tifffile package

Submodules

tifffile.lsm2bin module

Convert TZCYX LSM file to series of BIN files.

Usage: lsm2bin lsm_filename [bin_filename]

tifffile.lsm2bin.main(argv=None)

Lsm2bin command line usage main function.

tifffile.tifffile module

Read and write TIFF(r) files.

Tifffile is a Python library to

  1. store numpy arrays in TIFF (Tagged Image File Format) files, and

  2. read image and metadata from TIFF-like files used in bioimaging.

Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, SGI, NIHImage, ImageJ, MicroManager, FluoView, ScanImage, SEQ, GEL, SVS, SCN, SIS, ZIF, QPI, NDPI, and GeoTIFF files.

Numpy arrays can be written to TIFF, BigTIFF, and ImageJ hyperstack compatible files in multi-page, memory-mappable, tiled, predicted, or compressed form.

Only a subset of the TIFF specification is supported, mainly uncompressed and losslessly compressed 1, 8, 16, 32 and 64-bit integer, 16, 32 and 64-bit float, grayscale and RGB(A) images. Specifically, reading slices of image data, CCITT and OJPEG compression, chroma subsampling without JPEG compression, or IPTC and XMP metadata are not implemented.

TIFF(r), 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 respectively.

For command line usage run python -m tifffile --help

Author

Christoph Gohlke

Organization

Laboratory for Fluorescence Dynamics, University of California, Irvine

License

3-clause BSD

Version

2019.7.26

Requirements

This release has been tested with the following requirements and dependencies (other versions may work):

Revisions

2019.7.26

Pass 2869 tests. Fix infinite loop reading more than two tags of same code in IFD. Delay import of logging module.

2019.7.20

Fix OME-XML detection for files created by Imaris. Remove or replace assert statements.

2019.7.2

Do not write SampleFormat tag for unsigned data types. Write ByteCount tag values as SHORT or LONG if possible. Allow to specify axes in FileSequence pattern via group names. Add option to concurrently read FileSequence using threads. Derive TiffSequence from FileSequence. Use str(datetime.timedelta) to format Timer duration. Use perf_counter for Timer if possible.

2019.6.18

Fix reading planar RGB ImageJ files created by Bio-Formats. Fix reading single-file, multi-image OME-TIFF without UUID. Presume LSM stores uncompressed images contiguously per page. Reformat some complex expressions.

2019.5.30

Ignore invalid frames in OME-TIFF. Set default subsampling to (2, 2) for RGB JPEG compression. Fix reading and writing planar RGB JPEG compression. Replace buffered_read with FileHandle.read_segments. Include page or frame numbers in exceptions and warnings. Add Timer class.

2019.5.22

Add optional chroma subsampling for JPEG compression. Enable writing PNG, JPEG, JPEGXR, and JPEG2000 compression (WIP). Fix writing tiled images with WebP compression. Improve handling GeoTIFF sparse files.

2019.3.18

Fix regression decoding JPEG with RGB photometrics. Fix reading OME-TIFF files with corrupted but unused pages. Allow to load TiffFrame without specifying keyframe. Calculate virtual TiffFrames for non-BigTIFF ScanImage files > 2GB. Rename property is_chroma_subsampled to is_subsampled (breaking). Make more attributes and methods private (WIP).

2019.3.8

Fix MemoryError when RowsPerStrip > ImageLength. Fix SyntaxWarning on Python 3.8. Fail to decode JPEG to planar RGB (tentative). Separate public from private test files (WIP). Allow testing without data files or imagecodecs.

2019.2.22

Use imagecodecs-lite as a fallback for imagecodecs. Simplify reading numpy arrays from file. Use TiffFrames when reading arrays from page sequences. Support slices and iterators in TiffPageSeries sequence interface. Auto-detect uniform series. Use page hash to determine generic series. Turn off page cache (tentative). Pass through more parameters in imread. Discontinue movie parameter in imread and TiffFile (breaking). Discontinue bigsize parameter in imwrite (breaking). Raise TiffFileError in case of issues with TIFF structure. Return TiffFile.ome_metadata as XML (breaking). Ignore OME series when last dimensions are not stored in TIFF pages.

2019.2.10

Assemble IFDs in memory to speed-up writing on some slow media. Handle discontinued arguments fastij, multifile_close, and pages.

2019.1.30

Use black background in imshow. Do not write datetime tag by default (breaking). Fix OME-TIFF with SamplesPerPixel > 1. Allow 64-bit IFD offsets for NDPI (files > 4GB still not supported).

2019.1.4

Fix decoding deflate without imagecodecs.

2019.1.1

Update copyright year. Require imagecodecs >= 2018.12.16. Do not use JPEG tables from keyframe. Enable decoding large JPEG in NDPI. Decode some old-style JPEG. Reorder OME channel axis to match PlanarConfiguration storage. Return tiled images as contiguous arrays. Add decode_lzw proxy function for compatibility with old czifile module. Use dedicated logger.

2018.11.28

Make SubIFDs accessible as TiffPage.pages. Make parsing of TiffSequence axes pattern optional (breaking). Limit parsing of TiffSequence axes pattern to file names, not path names. Do not interpolate in imshow if image dimensions <= 512, else use bilinear. Use logging.warning instead of warnings.warn in many cases. Fix numpy FutureWarning for out == ‘memmap’. Adjust ZSTD and WebP compression to libtiff-4.0.10 (WIP). Decode old-style LZW with imagecodecs >= 2018.11.8. Remove TiffFile.qptiff_metadata (QPI metadata are per page). Do not use keyword arguments before variable positional arguments. Make either all or none return statements in a function return expression. Use pytest parametrize to generate tests. Replace test classes with functions.

2018.11.6

Rename imsave function to imwrite. Readd Python implementations of packints, delta, and bitorder codecs. Fix TiffFrame.compression AttributeError.

2018.10.18

Rename tiffile package to tifffile.

2018.10.10

Read ZIF, the Zoomable Image Format (WIP). Decode YCbCr JPEG as RGB (tentative). Improve restoration of incomplete tiles. Allow to write grayscale with extrasamples without specifying planarconfig. Enable decoding of PNG and JXR via imagecodecs. Deprecate 32-bit platforms (too many memory errors during tests).

2018.9.27

Read Olympus SIS (WIP). Allow to write non-BigTIFF files up to ~4 GB (fix). Fix parsing date and time fields in SEM metadata. Detect some circular IFD references. Enable WebP codecs via imagecodecs. Add option to read TiffSequence from ZIP containers. Remove TiffFile.isnative. Move TIFF struct format constants out of TiffFile namespace.

2018.8.31

Fix wrong TiffTag.valueoffset. Towards reading Hamamatsu NDPI (WIP). Enable PackBits compression of byte and bool arrays. Fix parsing NULL terminated CZ_SEM strings.

2018.8.24

Move tifffile.py and related modules into tiffile package. Move usage examples to module docstring. Enable multi-threading for compressed tiles and pages by default. Add option to concurrently decode image tiles using threads. Do not skip empty tiles (fix). Read JPEG and J2K compressed strips and tiles. Allow floating-point predictor on write. Add option to specify subfiletype on write. Depend on imagecodecs package instead of _tifffile, lzma, etc modules. Remove reverse_bitorder, unpack_ints, and decode functions. Use pytest instead of unittest.

2018.6.20

Save RGBA with unassociated extrasample by default (breaking). Add option to specify ExtraSamples values.

2018.6.17 (included with 0.15.1)

Towards reading JPEG and other compressions via imagecodecs package (WIP). Read SampleFormat VOID as UINT. Add function to validate TIFF using ‘jhove -m TIFF-hul’. Save bool arrays as bilevel TIFF. Accept pathlib.Path as filenames. Move ‘software’ argument from TiffWriter __init__ to save. Raise DOS limit to 16 TB. Lazy load LZMA and ZSTD compressors and decompressors. Add option to save IJMetadata tags. Return correct number of pages for truncated series (fix). Move EXIF tags to TIFF.TAG as per TIFF/EP standard.

2018.2.18

Always save RowsPerStrip and Resolution tags as required by TIFF standard. Do not use badly typed ImageDescription. Coherce bad ASCII string tags to bytes. Tuning of __str__ functions. Fix reading ‘undefined’ tag values. Read and write ZSTD compressed data. Use hexdump to print byte strings. Determine TIFF byte order from data dtype in imsave. Add option to specify RowsPerStrip for compressed strips. Allow memory-map of arrays with non-native byte order. Attempt to handle ScanImage <= 5.1 files. Restore TiffPageSeries.pages sequence interface. Use numpy.frombuffer instead of fromstring to read from binary data. Parse GeoTIFF metadata. Add option to apply horizontal differencing before compression. Towards reading PerkinElmer QPI (QPTIFF, no test files). Do not index out of bounds data in tifffile.c unpackbits and decodelzw.

2017.9.29

Many backward incompatible changes improving speed and resource usage: Add detail argument to __str__ function. Remove info functions. Fix potential issue correcting offsets of large LSM files with positions. Remove TiffFile sequence 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 pages 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. Do not modify input array in imshow (fix). Fix Python implementation of unpack_ints.

2017.5.23

Write correct number of SampleFormat values (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.

2017.3.17

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.

2017.1.12 (included with scikit-image 0.14.x)

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.

2017.1.1

…

Refer to the CHANGES file for older revisions.

Notes

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

Tested on little-endian platforms only.

Python 2.7 and 32-bit versions are deprecated.

Tifffile relies on the imagecodecs package for encoding and decoding LZW, JPEG, and other compressed images. The imagecodecs-lite package, which is easier to build, can be used for decoding LZW compressed images instead.

Several TIFF-like formats do not strictly adhere to the TIFF6 specification, some of which allow file or data sizes to exceed the 4 GB limit:

  • BigTIFF is identified by version number 43 and uses different file header, IFD, and tag structures with 64-bit offsets. It adds more data types. Tifffile can read and write BigTIFF files.

  • ImageJ hyperstacks store all image data, which may exceed 4 GB, contiguously after the first IFD. Files > 4 GB contain one IFD only. The size (shape and dtype) of the up to 6-dimensional image data can be determined from the ImageDescription tag of the first IFD, which is Latin-1 encoded. Tifffile can read and write ImageJ hyperstacks.

  • OME-TIFF stores up to 8-dimensional data in one or multiple TIFF of BigTIFF files. The 8-bit UTF-8 encoded OME-XML metadata found in the ImageDescription tag of the first IFD defines the position of TIFF IFDs in the high dimensional data. Tifffile can read OME-TIFF files, except when the OME-XML metadata is stored in a separate file.

  • LSM stores all IFDs below 4 GB but wraps around 32-bit StripOffsets. The StripOffsets of each series and position require separate unwrapping. The StripByteCounts tag contains the number of bytes for the uncompressed data. Tifffile can read large LSM files.

  • NDPI uses some 64-bit offsets in the file header, IFD, and tag structures and might require correcting 32-bit offsets found in tags. JPEG compressed tiles with dimensions > 65536 are not readable with libjpeg. Tifffile can read NDPI files < 4 GB and decompress large JPEG tiles using the imagecodecs library on Windows.

  • ScanImage optionally allows corrupt non-BigTIFF files > 2 GB. The values of StripOffsets and StripByteCounts can be recovered using the constant differences of the offsets of IFD and tag values throughout the file. Tifffile can read such files on Python 3 if the image data is stored contiguously in each page.

  • GeoTIFF sparse files allow strip or tile offsets and byte counts to be 0. Such segments are implicitly set to 0 or the NODATA value on reading. Tifffile can read GeoTIFF sparse files.

Other libraries for reading scientific TIFF files from Python:

Some libraries are using tifffile to write OME-TIFF files:

References

  1. TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated. https://www.adobe.io/open/standards/TIFF.html

  2. TIFF File Format FAQ. https://www.awaresystems.be/imaging/tiff/faq.html

  3. MetaMorph Stack (STK) Image File Format. http://mdc.custhelp.com/app/answers/detail/a_id/18862

  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. https://docs.openmicroscopy.org/ome-model/5.6.4/ome-tiff/

  6. UltraQuant(r) Version 6.0 for Windows Start-Up Guide. http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf

  7. Micro-Manager File Formats. https://micro-manager.org/wiki/Micro-Manager_File_Formats

  8. Tags for TIFF and Related Specifications. Digital Preservation. https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml

  9. ScanImage BigTiff Specification - ScanImage 2016. http://scanimage.vidriotechnologies.com/display/SI2016/ ScanImage+BigTiff+Specification

  10. CIPA DC-008-2016: Exchangeable image file format for digital still cameras: Exif Version 2.31. http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf

  11. ZIF, the Zoomable Image File format. http://zif.photo/

  12. GeoTIFF File Format https://www.gdal.org/frmt_gtiff.html

Examples

Save a 3D numpy array to a multi-page, 16-bit grayscale TIFF file:

>>> data = numpy.random.randint(0, 2**12, (4, 301, 219), 'uint16')
>>> imwrite('temp.tif', data, photometric='minisblack')

Read the whole image stack from the TIFF file as numpy array:

>>> image_stack = imread('temp.tif')
>>> image_stack.shape
(4, 301, 219)
>>> image_stack.dtype
dtype('uint16')

Read the image from first page in the TIFF file as numpy array:

>>> image = imread('temp.tif', key=0)
>>> image.shape
(301, 219)

Read images from a sequence of TIFF files as numpy array:

>>> image_sequence = imread(['temp.tif', 'temp.tif'])
>>> image_sequence.shape
(2, 4, 301, 219)

Save a numpy array to a single-page RGB TIFF file:

>>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8')
>>> imwrite('temp.tif', data, photometric='rgb')

Save a floating-point array and metadata, using zlib compression:

>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
>>> imwrite('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})

Save a volume with xyz voxel size 2.6755x2.6755x3.9474 碌m^3 to an ImageJ file:

>>> volume = numpy.random.randn(57*256*256).astype('float32')
>>> volume.shape = 1, 57, 1, 256, 256, 1  # dimensions in TZCYXS order
>>> imwrite('temp.tif', volume, imagej=True, resolution=(1./2.6755, 1./2.6755),
...         metadata={'spacing': 3.947368, 'unit': 'um'})

Get the shape and dtype of the images stored in the TIFF file:

>>> tif = TiffFile('temp.tif')
>>> len(tif.pages)  # number of pages in the file
57
>>> page = tif.pages[0]  # get shape and dtype of the image in the first page
>>> page.shape
(256, 256)
>>> page.dtype
dtype('float32')
>>> page.axes
'YX'
>>> series = tif.series[0]  # get shape and dtype of the first image series
>>> series.shape
(57, 256, 256)
>>> series.dtype
dtype('float32')
>>> series.axes
'ZYX'
>>> tif.close()

Read hyperstack and metadata from the ImageJ file:

>>> with TiffFile('temp.tif') as tif:
...     imagej_hyperstack = tif.asarray()
...     imagej_metadata = tif.imagej_metadata
>>> imagej_hyperstack.shape
(57, 256, 256)
>>> imagej_metadata['slices']
57

Read the “XResolution” tag from the first page in the TIFF file:

>>> with TiffFile('temp.tif') as tif:
...     tag = tif.pages[0].tags['XResolution']
>>> tag.value
(2000, 5351)
>>> tag.name
'XResolution'
>>> tag.code
282
>>> tag.count
1
>>> tag.dtype
'2I'
>>> tag.valueoffset
360

Read images from a selected range of pages:

>>> image = imread('temp.tif', key=range(4, 40, 2))
>>> image.shape
(18, 256, 256)

Create an empty TIFF file and write to the memory-mapped numpy array:

>>> memmap_image = memmap('temp.tif', shape=(256, 256), dtype='float32')
>>> memmap_image[255, 255] = 1.0
>>> memmap_image.flush()
>>> memmap_image.shape, memmap_image.dtype
((256, 256), dtype('float32'))
>>> del memmap_image

Memory-map image data of the first page in the TIFF file:

>>> memmap_image = memmap('temp.tif', page=0)
>>> memmap_image[255, 255]
1.0
>>> del memmap_image

Successively append images to a BigTIFF file, which can exceed 4 GB:

>>> data = numpy.random.randint(0, 255, (5, 2, 3, 301, 219), 'uint8')
>>> with TiffWriter('temp.tif', bigtiff=True) as tif:
...     for i in range(data.shape[0]):
...         tif.save(data[i], compress=6, photometric='minisblack')

Iterate over pages and tags in the TIFF file and successively read images:

>>> with TiffFile('temp.tif') as tif:
...     image_stack = tif.asarray()
...     for page in tif.pages:
...         for tag in page.tags.values():
...             tag_name, tag_value = tag.name, tag.value
...         image = page.asarray()

Save two image series to a TIFF file:

>>> data0 = numpy.random.randint(0, 255, (301, 219, 3), 'uint8')
>>> data1 = numpy.random.randint(0, 255, (5, 301, 219), 'uint16')
>>> with TiffWriter('temp.tif') as tif:
...     tif.save(data0, compress=6, photometric='rgb')
...     tif.save(data1, compress=6, photometric='minisblack', contiguous=False)

Read the second image series from the TIFF file:

>>> series1 = imread('temp.tif', series=1)
>>> series1.shape
(5, 301, 219)

Read an image stack from a series of TIFF files with a file name pattern:

>>> imwrite('temp_C001T001.tif', numpy.random.rand(64, 64))
>>> imwrite('temp_C001T002.tif', numpy.random.rand(64, 64))
>>> image_sequence = TiffSequence('temp_C001*.tif', pattern='axes')
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
'CT'
>>> data = image_sequence.asarray()
>>> data.shape
(1, 2, 64, 64)
tifffile.tifffile.imwrite(file, data=None, shape=None, dtype=None, **kwargs)

Write numpy array to TIFF file.

Refer to the TiffWriter class and its asarray function for documentation.

A BigTIFF file is created if the data size in bytes is larger than 4 GB minus 32 MB (for metadata), and ‘bigtiff’ is not specified, and ‘imagej’ or ‘truncate’ are not enabled.

Parameters

  • file (str or binary stream) – File name or writable binary stream, such as an open file or BytesIO.

  • data (array_like) – Input image. The last dimensions are assumed to be image depth, height, width, and samples. If None, an empty array of the specified shape and dtype is saved to file. Unless ‘byteorder’ is specified in ‘kwargs’, the TIFF file byte order is determined from the data’s dtype or the dtype argument.

  • shape (tuple) – If ‘data’ is None, shape of an empty array to save to the file.

  • dtype (numpy.dtype) – If ‘data’ is None, datatype of an empty array to save to the file.

  • kwargs (dict) – Parameters ‘append’, ‘byteorder’, ‘bigtiff’, and ‘imagej’, are passed to the TiffWriter constructor. Other parameters are passed to the TiffWriter.save function.

Returns

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

Return type

tuple or None

tifffile.tifffile.imread(files, **kwargs)

Return image data from TIFF file(s) as numpy array.

Refer to the TiffFile and TiffSequence classes and their asarray functions for documentation.

Parameters

  • files (str, binary stream, or sequence) – File name, seekable binary stream, glob pattern, or sequence of file names.

  • kwargs (dict) – Parameters ‘name’, ‘offset’, ‘size’, ‘multifile’, and ‘is_ome’ are passed to the TiffFile constructor. The ‘pattern’ and ‘ioworkers’ parameters are passed to the TiffSequence constructor. Other parameters are passed to the asarray functions. The first image series in the file is returned if no arguments are provided.

tifffile.tifffile.imshow(data, photometric=None, planarconfig=None, bitspersample=None, interpolation=None, cmap=None, vmin=None, vmax=None, figure=None, title=None, dpi=96, subplot=None, maxdim=None, **kwargs)

Plot n-dimensional images using matplotlib.pyplot.

Return figure, subplot and plot axis. Requires pyplot already imported C{from matplotlib import pyplot}.

Parameters

  • data (nd array) – The image data.

  • photometric ({'MINISWHITE', 'MINISBLACK', 'RGB', or 'PALETTE'}) – The color space of the image data.

  • planarconfig ({'CONTIG' or 'SEPARATE'}) – Defines how components of each pixel are stored.

  • bitspersample (int) – Number of bits per channel in integer RGB images.

  • interpolation (str) – The image interpolation method used in matplotlib.imshow. By default, ‘nearest’ will be used for image dimensions <= 512, else ‘bilinear’.

  • cmap (str or matplotlib.colors.Colormap) – The colormap maps non-RGBA scalar data to colors.

  • vmax (vmin,) – Data range covered by the colormap. By default, the complete range of the data is covered.

  • figure (matplotlib.figure.Figure) – Matplotlib figure to use for plotting.

  • title (str) – Window and subplot title.

  • subplot (int) – A matplotlib.pyplot.subplot axis.

