Merge branch 'main' into chore(deps)--update-ice-floe-tracker-to-0.6.…

…3-registry

IFTPipeline.jl/src/h5.jl

@@ -36,7 +36,6 @@ function choose_dtype(mx::T) where {T<:Integer}
     throw("$mx can't be represented by any of $types")
 end
 
-
 """
     makeh5files(pathtosampleimg, resdir)
 
@@ -79,7 +78,9 @@ Each HDF5 file has the following structure:
 The `floe_properties` group contains a floe properties matrix `properties` for `labeled_image` and associated `column_names`.
 The `index` group contains the spatial coordinates in the source image coordinate reference system (default NSIDC polar stereographic, meters) and geographic coordinates (latitude and longitude, decimal degrees). Estimated satellite overpass time `time` is provided in Unix timestamp format (seconds since 1970-01-01 00:00 UTC).
 """
-function makeh5files(; pathtosampleimg::String, resdir::String, iftversion=IceFloeTracker.IFTVERSION)
+function makeh5files(;
+    pathtosampleimg::String, resdir::String, iftversion=IceFloeTracker.IFTVERSION
+)
     latlondata = getlatlon(pathtosampleimg)
 
     ptpath = joinpath(resdir, "passtimes.jls")
@@ -137,8 +138,15 @@ function makeh5files(; pathtosampleimg::String, resdir::String, iftversion=IceFl
     return nothing
 end
 
-
-function makeh5files_single(; passtime::DateTime, iftversion::Union{String,Nothing}=nothing, truecolor::String, falsecolor::String, labeled::String, props::String, output::String)
+function makeh5files_single(;
+    passtime::DateTime,
+    iftversion::Union{String,Nothing}=nothing,
+    truecolor::String,
+    falsecolor::String,
+    labeled::String,
+    props::String,
+    output::String,
+)
     latlondata = getlatlon(truecolor)
     ptsunix = Int64(Dates.datetime2unix(passtime))
     labeled_ = load_labeled_img(labeled)
@@ -167,9 +175,13 @@ function makeh5files_single(; passtime::DateTime, iftversion::Union{String,Nothi
         g["y"] = latlondata["Y"]
 
         g = create_group(file, "floe_properties")
-        write_dataset(g, "properties", [copy(row) for row in eachrow(props_)])  # `copy(row)` converts the DataSetRow to a NamedTuple
-        attrs(g)["Description of properties"] = """ Area units (`area`, `convex_area`) are in sq. kilometers, length units (`minor_axis_length`, `major_axis_length`, and `perimeter`) in kilometers, and `orientation` in radians (see the description of properties attribute.) Latitude and longitude coordinates are in degrees, and the stereographic coordinates`x` and `y` are in meters relative to the NSIDC north polar stereographic projection. Generated using the `regionprops` function from the `skimage` package. See https://scikit-image.org/docs/0.20.x/api/skimage.measure.html#regionprops
-        """
+        @info nrow(props_)
+        if nrow(props_) > 0
+            write_dataset(g, "properties", [copy(row) for row in eachrow(props_)])  # `copy(row)` converts the DataSetRow to a NamedTuple
+            attrs(g)["Description of properties"] = """Area units (`area`, `convex_area`) are in sq. kilometers, length units (`minor_axis_length`, `major_axis_length`, and `perimeter`) in kilometers, and `orientation` in radians (see the description of properties attribute.) Latitude and longitude coordinates are in degrees, and the stereographic coordinates`x` and `y` are in meters relative to the NSIDC north polar stereographic projection. Generated using the `regionprops` function from the `skimage` package. See https://scikit-image.org/docs/0.20.x/api/skimage.measure.html#regionprops"""
+        else
+            attrs(g)["Description of properties"] = """No floes detected"""
+        end
 
         mx = maximum(labeled_)
         T = choose_dtype(mx)
@@ -182,7 +194,6 @@ function makeh5files_single(; passtime::DateTime, iftversion::Union{String,Nothi
 
         attrs(obj)["description"] = "Connected components of the segmented floe image using a 3x3 structuring element. The property matrix consists of the properties of each connected component."
         write_dataset(obj, dtype, imgdata)
-
     end
     return nothing
 end

