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PIMS 2 Design Document and Prototype


PIMS is a library for reading image series in a lazy fashion from various image and video formats via a consistent API. It could be reworked to leverage libraries, language features, and changes to the packaging ecosystem that have emerged since PIMS' first release in 2013.


What is PIMS? PIMS, short for Python Image Sequence, provides a common API to several underlying image or video I/O libraries. It exposes image sequences through an object that supports numpy array-like slicing with lazy evaluation.


# Several formats are built in.
import pims
images ='my_directory/*.png')  # many PNGs with sequential names
images ='my_directory/*.tif')  # many TIFs with sequential names
images ='tiff_stack.tif')  # one TIF file containing many frames
images ='movie.mp4')

# All of these indexing styles are supported and lazy --- no I/O performed yet.
subset = images[::2]
subset = images[:5]
subset = images[[13, 52, 99]] 

# When a single frame is accessed, via indexing...
first_image = subset[0]

# or iteration...
for image in subset:

# then I/O occurs, with one frame loaded at a time if possible.

Who uses PIMS? According to GitHub it is used by 102 other projects, and it has 51 forks. Dask-image uses pims, as a required dependency. Many users are concentrated in the bio sciences or soft matter physics area.

Who maintains PIMS? PIMS was split out of an image analysis codebase in 2013. Its main contributors are Dan Allan (BNL), Thomas Caswell (BNL), Nathan Keim (Penn State), Casper van der Wel (Nelen & Schuurmans), and Ruben Verweij (Leiden University). New development slowed around 2015, but the project is still tended to.

Room for Improvement

  • PIMS predated dask. Like PIMS, dask provides an object with numpy-like slicing semantics that can defer I/O and computation. PIMS could be updated to leverage dask.array, which has a much larger community behind it.
  • PIMS predated conda-forge. PIMS has minimal required dependencies (just numpy, six, and a pure-Python support package developed for PIMS)---good so far---but it bundles most of its readers in the main package and has many optional dependencies to support these readers. It might be better to distribute each reader (or groups of readers with the same underlying I/O library) in separate packages so that they can be separately released and installed.
  • PIMS predated the acceptance of entrypoints as an official PyPA specification (as opposed to a quirk/feature of only setuptools specifically). PIMS supports readers defined in external packages, but it discovers them by searching for subclasses of its base classes FramesSequences and FramesSequenceND. The downsides of this approach are (1) external packages with readers must import pims to subclass its objects and (2) the user must import the external package before using for PIMS to discover it.
  • PIMS predated xarray. The xarray.DataArray object could provide a natural way of encoding frame number, timecodes, "band" (i.e. color channel) labels. (For those unfamiliar, it's analogous to a pandas.Series, but with full ND support. It also plays very well with dask.)
  • PIMS readers return pims.Frame objects which subclass numpy.ndarray in order to tack a custom metadata on the array, rather than taking the xarray approach and putting metadata on an object that encapsulates the data through composition. The authors now appreciate the downsides of subclassing ndarray and the difficulties of propagating metadata through array operations.
  • detects file format and dispatches to a compatible reader using an ad hoc scheme rather than using an existing standard.

Proposal for PIMS 2.0

  • Use MIME Detect file format and dispatch to a compatible reader based on the file's MIME type, using the Python standard library module mimetypes and potentially other third-party libraries in this space.

    IANA maintains an official registry of formats (e.g. 'image/png') but it also defines a standard for adding application-specific formats outside of the official standard (e.g. 'application/x-hdf'). PIMS can use official MIME types where possible and use this extension mechanism for formats that are not registered, such as one-off vendor-specific microscopy formats.

    Within a given MIME type there can be significant variety of internal structure or metadata conventions. For this, a nested dispatch may be the right idea: PIMS dispatches based on MIME type, and the reader registered for that MIME type may inspect the file and do a second layer of dispatch based on its contents/layout. Nothing in PIMS currently requires this level of complexity, but it might be useful if we were to add a family of, say, NeXuS readers or more sophisticated TIFF readers.

    Although MIME types are not as well known to the scientific user--programmers that PIMS aims to serve as they are to web-oriented software engineers, MIME types do already have foothold in SciPy via IPython rich display's _repr_mimebundle_ and the Jupyter data explorer.

  • Use Entrypoints Perform reader discovery using entrypoints. Packages can declare in their that they provide PIMS-compatible objects like so:

        entry_points = {'TBD.readers': ['image/tiff = my_package:TIFFReader']}

    In this way, packages can declare that they include objects with a PIMS-compatible interface without actually importing PIMS or subclassing any PIMS objects. (PIMS may provide base classes as optional scaffolds for convenience or code reuse, but the key point is that packages are not required to use them.)

    Providing the option not to depend on PIMS may help gain adoption among packages that may add PIMS support experimentally or only incidentally. Established I/O packages might be open to adding PIMS-compatible readers to their API if it costs them only tens of lines of code and no new dependencies, not even optional ones.

    Also, PIMS can use entrypoints to construct a registry of all installed readers without actually importing the corresponding libraries unless/until their objects are actually used.