  • maxdim (int) – Maximum image width and length.

  • kwargs (dict) – Additional arguments for matplotlib.pyplot.imshow.

tifffile.tifffile.memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+', **kwargs)

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.

Parameters

  • filename (str) – Name of the TIFF file which stores the array.

  • shape (tuple) – Shape of the empty array.

  • dtype (numpy.dtype) – Datatype 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'}) – The file open mode. Default is to open existing file for reading and writing (‘r+’).

  • kwargs (dict) – Additional parameters passed to imwrite() or TiffFile().

tifffile.tifffile.lsm2bin(lsmfile, binfile=None, tile=None, verbose=True)

Convert [MP]TZCYX LSM file to series of BIN files.

One BIN file containing ‘ZCYX’ data are created for each position, time, and tile. The position, time, and tile indices are encoded at the end of the filenames.

class tifffile.tifffile.TiffFile(arg, name=None, offset=None, size=None, multifile=True, _useframes=None, **kwargs)

Bases: 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.

TiffFile instances are not thread-safe.

pages

Sequence of TIFF pages in file.

Type

TiffPages

series

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, and compression.

Type

list of TiffPageSeries

is_flag

If True, file is of a certain format. Flags are: bigtiff, uniform, shaped, ome, imagej, stk, lsm, fluoview, nih, vista, micromanager, metaseries, mdgel, mediacy, tvips, fei, sem, scn, svs, scanimage, andor, epics, ndpi, pilatus, qpi.

Type

bool

All attributes are read-only.
andor_metadata

Return Andor tags as dict.

asarray(key=None, series=None, out=None, validate=True, maxworkers=None)

Return image data from selected TIFF page(s) as numpy array.

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

Parameters

  • key (int, slice, or sequence of indices) – Defines which pages to return as array. If None (default), data from a series (default 0) is returned. If not None, data from the specified pages in the whole file (if ‘series’ is None) or a specified series are returned as a stacked array. Requesting an array from multiple pages that are not compatible wrt. shape, dtype, compression etc is undefined, i.e. may crash or return incorrect values.

  • series (int or TiffPageSeries) – Defines which series of pages to return as array.

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, directly memory-map the image data in the TIFF file if possible; else create a memory-mapped array in a temporary file. 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.

  • validate (bool) – If True (default), validate various tags. Passed to TiffPage.asarray().

  • maxworkers (int or None) – Maximum number of threads to concurrently get data from multiple pages or compressed segments. If None (default), up to half the CPU cores are used. If 1, multi-threading is disabled. 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, e.g. in case of large LZW compressed LSM files or JPEG compressed tiled slides. If the bottleneck is I/O or pure Python code, using multiple threads might be detrimental.

Returns

Image data from the specified pages. See the TiffPage.asarray function for operations that are applied (or not) to the raw data stored in the file.

Return type

numpy.ndarray

byteorder
close()

Close open file handle(s).

epics_metadata

Return EPICS areaDetector tags as dict.

fei_metadata

Attribute whose value is computed on first access.

filehandle

Return file handle.

filename

Return name of file handle.

flags

Attribute whose value is computed on first access.

fluoview_metadata

Attribute whose value is computed on first access.

fstat

Attribute whose value is computed on first access.

geotiff_metadata

Return GeoTIFF metadata from first page as dict.

imagej_metadata

Attribute whose value is computed on first access.

is_appendable

Return if pages can be appended to file without corrupting.

is_bigtiff
is_mdgel

Attribute whose value is computed on first access.

is_uniform

Attribute whose value is computed on first access.

lsm_metadata

Return LSM metadata from CZ_LSMINFO tag as dict.

mdgel_metadata

Attribute whose value is computed on first access.

metaseries_metadata

Attribute whose value is computed on first access.

micromanager_metadata

Attribute whose value is computed on first access.

nih_metadata

Attribute whose value is computed on first access.

ome_metadata

Return OME XML.

pilatus_metadata

Attribute whose value is computed on first access.

scanimage_metadata

Attribute whose value is computed on first access.

sem_metadata

Return SEM metadata from CZ_SEM tag as dict.

series

Attribute whose value is computed on first access.

shaped_metadata

Attribute whose value is computed on first access.

sis_metadata

Attribute whose value is computed on first access.

stk_metadata

Attribute whose value is computed on first access.

tvips_metadata

Return TVIPS tag as dict.

exception tifffile.tifffile.TiffFileError

Bases: Exception

Exception to indicate invalid TIFF structure.

class tifffile.tifffile.TiffSequence(files=None, container=None, sort=None, pattern=None, imread=<function imread>)

Bases: tifffile.tifffile.FileSequence

Series of TIFF files.

class tifffile.tifffile.TiffWriter(file, bigtiff=False, byteorder=None, append=False, imagej=False)

Bases: 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 instances are not thread-safe.

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

close()

Write remaining pages and close file handle.

save(data=None, shape=None, dtype=None, returnoffset=False, photometric=None, planarconfig=None, extrasamples=None, tile=None, contiguous=True, align=16, truncate=False, compress=0, rowsperstrip=None, predictor=False, subsampling=None, colormap=None, description=None, datetime=None, resolution=None, subfiletype=0, software='tifffile.py', metadata={}, ijmetadata=None, 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 colormap. If ‘shape’ and ‘dtype’ are specified, an empty array is saved. This option cannot be used with compression or multiple tiles. Image data are written uncompressed in one strip 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.

Parameters

  • 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) – Datatype 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 interleaved or in separate planes. By default, this setting is inferred from the data shape. If this parameter is set, extra samples are used to store grayscale images. ‘CONTIG’: last dimension contains samples. ‘SEPARATE’: third last dimension contains samples.

  • extrasamples (tuple of {'UNSPECIFIED', 'ASSOCALPHA', 'UNASSALPHA'}) – Defines the interpretation of extra components in pixels. ‘UNSPECIFIED’: no transparency information (default). ‘ASSOCALPHA’: single, true transparency with pre-multiplied color. ‘UNASSALPHA’: independent transparency masks.

  • tile (tuple of int) – The shape ([depth,] length, width) of image tiles to write. If None (default), image data are written in strips. 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. In that case, ‘photometric’, ‘planarconfig’, ‘rowsperstrip’, are ignored. Metadata such as ‘description’, ‘metadata’, ‘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 str or (str, int)) – If 0 (default), data are written uncompressed. If 0-9, the level of ADOBE_DEFLATE compression. If a str, one of TIFF.COMPESSORS, e.g. ‘LZMA’ or ‘ZSTD’. If a tuple, the first item is one of TIFF.COMPESSORS and the second item is the compression level. Compression cannot be used to write contiguous files. Compressors may require certain data shapes, types or value ranges. For example, JPEG requires grayscale or RGB(A), uint8 or 12-bit uint16. JPEG compression is experimental. JPEG markers and TIFF tags may not match.

  • rowsperstrip (int) – The number of rows per strip. By default, strips will be ~64 KB if compression is enabled, else rowsperstrip is set to the image length. Bilevel images are always stored in one strip per plane.

  • predictor (bool) – If True, apply horizontal differencing or floating-point predictor before compression.

  • subsampling ({(1, 1), (2, 1), (2, 2), (4, 1)}) – The horizontal and vertical subsampling factors used for the chrominance components of images. The default is (2, 2). Currently applies to JPEG compression of RGB images only. Images will be stored in YCbCr colorspace. Segment widths must be a multiple of the horizontal factor. Segment lengths and rowsperstrip must be a multiple of the vertical factor.

  • 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, str, or bool) – Date and time of image creation in ‘%Y:%m:%d %H:%M:%S’ format or datetime object. Else if True, 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’.

  • subfiletype (int) – Bitfield to indicate the kind of data. Set bit 0 if the image is a reduced-resolution version of another image. Set bit 1 if the image is part of a multi-page image. Set bit 2 if the image is transparency mask for another image (photometric must be MASK, SamplesPerPixel and BitsPerSample must be 1).

  • software (str) – Name of the software used to create the file. Must be 7-bit ASCII. Saved with the first page only.

  • metadata (dict) – Additional metadata 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.

  • ijmetadata (dict) – Additional metadata to be saved in application specific IJMetadata and IJMetadataByteCounts tags. Refer to the imagej_metadata_tag function for valid keys and values. Saved with the first page only.

  • extratags (sequence of tuples) –

    Additional tags as [(code, dtype, count, value, writeonce)].

    codeint

    The TIFF tag Id.

    dtypestr

    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.

    countint

    Number of data values. Not used for string or byte string values.

    valuesequence

    ’Count’ values compatible with ‘dtype’. Byte strings must contain count values of dtype packed as binary data.

    writeoncebool

    If True, the tag is written to the first page only.

class tifffile.tifffile.TiffPage(parent, index, keyframe=None)

Bases: object

TIFF image file directory (IFD).

index

Index of page in file.

Type

int

dtype

Data type (native byte order) of the image in IFD.

Type

numpy.dtype or None

shape

Dimensions of the image in IFD.

Type

tuple

axes

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

Type

str

tags

Dictionary of tags in IFD. {tag.name: TiffTag}

Type

dict

colormap

Color look up table, if exists.

Type

numpy.ndarray

All attributes are read-only.

Notes

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.

andor_tags

Attribute whose value is computed on first access.

asarray(out=None, squeeze=True, lock=None, reopen=True, maxsize=None, maxworkers=None, validate=True)

Read image data from file and return as numpy array.

Raise ValueError if format is unsupported.

Parameters

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, directly memory-map the image data in the TIFF file if possible; else create a memory-mapped array in a temporary file. 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 (default), 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 synchronize seeks and 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) – Maximum size of data before a ValueError is raised. Can be used to catch DOS. Default: 16 TB.

  • maxworkers (int or None) – Maximum number of threads to concurrently decode compressed segments. If None (default), up to half the CPU cores are used. See remarks in TiffFile.asarray.

  • validate (bool) – If True (default), validate various parameters. If None, only validate parameters and return None.

Returns

Numpy array of decompressed, depredicted, and unpacked image data read from Strip/Tile Offsets/ByteCounts, formatted according to shape and dtype metadata found in tags and parameters. Photometric conversion, pre-multiplied alpha, orientation, and colorimetry corrections are not applied. Specifically, CMYK images are not converted to RGB, MinIsWhite images are not inverted, and color palettes are not applied. An exception are YCbCr JPEG compressed images, which will be converted to RGB.

Return type

numpy.ndarray

aspage()

Return self.

asrgb(uint8=False, alpha=None, colormap=None, dmin=None, dmax=None, **kwargs)

Return image data as RGB(A).

Work in progress.

bitspersample = 1
colormap = None
compression = 1
decode

Attribute whose value is computed on first access.

description = ''
description1 = ''
epics_tags

Attribute whose value is computed on first access.

extrasamples = 1
fillorder = 1
flags

Attribute whose value is computed on first access.

geotiff_tags

Attribute whose value is computed on first access.

hash

Return checksum to identify pages in same series.

imagedepth = 1
imagelength = 0
imagewidth = 0
is_andor

Page contains Andor Technology tags.

is_contiguous

Attribute whose value is computed on first access.

is_epics

Page contains EPICS areaDetector tags.

is_fei

Page contains SFEG or HELIOS metadata.

is_final

Attribute whose value is computed on first access.

is_fluoview

Page contains FluoView MM_STAMP tag.

is_geotiff

Page contains GeoTIFF metadata.

is_imagej

Attribute whose value is computed on first access.

is_lsm

Page contains CZ_LSMINFO tag.

is_mask

Page is transparency mask for another image.

is_mdgel

Page contains MDFileTag tag.

is_mediacy

Page contains Media Cybernetics Id tag.

is_memmappable

Attribute whose value is computed on first access.

is_metaseries

Page contains MDS MetaSeries metadata in ImageDescription tag.

is_micromanager

Page contains Micro-Manager metadata.

is_mrc

Page is part of Mixed Raster Content.

is_multipage

Page is part of multi-page image.

is_ndpi

Attribute whose value is computed on first access.

is_nih

Page contains NIH image header.

is_ome

Page contains OME-XML in ImageDescription tag.

is_pilatus

Page contains Pilatus tags.

is_qpi

Page contains PerkinElmer tissue images metadata.

is_reduced

Page is reduced image of another image.

is_scanimage

Page contains ScanImage metadata.

is_scn

Page contains Leica SCN XML in ImageDescription tag.

is_sem

Page contains Zeiss SEM metadata.

is_sgi

Page contains SGI image and tile depth tags.

is_shaped

Attribute whose value is computed on first access.

is_sis

Page contains Olympus SIS metadata.

is_stk

Page contains UIC2Tag tag.

is_subsampled

Page contains chroma subsampled image.

is_svs

Page contains Aperio metadata.

is_tiled

Page contains tiled image.

is_tvips

Page contains TVIPS metadata.

is_vista

Software tag is ‘ISS Vista’.

keyframe

Return keyframe, self.

ndim

Return number of array dimensions.

ndpi_tags

Attribute whose value is computed on first access.

nodata = 0
pages

Attribute whose value is computed on first access.

photometric = 0
planarconfig = 1
predictor = 1
rowsperstrip = 4294967295
sampleformat = 1
samplesperpixel = 1
size

Return number of elements in array.

software = ''
subfiletype = 0
tiledepth = 1
tilelength = 0
tilewidth = 0
class tifffile.tifffile.TiffPageSeries(pages, shape, dtype, axes, parent=None, name=None, transform=None, kind=None, truncated=False)

Bases: object

Series of TIFF pages with compatible shape and data type.

pages

Sequence of TiffPages in series.

Type

list of TiffPage

dtype

Data type (native byte order) of the image array in series.

Type

numpy.dtype

shape

Dimensions of the image array in series.

Type

tuple

axes

Labels of axes in shape. See TiffPage.axes.

Type

str

offset

Position of image data in file if memory-mappable, else None.

Type

int or None

asarray(out=None)

Return image data from series of TIFF pages as numpy array.

ndim

Return number of array dimensions.

offset

Attribute whose value is computed on first access.

pages

Return sequence of all pages in series.

size

Return number of elements in array.

class tifffile.tifffile.TiffFrame(parent, index, offset=None, keyframe=None, offsets=None, bytecounts=None)

Bases: 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.

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

Not compatible with Python 2.

asarray(*args, **kwargs)

Read image data from file and return as numpy array.

aspage()

Return TiffPage from file.

asrgb(*args, **kwargs)

Read image data from file and return RGB image as numpy array.

hash

Return checksum to identify pages in same series.

index
is_contiguous

Return offset and size of contiguous data, else None.

is_mdgel = False
is_memmappable

Return if page’s image data in file can be memory-mapped.

keyframe

Return keyframe.

offset
pages = None
parent
tags = {}
class tifffile.tifffile.TiffTag(parent, tagheader, tagoffset)

Bases: object

TIFF tag structure.

name

Name of tag.

Type

string

code

Decimal code of tag.

Type

int

dtype

Datatype of tag data. One of TIFF DATA_FORMATS.

Type

str

count

Number of values.

Type

int

value

Tag data as Python object.

Type

various types

ImageSourceData

Location of value in file.

Type

int

All attributes are read-only.
code
count
dtype
name

Return name of tag from TIFF.TAGS registry.

value
valueoffset
class tifffile.tifffile.FileHandle(file, mode='rb', name=None, offset=None, size=None)

Bases: object

Binary file handle.

A limited, special purpose file handle 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

Name of the file.

Type

str

path

Absolute path to file.

Type

str

size

Size of file in bytes.

Type

int

is_file

If True, file has a filno and can be memory-mapped.

Type

bool

All attributes are read-only.
close()

Close file.

closed
dirname
flush()

Flush write buffers if applicable.

is_file
lock
memmap_array(dtype, shape, offset=0, mode='r', order='C')

Return numpy.memmap of data stored in file.

name
open()

Open or re-open file.

path
read(size=-1)

Read ‘size’ bytes from file, or until EOF is reached.

read_array(dtype, count=-1, out=None)

Return numpy array from file in native byte order.

read_record(dtype, shape=1, byteorder=None)

Return numpy record from file.

read_segments(offsets, bytecounts, lock=None, buffersize=None)

Return iterator over segments read from file.

A reentrant lock can be used to synchronize seeks and reads up to buffersize bytes.

readinto(b)

Read up to len(b) bytes into b, and return number of bytes read.

seek(offset, whence=0)

Set file’s current position.

size
tell()

Return file’s current position.

write(bytestring)

Write bytestring to file.

write_array(data)

Write numpy array to binary file.

write_empty(size)

Append size bytes to file. Position must be at end of file.

class tifffile.tifffile.FileSequence(fromfile, files, container=None, sort=None, pattern=None)

Bases: object

Series of files containing array data of compatible shape and data type.

files

List of file names.

Type

list

shape

Shape of file series. Excludes shape of individual arrays.

Type

tuple

axes

Labels of axes in shape.

Type

str

asarray(file=None, ioworkers=1, out=None, **kwargs)

Read image data from files and return as numpy array.

Raise IndexError or ValueError if array shapes do not match.

Parameters

  • file (int or None) – Index or name of single file to read.

  • ioworkers (int or None) – Maximum number of threads to execute the array read function asynchronously. Default: 1. If None, default to the number of processors multiplied by 5. Using threads can significantly improve runtime when reading many small files from a network share.

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, create a memory-mapped array in a temporary file. 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.

  • kwargs (dict) – Additional parameters passed to the array read function.

close()
class tifffile.tifffile.Timer(message='', end=' ')

Bases: object

Stopwatch for timing execution speed.

clock()

perf_counter() -> float

Performance counter for benchmarking.

duration
print(message='', end=None)

Print duration from timer start till last stop or now.

start(message='', end=' ')

Start timer and return current time.

started
stop(message='', end=' ')

Return duration of timer till start.

stopped
class tifffile.tifffile.lazyattr(func)

Bases: object

Attribute whose value is computed on first access.

func
tifffile.tifffile.natural_sorted(iterable)

Return human sorted list of strings.

E.g. for sorting file names.

>>> natural_sorted(['f1', 'f2', 'f10'])
['f1', 'f2', 'f10']
tifffile.tifffile.stripnull(string, null=b'\x00')

Return string truncated at first null character.

Clean NULL terminated C strings. For unicode strings use null=‘0’.

>>> stripnull(b'string\x00')
b'string'
>>> stripnull('string\x00', null='\0')
'string'
tifffile.tifffile.transpose_axes(image, axes, asaxes=None)

Return image with its axes permuted to match specified axes.

A view is returned if possible.

>>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape
(5, 2, 1, 3, 4)
tifffile.tifffile.squeeze_axes(shape, axes, skip=None)

Return shape and axes with single-dimensional entries removed.

Remove unused dimensions unless their axes are listed in ‘skip’.

>>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
((5, 2, 1), 'TYX')
tifffile.tifffile.create_output(out, shape, dtype, mode='w+', suffix=None)

Return numpy array where image data of shape and dtype can be copied.

The ‘out’ parameter may have the following values or types:

None

An empty array of shape and dtype is created and returned.

numpy.ndarray

An existing writable array of compatible dtype and shape. A view of the same array is returned after verification.

‘memmap’ or ‘memmap:tempdir’

A memory-map to an array stored in a temporary binary file on disk is created and returned.

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. The created memory-mapped array is returned.

tifffile.tifffile.repeat_nd(a, repeats)

Return read-only view into input array with elements repeated.

Zoom nD image by integer factors using nearest neighbor interpolation (box filter).

Parameters

  • a (array_like) – Input array.

  • repeats (sequence of int) – The number of repetitions to apply along each dimension of input array.

Examples

>>> repeat_nd([[1, 2], [3, 4]], (2, 2))
array([[1, 1, 2, 2],
       [1, 1, 2, 2],
       [3, 3, 4, 4],
       [3, 3, 4, 4]])
tifffile.tifffile.format_size(size, threshold=1536)

Return file size as string from byte size.

>>> format_size(1234)
'1234 B'
>>> format_size(12345678901)
'11.50 GiB'
tifffile.tifffile.astype(value, types=None)

Return argument as one of types if possible.

>>> astype('42')
42
>>> astype('3.14')
3.14
>>> astype('True')
True
>>> astype(b'Neee-Wom')
'Neee-Wom'
tifffile.tifffile.product(iterable)

Return product of sequence of numbers.

Equivalent of functools.reduce(operator.mul, iterable, 1). Multiplying numpy integers might overflow.

>>> product([2**8, 2**30])
274877906944
>>> product([])
1
tifffile.tifffile.xml2dict(xml, sanitize=True, prefix=None)

Return XML as dict.

>>> xml2dict('<?xml version="1.0" ?><root attr="name"><key>1</key></root>')
{'root': {'key': 1, 'attr': 'name'}}
tifffile.tifffile.pformat(arg, width=79, height=24, compact=True)

Return pretty formatted representation of object as string.

