,
            'NextRecording': None,
            'MeanOfRoisOverlay': None,
            'TopoIsolineOverlay': None,
            'TopoProfileOverlay': None,
            'ChannelWavelength': None,
            'SphereCorrection': None,
            'ChannelFactors': None,
            'UnmixParameters': None,
            'AcquisitionParameters': None,
            'Characteristics': None,

        # Map CZ_LSMINFO.ScanType to dimension order
        return {
            0: 'XYZCT',  # 'Stack' normal x-y-z-scan
            1: 'XYZCT',  # 'Z-Scan' x-z-plane Y=1
            2: 'XYZCT',  # 'Line'
            3: 'XYTCZ',  # 'Time Series Plane' time series x-y  XYCTZ ? Z=1
            4: 'XYZTC',  # 'Time Series z-Scan' time series x-z
            5: 'XYTCZ',  # 'Time Series Mean-of-ROIs'
            6: 'XYZTC',  # 'Time Series Stack' time series x-y-z
            7: 'XYCTZ',  # Spline Scan
            8: 'XYCZT',  # Spline Plane x-z
            9: 'XYTCZ',  # Time Series Spline Plane x-z
            10: 'XYZCT',  # 'Time Series Point' point mode

        # Map dimension codes to CZ_LSMINFO attribute
        return {
            'X': 'DimensionX',
            'Y': 'DimensionY',
            'Z': 'DimensionZ',
            'C': 'DimensionChannels',
            'T': 'DimensionTime',
            'P': 'DimensionP',
            'M': 'DimensionM',

        # Description of CZ_LSMINFO.DataType
        return {
            0: 'varying data types',
            1: '8 bit unsigned integer',
            2: '12 bit unsigned integer',
            5: '32 bit float',

        # Description of CZ_LSMINFO.TypeOfData
        return {
            0: 'Original scan data',
            1: 'Calculated data',
            2: '3D reconstruction',
            3: 'Topography height map',

        return {
            0x20000000: 'Tracks',
            0x30000000: 'Lasers',
            0x60000000: 'DetectionChannels',
            0x80000000: 'IlluminationChannels',
            0xa0000000: 'BeamSplitters',
            0xc0000000: 'DataChannels',
            0x11000000: 'Timers',
            0x13000000: 'Markers',

        return {
            # 0x10000000: "Recording",
            0x40000000: 'Track',
            0x50000000: 'Laser',
            0x70000000: 'DetectionChannel',
            0x90000000: 'IlluminationChannel',
            0xb0000000: 'BeamSplitter',
            0xd0000000: 'DataChannel',
            0x12000000: 'Timer',
            0x14000000: 'Marker',

