Enable dim_order kwargs for direct image conversions

[elevans]

This pull request enables dim_order kwargs for the direct image converter methods: to_dataset(), to_img(), and to_xarray(). It also adds tests for this new feature in test_image_conversion.py and some additional features. There are also a couple of bugs/enhancements I made (e.g. we now calculate the slope for linear axis using all points instead of the first two elements of the scale array).

To get a good idea on the dim_order kwarg option and how it works with the direct image converters, check out the new section I wrote in the docs (06-Working-with-Images, section 6.9). There is a nice table that outlines the image conversion behavior with or without dim_order supplied.

changes

• Enable dim_order kwarg for direct image converions (to_dataset(), to_img(), to_xarray()).
• Add tests for dim_order kwargs
• Refactor image tests to use pytest's parameterize syntax.
• Add test to check dimension coordinates are preserved between image conversions.
• Replaced imagej.dims._get_scale() with imagej.dims._compute_slope(). (see latest comment)
• Allow for dim_order lengths that are shorter than the length of the array. Unknown dims are assigned dim_n like xarray.
• Add 6.9 -- information about using the direct image converters and dim_order.
• Add singleton scale hack.

[codecov-commenter]

Codecov Report

Base: 76.05% // Head: 77.40% // Increases project coverage by +1.35% 🎉

Coverage data is based on head (799113d) compared to base (bffc2d7).
Patch coverage: 92.96% of modified lines in pull request are covered.

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Additional details and impacted files

@@            Coverage Diff             @@
##             main     #238      +/-   ##
==========================================
+ Coverage   76.05%   77.40%   +1.35%     
==========================================
  Files          16       16              
  Lines        1829     1868      +39     
==========================================
+ Hits         1391     1446      +55     
+ Misses        438      422      -16     

Impacted Files	Coverage Δ
src/imagej/stack.py	96.96% <ø> (ø)
tests/test_labeling.py	100.00% <ø> (ø)
src/imagej/dims.py	63.10% <64.28%> (+0.45%)	⬆️
src/imagej/__init__.py	62.09% <80.00%> (+1.69%)	⬆️
tests/test_image_conversion.py	98.83% <98.03%> (-1.17%)	⬇️
src/imagej/convert.py	88.62% <100.00%> (+6.46%)	⬆️

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[gselzer]

I didn't take the time to understand to understand the dimension translations, but this looks like a great step forward @elevans!

Just had a couple minor suggestions, but I don't think anything was major.

[elevans]

Update:

I'm working on using xarray's global attribute's to store imagej/pyimagej specific metadata like the scale and CalibratedAxis type. See the axis-scale-logic branch for more details. The way things are going it seems this will be our preferred way to assign CalibratedAxis types. Thus I think we should scrap the scale work I've done here (i.e. _compute_scale() etc...).

[elevans]

Me again! I have reverted the dims.py module to a near original state. The difference from main is:

• Added _validate_dim_order() function which allows users to pass dimension lists that are smaller than the size of the data. The unknown dimensions are then populated with dim_n where n is incrementing. This allows us to label unknown dimensions in the same way as xarray.
• Added a hack to deal with converting numpy.ndarrays or xarray.DataArrays with a singleton dimension. If a singleton dimension is in the data we simply assign scale=1 for the axis. Without this hack you hit an index error when trying to do the scale math. See below for more details.

I have a nice metadata solution and a rework on how we assign axes for xarrays <-> datasets. That work is still in progress (almost done) on this branch/PR: #247

Singleton dimensions

The reason I originally added the work with axis scales was because I discovered a bug when creating more tests for the dim_order kwargs. When converting a numpy.ndarray or an xarray.DataArray that has a singleton dimension (i.e. one or more axes is size 1) we run into an index error when trying to perform a subtraction between the first and second elements of the axis/scale array here:

pyimagej/src/imagej/dims.py

Lines 277 to 289 in a5544fa

	def _get_scale(axis):
	"""
	Get the scale of an axis, assuming it is linear and so the scale is simply
	second - first coordinate.
	:param axis: A 1D list like entry accessible with indexing, which contains the
	axis coordinates
	:return: The scale for this axis or None if it is a non-numeric scale.
	"""
	try:
	return axis.values[1] - axis.values[0]
	except TypeError:
	return None

From what I gather this rather serious bug has been here the entire time. Here is how you can reproduce it on main:

import numpy as np
import imagej

ij = imagej.init()
nparr = np.random.rand(1, 2, 3, 4, 5)

# index error
ds = ij.py.to_dataset(nparr)

I think no one has ran into this yet (or very few people have) because when you slice an array to a singleton dimension its usually squashed. But anyone who is making an array with a singleton dimension will hit this bug when converting to a Dataset.