Whitespace might be altered.

tifffile.tifffile.str2bytes(s, encoding='cp1252')

Return bytes from unicode string.

tifffile.tifffile.nullfunc(*args, **kwargs)

Null function.

>>> nullfunc('arg', kwarg='kwarg')
tifffile.tifffile.update_kwargs(kwargs, **keyvalues)

Update dict with keys and values if keys do not already exist.

>>> kwargs = {'one': 1, }
>>> update_kwargs(kwargs, one=None, two=2)
>>> kwargs == {'one': 1, 'two': 2}
True
tifffile.tifffile.parse_kwargs(kwargs, *keys, **keyvalues)

Return dict with keys from keys|keyvals and values from kwargs|keyvals.

Existing keys are deleted from kwargs.

>>> kwargs = {'one': 1, 'two': 2, 'four': 4}
>>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5)
>>> kwargs == {'one': 1}
True
>>> kwargs2 == {'two': 2, 'four': 4, 'five': 5}
True
tifffile.tifffile.askopenfilename(**kwargs)

Return file name(s) from Tkinter’s file open dialog.

tifffile.tifffile.imsave(file, data=None, shape=None, dtype=None, **kwargs)

Write numpy array to TIFF file.

Refer to the TiffWriter class and its asarray function for documentation.

A BigTIFF file is created if the data size in bytes is larger than 4 GB minus 32 MB (for metadata), and ‘bigtiff’ is not specified, and ‘imagej’ or ‘truncate’ are not enabled.

Parameters

  • file (str or binary stream) – File name or writable binary stream, such as an open file or BytesIO.

  • data (array_like) – Input image. The last dimensions are assumed to be image depth, height, width, and samples. If None, an empty array of the specified shape and dtype is saved to file. Unless ‘byteorder’ is specified in ‘kwargs’, the TIFF file byte order is determined from the data’s dtype or the dtype argument.

  • shape (tuple) – If ‘data’ is None, shape of an empty array to save to the file.

  • dtype (numpy.dtype) – If ‘data’ is None, datatype of an empty array to save to the file.

  • kwargs (dict) – Parameters ‘append’, ‘byteorder’, ‘bigtiff’, and ‘imagej’, are passed to the TiffWriter constructor. Other parameters are passed to the TiffWriter.save function.

Returns

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

Return type

tuple or None

tifffile.tifffile.decode_lzw(encoded)

Decompress LZW encoded byte string.

tifffile.tifffile.decodelzw(encoded)

Decompress LZW encoded byte string.

tifffile.tifffile_geodb module

GeoTIFF GeoKey Database.

Adapted from http://gis.ess.washington.edu/data/raster/drg/docs/geotiff.txt

class tifffile.tifffile_geodb.Angular

Bases: enum.IntEnum

Angular Units.

Arc_Minute = 9103
Arc_Second = 9104
DMS = 9107
DMS_Hemisphere = 9108
Degree = 9102
Gon = 9106
Grad = 9105
Radian = 9101
Undefined = 0
User_Defined = 32767
class tifffile.tifffile_geodb.CT

Bases: enum.IntEnum

Coordinate Transformation Codes.

AlbersEqualArea = 11
AzimuthalEquidistant = 12
CassiniSoldner = 18
CylindricalEqualArea = 28
EquidistantConic = 13
Equirectangular = 17
Gnomonic = 19
HotineObliqueMercatorAzimuthCenter = 9815
LambertAzimEqualArea = 10
LambertConfConic_2SP = 8
LambertConfConic_Helmert = 9
Mercator = 7
MillerCylindrical = 20
NewZealandMapGrid = 26
ObliqueMercator = 3
ObliqueMercator_Laborde = 4
ObliqueMercator_Rosenmund = 5
ObliqueMercator_Spherical = 6
ObliqueStereographic = 16
Orthographic = 21
PolarStereographic = 15
Polyconic = 22
Robinson = 23
Sinusoidal = 24
Stereographic = 14
TransvMercator_Modified_Alaska = 2
TransvMercator_SouthOriented = 27
TransverseMercator = 1
Undefined = 0
User_Defined = 32767
VanDerGrinten = 25
class tifffile.tifffile_geodb.Datum

Bases: enum.IntEnum

Geodetic Datum Codes.

Adindan = 6201
Afgooye = 6205
Agadez = 6206
Ain_el_Abd_1970 = 6204
Amersfoort = 6289
Ancienne_Triangulation_Francaise = 6901
Aratu = 6208
Arc_1950 = 6209
Arc_1960 = 6210
Australian_Geodetic_Datum_1966 = 6202
Australian_Geodetic_Datum_1984 = 6203
Barbados = 6212
Batavia = 6211
Beduaram = 6213
Beijing_1954 = 6214
Bermuda_1957 = 6216
Bern_1898 = 6217
Bern_1938 = 6306
Bogota = 6218
Bukit_Rimpah = 6219
Camacupa = 6220
Campo_Inchauspe = 6221
Cape = 6222
Carthage = 6223
Chua = 6224
Conakry_1905 = 6315
Corrego_Alegre = 6225
Cote_d_Ivoire = 6226
Datum_73 = 6274
Dealul_Piscului_1970 = 6317
Deir_ez_Zor = 6227
Deutsche_Hauptdreiecksnetz = 6314
Douala = 6228
Egypt_1907 = 6229
European_Datum_1950 = 6230
European_Datum_1987 = 6231
European_Reference_System_1989 = 6258
Fahud = 6232
Gandajika_1970 = 6233
Garoua = 6234
Geocentric_Datum_of_Australia_1994 = 6283
Guyane_Francaise = 6235
Herat_North = 6255
Hito_XVIII_1963 = 6254
Hu_Tzu_Shan = 6236
Hungarian_Datum_1972 = 6237
Indian_1954 = 6239
Indian_1975 = 6240
Indonesian_Datum_1974 = 6238
Jamaica_1875 = 6241
Jamaica_1969 = 6242
Kalianpur = 6243
Kandawala = 6244
Kertau = 6245
Kuwait_Oil_Company = 6246
La_Canoa = 6247
Lake = 6249
Leigon = 6250
Liberia_1964 = 6251
Lisbon = 6207
Loma_Quintana = 6288
Lome = 6252
Luzon_1911 = 6253
M_poraloko = 6266
Mahe_1971 = 6256
Makassar = 6257
Malongo_1987 = 6259
Manoca = 6260
Massawa = 6262
Merchich = 6261
Mhast = 6264
Militar_Geographische_Institut = 6312
Minna = 6263
Monte_Mario = 6265
NAD_Michigan = 6268
NGO_1948 = 6273
NSWC_9Z_2 = 6276
Nahrwan_1967 = 6270
Naparima_1972 = 6271
New_Zealand_Geodetic_Datum_1949 = 6272
Nord_Sahara_1959 = 6307
Nord_de_Guerre = 6902
North_American_Datum_1927 = 6267
North_American_Datum_1983 = 6269
Nouvelle_Triangulation_Francaise = 6275
OSGB_1936 = 6277
OSGB_1970_SN = 6278
OS_SN_1980 = 6279
Padang_1884 = 6280
Palestine_1923 = 6281
Pointe_Noire = 6282
Provisional_S_American_Datum_1956 = 6248
Pulkovo_1942 = 6284
Qatar = 6285
Qatar_1948 = 6286
Qornoq = 6287
RT38 = 6290
Reseau_National_Belge_1950 = 6215
Reseau_National_Belge_1972 = 6313
Sapper_Hill_1943 = 6292
Schwarzeck = 6293
Segora = 6294
Serindung = 6295
South_American_Datum_1969 = 6291
Stockholm_1938 = 6308
Sudan = 6296
TM65 = 6299
TM75 = 6300
Tananarive_1925 = 6297
Timbalai_1948 = 6298
Tokyo = 6301
Trinidad_1903 = 6302
Trucial_Coast_1948 = 6303
Undefined = 0
User_Defined = 32767
Voirol_1875 = 6304
Voirol_Unifie_1960 = 6305
WGS72 = 6322
WGS72_Transit_Broadcast_Ephemeris = 6324
WGS84 = 6326
Yacare = 6309
Yoff = 6310
Zanderij = 6311
class tifffile.tifffile_geodb.DatumE

Bases: enum.IntEnum

Ellipsoid-Only Geodetic Datum Codes.

Airy1830 = 6001
AiryModified1849 = 6002
AustralianNationalSpheroid = 6003
Bessel1841 = 6004
BesselModified = 6005
BesselNamibia = 6006
Clarke1858 = 6007
Clarke1866 = 6008
Clarke1866Michigan = 6009
Clarke1880 = 6034
Clarke1880_Arc = 6013
Clarke1880_Benoit = 6010
Clarke1880_IGN = 6011
Clarke1880_RGS = 6012
Clarke1880_SGA1922 = 6014
Everest1830Modified = 6018
Everest1830_1937Adjustment = 6015
Everest1830_1967Definition = 6016
Everest1830_1975Definition = 6017
GEM10C = 6031
GRS1980 = 6019
Helmert1906 = 6020
IndonesianNationalSpheroid = 6021
International1924 = 6022
International1967 = 6023
Krassowsky1960 = 6024
NWL10D = 6026
NWL9D = 6025
OSU86F = 6032
OSU91A = 6033
Plessis1817 = 6027
Sphere = 6035
Struve1860 = 6028
Undefined = 0
User_Defined = 32767
WGS84 = 6030
WarOffice = 6029
class tifffile.tifffile_geodb.Ellipse

Bases: enum.IntEnum

Ellipsoid Codes.

Airy_1830 = 7001
Airy_Modified_1849 = 7002
Australian_National_Spheroid = 7003
Bessel_1841 = 7004
Bessel_Modified = 7005
Bessel_Namibia = 7006
Clarke_1858 = 7007
Clarke_1866 = 7008
Clarke_1866_Michigan = 7009
Clarke_1880 = 7034
Clarke_1880_Arc = 7013
Clarke_1880_Benoit = 7010
Clarke_1880_IGN = 7011
Clarke_1880_RGS = 7012
Clarke_1880_SGA_1922 = 7014
Everest_1830_1937_Adjustment = 7015
Everest_1830_1967_Definition = 7016
Everest_1830_1975_Definition = 7017
Everest_1830_Modified = 7018
GEM_10C = 7031
GRS_1980 = 7019
Helmert_1906 = 7020
Indonesian_National_Spheroid = 7021
International_1924 = 7022
International_1967 = 7023
Krassowsky_1940 = 7024
NWL_10D = 7026
NWL_9D = 7025
OSU86F = 7032
OSU91A = 7033
Plessis_1817 = 7027
Sphere = 7035
Struve_1860 = 7028
Undefined = 0
User_Defined = 32767
WGS_84 = 7030
War_Office = 7029
class tifffile.tifffile_geodb.GCS

Bases: enum.IntEnum

Geographic CS Type Codes.

AGD66 = 4202
AGD84 = 4203
ATF_Paris = 4901
Adindan = 4201
Afgooye = 4205
Agadez = 4206
Ain_el_Abd = 4204
Amersfoort = 4289
Aratu = 4208
Arc_1950 = 4209
Arc_1960 = 4210
Barbados = 4212
Batavia = 4211
Batavia_Jakarta = 4813
Beduaram = 4213
Beijing_1954 = 4214
Belge_1950 = 4215
Belge_1950_Brussels = 4809
Belge_1972 = 4313
Bermuda_1957 = 4216
Bern_1898 = 4217
Bern_1898_Bern = 4801
Bern_1938 = 4306
Bogota = 4218
Bogota_Bogota = 4802
Bukit_Rimpah = 4219
Camacupa = 4220
Campo_Inchauspe = 4221
Cape = 4222
Carthage = 4223
Chua = 4224
Conakry_1905 = 4315
Corrego_Alegre = 4225
Cote_d_Ivoire = 4226
DHDN = 4314
Datum_73 = 4274
Dealul_Piscului_1970 = 4317
Deir_ez_Zor = 4227
Douala = 4228
ED50 = 4230
ED87 = 4231
EST92 = 4133
EUREF89 = 4258
Egypt_1907 = 4229
Fahud = 4232
GD49 = 4272
GDA94 = 4283
GGRS87 = 4121
Gandajika_1970 = 4233
Garoua = 4234
Greek = 4120
Greek_Athens = 4815
Guyane_Francaise = 4235
HD72 = 4237
Herat_North = 4255
Hito_XVIII_1963 = 4254
Hu_Tzu_Shan = 4236
ID74 = 4238
Indian_1954 = 4239
Indian_1975 = 4240
JAD69 = 4242
Jamaica_1875 = 4241
KKJ = 4123
KOC = 4246
Kalianpur = 4243
Kandawala = 4244
Kertau = 4245
La_Canoa = 4247
Lake = 4249
Leigon = 4250
Liberia_1964 = 4251
Lisbon = 4207
Lisbon_Lisbon = 4803
Loma_Quintana = 4288
Lome = 4252
Luzon_1911 = 4253
MGI = 4312
MGI_Ferro = 4805
M_poraloko = 4266
Mahe_1971 = 4256
Makassar = 4257
Makassar_Jakarta = 4804
Malongo_1987 = 4259
Manoca = 4260
Massawa = 4262
Merchich = 4261
Mhast = 4264
Minna = 4263
Monte_Mario = 4265
Monte_Mario_Rome = 4806
NAD27 = 4267
NAD83 = 4269
NAD_Michigan = 4268
NDG_Paris = 4902
NGO_1948 = 4273
NSWC_9Z_2 = 4276
NTF = 4275
NTF_Paris = 4807
Nahrwan_1967 = 4270
Naparima_1972 = 4271
Nord_Sahara_1959 = 4307
OSGB70 = 4278
OSGB_1936 = 4277
OS_SN80 = 4279
PSAD56 = 4248
Padang = 4280
Padang_Jakarta = 4808
Palestine_1923 = 4281
Pointe_Noire = 4282
Pulkovo_1942 = 4284
Qatar = 4285
Qatar_1948 = 4286
Qornoq = 4287
RT38 = 4290
RT90 = 4124
SAD69 = 4291
Sapper_Hill_1943 = 4292
Schwarzeck = 4293
Segora = 4294
Serindung = 4295
Stockholm_1938 = 4308
Sudan = 4296
TC_1948 = 4303
TM65 = 4299
TM75 = 4300
Tananarive = 4297
Tananarive_Paris = 4810
Timbalai_1948 = 4298
Tokyo = 4301
Trinidad_1903 = 4302
Undefined = 0
User_Defined = 32767
Voirol_1875 = 4304
Voirol_1875_Paris = 4811
Voirol_Unifie = 4305
Voirol_Unifie_Paris = 4812
WGS_72 = 4322
WGS_72BE = 4324
WGS_84 = 4326
Yacare = 4309
Yoff = 4310
Zanderij = 4311
class tifffile.tifffile_geodb.GCSE

Bases: enum.IntEnum

Unspecified GCS based on ellipsoid.

Airy1830 = 4001
AiryModified1849 = 4002
AustralianNationalSpheroid = 4003
Bessel1841 = 4004
BesselModified = 4005
BesselNamibia = 4006
Clarke1858 = 4007
Clarke1866 = 4008
Clarke1866Michigan = 4009
Clarke1880 = 4034
Clarke1880_Arc = 4013
Clarke1880_Benoit = 4010
Clarke1880_IGN = 4011
Clarke1880_RGS = 4012
Clarke1880_SGA1922 = 4014
Everest1830Modified = 4018
Everest1830_1937Adjustment = 4015
Everest1830_1967Definition = 4016
Everest1830_1975Definition = 4017
GEM10C = 4031
GRS1980 = 4019
Helmert1906 = 4020
IndonesianNationalSpheroid = 4021
International1924 = 4022
International1967 = 4023
Krassowsky1940 = 4024
NWL10D = 4026
NWL9D = 4025
OSU86F = 4032
OSU91A = 4033
Plessis1817 = 4027
Sphere = 4035
Struve1860 = 4028
Undefined = 0
User_Defined = 32767
WGS84 = 4030
WarOffice = 4029
class tifffile.tifffile_geodb.Linear

Bases: enum.IntEnum

Linear Units.

Chain_Benoit = 9010
Chain_Sears = 9011
Fathom = 9014
Foot = 9002
Foot_Clarke = 9005
Foot_Indian = 9006
Foot_Modified_American = 9004
Foot_US_Survey = 9003
Meter = 9001
Mile_International_Nautical = 9015
Undefined = 0
User_Defined = 32767
Yard_Indian = 9013
Yard_Sears = 9012
class tifffile.tifffile_geodb.ModelType

Bases: enum.IntEnum

Model Type Codes.

Geocentric = 3
Geographic = 2
Projected = 1
Undefined = 0
User_Defined = 32767
class tifffile.tifffile_geodb.PCS

Bases: enum.IntEnum

Projected CS Type Codes.