        return {
            # Recording
            0x10000001: 'Name',
            0x10000002: 'Description',
            0x10000003: 'Notes',
            0x10000004: 'Objective',
            0x10000005: 'ProcessingSummary',
            0x10000006: 'SpecialScanMode',
            0x10000007: 'ScanType',
            0x10000008: 'ScanMode',
            0x10000009: 'NumberOfStacks',
            0x1000000a: 'LinesPerPlane',
            0x1000000b: 'SamplesPerLine',
            0x1000000c: 'PlanesPerVolume',
            0x1000000d: 'ImagesWidth',
            0x1000000e: 'ImagesHeight',
            0x1000000f: 'ImagesNumberPlanes',
            0x10000010: 'ImagesNumberStacks',
            0x10000011: 'ImagesNumberChannels',
            0x10000012: 'LinscanXySize',
            0x10000013: 'ScanDirection',
            0x10000014: 'TimeSeries',
            0x10000015: 'OriginalScanData',
            0x10000016: 'ZoomX',
            0x10000017: 'ZoomY',
            0x10000018: 'ZoomZ',
            0x10000019: 'Sample0X',
            0x1000001a: 'Sample0Y',
            0x1000001b: 'Sample0Z',
            0x1000001c: 'SampleSpacing',
            0x1000001d: 'LineSpacing',
            0x1000001e: 'PlaneSpacing',
            0x1000001f: 'PlaneWidth',
            0x10000020: 'PlaneHeight',
            0x10000021: 'VolumeDepth',
            0x10000023: 'Nutation',
            0x10000034: 'Rotation',
            0x10000035: 'Precession',
            0x10000036: 'Sample0time',
            0x10000037: 'StartScanTriggerIn',
            0x10000038: 'StartScanTriggerOut',
            0x10000039: 'StartScanEvent',
            0x10000040: 'StartScanTime',
            0x10000041: 'StopScanTriggerIn',
            0x10000042: 'StopScanTriggerOut',
            0x10000043: 'StopScanEvent',
            0x10000044: 'StopScanTime',
            0x10000045: 'UseRois',
            0x10000046: 'UseReducedMemoryRois',
            0x10000047: 'User',
            0x10000048: 'UseBcCorrection',
            0x10000049: 'PositionBcCorrection1',
            0x10000050: 'PositionBcCorrection2',
            0x10000051: 'InterpolationY',
            0x10000052: 'CameraBinning',
            0x10000053: 'CameraSupersampling',
            0x10000054: 'CameraFrameWidth',
            0x10000055: 'CameraFrameHeight',
            0x10000056: 'CameraOffsetX',
            0x10000057: 'CameraOffsetY',
            0x10000059: 'RtBinning',
            0x1000005a: 'RtFrameWidth',
            0x1000005b: 'RtFrameHeight',
            0x1000005c: 'RtRegionWidth',
            0x1000005d: 'RtRegionHeight',
            0x1000005e: 'RtOffsetX',
            0x1000005f: 'RtOffsetY',
            0x10000060: 'RtZoom',
            0x10000061: 'RtLinePeriod',
            0x10000062: 'Prescan',
            0x10000063: 'ScanDirectionZ',
            # Track
            0x40000001: 'MultiplexType',  # 0 After Line; 1 After Frame
            0x40000002: 'MultiplexOrder',
            0x40000003: 'SamplingMode',  # 0 Sample; 1 Line Avg; 2 Frame Avg
            0x40000004: 'SamplingMethod',  # 1 Mean; 2 Sum
            0x40000005: 'SamplingNumber',
            0x40000006: 'Acquire',
            0x40000007: 'SampleObservationTime',
            0x4000000b: 'TimeBetweenStacks',
            0x4000000c: 'Name',
            0x4000000d: 'Collimator1Name',
            0x4000000e: 'Collimator1Position',
            0x4000000f: 'Collimator2Name',
            0x40000010: 'Collimator2Position',
            0x40000011: 'IsBleachTrack',
            0x40000012: 'IsBleachAfterScanNumber',
            0x40000013: 'BleachScanNumber',
            0x40000014: 'TriggerIn',
            0x40000015: 'TriggerOut',
            0x40000016: 'IsRatioTrack',
            0x40000017: 'BleachCount',
            0x40000018: 'SpiCenterWavelength',
            0x40000019: 'PixelTime',
            0x40000021: 'CondensorFrontlens',
            0x40000023: 'FieldStopValue',