IFTPipeline.jl/test/test-h5.jl

@@ -1,89 +1,122 @@
-testlisteq = (a, b) -> @test Set(a) == Set(b)
-pathtosampleimg = joinpath(@__DIR__, "test_inputs/input_pipeline/20220914.aqua.falsecolor.250m.tiff")
-resdir = joinpath(dirname(pathtosampleimg), "h5")
-
-originalbbox = (latitude=[81, 79], longitude=[-22, -12])
-
-latlondata = getlatlon(pathtosampleimg)
-
-getcorners(m) = [m[1, 1], m[end, end]]
-latcorners = getcorners(latlondata["latitude"])
-loncorners = getcorners(latlondata["longitude"])
-
-ptpath = joinpath(resdir, "passtimes.jls")
-passtimes = deserialize(ptpath)
-ptsunix = Int64.(Dates.datetime2unix.(passtimes))
-
-fnpath = joinpath(resdir, "filenames.jls")
-truecolor_refs, falsecolor_refs = deserialize(fnpath)
-
-floespath = joinpath(resdir, "segmented_floes.jls") # for labeled_image
-floes = deserialize(floespath)
-
-propspath = joinpath(resdir, "floe_props.jls")
-props = deserialize(propspath)
-
-lb = label_components(floes[1])
-
-makeh5files(; pathtosampleimg, resdir)
-
-h5path = joinpath(resdir, "hdf5-files", "20220914T1244.aqua.labeled_image.250m.h5")
-
-@testset "h5.jl" begin
-
-    # validate computed lat/lon corners
-    @test all(originalbbox.latitude .≈ round.(latcorners))
-    @test all(originalbbox.longitude .≈ round.(loncorners))
-
-
-    # open h5 file
-    fid = h5open(h5path, "r")
-
-    @test typeof(fid) == HDF5.File
-
-    # top level attributes
-    @test attrs(fid)["iftversion"] == string(IceFloeTracker.IFTVERSION)
-    @test attrs(fid)["fname_falsecolor"] == falsecolor_refs[1]
-    @test attrs(fid)["fname_truecolor"] == truecolor_refs[1]
-    @test attrs(fid)["crs"] == latlondata["crs"]
-
-    # groups
-    testlisteq(keys(fid), ["floe_properties", "index"])
-    keys_index = [k for k in keys(fid["index"]) if k ∉ ["latitude", "longitude"]]
-    testlisteq(keys_index, ["time", "x", "y"])
-    testlisteq(keys(fid["floe_properties"]), ["column_names", "labeled_image", "properties"])
-
-    # check index group datasets
-    g = fid["index"]
-    t = read(g["time"])
-    x = read(g["x"])
-    y = read(g["y"])
-
-    @test t == ptsunix[1]
-    @test x == latlondata["X"]
-    @test y == latlondata["Y"]
-
-    # check floe_properties group datasets
-    g = fid["floe_properties"]
-    colnames = read(g["column_names"])
-    lb = read(g["labeled_image"])
-    props = read(g["properties"])
-
-    testlisteq(colnames, ["area", "convex_area", "major_axis_length", "minor_axis_length", "orientation", "perimeter", "latitude", "longitude", "x", "y"])
-
-    @test typeof(lb) == Matrix{UInt8}
-    @test typeof(props) == Matrix{Float64}
-    close(fid)
-
-    @test_throws "can't be represented" choose_dtype(-1) 
-    @test choose_dtype(100) == UInt8
-    @test choose_dtype(300) == UInt16
-    @test choose_dtype(70_000) == UInt32
-    @test choose_dtype(BigInt(2)^64 - 1) == UInt64
-    @test choose_dtype(BigInt(2)^64) == UInt128
-    @test choose_dtype(BigInt(2)^128 - 1) == UInt128
-    @test_throws "can't be represented" choose_dtype(BigInt(2)^128) 
+@testset "hdf5 export" begin
+    @testset "normal case" begin
+        testlisteq = (a, b) -> @test Set(a) == Set(b)
+        pathtosampleimg = joinpath(
+            @__DIR__, "test_inputs/input_pipeline/20220914.aqua.falsecolor.250m.tiff"
+        )
+        resdir = joinpath(dirname(pathtosampleimg), "h5")
+
+        originalbbox = (latitude=[81, 79], longitude=[-22, -12])
+
+        latlondata = getlatlon(pathtosampleimg)
+
+        getcorners(m) = [m[1, 1], m[end, end]]
+        latcorners = getcorners(latlondata["latitude"])
+        loncorners = getcorners(latlondata["longitude"])
+
+        ptpath = joinpath(resdir, "passtimes.jls")
+        passtimes = deserialize(ptpath)
+        ptsunix = Int64.(Dates.datetime2unix.(passtimes))
+
+        fnpath = joinpath(resdir, "filenames.jls")
+        truecolor_refs, falsecolor_refs = deserialize(fnpath)
+
+        floespath = joinpath(resdir, "segmented_floes.jls") # for labeled_image
+        floes = deserialize(floespath)
+