AGD66_AMG_zone_48 = 20248
AGD66_AMG_zone_49 = 20249
AGD66_AMG_zone_50 = 20250
AGD66_AMG_zone_51 = 20251
AGD66_AMG_zone_52 = 20252
AGD66_AMG_zone_53 = 20253
AGD66_AMG_zone_54 = 20254
AGD66_AMG_zone_55 = 20255
AGD66_AMG_zone_56 = 20256
AGD66_AMG_zone_57 = 20257
AGD66_AMG_zone_58 = 20258
AGD84_AMG_zone_48 = 20348
AGD84_AMG_zone_49 = 20349
AGD84_AMG_zone_50 = 20350
AGD84_AMG_zone_51 = 20351
AGD84_AMG_zone_52 = 20352
AGD84_AMG_zone_53 = 20353
AGD84_AMG_zone_54 = 20354
AGD84_AMG_zone_55 = 20355
AGD84_AMG_zone_56 = 20356
AGD84_AMG_zone_57 = 20357
AGD84_AMG_zone_58 = 20358
ATF_Nord_de_Guerre = 27500
Adindan_UTM_zone_37N = 20137
Adindan_UTM_zone_38N = 20138
Afgooye_UTM_zone_38N = 20538
Afgooye_UTM_zone_39N = 20539
Ain_el_Abd_Bahrain_Grid = 20499
Ain_el_Abd_UTM_zone_37N = 20437
Ain_el_Abd_UTM_zone_38N = 20438
Ain_el_Abd_UTM_zone_39N = 20439
Aratu_UTM_zone_22S = 20822
Aratu_UTM_zone_23S = 20823
Aratu_UTM_zone_24S = 20824
Arc_1950_Lo13 = 20973
Arc_1950_Lo15 = 20975
Arc_1950_Lo17 = 20977
Arc_1950_Lo19 = 20979
Arc_1950_Lo21 = 20981
Arc_1950_Lo23 = 20983
Arc_1950_Lo25 = 20985
Arc_1950_Lo27 = 20987
Arc_1950_Lo29 = 20989
Arc_1950_Lo31 = 20991
Arc_1950_Lo33 = 20993
Arc_1950_Lo35 = 20995
Batavia_NEIEZ = 21100
Batavia_UTM_zone_48S = 21148
Batavia_UTM_zone_49S = 21149
Batavia_UTM_zone_50S = 21150
Beijing_Gauss_13N = 21473
Beijing_Gauss_14N = 21474
Beijing_Gauss_15N = 21475
Beijing_Gauss_16N = 21476
Beijing_Gauss_17N = 21477
Beijing_Gauss_18N = 21478
Beijing_Gauss_19N = 21479
Beijing_Gauss_20N = 21480
Beijing_Gauss_21N = 21481
Beijing_Gauss_22N = 21482
Beijing_Gauss_23N = 21483
Beijing_Gauss_zone_13 = 21413
Beijing_Gauss_zone_14 = 21414
Beijing_Gauss_zone_15 = 21415
Beijing_Gauss_zone_16 = 21416
Beijing_Gauss_zone_17 = 21417
Beijing_Gauss_zone_18 = 21418
Beijing_Gauss_zone_19 = 21419
Beijing_Gauss_zone_20 = 21420
Beijing_Gauss_zone_21 = 21421
Beijing_Gauss_zone_22 = 21422
Beijing_Gauss_zone_23 = 21423
Belge_Lambert_50 = 21500
Belge_Lambert_72 = 31300
Bern_1898_Swiss_Old = 21790
Bern_1938_Swiss_New = 30600
Bogota_Colombia_3E = 21893
Bogota_Colombia_3W = 21891
Bogota_Colombia_6E = 21894
Bogota_Colombia_Bogota = 21892
Bogota_UTM_zone_17N = 21817
Bogota_UTM_zone_18N = 21818
British_National_Grid = 27700
C_Inchauspe_Argentina_1 = 22191
C_Inchauspe_Argentina_2 = 22192
C_Inchauspe_Argentina_3 = 22193
C_Inchauspe_Argentina_4 = 22194
C_Inchauspe_Argentina_5 = 22195
C_Inchauspe_Argentina_6 = 22196
C_Inchauspe_Argentina_7 = 22197
Camacupa_UTM_32S = 22032
Camacupa_UTM_33S = 22033
Carthage_Nord_Tunisie = 22391
Carthage_Sud_Tunisie = 22392
Carthage_UTM_zone_32N = 22332
Corrego_Alegre_UTM_23S = 22523
Corrego_Alegre_UTM_24S = 22524
DHDN_Germany_zone_1 = 31491
DHDN_Germany_zone_2 = 31492
DHDN_Germany_zone_3 = 31493
DHDN_Germany_zone_4 = 31494
DHDN_Germany_zone_5 = 31495
Datum_73_UTM_zone_29N = 27429
Dealul_Piscului_1970_Stereo_70 = 31700
Douala_UTM_zone_32N = 22832
ED50_UTM_zone_28N = 23028
ED50_UTM_zone_29N = 23029
ED50_UTM_zone_30N = 23030
ED50_UTM_zone_31N = 23031
ED50_UTM_zone_32N = 23032
ED50_UTM_zone_33N = 23033
ED50_UTM_zone_34N = 23034
ED50_UTM_zone_35N = 23035
ED50_UTM_zone_36N = 23036
ED50_UTM_zone_37N = 23037
ED50_UTM_zone_38N = 23038
ETRS89_Poland_CS2000_zone_5 = 2176
ETRS89_Poland_CS2000_zone_6 = 2177
ETRS89_Poland_CS2000_zone_7 = 2177
ETRS89_Poland_CS2000_zone_8 = 2178
ETRS89_Poland_CS92 = 2180
Egypt_1907_Ext_Purple = 22994
Egypt_1907_Purple_Belt = 22993
Egypt_1907_Red_Belt = 22992
Estonian_Coordinate_System_of_1992 = 3300
Fahud_UTM_zone_39N = 23239
Fahud_UTM_zone_40N = 23240
GD49_NZ_Map_Grid = 27200
GD49_North_Island_Grid = 27291
GD49_South_Island_Grid = 27292
GDA94_MGA_zone_48 = 28348
GDA94_MGA_zone_49 = 28349
GDA94_MGA_zone_50 = 28350
GDA94_MGA_zone_51 = 28351
GDA94_MGA_zone_52 = 28352
GDA94_MGA_zone_53 = 28353
GDA94_MGA_zone_54 = 28354
GDA94_MGA_zone_55 = 28355
GDA94_MGA_zone_56 = 28356
GDA94_MGA_zone_57 = 28357
GDA94_MGA_zone_58 = 28358
GGRS87_Greek_Grid = 2100
Garoua_UTM_zone_33N = 23433
HD72_EOV = 23700
Hjorsey_1955_Lambert = 3053
ID74_UTM_zone_46N = 23846
ID74_UTM_zone_46S = 23886
ID74_UTM_zone_47N = 23847
ID74_UTM_zone_47S = 23887
ID74_UTM_zone_48N = 23848
ID74_UTM_zone_48S = 23888
ID74_UTM_zone_49N = 23849
ID74_UTM_zone_49S = 23889
ID74_UTM_zone_50N = 23850
ID74_UTM_zone_50S = 23890
ID74_UTM_zone_51N = 23851
ID74_UTM_zone_51S = 23891
ID74_UTM_zone_52N = 23852
ID74_UTM_zone_52S = 23892
ID74_UTM_zone_53N = 23853
ID74_UTM_zone_53S = 23893
ID74_UTM_zone_54S = 23894
ISN93_Lambert_1993 = 3057
Indian_1954_UTM_47N = 23947
Indian_1954_UTM_48N = 23948
Indian_1975_UTM_47N = 24047
Indian_1975_UTM_48N = 24048
JAD69_Jamaica_Grid = 24200
Jamaica_1875_Old_Grid = 24100
KKJ_Finland_zone_1 = 2391
KKJ_Finland_zone_2 = 2392
KKJ_Finland_zone_3 = 2393
KKJ_Finland_zone_4 = 2394
Kalianpur_India_0 = 24370
Kalianpur_India_I = 24371
Kalianpur_India_IIIa = 24373
Kalianpur_India_IIIb = 24383
Kalianpur_India_IIa = 24372
Kalianpur_India_IIb = 24382
Kalianpur_India_IVa = 24374
Kalianpur_India_IVb = 24384
Kertau_Singapore_Grid = 24500
Kertau_UTM_zone_47N = 24547
Kertau_UTM_zone_48N = 24548
La_Canoa_UTM_zone_20N = 24720
La_Canoa_UTM_zone_21N = 24721
Leigon_Ghana_Grid = 25000
Lietuvos_Koordinoei_Sistema_1994 = 2600
Lisbon_Portugese_Grid = 20700
Lome_UTM_zone_31N = 25231
Luzon_Philippines_I = 25391
Luzon_Philippines_II = 25392
Luzon_Philippines_III = 25393
Luzon_Philippines_IV = 25394
Luzon_Philippines_V = 25395
MGI_Austria_Central = 31292
MGI_Austria_East = 31293
MGI_Austria_West = 31291
M_poraloko_UTM_32N = 26632
M_poraloko_UTM_32S = 26692
Makassar_NEIEZ = 25700
Malongo_1987_UTM_32S = 25932
Massawa_UTM_zone_37N = 26237
Merchich_Nord_Maroc = 26191
Merchich_Sahara = 26193
Merchich_Sud_Maroc = 26192
Mhast_UTM_zone_32S = 26432
Minna_Nigeria_East = 26393
Minna_Nigeria_Mid_Belt = 26392
Minna_Nigeria_West = 26391
Minna_UTM_zone_31N = 26331
Minna_UTM_zone_32N = 26332
Monte_Mario_Italy_1 = 26591
Monte_Mario_Italy_2 = 26592
NAD27_Alabama_East = 26729
NAD27_Alabama_West = 26730
NAD27_Alaska_zone_1 = 26731
NAD27_Alaska_zone_10 = 26740
NAD27_Alaska_zone_2 = 26732
NAD27_Alaska_zone_3 = 26733
NAD27_Alaska_zone_4 = 26734
NAD27_Alaska_zone_5 = 26735
NAD27_Alaska_zone_6 = 26736
NAD27_Alaska_zone_7 = 26737
NAD27_Alaska_zone_8 = 26738
NAD27_Alaska_zone_9 = 26739
NAD27_Arizona_Central = 26749
NAD27_Arizona_East = 26748
NAD27_Arizona_West = 26750
NAD27_Arkansas_North = 26751
NAD27_Arkansas_South = 26752
NAD27_BLM_14N_feet = 26774
NAD27_BLM_15N_feet = 26775
NAD27_BLM_16N_feet = 26776
NAD27_BLM_17N_feet = 26777
NAD27_California_I = 26741
NAD27_California_II = 26742
NAD27_California_III = 26743
NAD27_California_IV = 26744
NAD27_California_V = 26745
NAD27_California_VI = 26746
NAD27_California_VII = 26747
NAD27_Colorado_Central = 26754
NAD27_Colorado_North = 26753
NAD27_Colorado_South = 26755
NAD27_Connecticut = 26756
NAD27_Delaware = 26757
NAD27_Florida_East = 26758
NAD27_Florida_North = 26760
NAD27_Florida_West = 26759
NAD27_Georgia_East = 26766
NAD27_Georgia_West = 26767
NAD27_Hawaii_zone_1 = 26761
NAD27_Hawaii_zone_2 = 26762
NAD27_Hawaii_zone_3 = 26763
NAD27_Hawaii_zone_4 = 26764
NAD27_Hawaii_zone_5 = 26765
NAD27_Idaho_Central = 26769
NAD27_Idaho_East = 26768
NAD27_Idaho_West = 26770
NAD27_Illinois_East = 26771
NAD27_Illinois_West = 26772
NAD27_Indiana_East = 26773
NAD27_Indiana_West = 26774
NAD27_Iowa_North = 26775
NAD27_Iowa_South = 26776
NAD27_Kansas_North = 26777
NAD27_Kansas_South = 26778
NAD27_Kentucky_North = 26779
NAD27_Kentucky_South = 26780
NAD27_Louisiana_North = 26781
NAD27_Louisiana_South = 26782
NAD27_Maine_East = 26783
NAD27_Maine_West = 26784
NAD27_Maryland = 26785
NAD27_Massachusetts = 26786
NAD27_Massachusetts_Is = 26787
NAD27_Michigan_Central = 26789
NAD27_Michigan_North = 26788
NAD27_Michigan_South = 26790
NAD27_Minnesota_Cent = 26792
NAD27_Minnesota_North = 26791
NAD27_Minnesota_South = 26793
NAD27_Mississippi_East = 26794
NAD27_Mississippi_West = 26795
NAD27_Missouri_Central = 26797
NAD27_Missouri_East = 26796
NAD27_Missouri_West = 26798
NAD27_Montana_Central = 32002
NAD27_Montana_North = 32001
NAD27_Montana_South = 32003
NAD27_Nebraska_North = 32005
NAD27_Nebraska_South = 32006
NAD27_Nevada_Central = 32008
NAD27_Nevada_East = 32007
NAD27_Nevada_West = 32009
NAD27_New_Hampshire = 32010
NAD27_New_Jersey = 32011
NAD27_New_Mexico_Cent = 32013
NAD27_New_Mexico_East = 32012
NAD27_New_Mexico_West = 32014
NAD27_New_York_Central = 32016
NAD27_New_York_East = 32015
NAD27_New_York_Long_Is = 32018
NAD27_New_York_West = 32017
NAD27_North_Carolina = 32019
NAD27_North_Dakota_N = 32020
NAD27_North_Dakota_S = 32021
NAD27_Ohio_North = 32022
NAD27_Ohio_South = 32023
NAD27_Oklahoma_North = 32024
NAD27_Oklahoma_South = 32025
NAD27_Oregon_North = 32026
NAD27_Oregon_South = 32027
NAD27_Pennsylvania_N = 32028
NAD27_Pennsylvania_S = 32029
NAD27_Puerto_Rico = 32059
NAD27_Rhode_Island = 32030
NAD27_South_Carolina_N = 32031
NAD27_South_Carolina_S = 32033
NAD27_South_Dakota_N = 32034
NAD27_South_Dakota_S = 32035
NAD27_St_Croix = 32060
NAD27_Tennessee = 2204
NAD27_Texas_Central = 32039
NAD27_Texas_North = 32037
NAD27_Texas_North_Cen = 32038
NAD27_Texas_South = 32041
NAD27_Texas_South_Cen = 32040
NAD27_UTM_zone_10N = 26710
NAD27_UTM_zone_11N = 26711
NAD27_UTM_zone_12N = 26712
NAD27_UTM_zone_13N = 26713
NAD27_UTM_zone_14N = 26714
NAD27_UTM_zone_15N = 26715
NAD27_UTM_zone_16N = 26716
NAD27_UTM_zone_17N = 26717
NAD27_UTM_zone_18N = 26718
NAD27_UTM_zone_19N = 26719
NAD27_UTM_zone_20N = 26720
NAD27_UTM_zone_21N = 26721
NAD27_UTM_zone_22N = 26722
NAD27_UTM_zone_3N = 26703
NAD27_UTM_zone_4N = 26704
NAD27_UTM_zone_5N = 26705
NAD27_UTM_zone_6N = 26706
NAD27_UTM_zone_7N = 26707
NAD27_UTM_zone_8N = 26708
NAD27_UTM_zone_9N = 26709
NAD27_Utah_Central = 32043
NAD27_Utah_North = 32042
NAD27_Utah_South = 32044
NAD27_Vermont = 32045
NAD27_Virginia_North = 32046
NAD27_Virginia_South = 32047
NAD27_Washington_North = 32048
NAD27_Washington_South = 32049
NAD27_West_Virginia_N = 32050
NAD27_West_Virginia_S = 32051
NAD27_Wisconsin_Cen = 32053
NAD27_Wisconsin_North = 32052
NAD27_Wisconsin_South = 32054
NAD27_Wyoming_E_Cen = 32056
NAD27_Wyoming_East = 32055
NAD27_Wyoming_W_Cen = 32057
NAD27_Wyoming_West = 32058
NAD83_Alabama_East = 26929
NAD83_Alabama_West = 26930
NAD83_Alaska_zone_1 = 26931
NAD83_Alaska_zone_10 = 26940
NAD83_Alaska_zone_2 = 26932
NAD83_Alaska_zone_3 = 26933
NAD83_Alaska_zone_4 = 26934
NAD83_Alaska_zone_5 = 26935
NAD83_Alaska_zone_6 = 26936
NAD83_Alaska_zone_7 = 26937
NAD83_Alaska_zone_8 = 26938
NAD83_Alaska_zone_9 = 26939
NAD83_Arizona_Central = 26949
NAD83_Arizona_East = 26948
NAD83_Arizona_West = 26950
NAD83_Arkansas_North = 26951
NAD83_Arkansas_South = 26952
NAD83_California_1 = 26941
NAD83_California_2 = 26942
NAD83_California_3 = 26943
NAD83_California_4 = 26944
NAD83_California_5 = 26945
NAD83_California_6 = 26946
NAD83_Colorado_Central = 26954
NAD83_Colorado_North = 26953
NAD83_Colorado_South = 26955
NAD83_Connecticut = 26956
NAD83_Delaware = 26957
NAD83_Florida_East = 26958
NAD83_Florida_North = 26960
NAD83_Florida_West = 26959
NAD83_Georgia_East = 26966
NAD83_Georgia_West = 26967
NAD83_Hawaii_zone_1 = 26961
NAD83_Hawaii_zone_2 = 26962
NAD83_Hawaii_zone_3 = 26963
NAD83_Hawaii_zone_4 = 26964
NAD83_Hawaii_zone_5 = 26965
NAD83_Idaho_Central = 26969
NAD83_Idaho_East = 26968
NAD83_Idaho_West = 26970
NAD83_Illinois_East = 26971
NAD83_Illinois_West = 26972
NAD83_Indiana_East = 26973
NAD83_Indiana_West = 26974
NAD83_Iowa_North = 26975
NAD83_Iowa_South = 26976
NAD83_Kansas_North = 26977
NAD83_Kansas_South = 26978
NAD83_Kentucky_North = 2205
NAD83_Kentucky_South = 26980
NAD83_Louisiana_North = 26981
NAD83_Louisiana_South = 26982
NAD83_Maine_East = 26983
NAD83_Maine_West = 26984
NAD83_Maryland = 26985
NAD83_Massachusetts = 26986
NAD83_Massachusetts_Is = 26987
NAD83_Michigan_Central = 26989
NAD83_Michigan_North = 26988
NAD83_Michigan_South = 26990
NAD83_Minnesota_Cent = 26992
NAD83_Minnesota_North = 26991
NAD83_Minnesota_South = 26993
NAD83_Mississippi_East = 26994
NAD83_Mississippi_West = 26995
NAD83_Missouri_Central = 26997
NAD83_Missouri_East = 26996
NAD83_Missouri_West = 26998
NAD83_Montana = 32100
NAD83_Nebraska = 32104
NAD83_Nevada_Central = 32108
NAD83_Nevada_East = 32107
NAD83_Nevada_West = 32109
NAD83_New_Hampshire = 32110
NAD83_New_Jersey = 32111
NAD83_New_Mexico_Cent = 32113
NAD83_New_Mexico_East = 32112
NAD83_New_Mexico_West = 32114
NAD83_New_York_Central = 32116
NAD83_New_York_East = 32115
NAD83_New_York_Long_Is = 32118
NAD83_New_York_West = 32117
NAD83_North_Carolina = 32119
NAD83_North_Dakota_N = 32120
NAD83_North_Dakota_S = 32121
NAD83_Ohio_North = 32122
NAD83_Ohio_South = 32123
NAD83_Oklahoma_North = 32124
NAD83_Oklahoma_South = 32125
NAD83_Oregon_North = 32126
NAD83_Oregon_South = 32127
NAD83_Pennsylvania_N = 32128
NAD83_Pennsylvania_S = 32129
NAD83_Puerto_Rico_Virgin_Is = 32161
NAD83_Rhode_Island = 32130
NAD83_South_Carolina = 32133
NAD83_South_Dakota_N = 32134
NAD83_South_Dakota_S = 32135
NAD83_Tennessee = 32136
NAD83_Texas_Central = 32139
NAD83_Texas_North = 32137
NAD83_Texas_North_Cen = 32138
NAD83_Texas_South = 32141
NAD83_Texas_South_Cen = 32140
NAD83_UTM_zone_10N = 26910
NAD83_UTM_zone_11N = 26911
NAD83_UTM_zone_12N = 26912
NAD83_UTM_zone_13N = 26913
NAD83_UTM_zone_14N = 26914
NAD83_UTM_zone_15N = 26915
NAD83_UTM_zone_16N = 26916
NAD83_UTM_zone_17N = 26917
NAD83_UTM_zone_18N = 26918
NAD83_UTM_zone_19N = 26919
NAD83_UTM_zone_20N = 26920
NAD83_UTM_zone_21N = 26921
NAD83_UTM_zone_22N = 26922
NAD83_UTM_zone_23N = 26923
NAD83_UTM_zone_3N = 26903
NAD83_UTM_zone_4N = 26904
NAD83_UTM_zone_5N = 26905
NAD83_UTM_zone_6N = 26906
NAD83_UTM_zone_7N = 26907
NAD83_UTM_zone_8N = 26908
NAD83_UTM_zone_9N = 26909
NAD83_Utah_Central = 32143
NAD83_Utah_North = 32142
NAD83_Utah_South = 32144
NAD83_Vermont = 32145
NAD83_Virginia_North = 32146
NAD83_Virginia_South = 32147
NAD83_Washington_North = 32148
