Tagged image file format (TIFF)#

RosettaSciIO can read and write 2D and 3D .tiff files using using Christoph Gohlke’s tifffile library. In particular, it supports reading and writing of TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH, and FluoView files. Most of these are uncompressed or losslessly compressed 2**(0 to 6) bit integer, 16, 32 and 64-bit float, grayscale and RGB(A) images, which are commonly used in bio-scientific imaging. See the library webpage for more details.

Currently RosettaSciIO has limited support for reading and saving the TIFF tags. However, the way that RosettaSciIO reads and saves the scale and the units of .tiff files is compatible with ImageJ/Fiji and Gatan Digital Micrograph software. RosettaSciIO can also import the scale and the units from .tiff files saved using FEI, Zeiss SEM, Olympus SIS, Jeol SightX and Hamamatsu HPD-TA (streak camera) software.

Multipage tiff files are read using either series or pages interface built in tifffile, series interface (default) returns multipage series of images as a single array with single metadata and original metadata structure of first page. Using multipage_to_list=True will use pages interface and will return a list of separate arrays and metadata per page.

API functions#

rsciio.tiff.file_reader(filename, lazy=False, force_read_resolution=False, multipage_as_list=False, **kwds)#

Read data from tif files using Christoph Gohlke’s tifffile library. The units and the scale of images saved with ImageJ or Digital Micrograph is read. There is limited support for reading the scale of files created with Zeiss and FEI SEMs.

Parameters:

  • filename (str, pathlib.Path) – Filename of the file to read or corresponding pathlib.Path.

  • lazy (bool, Default=False) – Whether to open the file lazily or not.

  • force_read_resolution (bool, Default=False) – Force read image resolution using the x_resolution, y_resolution and resolution_unit tiff tags. Beware: most software don’t (properly) use these tags when saving .tiff files. See https://www.awaresystems.be/imaging/tiff/tifftags/resolutionunit.html.

  • multipage_as_list (bool, Default=False) – Read multipage tiff and return list with full content of every page. This utilises tifffile``s ``pages instead of series way of data access, which differently to series is able to return metadata per page, where series (default) is able to access only metadata from first page. This is recommended to be used when data is from dynamic experiments (where some of parameters of the instrument are changing during acquisition).

  • hamamatsu_streak_axis_type (str, optional) –

    Decide the type of the time axis for hamamatsu streak files:

    • uniform: the best-fit linear axis is used, inducing a (small) linearisation error. Initialise a UniformDataAxis.

    • data: the raw time axis parsed from the metadata is used. Initialise a DataAxis.

    • functional: the best-fit 3rd-order polynomial axis is used, avoiding linearisation error. Initialise a FunctionalDataAxis.

    By default, uniform is used but a warning of the linearisation error is issued. Explicitly passing hamamatsu_streak_axis_type='uniform' suppresses the warning. In all cases, the original axis values are stored in the original_metadata of the signal object.

  • **kwds (optional) – Additional arguments to be passed to the TiffFile class of the tifffile library.

Returns:

List of dictionaries containing the following fields:

  • ’data’ – multidimensional numpy array

  • ’axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ’metadata’ – dictionary containing the parsed metadata

  • ’original_metadata’ – dictionary containing the full metadata tree from the input file

Return type:

list of dicts

Examples

>>> # Force read image resolution using the x_resolution, y_resolution and
>>> # the resolution_unit of the TIFF tags.
>>> s = file_reader('file.tif', force_read_resolution=True)
>>> # Load a non-uniform axis from a hamamatsu streak file:
>>> s = file_reader('file.tif', hamamatsu_streak_axis_type='data')
rsciio.tiff.file_writer(filename, signal, export_scale=True, extratags=[], **kwds)#

Writes data to tif using Christoph Gohlke’s tifffile library.

Parameters:

  • filename (str, pathlib.Path) – Filename of the file to write to or corresponding pathlib.Path.

  • signal (dict) –

    Dictionary containing the signal object. Should contain the following fields:

    • ’data’ – multidimensional numpy array

    • ’axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

    • ’metadata’ – dictionary containing the metadata tree

  • export_scale (bool, default=True) – Export the scale and the units (compatible with DM and ImageJ) to appropriate tags.

  • extratags (tuple, list of tuples, optional) – Save custom tags through the tifffile library. Must conform to a specific convention (see tifffile documentation and example below).

  • **kwds (optional) –

    Additional arguments to be passed to the imwrite function of the tifffile library.

Examples

>>> # Saving the string 'Random metadata' in a custom tag (ID 65000)
>>> extratag = [(65000, 's', 1, "Random metadata", False)]
>>> file_writer('file.tif', signal, extratags=extratag)

>>> # Reading the string 'Random metadata' from a custom tag (ID 65000)
>>> s2 = file_reader('file.tif')
>>> s2.original_metadata['Number_65000']
b'Random metadata'

Warning

The file will be saved with the same bit depth as the signal. Since most processing operations in HyperSpy and numpy will result in 64-bit floats, this can result in 64-bit .tiff files, which are not always compatible with other imaging software.

You can first change the dtype of the signal before saving (example using HyperSpy):

>>> s.data.dtype
dtype('float64')
>>> s.change_dtype('float32')
>>> s.data.dtype
dtype('float32')
>>> s.save('file.tif')