+        propspath = joinpath(resdir, "floe_props.jls")
+        props = deserialize(propspath)
+
+        lb = label_components(floes[1])
+
+        makeh5files(; pathtosampleimg, resdir)
+
+        h5path = joinpath(resdir, "hdf5-files", "20220914T1244.aqua.labeled_image.250m.h5")
+
+        @testset "h5.jl" begin
+
+            # validate computed lat/lon corners
+            @test all(originalbbox.latitude .≈ round.(latcorners))
+            @test all(originalbbox.longitude .≈ round.(loncorners))
+
+            # open h5 file
+            fid = h5open(h5path, "r")
+
+            @test typeof(fid) == HDF5.File
+
+            # top level attributes
+            @test attrs(fid)["iftversion"] == string(IceFloeTracker.IFTVERSION)
+            @test attrs(fid)["fname_falsecolor"] == falsecolor_refs[1]
+            @test attrs(fid)["fname_truecolor"] == truecolor_refs[1]
+            @test attrs(fid)["crs"] == latlondata["crs"]
+
+            # groups
+            testlisteq(keys(fid), ["floe_properties", "index"])
+            keys_index = [k for k in keys(fid["index"]) if k ∉ ["latitude", "longitude"]]
+            testlisteq(keys_index, ["time", "x", "y"])
+            testlisteq(
+                keys(fid["floe_properties"]),
+                ["column_names", "labeled_image", "properties"],
+            )
+
+            # check index group datasets
+            g = fid["index"]
+            t = read(g["time"])
+            x = read(g["x"])
+            y = read(g["y"])
+
+            @test t == ptsunix[1]
+            @test x == latlondata["X"]
+            @test y == latlondata["Y"]
+
+            # check floe_properties group datasets
+            g = fid["floe_properties"]
+            colnames = read(g["column_names"])
+            lb = read(g["labeled_image"])
+            props = read(g["properties"])
+
+            testlisteq(
+                colnames,
+                [
+                    "area",
+                    "convex_area",
+                    "major_axis_length",
+                    "minor_axis_length",
+                    "orientation",
+                    "perimeter",
+                    "latitude",
+                    "longitude",
+                    "x",
+                    "y",
+                ],
+            )
+
+            @test typeof(lb) == Matrix{UInt8}
+            @test typeof(props) == Matrix{Float64}
+            close(fid)
+
+            @test_throws "can't be represented" choose_dtype(-1)
+            @test choose_dtype(100) == UInt8
+            @test choose_dtype(300) == UInt16
+            @test choose_dtype(70_000) == UInt32
+            @test choose_dtype(BigInt(2)^64 - 1) == UInt64
+            @test choose_dtype(BigInt(2)^64) == UInt128
+            @test choose_dtype(BigInt(2)^128 - 1) == UInt128
+            @test_throws "can't be represented" choose_dtype(BigInt(2)^128)
+        end
+
+        # clean up
+        rm(dirname(h5path); recursive=true)
+    end
+    @testset "empty props" begin
+        example_data_dir = joinpath(test_data_dir, "cloudy")
+        IFTPipeline.makeh5files_single(;
+            passtime=Dates.DateTime("2006-05-13T14:55:34"),
+            truecolor=joinpath(example_data_dir, "truecolor.tiff"),
+            falsecolor=joinpath(example_data_dir, "falsecolor.tiff"),
+            labeled=joinpath(example_data_dir, "labeled.tiff"),
+            props=joinpath(example_data_dir, "labeled.props.csv"),
+            output="example.h5", #mktemp()[1],
+        )
+    end
 end
-
-# clean up
-rm(dirname(h5path), recursive=true)

IFTPipeline.jl/test/test_inputs/cloudy/README

@@ -0,0 +1 @@
+These data are from the Fram Strait, observed using Terra on 2006-05-13 and resolved at 250m.

IFTPipeline.jl/test/test_inputs/cloudy/labeled.props.csv

@@ -0,0 +1 @@
+"area","min_row","min_col","max_row","max_col","row_centroid","col_centroid","convex_area","label","major_axis_length","minor_axis_length","orientation","perimeter"

IFTPipeline.jl/test/test_inputs/cloudy/overpass.txt

@@ -0,0 +1 @@
+2006-05-13T14:55:34Z