NAD83_Washington_South = 32149
NAD83_West_Virginia_N = 32150
NAD83_West_Virginia_S = 32151
NAD83_Wisconsin_Cen = 32153
NAD83_Wisconsin_North = 32152
NAD83_Wisconsin_South = 32154
NAD83_Wyoming_E_Cen = 32156
NAD83_Wyoming_East = 32155
NAD83_Wyoming_W_Cen = 32157
NAD83_Wyoming_West = 32158
NAD_Michigan_Michigan_East = 26801
NAD_Michigan_Michigan_Old_Central = 26802
NAD_Michigan_Michigan_West = 26803
NTF_Centre_France = 27592
NTF_France_I = 27581
NTF_France_II = 27582
NTF_France_III = 27583
NTF_Nord_France = 27591
NTF_Sud_France = 27593
Nahrwan_1967_UTM_38N = 27038
Nahrwan_1967_UTM_39N = 27039
Nahrwan_1967_UTM_40N = 27040
Naparima_UTM_20N = 27120
Nord_Sahara_UTM_29N = 30729
Nord_Sahara_UTM_30N = 30730
Nord_Sahara_UTM_31N = 30731
Nord_Sahara_UTM_32N = 30732
PSAD56_Peru_central = 24892
PSAD56_Peru_east_zone = 24893
PSAD56_Peru_west_zone = 24891
PSAD56_UTM_zone_17S = 24877
PSAD56_UTM_zone_18N = 24818
PSAD56_UTM_zone_18S = 24878
PSAD56_UTM_zone_19N = 24819
PSAD56_UTM_zone_19S = 24879
PSAD56_UTM_zone_20N = 24820
PSAD56_UTM_zone_20S = 24880
PSAD56_UTM_zone_21N = 24821
Point_Noire_UTM_32S = 28232
Pulkovo_Gauss_10N = 28470
Pulkovo_Gauss_11N = 28471
Pulkovo_Gauss_12N = 28472
Pulkovo_Gauss_13N = 28473
Pulkovo_Gauss_14N = 28474
Pulkovo_Gauss_15N = 28475
Pulkovo_Gauss_16N = 28476
Pulkovo_Gauss_17N = 28477
Pulkovo_Gauss_18N = 28478
Pulkovo_Gauss_19N = 28479
Pulkovo_Gauss_20N = 28480
Pulkovo_Gauss_21N = 28481
Pulkovo_Gauss_22N = 28482
Pulkovo_Gauss_23N = 28483
Pulkovo_Gauss_24N = 28484
Pulkovo_Gauss_25N = 28485
Pulkovo_Gauss_26N = 28486
Pulkovo_Gauss_27N = 28487
Pulkovo_Gauss_28N = 28488
Pulkovo_Gauss_29N = 28489
Pulkovo_Gauss_30N = 28490
Pulkovo_Gauss_31N = 28491
Pulkovo_Gauss_32N = 28492
Pulkovo_Gauss_4N = 28464
Pulkovo_Gauss_5N = 28465
Pulkovo_Gauss_6N = 28466
Pulkovo_Gauss_7N = 28467
Pulkovo_Gauss_8N = 28468
Pulkovo_Gauss_9N = 28469
Pulkovo_Gauss_zone_10 = 28410
Pulkovo_Gauss_zone_11 = 28411
Pulkovo_Gauss_zone_12 = 28412
Pulkovo_Gauss_zone_13 = 28413
Pulkovo_Gauss_zone_14 = 28414
Pulkovo_Gauss_zone_15 = 28415
Pulkovo_Gauss_zone_16 = 28416
Pulkovo_Gauss_zone_17 = 28417
Pulkovo_Gauss_zone_18 = 28418
Pulkovo_Gauss_zone_19 = 28419
Pulkovo_Gauss_zone_20 = 28420
Pulkovo_Gauss_zone_21 = 28421
Pulkovo_Gauss_zone_22 = 28422
Pulkovo_Gauss_zone_23 = 28423
Pulkovo_Gauss_zone_24 = 28424
Pulkovo_Gauss_zone_25 = 28425
Pulkovo_Gauss_zone_26 = 28426
Pulkovo_Gauss_zone_27 = 28427
Pulkovo_Gauss_zone_28 = 28428
Pulkovo_Gauss_zone_29 = 28429
Pulkovo_Gauss_zone_30 = 28430
Pulkovo_Gauss_zone_31 = 28431
Pulkovo_Gauss_zone_32 = 28432
Pulkovo_Gauss_zone_4 = 28404
Pulkovo_Gauss_zone_5 = 28405
Pulkovo_Gauss_zone_6 = 28406
Pulkovo_Gauss_zone_7 = 28407
Pulkovo_Gauss_zone_8 = 28408
Pulkovo_Gauss_zone_9 = 28409
Qatar_National_Grid = 28600
RD_Netherlands_New = 28992
RD_Netherlands_Old = 28991
RT90_2_5_gon_W = 2400
SAD69_UTM_zone_17S = 29177
SAD69_UTM_zone_18N = 29118
SAD69_UTM_zone_18S = 29178
SAD69_UTM_zone_19N = 29119
SAD69_UTM_zone_19S = 29179
SAD69_UTM_zone_20N = 29120
SAD69_UTM_zone_20S = 29180
SAD69_UTM_zone_21N = 29121
SAD69_UTM_zone_21S = 29181
SAD69_UTM_zone_22N = 29122
SAD69_UTM_zone_22S = 29182
SAD69_UTM_zone_23S = 29183
SAD69_UTM_zone_24S = 29184
SAD69_UTM_zone_25S = 29185
Sapper_Hill_UTM_20S = 29220
Sapper_Hill_UTM_21S = 29221
Schwarzeck_UTM_33S = 29333
Sudan_UTM_zone_35N = 29635
Sudan_UTM_zone_36N = 29636
TC_1948_UTM_zone_39N = 30339
TC_1948_UTM_zone_40N = 30340
TM65_Irish_Nat_Grid = 29900
Tananarive_Laborde = 29700
Tananarive_UTM_38S = 29738
Tananarive_UTM_39S = 29739
Timbalai_1948_Borneo = 29800
Timbalai_1948_UTM_49N = 29849
Timbalai_1948_UTM_50N = 29850
Trinidad_1903_Trinidad = 30200
Undefined = 0
User_Defined = 32767
Voirol_N_Algerie_ancien = 30491
Voirol_S_Algerie_ancien = 30492
Voirol_Unifie_N_Algerie = 30591
Voirol_Unifie_S_Algerie = 30592
WGS72BE_UTM_zone_10N = 32410
WGS72BE_UTM_zone_10S = 32510
WGS72BE_UTM_zone_11N = 32411
WGS72BE_UTM_zone_11S = 32511
WGS72BE_UTM_zone_12N = 32412
WGS72BE_UTM_zone_12S = 32512
WGS72BE_UTM_zone_13N = 32413
WGS72BE_UTM_zone_13S = 32513
WGS72BE_UTM_zone_14N = 32414
WGS72BE_UTM_zone_14S = 32514
WGS72BE_UTM_zone_15N = 32415
WGS72BE_UTM_zone_15S = 32515
WGS72BE_UTM_zone_16N = 32416
WGS72BE_UTM_zone_16S = 32516
WGS72BE_UTM_zone_17N = 32417
WGS72BE_UTM_zone_17S = 32517
WGS72BE_UTM_zone_18N = 32418
WGS72BE_UTM_zone_18S = 32518
WGS72BE_UTM_zone_19N = 32419
WGS72BE_UTM_zone_19S = 32519
WGS72BE_UTM_zone_1N = 32401
WGS72BE_UTM_zone_1S = 32501
WGS72BE_UTM_zone_20N = 32420
WGS72BE_UTM_zone_20S = 32520
WGS72BE_UTM_zone_21N = 32421
WGS72BE_UTM_zone_21S = 32521
WGS72BE_UTM_zone_22N = 32422
WGS72BE_UTM_zone_22S = 32522
WGS72BE_UTM_zone_23N = 32423
WGS72BE_UTM_zone_23S = 32523
WGS72BE_UTM_zone_24N = 32424
WGS72BE_UTM_zone_24S = 32524
WGS72BE_UTM_zone_25N = 32425
WGS72BE_UTM_zone_25S = 32525
WGS72BE_UTM_zone_26N = 32426
WGS72BE_UTM_zone_26S = 32526
WGS72BE_UTM_zone_27N = 32427
WGS72BE_UTM_zone_27S = 32527
WGS72BE_UTM_zone_28N = 32428
WGS72BE_UTM_zone_28S = 32528
WGS72BE_UTM_zone_29N = 32429
WGS72BE_UTM_zone_29S = 32529
WGS72BE_UTM_zone_2N = 32402
WGS72BE_UTM_zone_2S = 32502
WGS72BE_UTM_zone_30N = 32430
WGS72BE_UTM_zone_30S = 32530
WGS72BE_UTM_zone_31N = 32431
WGS72BE_UTM_zone_31S = 32531
WGS72BE_UTM_zone_32N = 32432
WGS72BE_UTM_zone_32S = 32532
WGS72BE_UTM_zone_33N = 32433
WGS72BE_UTM_zone_33S = 32533
WGS72BE_UTM_zone_34N = 32434
WGS72BE_UTM_zone_34S = 32534
WGS72BE_UTM_zone_35N = 32435
WGS72BE_UTM_zone_35S = 32535
WGS72BE_UTM_zone_36N = 32436
WGS72BE_UTM_zone_36S = 32536
WGS72BE_UTM_zone_37N = 32437
WGS72BE_UTM_zone_37S = 32537
WGS72BE_UTM_zone_38N = 32438
WGS72BE_UTM_zone_38S = 32538
WGS72BE_UTM_zone_39N = 32439
WGS72BE_UTM_zone_39S = 32539
WGS72BE_UTM_zone_3N = 32403
WGS72BE_UTM_zone_3S = 32503
WGS72BE_UTM_zone_40N = 32440
WGS72BE_UTM_zone_40S = 32540
WGS72BE_UTM_zone_41N = 32441
WGS72BE_UTM_zone_41S = 32541
WGS72BE_UTM_zone_42N = 32442
WGS72BE_UTM_zone_42S = 32542
WGS72BE_UTM_zone_43N = 32443
WGS72BE_UTM_zone_43S = 32543
WGS72BE_UTM_zone_44N = 32444
WGS72BE_UTM_zone_44S = 32544
WGS72BE_UTM_zone_45N = 32445
WGS72BE_UTM_zone_45S = 32545
WGS72BE_UTM_zone_46N = 32446
WGS72BE_UTM_zone_46S = 32546
WGS72BE_UTM_zone_47N = 32447
WGS72BE_UTM_zone_47S = 32547
WGS72BE_UTM_zone_48N = 32448
WGS72BE_UTM_zone_48S = 32548
WGS72BE_UTM_zone_49N = 32449
WGS72BE_UTM_zone_49S = 32549
WGS72BE_UTM_zone_4N = 32404
WGS72BE_UTM_zone_4S = 32504
WGS72BE_UTM_zone_50N = 32450
WGS72BE_UTM_zone_50S = 32550
WGS72BE_UTM_zone_51N = 32451
WGS72BE_UTM_zone_51S = 32551
WGS72BE_UTM_zone_52N = 32452
WGS72BE_UTM_zone_52S = 32552
WGS72BE_UTM_zone_53N = 32453
WGS72BE_UTM_zone_53S = 32553
WGS72BE_UTM_zone_54N = 32454
WGS72BE_UTM_zone_54S = 32554
WGS72BE_UTM_zone_55N = 32455
WGS72BE_UTM_zone_55S = 32555
WGS72BE_UTM_zone_56N = 32456
WGS72BE_UTM_zone_56S = 32556
WGS72BE_UTM_zone_57N = 32457
WGS72BE_UTM_zone_57S = 32557
WGS72BE_UTM_zone_58N = 32458
WGS72BE_UTM_zone_58S = 32558
WGS72BE_UTM_zone_59N = 32459
WGS72BE_UTM_zone_59S = 32559
WGS72BE_UTM_zone_5N = 32405
WGS72BE_UTM_zone_5S = 32505
WGS72BE_UTM_zone_60N = 32460
WGS72BE_UTM_zone_60S = 32560
WGS72BE_UTM_zone_6N = 32406
WGS72BE_UTM_zone_6S = 32506
WGS72BE_UTM_zone_7N = 32407
WGS72BE_UTM_zone_7S = 32507
WGS72BE_UTM_zone_8N = 32408
WGS72BE_UTM_zone_8S = 32508
WGS72BE_UTM_zone_9N = 32409
WGS72BE_UTM_zone_9S = 32509
WGS72_UTM_zone_10N = 32210
WGS72_UTM_zone_10S = 32310
WGS72_UTM_zone_11N = 32211
WGS72_UTM_zone_11S = 32311
WGS72_UTM_zone_12N = 32212
WGS72_UTM_zone_12S = 32312
WGS72_UTM_zone_13N = 32213
WGS72_UTM_zone_13S = 32313
WGS72_UTM_zone_14N = 32214
WGS72_UTM_zone_14S = 32314
WGS72_UTM_zone_15N = 32215
WGS72_UTM_zone_15S = 32315
WGS72_UTM_zone_16N = 32216
WGS72_UTM_zone_16S = 32316
WGS72_UTM_zone_17N = 32217
WGS72_UTM_zone_17S = 32317
WGS72_UTM_zone_18N = 32218
WGS72_UTM_zone_18S = 32318
WGS72_UTM_zone_19N = 32219
WGS72_UTM_zone_19S = 32319
WGS72_UTM_zone_1N = 32201
WGS72_UTM_zone_1S = 32301
WGS72_UTM_zone_20N = 32220
WGS72_UTM_zone_20S = 32320
WGS72_UTM_zone_21N = 32221
WGS72_UTM_zone_21S = 32321
WGS72_UTM_zone_22N = 32222
WGS72_UTM_zone_22S = 32322
WGS72_UTM_zone_23N = 32223
WGS72_UTM_zone_23S = 32323
WGS72_UTM_zone_24N = 32224
WGS72_UTM_zone_24S = 32324
WGS72_UTM_zone_25N = 32225
WGS72_UTM_zone_25S = 32325
WGS72_UTM_zone_26N = 32226
WGS72_UTM_zone_26S = 32326
WGS72_UTM_zone_27N = 32227
WGS72_UTM_zone_27S = 32327
WGS72_UTM_zone_28N = 32228
WGS72_UTM_zone_28S = 32328
WGS72_UTM_zone_29N = 32229
WGS72_UTM_zone_29S = 32329
WGS72_UTM_zone_2N = 32202
WGS72_UTM_zone_2S = 32302
WGS72_UTM_zone_30N = 32230
WGS72_UTM_zone_30S = 32330
WGS72_UTM_zone_31N = 32231
WGS72_UTM_zone_31S = 32331
WGS72_UTM_zone_32N = 32232
WGS72_UTM_zone_32S = 32332
WGS72_UTM_zone_33N = 32233
WGS72_UTM_zone_33S = 32333
WGS72_UTM_zone_34N = 32234
WGS72_UTM_zone_34S = 32334
WGS72_UTM_zone_35N = 32235
WGS72_UTM_zone_35S = 32335
WGS72_UTM_zone_36N = 32236
WGS72_UTM_zone_36S = 32336
WGS72_UTM_zone_37N = 32237
WGS72_UTM_zone_37S = 32337
WGS72_UTM_zone_38N = 32238
WGS72_UTM_zone_38S = 32338
WGS72_UTM_zone_39N = 32239
WGS72_UTM_zone_39S = 32339
WGS72_UTM_zone_3N = 32203
WGS72_UTM_zone_3S = 32303
WGS72_UTM_zone_40N = 32240
WGS72_UTM_zone_40S = 32340
WGS72_UTM_zone_41N = 32241
WGS72_UTM_zone_41S = 32341
WGS72_UTM_zone_42N = 32242
WGS72_UTM_zone_42S = 32342
WGS72_UTM_zone_43N = 32243
WGS72_UTM_zone_43S = 32343
WGS72_UTM_zone_44N = 32244
WGS72_UTM_zone_44S = 32344
WGS72_UTM_zone_45N = 32245
WGS72_UTM_zone_45S = 32345
WGS72_UTM_zone_46N = 32246
WGS72_UTM_zone_46S = 32346
WGS72_UTM_zone_47N = 32247
WGS72_UTM_zone_47S = 32347
WGS72_UTM_zone_48N = 32248
WGS72_UTM_zone_48S = 32348
WGS72_UTM_zone_49N = 32249
WGS72_UTM_zone_49S = 32349
WGS72_UTM_zone_4N = 32204
WGS72_UTM_zone_4S = 32304
WGS72_UTM_zone_50N = 32250
WGS72_UTM_zone_50S = 32350
WGS72_UTM_zone_51N = 32251
WGS72_UTM_zone_51S = 32351
WGS72_UTM_zone_52N = 32252
WGS72_UTM_zone_52S = 32352
WGS72_UTM_zone_53N = 32253
WGS72_UTM_zone_53S = 32353
WGS72_UTM_zone_54N = 32254
WGS72_UTM_zone_54S = 32354
WGS72_UTM_zone_55N = 32255
WGS72_UTM_zone_55S = 32355
WGS72_UTM_zone_56N = 32256
WGS72_UTM_zone_56S = 32356
WGS72_UTM_zone_57N = 32257
WGS72_UTM_zone_57S = 32357
WGS72_UTM_zone_58N = 32258
WGS72_UTM_zone_58S = 32358
WGS72_UTM_zone_59N = 32259
WGS72_UTM_zone_59S = 32359
WGS72_UTM_zone_5N = 32205
WGS72_UTM_zone_5S = 32305
WGS72_UTM_zone_60N = 32260
WGS72_UTM_zone_60S = 32360
WGS72_UTM_zone_6N = 32206
WGS72_UTM_zone_6S = 32306
WGS72_UTM_zone_7N = 32207
WGS72_UTM_zone_7S = 32307
WGS72_UTM_zone_8N = 32208
WGS72_UTM_zone_8S = 32308
WGS72_UTM_zone_9N = 32209
WGS72_UTM_zone_9S = 32309
WGS84_UTM_zone_10N = 32610
WGS84_UTM_zone_10S = 32710
WGS84_UTM_zone_11N = 32611
WGS84_UTM_zone_11S = 32711
WGS84_UTM_zone_12N = 32612
WGS84_UTM_zone_12S = 32712
WGS84_UTM_zone_13N = 32613
WGS84_UTM_zone_13S = 32713
WGS84_UTM_zone_14N = 32614
WGS84_UTM_zone_14S = 32714
WGS84_UTM_zone_15N = 32615
WGS84_UTM_zone_15S = 32715
WGS84_UTM_zone_16N = 32616
WGS84_UTM_zone_16S = 32716
WGS84_UTM_zone_17N = 32617
WGS84_UTM_zone_17S = 32717
WGS84_UTM_zone_18N = 32618
WGS84_UTM_zone_18S = 32718
WGS84_UTM_zone_19N = 32619
WGS84_UTM_zone_19S = 32719
WGS84_UTM_zone_1N = 32601
WGS84_UTM_zone_1S = 32701
WGS84_UTM_zone_20N = 32620
WGS84_UTM_zone_20S = 32720
WGS84_UTM_zone_21N = 32621
WGS84_UTM_zone_21S = 32721
WGS84_UTM_zone_22N = 32622
WGS84_UTM_zone_22S = 32722
WGS84_UTM_zone_23N = 32623
WGS84_UTM_zone_23S = 32723
WGS84_UTM_zone_24N = 32624
WGS84_UTM_zone_24S = 32724
WGS84_UTM_zone_25N = 32625
WGS84_UTM_zone_25S = 32725
WGS84_UTM_zone_26N = 32626
WGS84_UTM_zone_26S = 32726
WGS84_UTM_zone_27N = 32627
WGS84_UTM_zone_27S = 32727
WGS84_UTM_zone_28N = 32628
WGS84_UTM_zone_28S = 32728
WGS84_UTM_zone_29N = 32629
WGS84_UTM_zone_29S = 32729
WGS84_UTM_zone_2N = 32602
WGS84_UTM_zone_2S = 32702
WGS84_UTM_zone_30N = 32630
WGS84_UTM_zone_30S = 32730
WGS84_UTM_zone_31N = 32631
WGS84_UTM_zone_31S = 32731
WGS84_UTM_zone_32N = 32632
WGS84_UTM_zone_32S = 32732
WGS84_UTM_zone_33N = 32633
WGS84_UTM_zone_33S = 32733
WGS84_UTM_zone_34N = 32634
WGS84_UTM_zone_34S = 32734
WGS84_UTM_zone_35N = 32635
WGS84_UTM_zone_35S = 32735
WGS84_UTM_zone_36N = 32636
WGS84_UTM_zone_36S = 32736
WGS84_UTM_zone_37N = 32637
WGS84_UTM_zone_37S = 32737
WGS84_UTM_zone_38N = 32638
WGS84_UTM_zone_38S = 32738
WGS84_UTM_zone_39N = 32639
WGS84_UTM_zone_39S = 32739
WGS84_UTM_zone_3N = 32603
WGS84_UTM_zone_3S = 32703
WGS84_UTM_zone_40N = 32640
WGS84_UTM_zone_40S = 32740
WGS84_UTM_zone_41N = 32641
WGS84_UTM_zone_41S = 32741
WGS84_UTM_zone_42N = 32642
WGS84_UTM_zone_42S = 32742
WGS84_UTM_zone_43N = 32643
WGS84_UTM_zone_43S = 32743
WGS84_UTM_zone_44N = 32644
WGS84_UTM_zone_44S = 32744
WGS84_UTM_zone_45N = 32645
WGS84_UTM_zone_45S = 32745
WGS84_UTM_zone_46N = 32646
WGS84_UTM_zone_46S = 32746
WGS84_UTM_zone_47N = 32647
WGS84_UTM_zone_47S = 32747
WGS84_UTM_zone_48N = 32648
WGS84_UTM_zone_48S = 32748
WGS84_UTM_zone_49N = 32649
WGS84_UTM_zone_49S = 32749
WGS84_UTM_zone_4N = 32604
WGS84_UTM_zone_4S = 32704
WGS84_UTM_zone_50N = 32650
WGS84_UTM_zone_50S = 32750
WGS84_UTM_zone_51N = 32651
WGS84_UTM_zone_51S = 32751
WGS84_UTM_zone_52N = 32652
WGS84_UTM_zone_52S = 32752
WGS84_UTM_zone_53N = 32653
WGS84_UTM_zone_53S = 32753
WGS84_UTM_zone_54N = 32654
WGS84_UTM_zone_54S = 32754
WGS84_UTM_zone_55N = 32655
WGS84_UTM_zone_55S = 32755
WGS84_UTM_zone_56N = 32656
WGS84_UTM_zone_56S = 32756
WGS84_UTM_zone_57N = 32657
WGS84_UTM_zone_57S = 32757
WGS84_UTM_zone_58N = 32658
WGS84_UTM_zone_58S = 32758
WGS84_UTM_zone_59N = 32659
WGS84_UTM_zone_59S = 32759
WGS84_UTM_zone_5N = 32605
WGS84_UTM_zone_5S = 32705
WGS84_UTM_zone_60N = 32660
WGS84_UTM_zone_60S = 32760
WGS84_UTM_zone_6N = 32606
WGS84_UTM_zone_6S = 32706
WGS84_UTM_zone_7N = 32607
WGS84_UTM_zone_7S = 32707
WGS84_UTM_zone_8N = 32608
WGS84_UTM_zone_8S = 32708
WGS84_UTM_zone_9N = 32609
WGS84_UTM_zone_9S = 32709
Yoff_UTM_zone_28N = 31028
Zanderij_UTM_zone_21N = 31121
class tifffile.tifffile_geodb.PM

