Note that these functions exist as two versions, the full version with numpy (np) and the version without requirements (nonp) that doesn't do nunpy encoding/decoding.
If you import these functions directly from json_tricks, e.g. from json_tricks import dumps, then it will select np if numpy is available, and nonp otherwise. You can use json_tricks.NUMPY_MODE to see if numpy mode is being used.
This dual behaviour can lead to confusion, so it is recommended that you import directly from np or nonp.
.. autofunction:: json_tricks.nonp.dumps
.. autofunction:: json_tricks.np.dumps
.. autofunction:: json_tricks.nonp.dump
.. autofunction:: json_tricks.np.dump
.. autofunction:: json_tricks.nonp.loads
.. autofunction:: json_tricks.np.loads
.. autofunction:: json_tricks.nonp.load
.. autofunction:: json_tricks.np.load
.. autofunction:: json_tricks.comment.strip_comments
.. autofunction:: json_tricks.np.numpy_encode
.. autofunction:: json_tricks.np.json_numpy_obj_hook
.. autofunction:: json_tricks.encoders.class_instance_encode
.. autoclass:: json_tricks.decoders.ClassInstanceHook
.. autofunction:: json_tricks.encoders.json_date_time_encode
.. autofunction:: json_tricks.decoders.json_date_time_hook
It's not possible (without a lot of hacks) to encode numpy scalars. This is the case because some numpy scalars (float64, and depending on Python version also int64) are subclasses of float and int. This means that the Python json encoder will stringify them without them ever reaching the custom encoders.
So if you really want to encode numpy scalars, you'll have to do the conversion beforehand. For that purpose you can use encode_scalars_inplace, which mutates a nested data structure (in place!) to replace any numpy scalars by their representation. If you serialize this result, it can subsequently be loaded without further adaptations.
It's not great, but unless the Python json module changes, it's the best that can be done. See issue 18 for more details.
.. autofunction:: json_tricks.np_utils.encode_scalars_inplace
There are many test environments: with and without pandas, numpy or timezone support, and for each of the supported Python versions. You will need all the Python versions installed, as well as a number of packages available though. You can just install only detox (pip install detox), and others will be installed automatically as dependencies or during tests (like numpy, pandas, pytz, pytest, pytest-cov and tox).
To run all of these tests at once, simply run detox from the main directory. It usually takes roughly half a minute, but the first time takes longer because packages need to be installed.
To get coverage information from all these configurations, you first need to combine them using coverage combine .tox/coverage/* (once after each detox). You can then show results normally, e.g. coverage report. It should be about 90%.
If you want to show results in IntelliJ PyCharm with lines highlighted etc, you need several steps. First generate an XML-report with coverage xml. Then the paths must be made to start at the root of the project, or be absolute, which can be done using sed 's/filename="\([^"]*\)"/filename="pyjson_tricks\/\1"/g' coverage.xml or manually using find and replace. Finally, you can load the report using Tools > Show code coverage data and use the green +.
This is a simple module so the documentation is single-page.
.. toctree:: :maxdepth: 2