Package evaluation of IceFloeTracker on Julia 1.13.0-DEV.1080 (ed57414aec*) started at 2025-09-05T04:23:12.731 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 7.16s ################################################################################ # Installation # Installing IceFloeTracker... Resolving package versions... Installed Conda ── v1.10.2 Installed PyCall ─ v1.96.4 Updating `~/.julia/environments/v1.13/Project.toml` [04643c7a] + IceFloeTracker v0.9.0 Updating `~/.julia/environments/v1.13/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [c9ce4bd3] + ArchGDAL v0.10.10 [ec485272] + ArnoldiMethod v0.4.0 [4fba245c] + ArrayInterface v7.20.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.7 [0e736298] + Bessels v0.2.8 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [fa961155] + CEnum v0.5.0 [2a0fbf3d] + CPUSummary v0.2.7 [aafaddc9] + CatIndices v0.2.2 [d360d2e6] + ChainRulesCore v1.26.0 [fb6a15b2] + CloseOpenIntervals v0.1.13 [aaaa29a8] + Clustering v0.15.8 [35d6a980] + ColorSchemes v3.30.0 ⌅ [3da002f7] + ColorTypes v0.11.5 ⌃ [c3611d14] + ColorVectorSpace v0.10.0 [5ae59095] + Colors v0.13.1 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.0 [ed09eef8] + ComputationalResources v0.3.2 [8f4d0f93] + Conda v1.10.2 [187b0558] + ConstructionBase v1.6.0 [150eb455] + CoordinateTransformations v0.6.4 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [dc8bdbbb] + CustomUnitRanges v1.0.2 [717857b8] + DSP v0.8.4 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.1 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [3c3547ce] + DiskArrays v0.4.15 [b4f34e82] + Distances v0.10.12 [ffbed154] + DocStringExtensions v0.9.5 [e2ba6199] + ExprTools v0.1.10 [411431e0] + Extents v0.1.6 [4f61f5a4] + FFTViews v0.3.2 [7a1cc6ca] + FFTW v1.9.0 [5789e2e9] + FileIO v1.17.0 [53c48c17] + FixedPointNumbers v0.8.5 [add2ef01] + GDAL v1.11.0 [68eda718] + GeoFormatTypes v0.4.4 [cf35fbd7] + GeoInterface v1.5.0 [a2bd30eb] + Graphics v1.1.3 [86223c79] + Graphs v1.13.1 [f67ccb44] + HDF5 v0.17.2 [076d061b] + HashArrayMappedTries v0.2.0 [2c695a8d] + HistogramThresholding v0.3.1 [3e5b6fbb] + HostCPUFeatures v0.1.17 [04643c7a] + IceFloeTracker v0.9.0 [615f187c] + IfElse v0.1.1 [2803e5a7] + ImageAxes v0.6.12 [c817782e] + ImageBase v0.1.7 [cbc4b850] + ImageBinarization v0.3.1 [f332f351] + ImageContrastAdjustment v0.3.12 [a09fc81d] + ImageCore v0.10.5 [89d5987c] + ImageCorners v0.1.3 [51556ac3] + ImageDistances v0.2.17 [6a3955dd] + ImageFiltering v0.7.12 [82e4d734] + ImageIO v0.6.9 [6218d12a] + ImageMagick v1.4.2 [bc367c6b] + ImageMetadata v0.9.10 [787d08f9] + ImageMorphology v0.4.6 [2996bd0c] + ImageQualityIndexes v0.3.7 [80713f31] + ImageSegmentation v1.9.0 [4e3cecfd] + ImageShow v0.3.8 [02fcd773] + ImageTransformations v0.10.2 [916415d5] + Images v0.26.2 [9b13fd28] + IndirectArrays v1.0.0 [d25df0c9] + Inflate v0.1.5 [842dd82b] + InlineStrings v1.4.5 [1d092043] + IntegralArrays v0.1.6 [a98d9a8b] + Interpolations v0.16.2 [8197267c] + IntervalSets v0.7.11 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 ⌃ [033835bb] + JLD2 v0.5.15 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v0.21.4 [b835a17e] + JpegTurbo v0.1.6 [8ac3fa9e] + LRUCache v1.6.2 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 [8cdb02fc] + LazyModules v0.3.