            0x40000024: 'IdCondensorAperture',
            0x40000025: 'CondensorAperture',
            0x40000026: 'IdCondensorRevolver',
            0x40000027: 'CondensorFilter',
            0x40000028: 'IdTransmissionFilter1',
            0x40000029: 'IdTransmission1',
            0x40000030: 'IdTransmissionFilter2',
            0x40000031: 'IdTransmission2',
            0x40000032: 'RepeatBleach',
            0x40000033: 'EnableSpotBleachPos',
            0x40000034: 'SpotBleachPosx',
            0x40000035: 'SpotBleachPosy',
            0x40000036: 'SpotBleachPosz',
            0x40000037: 'IdTubelens',
            0x40000038: 'IdTubelensPosition',
            0x40000039: 'TransmittedLight',
            0x4000003a: 'ReflectedLight',
            0x4000003b: 'SimultanGrabAndBleach',
            0x4000003c: 'BleachPixelTime',
            # Laser
            0x50000001: 'Name',
            0x50000002: 'Acquire',
            0x50000003: 'Power',
            # DetectionChannel
            0x70000001: 'IntegrationMode',
            0x70000002: 'SpecialMode',
            0x70000003: 'DetectorGainFirst',
            0x70000004: 'DetectorGainLast',
            0x70000005: 'AmplifierGainFirst',
            0x70000006: 'AmplifierGainLast',
            0x70000007: 'AmplifierOffsFirst',
            0x70000008: 'AmplifierOffsLast',
            0x70000009: 'PinholeDiameter',
            0x7000000a: 'CountingTrigger',
            0x7000000b: 'Acquire',
            0x7000000c: 'PointDetectorName',
            0x7000000d: 'AmplifierName',
            0x7000000e: 'PinholeName',
            0x7000000f: 'FilterSetName',
            0x70000010: 'FilterName',
            0x70000013: 'IntegratorName',
            0x70000014: 'ChannelName',
            0x70000015: 'DetectorGainBc1',
            0x70000016: 'DetectorGainBc2',
            0x70000017: 'AmplifierGainBc1',
            0x70000018: 'AmplifierGainBc2',
            0x70000019: 'AmplifierOffsetBc1',
            0x70000020: 'AmplifierOffsetBc2',
            0x70000021: 'SpectralScanChannels',
            0x70000022: 'SpiWavelengthStart',
            0x70000023: 'SpiWavelengthStop',
            0x70000026: 'DyeName',
            0x70000027: 'DyeFolder',
            # IlluminationChannel
            0x90000001: 'Name',
            0x90000002: 'Power',
            0x90000003: 'Wavelength',
            0x90000004: 'Aquire',
            0x90000005: 'DetchannelName',
            0x90000006: 'PowerBc1',
            0x90000007: 'PowerBc2',
            # BeamSplitter
            0xb0000001: 'FilterSet',
            0xb0000002: 'Filter',
            0xb0000003: 'Name',
            # DataChannel
            0xd0000001: 'Name',
            0xd0000003: 'Acquire',
            0xd0000004: 'Color',
            0xd0000005: 'SampleType',
            0xd0000006: 'BitsPerSample',
            0xd0000007: 'RatioType',
            0xd0000008: 'RatioTrack1',
            0xd0000009: 'RatioTrack2',
            0xd000000a: 'RatioChannel1',
            0xd000000b: 'RatioChannel2',
            0xd000000c: 'RatioConst1',
            0xd000000d: 'RatioConst2',
            0xd000000e: 'RatioConst3',
            0xd000000f: 'RatioConst4',
            0xd0000010: 'RatioConst5',
            0xd0000011: 'RatioConst6',
            0xd0000012: 'RatioFirstImages1',
            0xd0000013: 'RatioFirstImages2',
            0xd0000014: 'DyeName',
            0xd0000015: 'DyeFolder',
            0xd0000016: 'Spectrum',
            0xd0000017: 'Acquire',
            # Timer
            0x12000001: 'Name',
            0x12000002: 'Description',
            0x12000003: 'Interval',
            0x12000004: 'TriggerIn',
            0x12000005: 'TriggerOut',
            0x12000006: 'ActivationTime',
            0x12000007: 'ActivationNumber',
            # Marker
            0x14000001: 'Name',
            0x14000002: 'Description',
            0x14000003: 'TriggerIn',
            0x14000004: 'TriggerOut',

