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Update tifffile/tifffile.py
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@cgohlke
cgohlke committed May 10, 2024
1 parent ae20115 commit 62c9ad2
Showing 1 changed file with 65 additions and 34 deletions.
99 changes: 65 additions & 34 deletions tifffile/tifffile.py
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Expand Up @@ -60,7 +60,7 @@

:Author: `Christoph Gohlke <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2024.5.3
:Version: 2024.5.10
:DOI: `10.5281/zenodo.6795860 <https://doi.org/10.5281/zenodo.6795860>`_

Quickstart
Expand Down Expand Up @@ -104,17 +104,22 @@
(required for plotting)
- `Lxml <https://pypi.org/project/lxml/>`_ 5.2.1
(required only for validating and printing XML)
- `Zarr <https://pypi.org/project/zarr/>`_ 2.17.2
- `Zarr <https://pypi.org/project/zarr/>`_ 2.18.0
(required only for opening Zarr stores)
- `Fsspec <https://pypi.org/project/fsspec/>`_ 2024.3.1
(required only for opening ReferenceFileSystem files)

Revisions
---------

2024.5.10

- Pass 5082 tests.
- Support reading JPEGXL compression in DNG 1.7.
- Read invalid TIFF created by IDEAS software.

2024.5.3

- Pass 5080 tests.
- Fix reading incompletely written LSM.
- Fix reading Philips DP with extra rows of tiles (#253, breaking).

Expand Down Expand Up @@ -414,8 +419,8 @@
http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf
- The EER (Electron Event Representation) file format.
https://github.com/fei-company/EerReaderLib
- Digital Negative (DNG) Specification. Version 1.5.0.0, June 2012.
https://www.adobe.com/content/dam/acom/en/products/photoshop/pdfs/dng_spec_1.5.0.0.pdf
- Digital Negative (DNG) Specification. Version 1.7.1.0, September 2023.
https://helpx.adobe.com/content/dam/help/en/photoshop/pdf/DNG_Spec_1_7_1_0.pdf
- Roche Digital Pathology. BIF image file format for digital pathology.
https://diagnostics.roche.com/content/dam/diagnostics/Blueprint/en/pdf/rmd/Roche-Digital-Pathology-BIF-Whitepaper.pdf
- Astro-TIFF specification. https://astro-tiff.sourceforge.io/
Expand Down Expand Up @@ -483,6 +488,7 @@
>>> with TiffFile('temp.tif') as tif:
... for page in tif.pages:
... image = page.asarray()
...

Get information about the image stack in the TIFF file without reading
any image data:
Expand Down Expand Up @@ -510,6 +516,7 @@

>>> with TiffFile('temp.tif') as tif:
... tag = tif.pages[0].tags['XResolution']
...
>>> tag.value
(1, 1)
>>> tag.name
Expand All @@ -527,11 +534,13 @@
... for page in tif.pages:
... for tag in page.tags:
... tag_name, tag_value = tag.name, tag.value
...

Overwrite the value of an existing tag, for example, XResolution:

>>> with TiffFile('temp.tif', mode='r+') as tif:
... _ = tif.pages[0].tags['XResolution'].overwrite((96000, 1000))
...

Write a 5-dimensional floating-point array using BigTIFF format, separate
color components, tiling, Zlib compression level 8, horizontal differencing
Expand All @@ -548,7 +557,7 @@
... compression='zlib',
... compressionargs={'level': 8},
... predictor=True,
... metadata={'axes': 'TZCYX'}
... metadata={'axes': 'TZCYX'},
... )

Write a 10 fps time series of volumes with xyz voxel size 2.6755x2.6755x3.9474
Expand All @@ -560,15 +569,15 @@
... 'temp.tif',
... volume,
... imagej=True,
... resolution=(1./2.6755, 1./2.6755),
... resolution=(1.0 / 2.6755, 1.0 / 2.6755),
... metadata={
... 'spacing': 3.947368,
... 'unit': 'um',
... 'finterval': 1/10,
... 'finterval': 1 / 10,
... 'fps': 10.0,
... 'axes': 'TZYX',
... 'Labels': image_labels,
... }
... },
... )

Read the volume and metadata from the ImageJ hyperstack file:
Expand All @@ -577,6 +586,7 @@
... volume = tif.asarray()
... axes = tif.series[0].axes
... imagej_metadata = tif.imagej_metadata
...
>>> volume.shape
(6, 57, 256, 256)
>>> axes
Expand All @@ -597,10 +607,7 @@
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 All @@ -617,6 +624,7 @@
>>> with TiffWriter('temp.tif') as tif:
... tif.write(series0, photometric='rgb')
... tif.write(series1, photometric='minisblack')
...

