Addition of constellation homologies workflow with tutorials #690
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Created plm_homologies directory containing de novo homology pipeline and migrated acute into this directory.
Cleaned up names of functions being called by init so that import of plm_homologies is successful.
The base functions required for homology grouping (space [optional], starscape, and constella as well as a QC screening script (constellaQC) have been added via the homology module in PCV. Markdown files have also been added which present the implementation of acute (tutorials 1 & 2) as well as the usage of homology on segmented plant data (tutorials 3 & 4).
Adjusting format size of graphs for ease of better display purposes.
Moving existing .md files from ~/docs/ to ~/docs/tutorials/ for the plm analysis and homology workflow.
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@jgerardhodge is the new pull request just for merging with 4.x? If so, we can update this PR to compare to 4.x instead |
nfahlgren
added
new feature
Python convention is lowercase variable names
float(len(acute_pos)) / float(len(obj)) was not assigned to a variable and acute_pos was only used in that calculation
Codecov Report
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In our testing we could not trigger either condition and originally had this commented out in `pcv.acute`
I broke it when changing to is/is not because of how the NumPy comparisons need to be done
nfahlgren
added
ready to review
and removed
work in progress
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Describe your changes
Adds new functions under the homology module of PlantCV which consists of:
*space - generates additional metavariables for acute pseudo-landmarks
*starscape - function for dimensionality reduction of segmented plant data to generate constellations of points in a time series step
*constella - function for de novo homology grouping of nearest neighbors in these constellations through agglomerative clustering
*constellaQC - scores homology groupings against a known standard to estimate valid call rates in addition to clumping and splitting errors.
While space and constellaQC are considered optional functions due to their limited scope for datatypes or utility as QC measures respectively starscape and constella comprise the bulk of the constellation approach to de novo homology assessment.
In addition 4 tutorial md files have been included which form a series of "acute_plm_tutorial". The first two tutorials demonstrate the usage of acute for pseudo-landmarking as well as an implementation for running this script on batch datasets. The second two tutorials (i.e. 3 & 4) consist of a demonstration of the constellation grouping methodology on this simplified dataset as well as best practices for it's usage.
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