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Release v2022.7.31
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@cgohlke
cgohlke committed Aug 1, 2022
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Expand Up @@ -24,7 +24,8 @@ many proprietary metadata formats.

:Author: `Christoph Gohlke <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2022.7.28
:Version: 2022.7.31
:DOI: 10.5281/zenodo.6795860

Installation
------------
Expand All @@ -46,7 +47,7 @@ This release has been tested with the following requirements and dependencies
- `CPython 3.8.10, 3.9.13, 3.10.5, 3.11.0b5 <https://www.python.org>`_
(AMD64 platforms, 32-bit platforms are deprecated)
- `NumPy 1.21.5 <https://pypi.org/project/numpy/>`_
- `Imagecodecs 2022.7.27 <https://pypi.org/project/imagecodecs/>`_
- `Imagecodecs 2022.7.31 <https://pypi.org/project/imagecodecs/>`_
(required for encoding or decoding LZW, JPEG, etc. compressed segments)
- `Matplotlib 3.5.2 <https://pypi.org/project/matplotlib/>`_
(required for plotting)
Expand All @@ -58,9 +59,14 @@ This release has been tested with the following requirements and dependencies
Revisions
---------

2022.7.31

- Pass 4907 tests.
- Fix reading corrupted WebP compressed segments missing alpha channel (#122).
- Fix regression reading compressed ImageJ files.

2022.7.28

- Pass 4906 tests.
- Rename FileSequence.labels attribute to dims (breaking).
- Rename tifffile_geodb module to geodb (breaking).
- Rename TiffFile._astuple method to astuple (breaking).
Expand Down Expand Up @@ -386,19 +392,35 @@ color components, tiling, Zlib compression level 8, horizontal differencing
predictor, and additional metadata:

>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
>>> imwrite('temp.tif', data, bigtiff=True, photometric='rgb',
... planarconfig='separate', tile=(32, 32),
... compression='zlib', compressionargs={'level': 8},
... predictor=True, metadata={'axes': 'TZCYX'})
>>> imwrite(
... 'temp.tif',
... data,
... bigtiff=True,
... photometric='rgb',
... planarconfig='separate',
... tile=(32, 32),
... compression='zlib',
... compressionargs={'level': 8},
... predictor=True,
... metadata={'axes': 'TZCYX'}
... )

Write a 10 fps time series of volumes with xyz voxel size 2.6755x2.6755x3.9474
micron^3 to an ImageJ hyperstack formatted TIFF file:

>>> volume = numpy.random.randn(6, 57, 256, 256).astype('float32')
>>> imwrite('temp.tif', volume, imagej=True,
... resolution=(1./2.6755, 1./2.6755),
... metadata={'spacing': 3.947368, 'unit': 'um',
... 'finterval': 1/10, 'axes': 'TZYX'})
>>> imwrite(
... 'temp.tif',
... volume,
... imagej=True,
... resolution=(1./2.6755, 1./2.6755),
... metadata={
... 'spacing': 3.947368,
... 'unit': 'um',
... 'finterval': 1/10,
... 'axes': 'TZYX'
... }
... )

Read the volume and metadata from the ImageJ file:

Expand All @@ -419,8 +441,10 @@ Create a TIFF file containing an empty image and write to the memory-mapped
NumPy array (note: this does not work with compression or tiling):

>>> memmap_image = memmap(
... 'temp.tif', shape=(256, 256, 3), dtype='float32',
... photometric='rgb'
... 'temp.tif',
... shape=(256, 256, 3),
... dtype='float32',
... photometric='rgb'
... )
>>> type(memmap_image)
<class 'numpy.memmap'>
Expand Down Expand Up @@ -472,8 +496,14 @@ Create a TIFF file from a generator of tiles:
... for y in range(0, data.shape[0], tileshape[0]):
... for x in range(0, data.shape[1], tileshape[1]):
... yield data[y : y + tileshape[0], x : x + tileshape[1]]
>>> imwrite('temp.tif', tiles(data, (16, 16)), tile=(16, 16),
... shape=data.shape, dtype=data.dtype, photometric='rgb')
>>> imwrite(
... 'temp.tif',
... tiles(data, (16, 16)),
... tile=(16, 16),
... shape=data.shape,
... dtype=data.dtype,
... photometric='rgb'
... )