Read the second image series from the TIFF file:

Expand All @@ -630,6 +638,7 @@
>>> with TiffWriter('temp.tif') as tif:
... for frame in data:
... tif.write(frame, contiguous=True)
...

Append an image series to the existing TIFF file (note: this does not work
with ImageJ hyperstack or OME-TIFF files):
Expand All @@ -644,13 +653,14 @@
... 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'
... photometric='rgb',
... )

Write a multi-dimensional, multi-resolution (pyramidal), multi-series OME-TIFF
Expand All @@ -662,7 +672,7 @@
>>> subresolutions = 2
>>> pixelsize = 0.29 # micrometer
>>> with TiffWriter('temp.ome.tif', bigtiff=True) as tif:
... metadata={
... metadata = {
... 'axes': 'TCYXS',
... 'SignificantBits': 8,
... 'TimeIncrement': 0.1,
Expand All @@ -672,36 +682,37 @@
... 'PhysicalSizeY': pixelsize,
... 'PhysicalSizeYUnit': 'µm',
... 'Channel': {'Name': ['Channel 1', 'Channel 2']},
... 'Plane': {'PositionX': [0.0] * 16, 'PositionXUnit': ['µm'] * 16}
... 'Plane': {'PositionX': [0.0] * 16, 'PositionXUnit': ['µm'] * 16},
... }
... options = dict(
... photometric='rgb',
... tile=(128, 128),
... compression='jpeg',
... resolutionunit='CENTIMETER',
... maxworkers=2
... maxworkers=2,
... )
... tif.write(
... data,
... subifds=subresolutions,
... resolution=(1e4 / pixelsize, 1e4 / pixelsize),
... metadata=metadata,
... **options
... **options,
... )
... # write pyramid levels to the two subifds
... # in production use resampling to generate sub-resolution images
... for level in range(subresolutions):
... mag = 2**(level + 1)
... mag = 2 ** (level + 1)
... tif.write(
... 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')
... tif.write(thumbnail, metadata={'Name': 'thumbnail'})
...

Access the image levels in the pyramidal OME-TIFF file:

Expand All @@ -710,6 +721,7 @@
>>> with TiffFile('temp.ome.tif') as tif:
... baseimage = tif.series[0].asarray()
... second_level = tif.series[0].levels[1].asarray()
...

Iterate over and decode single JPEG compressed tiles in the TIFF file:

Expand All @@ -724,6 +736,7 @@
... tile, indices, shape = page.decode(
... data, index, jpegtables=page.jpegtables
... )
...

Use Zarr to read parts of the tiled, pyramidal images in the TIFF file:

Expand Down Expand Up @@ -773,7 +786,7 @@
... dtype='uint16',
... photometric='minisblack',
... tile=(128, 128),
... metadata={'axes': 'CYX'}
... metadata={'axes': 'CYX'},
... )
>>> store = imread('temp.ome.tif', mode='r+', aszarr=True)
>>> z = zarr.open(store, mode='r+')
Expand All @@ -798,9 +811,7 @@
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+)'
... )
>>> image_sequence = TiffSequence('temp_C0*.tif', pattern=r'_(C)(\d+)(T)(\d+)')
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
Expand Down Expand Up @@ -837,7 +848,7 @@

from __future__ import annotations

__version__ = '2024.5.3'
__version__ = '2024.5.10'

__all__ = [
'TiffFile',
Expand Down Expand Up @@ -4138,6 +4149,7 @@ def _maxworkers(
33005, # JPEG2000
34712, # JPEG2000
50002, # JPEG XL
52546, # JPEG XL DNG
}:
return 1
if chunksize < 1024 and compression in {
Expand Down Expand Up @@ -8188,6 +8200,10 @@ def __init__(
self.subfiletype = 0b1 # reduced image
elif value == 3:
self.subfiletype = 0b10 # multi-page
elif not isinstance(self.subfiletype, int):
# files created by IDEAS
logger().warning(f'{self!r} invalid {self.subfiletype=}')
self.subfiletype = 0

# consolidate private tags; remove them from self.tags
# if self.is_andor:
Expand Down Expand Up @@ -11908,9 +11924,7 @@ class TiffTagRegistry:
arg: Mapping of codes to names.