        return [
            ('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'),
            ('Unused2', 'i2'),
            ('Unused3', 'i2'),
            ('UnitsID', 'i2'),  # NIH_UNITS_TYPE
            ('p1', [('x', 'i2'), ('y', 'i2')]),
            ('p2', [('x', 'i2'), ('y', 'i2')]),
            ('CurveFitType', 'i2'),  # NIH_CURVEFIT_TYPE
            ('nCoefficients', 'i2'),
            ('Coeff', 'f8', 6),
            ('UMsize', 'u1'),
            ('UM', 'a15'),
            ('UnusedBoolean', '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'),
            ('Table', 'u1'),  # NIH_COLORTABLE_TYPE
            ('LutMode', 'u1'),  # NIH_LUTMODE_TYPE
            ('InvertedTable', 'b1'),
            ('ZeroClip', 'b1'),
            ('XUnitSize', 'u1'),
            ('XUnit', 'a11'),
            ('StackType', 'i2'),  # NIH_STACKTYPE_TYPE
            # ('UnusedBytes', 'u1', 200)

        return ('CustomTable', 'AppleDefault', 'Pseudo20', 'Pseudo32',
                'Rainbow', 'Fire1', 'Fire2', 'Ice', 'Grays', 'Spectrum')

        return ('PseudoColor', 'OldAppleDefault', 'OldSpectrum', 'GrayScale',
                'ColorLut', 'CustomGrayscale')

        return ('StraightLine', 'Poly2', 'Poly3', 'Poly4', 'Poly5', 'ExpoFit',
                'PowerFit', 'LogFit', 'RodbardFit', 'SpareFit1',
                'Uncalibrated', 'UncalibratedOD')

    def NIH_UNITS_TYPE():
        return ('Nanometers', 'Micrometers', 'Millimeters', 'Centimeters',
                'Meters', 'Kilometers', 'Inches', 'Feet', 'Miles', 'Pixels',

        return ('VolumeStack', 'RGBStack', 'MovieStack', 'HSVStack')

    def TVIPS_HEADER_V1():
        # TVIPS TemData structure from EMMENU Help file
        return [
            ('Version', 'i4'),
            ('CommentV1', 'a80'),
            ('HighTension', 'i4'),
            ('SphericalAberration', 'i4'),
            ('IlluminationAperture', 'i4'),
            ('Magnification', 'i4'),
            ('PostMagnification', 'i4'),
            ('FocalLength', 'i4'),
            ('Defocus', 'i4'),
            ('Astigmatism', 'i4'),
            ('AstigmatismDirection', 'i4'),
            ('BiprismVoltage', 'i4'),
            ('SpecimenTiltAngle', 'i4'),
            ('SpecimenTiltDirection', 'i4'),
            ('IlluminationTiltDirection', 'i4'),
            ('IlluminationTiltAngle', 'i4'),
            ('ImageMode', 'i4'),
            ('EnergySpread', 'i4'),
            ('ChromaticAberration', 'i4'),
            ('ShutterType', 'i4'),
            ('DefocusSpread', 'i4'),
            ('CcdNumber', 'i4'),
            ('CcdSize', 'i4'),
            ('OffsetXV1', 'i4'),
            ('OffsetYV1', 'i4'),
            ('PhysicalPixelSize', 'i4'),
            ('Binning', 'i4'),
            ('ReadoutSpeed', 'i4'),
            ('GainV1', 'i4'),
            ('SensitivityV1', 'i4'),
            ('ExposureTimeV1', 'i4'),
            ('FlatCorrected', 'i4'),
            ('DeadPxCorrected', 'i4'),
            ('ImageMean', 'i4'),
            ('ImageStd', 'i4'),
            ('DisplacementX', 'i4'),
            ('DisplacementY', 'i4'),
            ('DateV1', 'i4'),
            ('TimeV1', 'i4'),
            ('ImageMin', 'i4'),
            ('ImageMax', 'i4'),
            ('ImageStatisticsQuality', 'i4'),