Write a multi-dimensional, multi-resolution (pyramidal), multi-series OME-TIFF
file with metadata. Sub-resolution images are written to SubIFDs. A thumbnail
Expand All @@ -484,22 +514,29 @@ image is written as a separate image series:
>>> pixelsize = 0.29 # micrometer
>>> with TiffWriter('temp.ome.tif', bigtiff=True) as tif:
... metadata={
... 'axes': 'TCYXS', 'SignificantBits': 10,
... 'Channel': {'Name': ['Channel 1', 'Channel 2']},
... 'TimeIncrement': 0.1, 'TimeIncrementUnit': 's',
... 'PhysicalSizeX': pixelsize, 'PhysicalSizeXUnit': 'µm',
... 'PhysicalSizeY': pixelsize, 'PhysicalSizeYUnit': 'µm'}
... 'axes': 'TCYXS',
... 'SignificantBits': 10,
... 'Channel': {'Name': ['Channel 1', 'Channel 2']},
... 'TimeIncrement': 0.1,
... 'TimeIncrementUnit': 's',
... 'PhysicalSizeX': pixelsize,
... 'PhysicalSizeXUnit': 'µm',
... 'PhysicalSizeY': pixelsize,
... 'PhysicalSizeYUnit': 'µm',
... }
... options = dict(
... photometric='rgb',
... tile=(128, 128),
... compression='jpeg',
... resolutionunit='CENTIMETER')
... resolutionunit='CENTIMETER'
... )
... tif.write(
... data,
... subifds=subresolutions,
... resolution=(1e4 / pixelsize, 1e4 / pixelsize),
... metadata=metadata,
... **options)
... **options
... )
... # save pyramid levels to the two subifds
... # in production use resampling to generate sub-resolution images
... for level in range(subresolutions):
Expand All @@ -508,7 +545,8 @@ image is written as a separate image series:
... data[..., ::mag, ::mag, :],
... subfiletype=1,
... resolution=(1e4 / mag / pixelsize, 1e4 / mag / pixelsize),
... **options)
... **options
... )
... # add a thumbnail image as a separate series
... # it is recognized by QuPath as an associated image
... thumbnail = (data[0, 0, ::8, ::8] >> 2).astype('uint8')
Expand Down Expand Up @@ -567,17 +605,23 @@ Open the fsspec ReferenceFileSystem as a Zarr group:
>>> import imagecodecs.numcodecs
>>> imagecodecs.numcodecs.register_codecs()
>>> mapper = fsspec.get_mapper(
... 'reference://', fo='temp.ome.tif.json', target_protocol='file')
... 'reference://', fo='temp.ome.tif.json', target_protocol='file'
... )
>>> z = zarr.open(mapper, mode='r')
>>> z
<zarr.hierarchy.Group '/' read-only>

Create an OME-TIFF file containing an empty, tiled image series and write
to it via the Zarr interface (note: this does not work with compression):

>>> imwrite('temp.ome.tif', shape=(8, 800, 600), dtype='uint16',
... photometric='minisblack', tile=(128, 128),
... metadata={'axes': 'CYX'})
>>> imwrite(
... 'temp.ome.tif',
... shape=(8, 800, 600),
... dtype='uint16',
... photometric='minisblack',
... tile=(128, 128),
... metadata={'axes': 'CYX'}
... )
>>> store = imread('temp.ome.tif', mode='r+', aszarr=True)
>>> z = zarr.open(store, mode='r+')
>>> z
Expand All @@ -599,7 +643,8 @@ Read an image stack from a series of TIFF files with a file name pattern
as NumPy or Zarr arrays:

>>> image_sequence = TiffSequence(
... 'temp_C0*.tif', pattern=r'_(C)(\d+)(T)(\d+)')
... 'temp_C0*.tif', pattern=r'_(C)(\d+)(T)(\d+)'
... )
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
Expand All @@ -623,10 +668,11 @@ Open the fsspec ReferenceFileSystem as a Zarr array:
>>> import tifffile.numcodecs
>>> tifffile.numcodecs.register_codec()
>>> mapper = fsspec.get_mapper(
... 'reference://', fo='temp.json', target_protocol='file')
... 'reference://', fo='temp.json', target_protocol='file'
... )
>>> zarr.open(mapper, mode='r')
<zarr.core.Array (1, 2, 64, 64) float64 read-only>

Tifffile can be used from the command line to inspect TIFF files:
Inspect the TIFF file from the command line::

``python -m tifffile --help``
$ python -m tifffile temp.ome.tif

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