Examples:
>>> tags = TiffTagRegistry(
... [(34853, 'GPSTag'), (34853, 'OlympusSIS2')]
... )
>>> tags = TiffTagRegistry([(34853, 'GPSTag'), (34853, 'OlympusSIS2')])
>>> tags.add(37387, 'FlashEnergy')
>>> tags.add(41483, 'FlashEnergy')
>>> tags['GPSTag']
Expand Down Expand Up @@ -12992,6 +13006,7 @@ def write_fsspec(
50000: 'imagecodecs_zstd', # numcodecs.zstd fails w/ unknown sizes
50001: 'imagecodecs_webp',
50002: 'imagecodecs_jpegxl',
52546: 'imagecodecs_jpegxl',
**({} if compressors is None else compressors),
}

Expand Down Expand Up @@ -15583,6 +15598,7 @@ class NullContext:

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

"""

Expand Down Expand Up @@ -16018,7 +16034,8 @@ def addimage(

# TODO: use user-specified start, stop, step, or labels
moduloalong = ''.join(
f'<ModuloAlong{ax} Type="{axtype}" Start="0" End="{size-1}"/>'
f'<ModuloAlong{ax} Type="{axtype}" '
f'Start="0" End="{size - 1}"/>'
for ax, (axtype, size) in modulo.items()
)
annotationref = f'<AnnotationRef ID="Annotation:{index}"/>'
Expand Down Expand Up @@ -16521,7 +16538,7 @@ def __getitem__(self, key: int, /) -> Callable[..., Any]:
codec = imagecodecs.webp_decode
elif key in {65000, 65001, 65002} and not self._encode:
codec = imagecodecs.eer_decode
elif key == 50002:
elif key in {50002, 52546}:
if self._encode:
codec = imagecodecs.jpegxl_encode
else:
Expand Down Expand Up @@ -16791,10 +16808,12 @@ class COMPRESSION(enum.IntEnum):
"""Zstandard."""
WEBP = 50001
"""WebP."""
JPEGXL = 50002
JPEGXL = 50002 # GDAL
"""JPEG XL."""
PIXTIFF = 50013
"""ZLIB (Atalasoft)."""
JPEGXL_DNG = 52546
"""JPEG XL (DNG)."""
EER_V0 = 65000 # FIXED82 Thermo Fisher Scientific
EER_V1 = 65001 # FIXED72 Thermo Fisher Scientific
EER_V2 = 65002 # VARIABLE Thermo Fisher Scientific
Expand Down Expand Up @@ -17772,6 +17791,15 @@ def TAGS(self) -> TiffTagRegistry:
(52533, 'IlluminantData1'), # DNG 1.6
(52534, 'IlluminantData2'), # DNG 1.6
(53535, 'IlluminantData3'), # DNG 1.6
(52544, 'ProfileGainTableMap2'), # DNG 1.7
(52547, 'ColumnInterleaveFactor'), # DNG 1.7
(52548, 'ImageSequenceInfo'), # DNG 1.7
(52550, 'ImageStats'), # DNG 1.7
(52551, 'ProfileDynamicRange'), # DNG 1.7
(52552, 'ProfileGroupName'), # DNG 1.7
(52553, 'JXLDistance'), # DNG 1.7
(52554, 'JXLEffort'), # DNG 1.7
(52555, 'JXLDecodeSpeed'), # DNG 1.7
(55000, 'AperioUnknown55000'),
(55001, 'AperioMagnification'),
(55002, 'AperioMPP'),
Expand Down Expand Up @@ -18321,6 +18349,7 @@ def IMAGE_COMPRESSIONS(self) -> set[int]:
48124, # jetraw
50001, # webp
50002, # jpegxl
52546, # jpegxl DNG
}

@cached_property
Expand Down Expand Up @@ -23640,8 +23669,10 @@ def byteorder_compare(byteorder: str, other: str, /) -> bool:
def recarray2dict(recarray: numpy.recarray, /) -> dict[str, Any]:
"""Return numpy.recarray as dictionary.

>>> r = numpy.array([(1., 2, 'a'), (3., 4, 'bc')],
... dtype=[('x', '<f4'), ('y', '<i4'), ('s', 'S2')])
>>> r = numpy.array(
... [(1.0, 2, 'a'), (3.0, 4, 'bc')],
... dtype=[('x', '<f4'), ('y', '<i4'), ('s', 'S2')],
... )
>>> recarray2dict(r)
{'x': [1.0, 3.0], 'y': [2, 4], 's': ['a', 'bc']}
>>> recarray2dict(r[1])
Expand Down Expand Up @@ -24178,7 +24209,7 @@ def parse_kwargs(
def update_kwargs(kwargs: dict[str, Any], /, **keyvalues: Any) -> None:
"""Update dict with keys and values if keys do not already exist.

>>> kwargs = {'one': 1, }
>>> kwargs = {'one': 1}
>>> update_kwargs(kwargs, one=None, two=2)
>>> kwargs == {'one': 1, 'two': 2}
True
Expand Down

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