350 pairing extra feats #352
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## main #352 +/- ##
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Distances are easily recovereable from the data Distances are easily recovereable from the data
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@danielmwatkins Below is what the output data structure is looking like after the matching criteria is addee. All units for lengths and areas are in km and sq. km, respectively; orientation is in radians. 6×14 DataFrame
The other 'goodness of fit' metrics are being excluded from the output as they can be easily computed from the inputs. I was thinking that the same is true for centroid distances (thus not included above). Should they? Any further thoughts? Thanks! |
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Yes, along-track distance is easily calculated from the other columns. I
think Float64 makes sense for area_under and corr as well, since we want to
be able to have NaN for the last timestamp in a trajectory. For clarity, I
wonder if we should make the names "area_under" and "corr" more
descriptive. The column as listed is actual 1 - correlation, is it not? If
so, we should convert it to correlation (so the first row would be ~0.99
instead of 0.01) since in the Lopez-Acosta paper psi-s correlation is
described with large values being a better match. For area_under, that's
the total area of mismatch isn't it? Like, a 0 means that after rotation,
the floes line up perfectly? If so, instead of calling it "area_under" we
could call it "area_mismatch". Thoughts?
…On Mon, Nov 6, 2023 at 11:38 AM Carlos Paniagua ***@***.***> wrote:
@danielmwatkins <https://github.com/danielmwatkins> Below is what the
output data structure is looking like after the matching criteria is addee.
All units for lengths and areas are in km and sq. km, respectively;
orientation is in radians. area_under (as named in the Matlab script) is
the registration "floe mismatch" (sometimes clamped to 0 if a certain
threshold is met); corr is the psi-s correlation. They are computed
between the floes in the current row and the next. Note that these values
are missing for the last floe in each group ID.
6×14 DataFrame
RowIDpasstimeareaconvex_areamajor_axis_lengthminor_axis_lengthorientation
perimeterlatitudelongitudexyarea_undercorr
Int64DateTimeFloat64Float64Float64Float64Float64Float64Float64Float64
Float64Float64Float64?Float64?
1 1 2022-09-14T12:44:49 31.7219 41.7497 8.85311 6.227 0.607172 28.5445
66.4384 -173.71 1.20753e6 -1.68165e6 0.0 0.0102782
2 1 2022-09-14T13:59:19 27.3962 37.3585 8.36951 6.02703 0.566405 27.3084
70.5095 -153.641 1.20779e6 -1.68216e6 0.0 0.0104143
3 1 2022-09-15T12:44:49 31.7219 41.7497 8.48116 6.45538 0.479664 28.7566
71.483 -131.167 1.20574e6 -1.68088e6 missing missing
4 2 2022-09-14T12:44:49 84.6792 101.523 20.8234 5.61064 -0.316888 48.9224
66.5149 -173.725 1.19448e6 -1.67628e6 0.0 0.00823861
5 2 2022-09-14T13:59:19 77.0764 94.5103 20.5266 5.27752 -0.340923 47.8983
71.4251 -127.612 1.19499e6 -1.67704e6 0.0 0.0126095
6 2 2022-09-15T12:44:49 84.6136 102.441 21.0872 5.65186 -0.358114 49.4344
71.5446 -131.022 1.19268e6 -1.67576e6 missing missing
The other 'goodness of fit' metrics are being excluded from the output as
they can be easily computed from the inputs. I was thinking that the same
is true for centroid distances (thus not included above). Should they? Any
further thoughts? Thanks!
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@danielmwatkins |
| # rename uuid to ID | ||
| rename!(propsvert, :uuid => :ID) | ||
| # sort by uuid_0, passtime and keep unique rows | ||
| _pairs = DataFrames.sort!(_pairs, [:uuid_0, :passtime]) |> unique |
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Nice! That's a great way to just get rid of the extra rows
| @@ -497,3 +497,58 @@ function addfloemasks!(props, imgs) | |||
| end | |||
| return nothing | |||
| end | |||
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| ## LatLon functions originally from IFTPipeline.jl | |||
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Will it be removed from ice-floe-tracker-pipeline? It looks like these were orginially in the h5.jl script in that repo.
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Needs merging WilhelmusLab/IceFloeTracker.jl#352
#350 #339