    def TVIPS_HEADER_V2():
        return [
            ('ImageName', 'V160'),  # utf16
            ('ImageFolder', 'V160'),
            ('ImageSizeX', 'i4'),
            ('ImageSizeY', 'i4'),
            ('ImageSizeZ', 'i4'),
            ('ImageSizeE', 'i4'),
            ('ImageDataType', 'i4'),
            ('Date', 'i4'),
            ('Time', 'i4'),
            ('Comment', 'V1024'),
            ('ImageHistory', 'V1024'),
            ('Scaling', '16f4'),
            ('ImageStatistics', '16c16'),
            ('ImageType', 'i4'),
            ('ImageDisplaType', 'i4'),
            ('PixelSizeX', 'f4'),  # distance between two px in x, [nm]
            ('PixelSizeY', 'f4'),  # distance between two px in y, [nm]
            ('ImageDistanceZ', 'f4'),
            ('ImageDistanceE', 'f4'),
            ('ImageMisc', '32f4'),
            ('TemType', 'V160'),
            ('TemHighTension', 'f4'),
            ('TemAberrations', '32f4'),
            ('TemEnergy', '32f4'),
            ('TemMode', 'i4'),
            ('TemMagnification', 'f4'),
            ('TemMagnificationCorrection', 'f4'),
            ('PostMagnification', 'f4'),
            ('TemStageType', 'i4'),
            ('TemStagePosition', '5f4'),  # x, y, z, a, b
            ('TemImageShift', '2f4'),
            ('TemBeamShift', '2f4'),
            ('TemBeamTilt', '2f4'),
            ('TilingParameters', '7f4'),  # 0: tiling? 1:x 2:y 3: max x
                                          # 4: max y 5: overlap x 6: overlap y
            ('TemIllumination', '3f4'),  # 0: spotsize 1: intensity
            ('TemShutter', 'i4'),
            ('TemMisc', '32f4'),
            ('CameraType', 'V160'),
            ('PhysicalPixelSizeX', 'f4'),
            ('PhysicalPixelSizeY', 'f4'),
            ('OffsetX', 'i4'),
            ('OffsetY', 'i4'),
            ('BinningX', 'i4'),
            ('BinningY', 'i4'),
            ('ExposureTime', 'f4'),
            ('Gain', 'f4'),
            ('ReadoutRate', 'f4'),
            ('FlatfieldDescription', 'V160'),
            ('Sensitivity', 'f4'),
            ('Dose', 'f4'),
            ('CamMisc', '32f4'),
            ('FeiMicroscopeInformation', 'V1024'),
            ('FeiSpecimenInformation', 'V1024'),
            ('Magic', 'u4'),

    def MM_HEADER():
        # Olympus FluoView MM_Header
        MM_DIMENSION = [
            ('Name', 'a16'),
            ('Size', 'i4'),
            ('Origin', 'f8'),
            ('Resolution', 'f8'),
            ('Unit', 'a64')]
        return [
            ('HeaderFlag', 'i2'),
            ('ImageType', 'u1'),
            ('ImageName', 'a257'),
            ('OffsetData', 'u4'),
            ('PaletteSize', 'i4'),
            ('OffsetPalette0', 'u4'),
            ('OffsetPalette1', 'u4'),
            ('CommentSize', 'i4'),
            ('OffsetComment', 'u4'),
            ('Dimensions', MM_DIMENSION, 10),
            ('OffsetPosition', 'u4'),
            ('MapType', 'i2'),
            ('MapMin', 'f8'),
            ('MapMax', 'f8'),
            ('MinValue', 'f8'),
            ('MaxValue', 'f8'),
            ('OffsetMap', 'u4'),
            ('Gamma', 'f8'),
            ('Offset', 'f8'),
            ('GrayChannel', MM_DIMENSION),
            ('OffsetThumbnail', 'u4'),
            ('VoiceField', 'i4'),
            ('OffsetVoiceField', 'u4'),

    def MM_DIMENSIONS():
        # Map FluoView MM_Header.Dimensions to axes characters
        return {
            'X': 'X',
            'Y': 'Y',
            'Z': 'Z',
            'T': 'T',
            'CH': 'C',
            'WAVELENGTH': 'C',
            'TIME': 'T',
            'XY': 'R',
            'EVENT': 'V',
            'EXPOSURE': 'L',

    def UIC_TAGS():
        # Map Universal Imaging Corporation MetaMorph internal tag ids to
        # name and type
        from fractions import Fraction

        return [
            ('AutoScale', int),
            ('MinScale', int),