Bases: enum.IntEnum

Prime Meridian Codes.

Bern = 8907
Bogota = 8904
Brussels = 8910
Ferro = 8909
Greenwich = 8901
Jakarta = 8908
Lisbon = 8902
Madrid = 8905
Paris = 8903
Rome = 8906
Stockholm = 8911
Undefined = 0
User_Defined = 32767
class tifffile.tifffile_geodb.Proj

Bases: enum.IntEnum

Projection Codes.

Alabama_CS27_East = 10101
Alabama_CS27_West = 10102
Alabama_CS83_East = 10131
Alabama_CS83_West = 10132
Alaska_CS27_1 = 15001
Alaska_CS27_10 = 15010
Alaska_CS27_2 = 15002
Alaska_CS27_3 = 15003
Alaska_CS27_4 = 15004
Alaska_CS27_5 = 15005
Alaska_CS27_6 = 15006
Alaska_CS27_7 = 15007
Alaska_CS27_8 = 15008
Alaska_CS27_9 = 15009
Alaska_CS83_1 = 15031
Alaska_CS83_10 = 15040
Alaska_CS83_2 = 15032
Alaska_CS83_3 = 15033
Alaska_CS83_4 = 15034
Alaska_CS83_5 = 15035
Alaska_CS83_6 = 15036
Alaska_CS83_7 = 15037
Alaska_CS83_8 = 15038
Alaska_CS83_9 = 15039
Argentina_1 = 18031
Argentina_2 = 18032
Argentina_3 = 18033
Argentina_4 = 18034
Argentina_5 = 18035
Argentina_6 = 18036
Argentina_7 = 18037
Arizona_CS83_Central = 10232
Arizona_CS83_east = 10231
Arizona_CS83_west = 10233
Arizona_Coordinate_System_Central = 10202
Arizona_Coordinate_System_east = 10201
Arizona_Coordinate_System_west = 10203
Arkansas_CS27_North = 10301
Arkansas_CS27_South = 10302
Arkansas_CS83_North = 10331
Arkansas_CS83_South = 10332
Australian_Map_Grid_48 = 17448
Australian_Map_Grid_49 = 17449
Australian_Map_Grid_50 = 17450
Australian_Map_Grid_51 = 17451
Australian_Map_Grid_52 = 17452
Australian_Map_Grid_53 = 17453
Australian_Map_Grid_54 = 17454
Australian_Map_Grid_55 = 17455
Australian_Map_Grid_56 = 17456
Australian_Map_Grid_57 = 17457
Australian_Map_Grid_58 = 17458
BLM_14N_feet = 15914
BLM_15N_feet = 15915
BLM_16N_feet = 15916
BLM_17N_feet = 15917
Bahrain_Grid = 19900
California_CS27_I = 10401
California_CS27_II = 10402
California_CS27_III = 10403
California_CS27_IV = 10404
California_CS27_V = 10405
California_CS27_VI = 10406
California_CS27_VII = 10407
California_CS83_1 = 10431
California_CS83_2 = 10432
California_CS83_3 = 10433
California_CS83_4 = 10434
California_CS83_5 = 10435
California_CS83_6 = 10436
Colombia_3E = 18053
Colombia_3W = 18051
Colombia_6E = 18054
Colombia_Bogota = 18052
Colorado_CS27_Central = 10502
Colorado_CS27_North = 10501
Colorado_CS27_South = 10503
Colorado_CS83_Central = 10532
Colorado_CS83_North = 10531
Colorado_CS83_South = 10533
Connecticut_CS27 = 10600
Connecticut_CS83 = 10630
Delaware_CS27 = 10700
Delaware_CS83 = 10730
Egypt_Purple_Belt = 18073
Egypt_Red_Belt = 18072
Extended_Purple_Belt = 18074
Florida_CS27_East = 10901
Florida_CS27_North = 10903
Florida_CS27_West = 10902
Florida_CS83_East = 10931
Florida_CS83_North = 10933
Florida_CS83_West = 10932
Gauss_Kruger_zone_0 = 16200
Gauss_Kruger_zone_1 = 16201
Gauss_Kruger_zone_2 = 16202
Gauss_Kruger_zone_3 = 16203
Gauss_Kruger_zone_4 = 16204
Gauss_Kruger_zone_5 = 16205
Georgia_CS27_East = 11001
Georgia_CS27_West = 11002
Georgia_CS83_East = 11031
Georgia_CS83_West = 11032
Hawaii_CS27_1 = 15101
Hawaii_CS27_2 = 15102
Hawaii_CS27_3 = 15103
Hawaii_CS27_4 = 15104
Hawaii_CS27_5 = 15105
Hawaii_CS83_1 = 15131
Hawaii_CS83_2 = 15132
Hawaii_CS83_3 = 15133
Hawaii_CS83_4 = 15134
Hawaii_CS83_5 = 15135
Idaho_CS27_Central = 11102
Idaho_CS27_East = 11101
Idaho_CS27_West = 11103
Idaho_CS83_Central = 11132
Idaho_CS83_East = 11131
Idaho_CS83_West = 11133
Illinois_CS27_East = 11201
Illinois_CS27_West = 11202
Illinois_CS83_East = 11231
Illinois_CS83_West = 11232
Indiana_CS27_East = 11301
Indiana_CS27_West = 11302
Indiana_CS83_East = 11331
Indiana_CS83_West = 11332
Iowa_CS27_North = 11401
Iowa_CS27_South = 11402
Iowa_CS83_North = 11431
Iowa_CS83_South = 11432
Kansas_CS27_North = 11501
Kansas_CS27_South = 11502
Kansas_CS83_North = 11531
Kansas_CS83_South = 11532
Kentucky_CS27_North = 11601
Kentucky_CS27_South = 11602
Kentucky_CS83_North = 15303
Kentucky_CS83_South = 11632
Louisiana_CS27_North = 11701
Louisiana_CS27_South = 11702
Louisiana_CS83_North = 11731
Louisiana_CS83_South = 11732
Maine_CS27_East = 11801
Maine_CS27_West = 11802
Maine_CS83_East = 11831
Maine_CS83_West = 11832
Map_Grid_of_Australia_48 = 17348
Map_Grid_of_Australia_49 = 17349
Map_Grid_of_Australia_50 = 17350
Map_Grid_of_Australia_51 = 17351
Map_Grid_of_Australia_52 = 17352
Map_Grid_of_Australia_53 = 17353
Map_Grid_of_Australia_54 = 17354
Map_Grid_of_Australia_55 = 17355
Map_Grid_of_Australia_56 = 17356
Map_Grid_of_Australia_57 = 17357
Map_Grid_of_Australia_58 = 17358
Maryland_CS27 = 11900
Maryland_CS83 = 11930
Massachusetts_CS27_Island = 12002
Massachusetts_CS27_Mainland = 12001
Massachusetts_CS83_Island = 12032
Massachusetts_CS83_Mainland = 12031
Michigan_CS27_Central = 12112
Michigan_CS27_North = 12111
Michigan_CS27_South = 12113
Michigan_CS83_Central = 12142
Michigan_CS83_North = 12141
Michigan_CS83_South = 12143
Michigan_State_Plane_East = 12101
Michigan_State_Plane_Old_Central = 12102
Michigan_State_Plane_West = 12103
Minnesota_CS27_Central = 12202
Minnesota_CS27_North = 12201
Minnesota_CS27_South = 12203
Minnesota_CS83_Central = 12232
Minnesota_CS83_North = 12231
Minnesota_CS83_South = 12233
Mississippi_CS27_East = 12301
Mississippi_CS27_West = 12302
Mississippi_CS83_East = 12331
Mississippi_CS83_West = 12332
Missouri_CS27_Central = 12402
Missouri_CS27_East = 12401
Missouri_CS27_West = 12403
Missouri_CS83_Central = 12432
Missouri_CS83_East = 12431
Missouri_CS83_West = 12433
Montana_CS27_Central = 12502
Montana_CS27_North = 12501
Montana_CS27_South = 12503
Montana_CS83 = 12530
Nebraska_CS27_North = 12601
Nebraska_CS27_South = 12602
Nebraska_CS83 = 12630
Netherlands_E_Indies_Equatorial = 19905
Nevada_CS27_Central = 12702
Nevada_CS27_East = 12701
Nevada_CS27_West = 12703
Nevada_CS83_Central = 12732
Nevada_CS83_East = 12731
Nevada_CS83_West = 12733
New_Hampshire_CS27 = 12800
New_Hampshire_CS83 = 12830
New_Jersey_CS27 = 12900
New_Jersey_CS83 = 12930
New_Mexico_CS27_Central = 13002
New_Mexico_CS27_East = 13001
New_Mexico_CS27_West = 13003
New_Mexico_CS83_Central = 13032
New_Mexico_CS83_East = 13031
New_Mexico_CS83_West = 13033
New_York_CS27_Central = 13102
New_York_CS27_East = 13101
New_York_CS27_Long_Island = 13104
New_York_CS27_West = 13103
New_York_CS83_Central = 13132
New_York_CS83_East = 13131
New_York_CS83_Long_Island = 13134
New_York_CS83_West = 13133
New_Zealand_North_Island_Nat_Grid = 18141
New_Zealand_South_Island_Nat_Grid = 18142
North_Carolina_CS27 = 13200
North_Carolina_CS83 = 13230
North_Dakota_CS27_North = 13301
North_Dakota_CS27_South = 13302
North_Dakota_CS83_North = 13331
North_Dakota_CS83_South = 13332
Ohio_CS27_North = 13401
Ohio_CS27_South = 13402
Ohio_CS83_North = 13431
Ohio_CS83_South = 13432
Oklahoma_CS27_North = 13501
Oklahoma_CS27_South = 13502
Oklahoma_CS83_North = 13531
Oklahoma_CS83_South = 13532
Oregon_CS27_North = 13601
Oregon_CS27_South = 13602
Oregon_CS83_North = 13631
Oregon_CS83_South = 13632
Pennsylvania_CS27_North = 13701
Pennsylvania_CS27_South = 13702
Pennsylvania_CS83_North = 13731
Pennsylvania_CS83_South = 13732
Puerto_Rico_CS27 = 15201
Puerto_Rico_Virgin_Is = 15230
RSO_Borneo = 19912
Rhode_Island_CS27 = 13800
Rhode_Island_CS83 = 13830
South_Carolina_CS27_North = 13901
South_Carolina_CS27_South = 13902
South_Carolina_CS83 = 13930
South_Dakota_CS27_North = 14001
South_Dakota_CS27_South = 14002
South_Dakota_CS83_North = 14031
South_Dakota_CS83_South = 14032
St_Croix = 15202
Stereo_70 = 19926
Tennessee_CS27 = 15302
Tennessee_CS83 = 14130
Texas_CS27_Central = 14203
Texas_CS27_North = 14201
Texas_CS27_North_Central = 14202
Texas_CS27_South = 14205
Texas_CS27_South_Central = 14204
Texas_CS83_Central = 14233
Texas_CS83_North = 14231
Texas_CS83_North_Central = 14232
Texas_CS83_South = 14235
Texas_CS83_South_Central = 14234
UTM_zone_10N = 16010
UTM_zone_10S = 16110
UTM_zone_11N = 16011
UTM_zone_11S = 16111
UTM_zone_12N = 16012
UTM_zone_12S = 16112
UTM_zone_13N = 16013
UTM_zone_13S = 16113
UTM_zone_14N = 16014
UTM_zone_14S = 16114
UTM_zone_15N = 16015
UTM_zone_15S = 16115
UTM_zone_16N = 16016
UTM_zone_16S = 16116
UTM_zone_17N = 16017
UTM_zone_17S = 16117
UTM_zone_18N = 16018
UTM_zone_18S = 16118
UTM_zone_19N = 16019
UTM_zone_19S = 16119
UTM_zone_1N = 16001
UTM_zone_1S = 16101
UTM_zone_20N = 16020
UTM_zone_20S = 16120
UTM_zone_21N = 16021
UTM_zone_21S = 16121
UTM_zone_22N = 16022
UTM_zone_22S = 16122
UTM_zone_23N = 16023
UTM_zone_23S = 16123
UTM_zone_24N = 16024
UTM_zone_24S = 16124
UTM_zone_25N = 16025
UTM_zone_25S = 16125
UTM_zone_26N = 16026
UTM_zone_26S = 16126
UTM_zone_27N = 16027
UTM_zone_27S = 16127
UTM_zone_28N = 16028
UTM_zone_28S = 16128
UTM_zone_29N = 16029
UTM_zone_29S = 16129
UTM_zone_2N = 16002
UTM_zone_2S = 16102
UTM_zone_30N = 16030
UTM_zone_30S = 16130
UTM_zone_31N = 16031
UTM_zone_31S = 16131
UTM_zone_32N = 16032
UTM_zone_32S = 16132
UTM_zone_33N = 16033
UTM_zone_33S = 16133
UTM_zone_34N = 16034
UTM_zone_34S = 16134
UTM_zone_35N = 16035
UTM_zone_35S = 16135
UTM_zone_36N = 16036
UTM_zone_36S = 16136
UTM_zone_37N = 16037
UTM_zone_37S = 16137
UTM_zone_38N = 16038
UTM_zone_38S = 16138
UTM_zone_39N = 16039
UTM_zone_39S = 16139
UTM_zone_3N = 16003
UTM_zone_3S = 16103
UTM_zone_40N = 16040
UTM_zone_40S = 16140
UTM_zone_41N = 16041
UTM_zone_41S = 16141
UTM_zone_42N = 16042
UTM_zone_42S = 16142
UTM_zone_43N = 16043
UTM_zone_43S = 16143
UTM_zone_44N = 16044
UTM_zone_44S = 16144
UTM_zone_45N = 16045
UTM_zone_45S = 16145
UTM_zone_46N = 16046
UTM_zone_46S = 16146
UTM_zone_47N = 16047
UTM_zone_47S = 16147
UTM_zone_48N = 16048
UTM_zone_48S = 16148
UTM_zone_49N = 16049
UTM_zone_49S = 16149
UTM_zone_4N = 16004
UTM_zone_4S = 16104
UTM_zone_50N = 16050
UTM_zone_50S = 16150
UTM_zone_51N = 16051
UTM_zone_51S = 16151
UTM_zone_52N = 16052
UTM_zone_52S = 16152
UTM_zone_53N = 16053
UTM_zone_53S = 16153
UTM_zone_54N = 16054
UTM_zone_54S = 16154
UTM_zone_55N = 16055
UTM_zone_55S = 16155
UTM_zone_56N = 16056
UTM_zone_56S = 16156
UTM_zone_57N = 16057
UTM_zone_57S = 16157
UTM_zone_58N = 16058
UTM_zone_58S = 16158
UTM_zone_59N = 16059
UTM_zone_59S = 16159
UTM_zone_5N = 16005
UTM_zone_5S = 16105
UTM_zone_60N = 16060
UTM_zone_60S = 16160
UTM_zone_6N = 16006
UTM_zone_6S = 16106
UTM_zone_7N = 16007
UTM_zone_7S = 16107
UTM_zone_8N = 16008
UTM_zone_8S = 16108
UTM_zone_9N = 16009
UTM_zone_9S = 16109
Undefined = 0
User_Defined = 32767
Utah_CS27_Central = 14302
Utah_CS27_North = 14301
Utah_CS27_South = 14303
Utah_CS83_Central = 14332
Utah_CS83_North = 14331
Utah_CS83_South = 14333
Vermont_CS27 = 14400
Vermont_CS83 = 14430
Virginia_CS27_North = 14501
Virginia_CS27_South = 14502
Virginia_CS83_North = 14531
Virginia_CS83_South = 14532
Washington_CS27_North = 14601
Washington_CS27_South = 14602
Washington_CS83_North = 14631
Washington_CS83_South = 14632
West_Virginia_CS27_North = 14701
West_Virginia_CS27_South = 14702
West_Virginia_CS83_North = 14731
West_Virginia_CS83_South = 14732
Wisconsin_CS27_Central = 14802
Wisconsin_CS27_North = 14801
Wisconsin_CS27_South = 14803
Wisconsin_CS83_Central = 14832
Wisconsin_CS83_North = 14831
Wisconsin_CS83_South = 14833
Wyoming_CS27_East = 14901
Wyoming_CS27_East_Central = 14902
Wyoming_CS27_West = 14904
Wyoming_CS27_West_Central = 14903
Wyoming_CS83_East = 14931
Wyoming_CS83_East_Central = 14932
Wyoming_CS83_West = 14934
Wyoming_CS83_West_Central = 14933
class tifffile.tifffile_geodb.RasterPixel

Bases: enum.IntEnum

Raster Type Codes.

IsArea = 1
IsPoint = 2
Undefined = 0
User_Defined = 32767
class tifffile.tifffile_geodb.VertCS

Bases: enum.IntEnum

Vertical CS Type Codes.

ANS_ellipsoid = 5003
Airy_1830_ellipsoid = 5001
Airy_Modified_1849_ellipsoid = 5002
Baltic_Sea = 5105
Bessel_1841_ellipsoid = 5004
Bessel_Modified_ellipsoid = 5005
Bessel_Namibia_ellipsoid = 5006
Caspian_Sea = 5106
Clarke_1858_ellipsoid = 5007
Clarke_1866_ellipsoid = 5008
Clarke_1880_Arc_ellipsoid = 5013
Clarke_1880_Benoit_ellipsoid = 5010
Clarke_1880_IGN_ellipsoid = 5011
Clarke_1880_RGS_ellipsoid = 5012
Clarke_1880_SGA_1922_ellipsoid = 5014
Everest_1830_1937_Adjustment_ellipsoid = 5015
Everest_1830_1967_Definition_ellipsoid = 5016
Everest_1830_1975_Definition_ellipsoid = 5017
Everest_1830_Modified_ellipsoid = 5018
GEM_10C_ellipsoid = 5031
GRS_1980_ellipsoid = 5019
Helmert_1906_ellipsoid = 5020
INS_ellipsoid = 5021
International_1924_ellipsoid = 5022
International_1967_ellipsoid = 5023
Krassowsky_1940_ellipsoid = 5024
NWL_10D_ellipsoid = 5026
NWL_9D_ellipsoid = 5025
Newlyn = 5101
North_American_Vertical_Datum_1929 = 5102
North_American_Vertical_Datum_1988 = 5103
OSU86F_ellipsoid = 5032
OSU91A_ellipsoid = 5033
Plessis_1817_ellipsoid = 5027
Struve_1860_ellipsoid = 5028
Undefined = 0
User_Defined = 32767
WGS_84_ellipsoid = 5030
War_Office_ellipsoid = 5029
Yellow_Sea_1956 = 5104

Module contents

Read and write TIFF(r) files.

Tifffile is a Python library to

  1. store numpy arrays in TIFF (Tagged Image File Format) files, and

  2. read image and metadata from TIFF-like files used in bioimaging.

Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, SGI, NIHImage, ImageJ, MicroManager, FluoView, ScanImage, SEQ, GEL, SVS, SCN, SIS, ZIF, QPI, NDPI, and GeoTIFF files.

Numpy arrays can be written to TIFF, BigTIFF, and ImageJ hyperstack compatible files in multi-page, memory-mappable, tiled, predicted, or compressed form.

Only a subset of the TIFF specification is supported, mainly uncompressed and losslessly compressed 1, 8, 16, 32 and 64-bit integer, 16, 32 and 64-bit float, grayscale and RGB(A) images. Specifically, reading slices of image data, CCITT and OJPEG compression, chroma subsampling without JPEG compression, or IPTC and XMP metadata are not implemented.

TIFF(r), 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 respectively.

For command line usage run python -m tifffile --help

Author

Christoph Gohlke

Organization

Laboratory for Fluorescence Dynamics, University of California, Irvine

License

3-clause BSD

Version

2019.7.26

Requirements

This release has been tested with the following requirements and dependencies (other versions may work):

Revisions

2019.7.26

Pass 2869 tests. Fix infinite loop reading more than two tags of same code in IFD. Delay import of logging module.

2019.7.20

Fix OME-XML detection for files created by Imaris. Remove or replace assert statements.

2019.7.2

Do not write SampleFormat tag for unsigned data types. Write ByteCount tag values as SHORT or LONG if possible. Allow to specify axes in FileSequence pattern via group names. Add option to concurrently read FileSequence using threads. Derive TiffSequence from FileSequence. Use str(datetime.timedelta) to format Timer duration. Use perf_counter for Timer if possible.

