Hi Qianqian,
What is your concrete proposal for NumPy here?
Are you suggesting new methods or functions like to_json/from_json in NumPy itself?
that would work - either define a subclass of JSONEncoder to serialize ndarray and allow users to pass it to cls in json.dump, or, as you mentioned, define to_json/from_json like pandas DataFrame would save people from writing customized codes/formats.
I am also wondering if there is a more automated way to tell
json.dump/dumps to use a default serializer for ndarray without
using cls=...? I saw a SO post mentioned about a method called
"__serialize__" in a class, but can't find it in the official doc.
I am wondering if anyone is aware of the method defining a default
json serializer in an object?
As far as I can tell, reading/writing in your custom JSON format already works with your jdata library.
ideally, I was hoping the small jdata encoder/decoder functions can be integrated into numpy; it can help avoid the "TypeError: Object of type ndarray is not JSON serializable" in json.dump/dumps without needing additional modules; more importantly, it simplifies users experience in exchanging complex arrays (complex valued, sparse, special shapes) with other programming environments.
Qianqian
Best,Stephan
On Thu, Nov 25, 2021 at 2:35 PM Qianqian Fang <q.fang@neu.edu> wrote:
_______________________________________________Dear numpy developers,
I would like to share a proposal on making ndarray JSON serializable by default, as detailed in this github issue:
https://github.com/numpy/numpy/issues/20461
briefly, my group and collaborators are working on a new NIH (National Institute of Health) funded initiative - NeuroJSON (http://neurojson.org) - to further disseminate a lightweight data annotation specification (JData) among the broad neuroimaging/scientific community. Python and numpy have been widely used in neuroimaging data analysis pipelines (nipy, nibabel, mne-python, PySurfer ... ), because N-D array is THE most important data structure used in scientific data. However, numpy currently does not support JSON serialization by default. This is one of the frequently requested features on github (#16432, #12481).
We have developed a lightweight python modules (jdata, bjdata) to help export/import ndarray objects to/from JSON (and a binary JSON format - BJData/UBJSON - to gain efficiency). The approach is to convert ndarray objects to a dictionary with subfields using standardized JData annotation tags. The JData spec can serialize complex data structures such as N-D arrays (solid, sparse, complex). trees, graphs, tables etc. It also permits data compression. These annotations have been implemented in my MATLAB toolbox - JSONLab - since 2011 to help import/export MATLAB data types, and have been broadly used among MATLAB/GNU Octave users.
Examples of these portable JSON annotation tags representing N-D arrays can be found at
http://openjdata.org/wiki/index.cgi?JData/Examples/Basic#2_D_arrays_in_the_annotated_format
http://openjdata.org/wiki/index.cgi?JData/Examples/Advancedand the detailed formats on N-D array annotations can be found in the spec:
our current python module to encode/decode ndarray to JSON serializable forms are implemented in these compact functions (handling lossless type/data conversion and data compression)
https://github.com/NeuroJSON/pyjdata/blob/63301d41c7b97fc678fa0ab0829f76c762a16354/jdata/jdata.py#L72-L97
https://github.com/NeuroJSON/pyjdata/blob/63301d41c7b97fc678fa0ab0829f76c762a16354/jdata/jdata.py#L126-L160We strongly believe that enabling JSON serialization by default will benefit the numpy user community, making it a lot easier to share complex data between platforms (MATLAB/Python/C/FORTRAN/JavaScript...) via a standardized/NIH-backed data annotation scheme.
We are happy to hear your thoughts, suggestions on how to contribute, and also glad to set up dedicated discussions.
Cheers
Qianqian
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