  • Put metadata beside data Rather than returning a lazy, array-ish object directly, as PIMS does now

    lazy_array =

    add one more layer of indirection, giving space for metadata which can be cheaply accessed and inspected before additional work is performed to build the lazy array.

    reader =
    lazy_array =

    The usage is still satisfyingly succinct and rhymes nicely with the syntax for opening files in Python, open(file).read(). Reading a file with PIMS will feel like reading a file with Python, but instead of returning an iterable of lines, it will return a lazy array-like object, which can be treated as an iterable sequence of images.

  • Use Dask Embrace dask arrays, integrating with or replacing PIMS' lazy array-like classes FramesSequence and FramesSequenceND. It is an open question whether dask arrays can fully support the same lazy slicing semantics; see #1.

Use Case

Suppose I have a library for reading and analyzing microscopy images in a particular file format. I already have I/O code. I could wrap it in a class that implements a PIMS-compatible reader. (As illusrated with reader above and in the example below, a "PIMS compatible reader" is a very small API, so this is likely < 100 lines of code.) I could make the reader "discoverable" to PIMS by declaring a 'TBD.readers' entrypoint in my As emphasized above, I could do both of these things without importing pims or adding it as a dependency.

What have I gained for my effort? If all I care about is my particular file format, not much. I might not find the new reader class any more convenient than my original I/O code which underlies it.

But when I or one of my collaborators needs to read two different kinds of formats, perhaps to align microscopy images with images from another instrument or another group, now PIMS adds value. If I can find or make PIMS-compatible readers for all the formats involved, my code will have to change very little moving from one format to another. And to smooth over the differences entirely, I may decide to install pims itself to engage the automatic MIME type detection and dispatch in

Migrating Existing Users

The proposed change to would be backward-incompatible, but all other objects in PIMS could remain in the PIMS codebase in their current form, deprecated but usable, alongside new objects that implement the PIMS 2 API.

Try the Prototype

  1. Install an example TIFF reader.

    git clone
    pip install -e tifffile_reader/
  2. Generate example data.

    pip install -r tifffile_reader/requirements_for_generate_example_data.txt
    python tifffile_reader/
  3. Try using reader directly to read one TIFF.

    In [1]: import tifffile_reader
    In [2]: reader = tifffile_reader.TIFFReader('tifffile_reader/example_data/coffee.tif')
    In [3]: reader
    Out[3]: TiffReader('tifffile_reader/example_data/coffee.tif')
    In [4]:
    Out[4]: dask.array<from-value, shape=(400, 600, 3), dtype=uint8, chunksize=(400, 600, 3), chunktype=numpy.ndarray>
    In [5]:
    <numpy array output, snipped>

    And try a TIFF series and stack as well.

    In [3]: tifffile_reader.TIFFReader('tifffile_reader/example_data/series/*.tif').read().shape
    Out[3]: (200, 25, 25)
    In [4]: tifffile_reader.TIFFReader('tifffile_reader/example_data/lfw_subset_as_stack.tif').read().shape
    Out[4]: (200, 25, 25)
  4. Install PIMS.

    git clone
    pip install -e pims2-prototype/
  5. Let detect the filetype and invoke the TIFF reader implicitly.

    In [1]: import pims
    In [2]:'tifffile_reader/example_data/coffee.tif').read().shape
    Out[2]: (400, 600, 3)

    Another example spells the file extension differently (tiff vs tif) but mimetypes still detects the filetype successfully.

    In [3]:'tifffile_reader/example_data/coffee.tiff').read().shape
    Out[3]: (400, 600, 3)

Things to notice:

  • We were able to use tifffile_reader without pims imported or even installed. If tifffile itself were to add a PIMS reader, it could do so without adding a pims dependency.
  • The core pims package provides the dispatch-on-filetype mechanism. It has one dependency (the tiny pure-Python package entrypoints) and 50 lines of code. The filetype detection is done via the Python standard library module mimetypes, which relies solely on the file extension. More sophisticated detection schemes that consider file signatures are availabe in third party libraries and should be considered.

Variety of Return Types

It is often useful to label axes of the data (color band, x vs y, etc.) and it may be useful to add coordinates like frame number and time code. Should PIMS 2 standardize on xarray.DataArray-wrapping-dask.array.Array instead of standardizing on dask.array.Array? Or should we explicitly support the possibility of multiple variations on a given reader (via thin subclasses) that implement different return types, potentially including xarray.DataArray, dask.array.Array, and plain numpy.ndarray?

Connection to Intake DataSources

Intake is a newer project that, like PIMS, wraps disparate file formats in a consistent interface. Unlike PIMS, intake handles many data structures, not just image series; in particular it has many readers that return tabular data as a pandas.DataFrame or xarray.Dataset. How should PIMS relate to intake?

There is something to be said for intake's all-encompassing generality. "You give me a data source; I give you a PyData object or its lazy (dask-backed) counterpart." There is also something to be said for the tight scope of "image series", which communicates a clear use case to users, lends itself to certain file formats, and lends type stability to the interface. That is, PIMS' read() always returns a series of image frames that the user can loop over; one never has to check whether it has returned a pandas.DataFrame.

Should PIMS carry on as a similar-but-distinct library to intake or should it become a distribution of intake drivers? One possible answer is, "Yes!" It is to automatically generate objects that satify the intake DataSource API from the proposed Reader objects. This is demonstrated in danielballan/reader-intake-adapter.


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