2019.6.18

Fix reading planar RGB ImageJ files created by Bio-Formats. Fix reading single-file, multi-image OME-TIFF without UUID. Presume LSM stores uncompressed images contiguously per page. Reformat some complex expressions.

2019.5.30

Ignore invalid frames in OME-TIFF. Set default subsampling to (2, 2) for RGB JPEG compression. Fix reading and writing planar RGB JPEG compression. Replace buffered_read with FileHandle.read_segments. Include page or frame numbers in exceptions and warnings. Add Timer class.

2019.5.22

Add optional chroma subsampling for JPEG compression. Enable writing PNG, JPEG, JPEGXR, and JPEG2000 compression (WIP). Fix writing tiled images with WebP compression. Improve handling GeoTIFF sparse files.

2019.3.18

Fix regression decoding JPEG with RGB photometrics. Fix reading OME-TIFF files with corrupted but unused pages. Allow to load TiffFrame without specifying keyframe. Calculate virtual TiffFrames for non-BigTIFF ScanImage files > 2GB. Rename property is_chroma_subsampled to is_subsampled (breaking). Make more attributes and methods private (WIP).

2019.3.8

Fix MemoryError when RowsPerStrip > ImageLength. Fix SyntaxWarning on Python 3.8. Fail to decode JPEG to planar RGB (tentative). Separate public from private test files (WIP). Allow testing without data files or imagecodecs.

2019.2.22

Use imagecodecs-lite as a fallback for imagecodecs. Simplify reading numpy arrays from file. Use TiffFrames when reading arrays from page sequences. Support slices and iterators in TiffPageSeries sequence interface. Auto-detect uniform series. Use page hash to determine generic series. Turn off page cache (tentative). Pass through more parameters in imread. Discontinue movie parameter in imread and TiffFile (breaking). Discontinue bigsize parameter in imwrite (breaking). Raise TiffFileError in case of issues with TIFF structure. Return TiffFile.ome_metadata as XML (breaking). Ignore OME series when last dimensions are not stored in TIFF pages.

2019.2.10

Assemble IFDs in memory to speed-up writing on some slow media. Handle discontinued arguments fastij, multifile_close, and pages.

2019.1.30

Use black background in imshow. Do not write datetime tag by default (breaking). Fix OME-TIFF with SamplesPerPixel > 1. Allow 64-bit IFD offsets for NDPI (files > 4GB still not supported).

2019.1.4

Fix decoding deflate without imagecodecs.

2019.1.1

Update copyright year. Require imagecodecs >= 2018.12.16. Do not use JPEG tables from keyframe. Enable decoding large JPEG in NDPI. Decode some old-style JPEG. Reorder OME channel axis to match PlanarConfiguration storage. Return tiled images as contiguous arrays. Add decode_lzw proxy function for compatibility with old czifile module. Use dedicated logger.

2018.11.28

Make SubIFDs accessible as TiffPage.pages. Make parsing of TiffSequence axes pattern optional (breaking). Limit parsing of TiffSequence axes pattern to file names, not path names. Do not interpolate in imshow if image dimensions <= 512, else use bilinear. Use logging.warning instead of warnings.warn in many cases. Fix numpy FutureWarning for out == ‘memmap’. Adjust ZSTD and WebP compression to libtiff-4.0.10 (WIP). Decode old-style LZW with imagecodecs >= 2018.11.8. Remove TiffFile.qptiff_metadata (QPI metadata are per page). Do not use keyword arguments before variable positional arguments. Make either all or none return statements in a function return expression. Use pytest parametrize to generate tests. Replace test classes with functions.

2018.11.6

Rename imsave function to imwrite. Readd Python implementations of packints, delta, and bitorder codecs. Fix TiffFrame.compression AttributeError.

2018.10.18

Rename tiffile package to tifffile.

2018.10.10

Read ZIF, the Zoomable Image Format (WIP). Decode YCbCr JPEG as RGB (tentative). Improve restoration of incomplete tiles. Allow to write grayscale with extrasamples without specifying planarconfig. Enable decoding of PNG and JXR via imagecodecs. Deprecate 32-bit platforms (too many memory errors during tests).

2018.9.27

Read Olympus SIS (WIP). Allow to write non-BigTIFF files up to ~4 GB (fix). Fix parsing date and time fields in SEM metadata. Detect some circular IFD references. Enable WebP codecs via imagecodecs. Add option to read TiffSequence from ZIP containers. Remove TiffFile.isnative. Move TIFF struct format constants out of TiffFile namespace.

2018.8.31

Fix wrong TiffTag.valueoffset. Towards reading Hamamatsu NDPI (WIP). Enable PackBits compression of byte and bool arrays. Fix parsing NULL terminated CZ_SEM strings.

2018.8.24

Move tifffile.py and related modules into tiffile package. Move usage examples to module docstring. Enable multi-threading for compressed tiles and pages by default. Add option to concurrently decode image tiles using threads. Do not skip empty tiles (fix). Read JPEG and J2K compressed strips and tiles. Allow floating-point predictor on write. Add option to specify subfiletype on write. Depend on imagecodecs package instead of _tifffile, lzma, etc modules. Remove reverse_bitorder, unpack_ints, and decode functions. Use pytest instead of unittest.

2018.6.20

Save RGBA with unassociated extrasample by default (breaking). Add option to specify ExtraSamples values.

2018.6.17 (included with 0.15.1)

Towards reading JPEG and other compressions via imagecodecs package (WIP). Read SampleFormat VOID as UINT. Add function to validate TIFF using ‘jhove -m TIFF-hul’. Save bool arrays as bilevel TIFF. Accept pathlib.Path as filenames. Move ‘software’ argument from TiffWriter __init__ to save. Raise DOS limit to 16 TB. Lazy load LZMA and ZSTD compressors and decompressors. Add option to save IJMetadata tags. Return correct number of pages for truncated series (fix). Move EXIF tags to TIFF.TAG as per TIFF/EP standard.

2018.2.18

Always save RowsPerStrip and Resolution tags as required by TIFF standard. Do not use badly typed ImageDescription. Coherce bad ASCII string tags to bytes. Tuning of __str__ functions. Fix reading ‘undefined’ tag values. Read and write ZSTD compressed data. Use hexdump to print byte strings. Determine TIFF byte order from data dtype in imsave. Add option to specify RowsPerStrip for compressed strips. Allow memory-map of arrays with non-native byte order. Attempt to handle ScanImage <= 5.1 files. Restore TiffPageSeries.pages sequence interface. Use numpy.frombuffer instead of fromstring to read from binary data. Parse GeoTIFF metadata. Add option to apply horizontal differencing before compression. Towards reading PerkinElmer QPI (QPTIFF, no test files). Do not index out of bounds data in tifffile.c unpackbits and decodelzw.

2017.9.29

Many backward incompatible changes improving speed and resource usage: Add detail argument to __str__ function. Remove info functions. Fix potential issue correcting offsets of large LSM files with positions. Remove TiffFile sequence 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 pages 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. Do not modify input array in imshow (fix). Fix Python implementation of unpack_ints.

2017.5.23

Write correct number of SampleFormat values (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.

2017.3.17

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.

2017.1.12 (included with scikit-image 0.14.x)

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.

2017.1.1

…

Refer to the CHANGES file for older revisions.

Notes

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

Tested on little-endian platforms only.

Python 2.7 and 32-bit versions are deprecated.

Tifffile relies on the imagecodecs package for encoding and decoding LZW, JPEG, and other compressed images. The imagecodecs-lite package, which is easier to build, can be used for decoding LZW compressed images instead.

Several TIFF-like formats do not strictly adhere to the TIFF6 specification, some of which allow file or data sizes to exceed the 4 GB limit:

  • BigTIFF is identified by version number 43 and uses different file header, IFD, and tag structures with 64-bit offsets. It adds more data types. Tifffile can read and write BigTIFF files.

  • ImageJ hyperstacks store all image data, which may exceed 4 GB, contiguously after the first IFD. Files > 4 GB contain one IFD only. The size (shape and dtype) of the up to 6-dimensional image data can be determined from the ImageDescription tag of the first IFD, which is Latin-1 encoded. Tifffile can read and write ImageJ hyperstacks.

  • OME-TIFF stores up to 8-dimensional data in one or multiple TIFF of BigTIFF files. The 8-bit UTF-8 encoded OME-XML metadata found in the ImageDescription tag of the first IFD defines the position of TIFF IFDs in the high dimensional data. Tifffile can read OME-TIFF files, except when the OME-XML metadata is stored in a separate file.

  • LSM stores all IFDs below 4 GB but wraps around 32-bit StripOffsets. The StripOffsets of each series and position require separate unwrapping. The StripByteCounts tag contains the number of bytes for the uncompressed data. Tifffile can read large LSM files.

  • NDPI uses some 64-bit offsets in the file header, IFD, and tag structures and might require correcting 32-bit offsets found in tags. JPEG compressed tiles with dimensions > 65536 are not readable with libjpeg. Tifffile can read NDPI files < 4 GB and decompress large JPEG tiles using the imagecodecs library on Windows.

  • ScanImage optionally allows corrupt non-BigTIFF files > 2 GB. The values of StripOffsets and StripByteCounts can be recovered using the constant differences of the offsets of IFD and tag values throughout the file. Tifffile can read such files on Python 3 if the image data is stored contiguously in each page.

  • GeoTIFF sparse files allow strip or tile offsets and byte counts to be 0. Such segments are implicitly set to 0 or the NODATA value on reading. Tifffile can read GeoTIFF sparse files.

Other libraries for reading scientific TIFF files from Python:

Some libraries are using tifffile to write OME-TIFF files:

References

  1. TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated. https://www.adobe.io/open/standards/TIFF.html

  2. TIFF File Format FAQ. https://www.awaresystems.be/imaging/tiff/faq.html

  3. MetaMorph Stack (STK) Image File Format. http://mdc.custhelp.com/app/answers/detail/a_id/18862

  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. https://docs.openmicroscopy.org/ome-model/5.6.4/ome-tiff/

  6. UltraQuant(r) Version 6.0 for Windows Start-Up Guide. http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf

  7. Micro-Manager File Formats. https://micro-manager.org/wiki/Micro-Manager_File_Formats

  8. Tags for TIFF and Related Specifications. Digital Preservation. https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml

  9. ScanImage BigTiff Specification - ScanImage 2016. http://scanimage.vidriotechnologies.com/display/SI2016/ ScanImage+BigTiff+Specification

  10. CIPA DC-008-2016: Exchangeable image file format for digital still cameras: Exif Version 2.31. http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf

  11. ZIF, the Zoomable Image File format. http://zif.photo/

  12. GeoTIFF File Format https://www.gdal.org/frmt_gtiff.html

Examples

Save a 3D numpy array to a multi-page, 16-bit grayscale TIFF file:

>>> data = numpy.random.randint(0, 2**12, (4, 301, 219), 'uint16')
>>> imwrite('temp.tif', data, photometric='minisblack')

Read the whole image stack from the TIFF file as numpy array:

>>> image_stack = imread('temp.tif')
>>> image_stack.shape
(4, 301, 219)
>>> image_stack.dtype
dtype('uint16')

Read the image from first page in the TIFF file as numpy array:

>>> image = imread('temp.tif', key=0)
>>> image.shape
(301, 219)

Read images from a sequence of TIFF files as numpy array:

>>> image_sequence = imread(['temp.tif', 'temp.tif'])
>>> image_sequence.shape
(2, 4, 301, 219)

Save a numpy array to a single-page RGB TIFF file:

>>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8')
>>> imwrite('temp.tif', data, photometric='rgb')

Save a floating-point array and metadata, using zlib compression:

>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
>>> imwrite('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})

Save a volume with xyz voxel size 2.6755x2.6755x3.9474 碌m^3 to an ImageJ file:

>>> volume = numpy.random.randn(57*256*256).astype('float32')
>>> volume.shape = 1, 57, 1, 256, 256, 1  # dimensions in TZCYXS order
>>> imwrite('temp.tif', volume, imagej=True, resolution=(1./2.6755, 1./2.6755),
...         metadata={'spacing': 3.947368, 'unit': 'um'})

Get the shape and dtype of the images stored in the TIFF file:

>>> tif = TiffFile('temp.tif')
>>> len(tif.pages)  # number of pages in the file
57
>>> page = tif.pages[0]  # get shape and dtype of the image in the first page
>>> page.shape
(256, 256)
>>> page.dtype
dtype('float32')
>>> page.axes
'YX'
>>> series = tif.series[0]  # get shape and dtype of the first image series
>>> series.shape
(57, 256, 256)
>>> series.dtype
dtype('float32')
>>> series.axes
'ZYX'
>>> tif.close()

Read hyperstack and metadata from the ImageJ file:

>>> with TiffFile('temp.tif') as tif:
...     imagej_hyperstack = tif.asarray()
...     imagej_metadata = tif.imagej_metadata
>>> imagej_hyperstack.shape
(57, 256, 256)
>>> imagej_metadata['slices']
57

Read the “XResolution” tag from the first page in the TIFF file:

>>> with TiffFile('temp.tif') as tif:
...     tag = tif.pages[0].tags['XResolution']
>>> tag.value
(2000, 5351)
>>> tag.name
'XResolution'
>>> tag.code
282
>>> tag.count
1
>>> tag.dtype
'2I'
>>> tag.valueoffset
360

Read images from a selected range of pages:

>>> image = imread('temp.tif', key=range(4, 40, 2))
>>> image.shape
(18, 256, 256)

Create an empty TIFF file and write to the memory-mapped numpy array:

>>> memmap_image = memmap('temp.tif', shape=(256, 256), dtype='float32')
>>> memmap_image[255, 255] = 1.0
>>> memmap_image.flush()
>>> memmap_image.shape, memmap_image.dtype
((256, 256), dtype('float32'))
>>> del memmap_image

Memory-map image data of the first page in the TIFF file:

>>> memmap_image = memmap('temp.tif', page=0)
>>> memmap_image[255, 255]
1.0
>>> del memmap_image

Successively append images to a BigTIFF file, which can exceed 4 GB:

>>> data = numpy.random.randint(0, 255, (5, 2, 3, 301, 219), 'uint8')
>>> with TiffWriter('temp.tif', bigtiff=True) as tif:
...     for i in range(data.shape[0]):
...         tif.save(data[i], compress=6, photometric='minisblack')

Iterate over pages and tags in the TIFF file and successively read images:

>>> with TiffFile('temp.tif') as tif:
...     image_stack = tif.asarray()
...     for page in tif.pages:
...         for tag in page.tags.values():
...             tag_name, tag_value = tag.name, tag.value
...         image = page.asarray()

Save two image series to a TIFF file:

>>> data0 = numpy.random.randint(0, 255, (301, 219, 3), 'uint8')
>>> data1 = numpy.random.randint(0, 255, (5, 301, 219), 'uint16')
>>> with TiffWriter('temp.tif') as tif:
...     tif.save(data0, compress=6, photometric='rgb')
...     tif.save(data1, compress=6, photometric='minisblack', contiguous=False)

Read the second image series from the TIFF file:

>>> series1 = imread('temp.tif', series=1)
>>> series1.shape
(5, 301, 219)

Read an image stack from a series of TIFF files with a file name pattern:

>>> imwrite('temp_C001T001.tif', numpy.random.rand(64, 64))
>>> imwrite('temp_C001T002.tif', numpy.random.rand(64, 64))
>>> image_sequence = TiffSequence('temp_C001*.tif', pattern='axes')
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
'CT'
>>> data = image_sequence.asarray()
>>> data.shape
(1, 2, 64, 64)
tifffile.imwrite(file, data=None, shape=None, dtype=None, **kwargs)

Write numpy array to TIFF file.

Refer to the TiffWriter class and its asarray function for documentation.

A BigTIFF file is created if the data size in bytes is larger than 4 GB minus 32 MB (for metadata), and ‘bigtiff’ is not specified, and ‘imagej’ or ‘truncate’ are not enabled.

Parameters

  • file (str or binary stream) – File name or writable binary stream, such as an open file or BytesIO.

  • data (array_like) – Input image. The last dimensions are assumed to be image depth, height, width, and samples. If None, an empty array of the specified shape and dtype is saved to file. Unless ‘byteorder’ is specified in ‘kwargs’, the TIFF file byte order is determined from the data’s dtype or the dtype argument.

  • shape (tuple) – If ‘data’ is None, shape of an empty array to save to the file.

  • dtype (numpy.dtype) – If ‘data’ is None, datatype of an empty array to save to the file.

  • kwargs (dict) – Parameters ‘append’, ‘byteorder’, ‘bigtiff’, and ‘imagej’, are passed to the TiffWriter constructor. Other parameters are passed to the TiffWriter.save function.

Returns

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

Return type

tuple or None

tifffile.imread(files, **kwargs)

Return image data from TIFF file(s) as numpy array.

Refer to the TiffFile and TiffSequence classes and their asarray functions for documentation.

Parameters

  • files (str, binary stream, or sequence) – File name, seekable binary stream, glob pattern, or sequence of file names.

  • kwargs (dict) – Parameters ‘name’, ‘offset’, ‘size’, ‘multifile’, and ‘is_ome’ are passed to the TiffFile constructor. The ‘pattern’ and ‘ioworkers’ parameters are passed to the TiffSequence constructor. Other parameters are passed to the asarray functions. The first image series in the file is returned if no arguments are provided.

tifffile.imshow(data, photometric=None, planarconfig=None, bitspersample=None, interpolation=None, cmap=None, vmin=None, vmax=None, figure=None, title=None, dpi=96, subplot=None, maxdim=None, **kwargs)

Plot n-dimensional images using matplotlib.pyplot.

Return figure, subplot and plot axis. Requires pyplot already imported C{from matplotlib import pyplot}.

Parameters

  • data (nd array) – The image data.

  • photometric ({'MINISWHITE', 'MINISBLACK', 'RGB', or 'PALETTE'}) – The color space of the image data.

  • planarconfig ({'CONTIG' or 'SEPARATE'}) – Defines how components of each pixel are stored.

  • bitspersample (int) – Number of bits per channel in integer RGB images.

  • interpolation (str) – The image interpolation method used in matplotlib.imshow. By default, ‘nearest’ will be used for image dimensions <= 512, else ‘bilinear’.

  • cmap (str or matplotlib.colors.Colormap) – The colormap maps non-RGBA scalar data to colors.

  • vmax (vmin,) – Data range covered by the colormap. By default, the complete range of the data is covered.

  • figure (matplotlib.figure.Figure) – Matplotlib figure to use for plotting.

  • title (str) – Window and subplot title.

  • subplot (int) – A matplotlib.pyplot.subplot axis.

  • maxdim (int) – Maximum image width and length.

  • kwargs (dict) – Additional arguments for matplotlib.pyplot.imshow.

tifffile.memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+', **kwargs)

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.

Parameters

  • filename (str) – Name of the TIFF file which stores the array.

  • shape (tuple) – Shape of the empty array.

  • dtype (numpy.dtype) – Datatype 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'}) – The file open mode. Default is to open existing file for reading and writing (‘r+’).

  • kwargs (dict) – Additional parameters passed to imwrite() or TiffFile().

class tifffile.TiffFile(arg, name=None, offset=None, size=None, multifile=True, _useframes=None, **kwargs)

Bases: 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.

TiffFile instances are not thread-safe.

pages

Sequence of TIFF pages in file.

Type

TiffPages

series

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, and compression.

Type

list of TiffPageSeries

is_flag

If True, file is of a certain format. Flags are: bigtiff, uniform, shaped, ome, imagej, stk, lsm, fluoview, nih, vista, micromanager, metaseries, mdgel, mediacy, tvips, fei, sem, scn, svs, scanimage, andor, epics, ndpi, pilatus, qpi.

Type

bool

All attributes are read-only.
andor_metadata

Return Andor tags as dict.

asarray(key=None, series=None, out=None, validate=True, maxworkers=None)

Return image data from selected TIFF page(s) as numpy array.

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

Parameters

  • key (int, slice, or sequence of indices) – Defines which pages to return as array. If None (default), data from a series (default 0) is returned. If not None, data from the specified pages in the whole file (if ‘series’ is None) or a specified series are returned as a stacked array. Requesting an array from multiple pages that are not compatible wrt. shape, dtype, compression etc is undefined, i.e. may crash or return incorrect values.

  • series (int or TiffPageSeries) – Defines which series of pages to return as array.

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, directly memory-map the image data in the TIFF file if possible; else create a memory-mapped array in a temporary file. 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.

  • validate (bool) – If True (default), validate various tags. Passed to TiffPage.asarray().

