Refactoring and tests of dnatracing.py

[ns-rse]

topostats.tracing.dnatracing is the module that covers the processing after "topotracing" has processed an image via Filters(), Grains() and GrainStats(). It contains a couple of classes dnaTrace() and traceStats() and some work has been done on these to align them with the new workflow (see #166).

But there are no tests in place and the code can be refactored to be cleaner which in turn will allow for finder-grained tests to be written. Further docstrings need writing for all modules, classes and methods, there is some information there but not fully qualified numpy docstrings.

Many of the methods have for: loops in them, each of the contents of these loops should be abstracted out into its own _[method] and that method then called from within a loop of the associated [method]. This makes it easier to write tests on a single case.

There is a lot of old code left in various comments or documentation blocks that will need cleaning out.

• [x] #27
• [x] #289
• [ ] #290
• [x] #294
• [ ] #295 (blocks #294 though so needs completing first)
• [x] #402
• [x] #403
• [x] #595

[ns-rse]

One thing to consider is replacing the skeletonize solution that dnatracing calls with the same functionality from scikit-image skeletonize.

[alicepyne]

Definitely agree with this. Makes it more flexible too. Unless @joe thinks differently? It’s his initial code!

On Wed, 20 Jul 2022 at 09:54, Neil Shephard @.***> wrote:

One thing to consider is replacing the skeletonize solution that dnatracing calls with the same functionality from scikit-image skeletonize https://scikit-image.org/docs/stable/auto_examples/edges/plot_skeleton.html .

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[ns-rse]

This is quite a big refactor and so I'm going to break it down into smaller tasks, and so will use this issue as an "Epic" and break it down into smaller tasks and list the issues below.

• [x] #27
• [x] #289
• [ ] #290
• [ ] #294
• [ ] #295 (blocks #294 though so needs completing first)
• [ ] #403

[ns-rse]

I've started work on this and am aiming to simplify a lot of the code by making the class process a single grain at a time, that removes all the complexity that is scattered around that repeats each step on multiple grains.

Processing multiple grains is then simplified by iterating over a dictionary of grains which itself may contain several different sets of information such as the grain in its raw processed form (post Filters), the mask of the grain (as skeletonisation currently works on binary arrays), the original coordinates should there be a desire to reconstruct the whole image with skeletons for example.

[ns-rse]

Belatedly adding notes from meeting 2023-01-17

• Make binary dilation optional, this sometimes results in grains touching the border and when this happens skeletonisation doesn't always work correctly (unclear what method though whether this is scikit-image or "Joe's". A possible solution to deal with this might be to np.pad() the dilated arrays (masked or otherwise) so that there are blanks/zero's and dilated images don't touch the edges.

• Rather than ~purge_obvious_crap~ we need to have the option to remove objects that after skeletonisation are smaller than a minimum size. Despite removing small objects during filtering and grain detection we can still end up with blobs (roughly circular artefacts) that when skeletonised reduce down to very small skeletons.

As a dummy example...

[
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
 [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
 [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
 [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
 [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
 [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
 [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
 [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
 [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]

...might skeletonise to...

[
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]

Strictly it has skeletonised, but its neither circular nor linear and should be removed.

[ns-rse]

Add a function to topostats.io to write co-ordinates of traces to CSV.

Documented in #595 and added to the todo list.

[ns-rse]

After #610 has been completed the next step is to spline the traces, current code-base only performs this for linear molecules, be sure to git rebase and include the changes in #653.

[ns-rse]

Closing as work has been undertaken under the EPIC #800 and related issues linked from there.