  • maxworkers (int or None) – Maximum number of threads to concurrently get data from multiple pages or compressed segments. If None (default), up to half the CPU cores are used. If 1, multi-threading is disabled. 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, e.g. in case of large LZW compressed LSM files or JPEG compressed tiled slides. If the bottleneck is I/O or pure Python code, using multiple threads might be detrimental.

Returns

Image data from the specified pages. See the TiffPage.asarray function for operations that are applied (or not) to the raw data stored in the file.

Return type

numpy.ndarray

byteorder
close()

Close open file handle(s).

epics_metadata

Return EPICS areaDetector tags as dict.

fei_metadata

Attribute whose value is computed on first access.

filehandle

Return file handle.

filename

Return name of file handle.

flags

Attribute whose value is computed on first access.

fluoview_metadata

Attribute whose value is computed on first access.

fstat

Attribute whose value is computed on first access.

geotiff_metadata

Return GeoTIFF metadata from first page as dict.

imagej_metadata

Attribute whose value is computed on first access.

is_appendable

Return if pages can be appended to file without corrupting.

is_bigtiff
is_mdgel

Attribute whose value is computed on first access.

is_uniform

Attribute whose value is computed on first access.

lsm_metadata

Return LSM metadata from CZ_LSMINFO tag as dict.

mdgel_metadata

Attribute whose value is computed on first access.

metaseries_metadata

Attribute whose value is computed on first access.

micromanager_metadata

Attribute whose value is computed on first access.

nih_metadata

Attribute whose value is computed on first access.

ome_metadata

Return OME XML.

pilatus_metadata

Attribute whose value is computed on first access.

scanimage_metadata

Attribute whose value is computed on first access.

sem_metadata

Return SEM metadata from CZ_SEM tag as dict.

series

Attribute whose value is computed on first access.

shaped_metadata

Attribute whose value is computed on first access.

sis_metadata

Attribute whose value is computed on first access.

stk_metadata

Attribute whose value is computed on first access.

tvips_metadata

Return TVIPS tag as dict.

exception tifffile.TiffFileError

Bases: Exception

Exception to indicate invalid TIFF structure.

class tifffile.TiffSequence(files=None, container=None, sort=None, pattern=None, imread=<function imread>)

Bases: tifffile.tifffile.FileSequence

Series of TIFF files.

class tifffile.TiffWriter(file, bigtiff=False, byteorder=None, append=False, imagej=False)

Bases: 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 instances are not thread-safe.

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

close()

Write remaining pages and close file handle.

save(data=None, shape=None, dtype=None, returnoffset=False, photometric=None, planarconfig=None, extrasamples=None, tile=None, contiguous=True, align=16, truncate=False, compress=0, rowsperstrip=None, predictor=False, subsampling=None, colormap=None, description=None, datetime=None, resolution=None, subfiletype=0, software='tifffile.py', metadata={}, ijmetadata=None, 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 colormap. If ‘shape’ and ‘dtype’ are specified, an empty array is saved. This option cannot be used with compression or multiple tiles. Image data are written uncompressed in one strip 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.

Parameters

  • 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) – Datatype 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 interleaved or in separate planes. By default, this setting is inferred from the data shape. If this parameter is set, extra samples are used to store grayscale images. ‘CONTIG’: last dimension contains samples. ‘SEPARATE’: third last dimension contains samples.

  • extrasamples (tuple of {'UNSPECIFIED', 'ASSOCALPHA', 'UNASSALPHA'}) – Defines the interpretation of extra components in pixels. ‘UNSPECIFIED’: no transparency information (default). ‘ASSOCALPHA’: single, true transparency with pre-multiplied color. ‘UNASSALPHA’: independent transparency masks.

  • tile (tuple of int) – The shape ([depth,] length, width) of image tiles to write. If None (default), image data are written in strips. 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. In that case, ‘photometric’, ‘planarconfig’, ‘rowsperstrip’, are ignored. Metadata such as ‘description’, ‘metadata’, ‘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 str or (str, int)) – If 0 (default), data are written uncompressed. If 0-9, the level of ADOBE_DEFLATE compression. If a str, one of TIFF.COMPESSORS, e.g. ‘LZMA’ or ‘ZSTD’. If a tuple, the first item is one of TIFF.COMPESSORS and the second item is the compression level. Compression cannot be used to write contiguous files. Compressors may require certain data shapes, types or value ranges. For example, JPEG requires grayscale or RGB(A), uint8 or 12-bit uint16. JPEG compression is experimental. JPEG markers and TIFF tags may not match.

  • rowsperstrip (int) – The number of rows per strip. By default, strips will be ~64 KB if compression is enabled, else rowsperstrip is set to the image length. Bilevel images are always stored in one strip per plane.

  • predictor (bool) – If True, apply horizontal differencing or floating-point predictor before compression.

  • subsampling ({(1, 1), (2, 1), (2, 2), (4, 1)}) – The horizontal and vertical subsampling factors used for the chrominance components of images. The default is (2, 2). Currently applies to JPEG compression of RGB images only. Images will be stored in YCbCr colorspace. Segment widths must be a multiple of the horizontal factor. Segment lengths and rowsperstrip must be a multiple of the vertical factor.

  • 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, str, or bool) – Date and time of image creation in ‘%Y:%m:%d %H:%M:%S’ format or datetime object. Else if True, 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’.

  • subfiletype (int) – Bitfield to indicate the kind of data. Set bit 0 if the image is a reduced-resolution version of another image. Set bit 1 if the image is part of a multi-page image. Set bit 2 if the image is transparency mask for another image (photometric must be MASK, SamplesPerPixel and BitsPerSample must be 1).

  • software (str) – Name of the software used to create the file. Must be 7-bit ASCII. Saved with the first page only.

  • metadata (dict) – Additional metadata 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.

  • ijmetadata (dict) – Additional metadata to be saved in application specific IJMetadata and IJMetadataByteCounts tags. Refer to the imagej_metadata_tag function for valid keys and values. Saved with the first page only.

  • extratags (sequence of tuples) –

    Additional tags as [(code, dtype, count, value, writeonce)].

    codeint

    The TIFF tag Id.

    dtypestr

    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.

    countint

    Number of data values. Not used for string or byte string values.

    valuesequence

    ’Count’ values compatible with ‘dtype’. Byte strings must contain count values of dtype packed as binary data.

    writeoncebool

    If True, the tag is written to the first page only.

class tifffile.TiffPage(parent, index, keyframe=None)

Bases: object

TIFF image file directory (IFD).

index

Index of page in file.

Type

int

dtype

Data type (native byte order) of the image in IFD.

Type

numpy.dtype or None

shape

Dimensions of the image in IFD.

Type

tuple

axes

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

Type

str

tags

Dictionary of tags in IFD. {tag.name: TiffTag}

Type

dict

colormap

Color look up table, if exists.

Type

numpy.ndarray

All attributes are read-only.

Notes

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.

andor_tags

Attribute whose value is computed on first access.

asarray(out=None, squeeze=True, lock=None, reopen=True, maxsize=None, maxworkers=None, validate=True)

Read image data from file and return as numpy array.

Raise ValueError if format is unsupported.

Parameters

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, directly memory-map the image data in the TIFF file if possible; else create a memory-mapped array in a temporary file. 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 (default), 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 synchronize seeks and 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) – Maximum size of data before a ValueError is raised. Can be used to catch DOS. Default: 16 TB.

  • maxworkers (int or None) – Maximum number of threads to concurrently decode compressed segments. If None (default), up to half the CPU cores are used. See remarks in TiffFile.asarray.

  • validate (bool) – If True (default), validate various parameters. If None, only validate parameters and return None.

Returns

Numpy array of decompressed, depredicted, and unpacked image data read from Strip/Tile Offsets/ByteCounts, formatted according to shape and dtype metadata found in tags and parameters. Photometric conversion, pre-multiplied alpha, orientation, and colorimetry corrections are not applied. Specifically, CMYK images are not converted to RGB, MinIsWhite images are not inverted, and color palettes are not applied. An exception are YCbCr JPEG compressed images, which will be converted to RGB.

Return type

numpy.ndarray

aspage()

Return self.

asrgb(uint8=False, alpha=None, colormap=None, dmin=None, dmax=None, **kwargs)

Return image data as RGB(A).

Work in progress.

bitspersample = 1
colormap = None
compression = 1
decode

Attribute whose value is computed on first access.

description = ''
description1 = ''
epics_tags

Attribute whose value is computed on first access.

extrasamples = 1
fillorder = 1
flags

Attribute whose value is computed on first access.

geotiff_tags

Attribute whose value is computed on first access.

hash

Return checksum to identify pages in same series.

imagedepth = 1
imagelength = 0
imagewidth = 0
is_andor

Page contains Andor Technology tags.

is_contiguous

Attribute whose value is computed on first access.

is_epics

Page contains EPICS areaDetector tags.

is_fei

Page contains SFEG or HELIOS metadata.

is_final

Attribute whose value is computed on first access.

is_fluoview

Page contains FluoView MM_STAMP tag.

is_geotiff

Page contains GeoTIFF metadata.

is_imagej

Attribute whose value is computed on first access.

is_lsm

Page contains CZ_LSMINFO tag.

is_mask

Page is transparency mask for another image.

is_mdgel

Page contains MDFileTag tag.

is_mediacy

Page contains Media Cybernetics Id tag.

is_memmappable

Attribute whose value is computed on first access.

is_metaseries

Page contains MDS MetaSeries metadata in ImageDescription tag.

is_micromanager

Page contains Micro-Manager metadata.

is_mrc

Page is part of Mixed Raster Content.

is_multipage

Page is part of multi-page image.

is_ndpi

Attribute whose value is computed on first access.

is_nih

Page contains NIH image header.

is_ome

Page contains OME-XML in ImageDescription tag.

is_pilatus

Page contains Pilatus tags.

is_qpi

Page contains PerkinElmer tissue images metadata.

is_reduced

Page is reduced image of another image.

is_scanimage

Page contains ScanImage metadata.

is_scn

Page contains Leica SCN XML in ImageDescription tag.

is_sem

Page contains Zeiss SEM metadata.

is_sgi

Page contains SGI image and tile depth tags.

is_shaped

Attribute whose value is computed on first access.

is_sis

Page contains Olympus SIS metadata.

is_stk

Page contains UIC2Tag tag.

is_subsampled

Page contains chroma subsampled image.

is_svs

Page contains Aperio metadata.

is_tiled

Page contains tiled image.

is_tvips

Page contains TVIPS metadata.

is_vista

Software tag is ‘ISS Vista’.

keyframe

Return keyframe, self.

ndim

Return number of array dimensions.

ndpi_tags

Attribute whose value is computed on first access.

nodata = 0
pages

Attribute whose value is computed on first access.

photometric = 0
planarconfig = 1
predictor = 1
rowsperstrip = 4294967295
sampleformat = 1
samplesperpixel = 1
size

Return number of elements in array.

software = ''
subfiletype = 0
tiledepth = 1
tilelength = 0
tilewidth = 0
class tifffile.TiffPageSeries(pages, shape, dtype, axes, parent=None, name=None, transform=None, kind=None, truncated=False)

Bases: object

Series of TIFF pages with compatible shape and data type.

pages

Sequence of TiffPages in series.

Type

list of TiffPage

dtype

Data type (native byte order) of the image array in series.

Type

numpy.dtype

shape

Dimensions of the image array in series.

Type

tuple

axes

Labels of axes in shape. See TiffPage.axes.

Type

str

offset

Position of image data in file if memory-mappable, else None.

Type

int or None

asarray(out=None)

Return image data from series of TIFF pages as numpy array.

ndim

Return number of array dimensions.

offset

Attribute whose value is computed on first access.

pages

Return sequence of all pages in series.

size

Return number of elements in array.

class tifffile.TiffFrame(parent, index, offset=None, keyframe=None, offsets=None, bytecounts=None)

Bases: 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.

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

Not compatible with Python 2.

asarray(*args, **kwargs)

Read image data from file and return as numpy array.

aspage()

Return TiffPage from file.

asrgb(*args, **kwargs)

Read image data from file and return RGB image as numpy array.

hash

Return checksum to identify pages in same series.

index
is_contiguous

Return offset and size of contiguous data, else None.

is_mdgel = False
is_memmappable

Return if page’s image data in file can be memory-mapped.

keyframe

Return keyframe.

offset
pages = None
parent
tags = {}
class tifffile.TiffTag(parent, tagheader, tagoffset)

Bases: object

TIFF tag structure.

name

Name of tag.

Type

string

code

Decimal code of tag.

Type

int

dtype

Datatype of tag data. One of TIFF DATA_FORMATS.

Type

str

count

Number of values.

Type

int

value

Tag data as Python object.

Type

various types

ImageSourceData

Location of value in file.

Type

int

All attributes are read-only.
code
count
dtype
name

Return name of tag from TIFF.TAGS registry.

value
valueoffset
class tifffile.FileHandle(file, mode='rb', name=None, offset=None, size=None)

Bases: object

Binary file handle.

A limited, special purpose file handle 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

Name of the file.

Type

str

path

Absolute path to file.

Type

str

size

Size of file in bytes.

Type

int

is_file

If True, file has a filno and can be memory-mapped.

Type

bool

All attributes are read-only.
close()

Close file.

closed
dirname
flush()

Flush write buffers if applicable.

is_file
lock
memmap_array(dtype, shape, offset=0, mode='r', order='C')

Return numpy.memmap of data stored in file.

name
open()

Open or re-open file.

path
read(size=-1)

Read ‘size’ bytes from file, or until EOF is reached.

read_array(dtype, count=-1, out=None)

Return numpy array from file in native byte order.

read_record(dtype, shape=1, byteorder=None)

Return numpy record from file.

read_segments(offsets, bytecounts, lock=None, buffersize=None)

Return iterator over segments read from file.

A reentrant lock can be used to synchronize seeks and reads up to buffersize bytes.

readinto(b)

Read up to len(b) bytes into b, and return number of bytes read.

seek(offset, whence=0)

Set file’s current position.

size
tell()

Return file’s current position.

write(bytestring)

Write bytestring to file.

write_array(data)

Write numpy array to binary file.

write_empty(size)

Append size bytes to file. Position must be at end of file.

class tifffile.FileSequence(fromfile, files, container=None, sort=None, pattern=None)

Bases: object

Series of files containing array data of compatible shape and data type.

files

List of file names.

Type

list

shape

Shape of file series. Excludes shape of individual arrays.

Type

tuple

axes

Labels of axes in shape.

Type

str

asarray(file=None, ioworkers=1, out=None, **kwargs)

Read image data from files and return as numpy array.

Raise IndexError or ValueError if array shapes do not match.

Parameters

  • file (int or None) – Index or name of single file to read.

  • ioworkers (int or None) – Maximum number of threads to execute the array read function asynchronously. Default: 1. If None, default to the number of processors multiplied by 5. Using threads can significantly improve runtime when reading many small files from a network share.

  • out (numpy.ndarray, str, or file-like object) – Buffer where image data will be saved. If None (default), a new array will be created. If numpy.ndarray, a writable array of compatible dtype and shape. If ‘memmap’, create a memory-mapped array in a temporary file. 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.

  • kwargs (dict) – Additional parameters passed to the array read function.

close()
class tifffile.Timer(message='', end=' ')

Bases: object

Stopwatch for timing execution speed.

clock()

perf_counter() -> float

Performance counter for benchmarking.

duration
print(message='', end=None)

Print duration from timer start till last stop or now.

start(message='', end=' ')

Start timer and return current time.

started
stop(message='', end=' ')

Return duration of timer till start.

stopped
class tifffile.lazyattr(func)

Bases: object

Attribute whose value is computed on first access.

func
tifffile.natural_sorted(iterable)

Return human sorted list of strings.

E.g. for sorting file names.

>>> natural_sorted(['f1', 'f2', 'f10'])
['f1', 'f2', 'f10']
tifffile.stripnull(string, null=b'\x00')

Return string truncated at first null character.

Clean NULL terminated C strings. For unicode strings use null=‘0’.

>>> stripnull(b'string\x00')
b'string'
>>> stripnull('string\x00', null='\0')
'string'
tifffile.transpose_axes(image, axes, asaxes=None)

Return image with its axes permuted to match specified axes.

A view is returned if possible.

>>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape
(5, 2, 1, 3, 4)
tifffile.squeeze_axes(shape, axes, skip=None)

Return shape and axes with single-dimensional entries removed.

Remove unused dimensions unless their axes are listed in ‘skip’.

>>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
((5, 2, 1), 'TYX')
tifffile.create_output(out, shape, dtype, mode='w+', suffix=None)

Return numpy array where image data of shape and dtype can be copied.

The ‘out’ parameter may have the following values or types:

None

An empty array of shape and dtype is created and returned.

numpy.ndarray

An existing writable array of compatible dtype and shape. A view of the same array is returned after verification.

‘memmap’ or ‘memmap:tempdir’

A memory-map to an array stored in a temporary binary file on disk is created and returned.

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. The created memory-mapped array is returned.

tifffile.repeat_nd(a, repeats)

Return read-only view into input array with elements repeated.

Zoom nD image by integer factors using nearest neighbor interpolation (box filter).

Parameters

  • a (array_like) – Input array.

  • repeats (sequence of int) – The number of repetitions to apply along each dimension of input array.

Examples

>>> repeat_nd([[1, 2], [3, 4]], (2, 2))
array([[1, 1, 2, 2],
       [1, 1, 2, 2],
       [3, 3, 4, 4],
       [3, 3, 4, 4]])
tifffile.format_size(size, threshold=1536)

Return file size as string from byte size.

>>> format_size(1234)
'1234 B'
>>> format_size(12345678901)
'11.50 GiB'
tifffile.astype(value, types=None)

Return argument as one of types if possible.

>>> astype('42')
42
>>> astype('3.14')
3.14
>>> astype('True')
True
>>> astype(b'Neee-Wom')
'Neee-Wom'
tifffile.product(iterable)

Return product of sequence of numbers.

Equivalent of functools.reduce(operator.mul, iterable, 1). Multiplying numpy integers might overflow.

>>> product([2**8, 2**30])
274877906944
>>> product([])
1
tifffile.xml2dict(xml, sanitize=True, prefix=None)

Return XML as dict.

>>> xml2dict('<?xml version="1.0" ?><root attr="name"><key>1</key></root>')
{'root': {'key': 1, 'attr': 'name'}}
tifffile.pformat(arg, width=79, height=24, compact=True)

Return pretty formatted representation of object as string.

Whitespace might be altered.

tifffile.str2bytes(s, encoding='cp1252')

Return bytes from unicode string.

tifffile.nullfunc(*args, **kwargs)

Null function.

>>> nullfunc('arg', kwarg='kwarg')
tifffile.update_kwargs(kwargs, **keyvalues)

Update dict with keys and values if keys do not already exist.

>>> kwargs = {'one': 1, }
>>> update_kwargs(kwargs, one=None, two=2)
>>> kwargs == {'one': 1, 'two': 2}
True
tifffile.parse_kwargs(kwargs, *keys, **keyvalues)

Return dict with keys from keys|keyvals and values from kwargs|keyvals.

Existing keys are deleted from kwargs.

>>> kwargs = {'one': 1, 'two': 2, 'four': 4}
>>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5)
>>> kwargs == {'one': 1}
True
>>> kwargs2 == {'two': 2, 'four': 4, 'five': 5}
True
tifffile.askopenfilename(**kwargs)

Return file name(s) from Tkinter’s file open dialog.

tifffile.imsave(file, data=None, shape=None, dtype=None, **kwargs)

Write numpy array to TIFF file.

Refer to the TiffWriter class and its asarray function for documentation.

A BigTIFF file is created if the data size in bytes is larger than 4 GB minus 32 MB (for metadata), and ‘bigtiff’ is not specified, and ‘imagej’ or ‘truncate’ are not enabled.

Parameters

  • file (str or binary stream) – File name or writable binary stream, such as an open file or BytesIO.

  • data (array_like) – Input image. The last dimensions are assumed to be image depth, height, width, and samples. If None, an empty array of the specified shape and dtype is saved to file. Unless ‘byteorder’ is specified in ‘kwargs’, the TIFF file byte order is determined from the data’s dtype or the dtype argument.

  • shape (tuple) – If ‘data’ is None, shape of an empty array to save to the file.

  • dtype (numpy.dtype) – If ‘data’ is None, datatype of an empty array to save to the file.

  • kwargs (dict) – Parameters ‘append’, ‘byteorder’, ‘bigtiff’, and ‘imagej’, are passed to the TiffWriter constructor. Other parameters are passed to the TiffWriter.save function.

Returns

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

Return type

tuple or None

tifffile.decode_lzw(encoded)

Decompress LZW encoded byte string.

tifffile.decodelzw(encoded)

Decompress LZW encoded byte string.