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [bdcacae8] + LoopVectorization v0.12.172 [3da0fdf6] + MPIPreferences v0.1.11 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 [dbb5928d] + MappedArrays v0.4.2 [626554b9] + MetaGraphs v0.8.1 [e1d29d7a] + Missings v1.2.0 [78c3b35d] + Mocking v0.8.1 [e94cdb99] + MosaicViews v0.3.4 [77ba4419] + NaNMath v1.1.3 [b8a86587] + NearestNeighbors v0.4.22 [f09324ee] + Netpbm v1.1.1 [6fe1bfb0] + OffsetArrays v1.17.0 [52e1d378] + OpenEXR v0.3.3 [bac558e1] + OrderedCollections v1.8.1 [f57f5aa1] + PNGFiles v0.4.4 [5432bcbf] + PaddedViews v0.5.12 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [18e31ff7] + Peaks v0.5.3 [eebad327] + PkgVersion v0.3.3 [1d0040c9] + PolyesterWeave v0.2.2 [f27b6e38] + Polynomials v4.1.0 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 ⌅ [08abe8d2] + PrettyTables v2.4.0 [92933f4c] + ProgressMeter v1.11.0 [c94c279d] + Proj v1.9.0 [43287f4e] + PtrArrays v1.3.0 [438e738f] + PyCall v1.96.4 [4b34888f] + QOI v1.0.1 [94ee1d12] + Quaternions v0.7.6 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [dee08c22] + RegionTrees v0.3.2 [ae029012] + Requires v1.3.1 [6038ab10] + Rotations v1.7.1 [fdea26ae] + SIMD v3.7.1 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.43 [7e506255] + ScopedValues v1.5.0 [6c6a2e73] + Scratch v1.3.0 [91c51154] + SentinelArrays v1.4.8 [efcf1570] + Setfield v1.1.2 [699a6c99] + SimpleTraits v0.9.5 [47aef6b3] + SimpleWeightedGraphs v1.5.0 [45858cf5] + Sixel v0.1.5 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.5.1 [cae243ae] + StackViews v0.1.2 [aedffcd0] + Static v1.2.0 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.6 [892a3eda] + StringManipulation v0.4.1 [dc5dba14] + TZJData v1.5.0+2025b [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [62fd8b95] + TensorCore v0.1.1 [8290d209] + ThreadingUtilities v0.5.5 ⌅ [731e570b] + TiffImages v0.10.2 [06e1c1a7] + TiledIteration v0.5.0 [f269a46b] + TimeZones v1.22.0 [3bb67fe8] + TranscodingStreams v0.11.3 [3a884ed6] + UnPack v1.0.2 [3d5dd08c] + VectorizationBase v0.21.72 [81def892] + VersionParsing v1.3.0 [e3aaa7dc] + WebP v0.1.3 [efce3f68] + WoodburyMatrices v1.0.0 [8ce61222] + Arrow_jll v19.0.1+0 [0b7ba130] + Blosc_jll v1.21.7+0 [6e34b625] + Bzip2_jll v1.0.9+0 [2e619515] + Expat_jll v2.7.1+0 [f5851436] + FFTW_jll v3.3.11+0 [a7073274] + GDAL_jll v303.1100.300+0 [d604d12d] + GEOS_jll v3.13.1+1 [61579ee1] + Ghostscript_jll v9.55.1+0 [59f7168a] + Giflib_jll v5.2.3+0 [818ab7a1] + HDF4_jll v4.3.2+0 [0234f1f7] + HDF5_jll v1.14.6+0 [e33a78d0] + Hwloc_jll v2.12.2+0 [a51ab1cf] + ICU_jll v76.2.0+0 [c73af94c] + ImageMagick_jll v7.1.2002+0 [905a6f67] + Imath_jll v3.1.11+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [aacddb02] + JpegTurbo_jll v3.1.2+0 [b39eb1a6] + Kerberos_krb5_jll v1.21.3+0 [88015f11] + LERC_jll v4.0.1+0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 [08be9ffa] + LibPQ_jll v16.8.0+0 [7e76a0d4] + Libglvnd_jll v1.7.1+1 [94ce4f54] + Libiconv_jll v1.18.0+0 [89763e89] + Libtiff_jll v4.7.1+0 [d3a379c0] + LittleCMS_jll v2.17.0+0 [5ced341a] + Lz4_jll v1.10.1+0 [856f044c] + MKL_jll v2025.2.0+0 [7cb0a576] + MPICH_jll v4.3.1+0 [f1f71cc9] + MPItrampoline_jll v5.5.4+0 [9237b28f] + MicrosoftMPI_jll v10.1.4+3 [7243133f] + NetCDF_jll v401.900.300+0 [18a262bb] + OpenEXR_jll v3.2.4+0 [643b3616] + OpenJpeg_jll v2.5.4+0 [fe0851c0] + OpenMPI_jll v5.0.8+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [58948b4f] + PROJ_jll v902.600.200+0 [784f63db] + Qhull_jll v10008.0.1004+0 [76ed43ae] + SQLite_jll v3.48.0+0 [e0b8ae26] + Thrift_jll v0.21.1+0 ⌅ [02c8fc9c] + XML2_jll v2.13.6+1 [ffd25f8a] + XZ_jll v5.8.1+0 [4f6342f7] + Xorg_libX11_jll v1.8.12+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.7+0 [a65dc6b1] + Xorg_libpciaccess_jll v0.18.1+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [28df3c45] + boost_jll v1.87.0+0 [4611771a] + brotli_jll v1.1.1+0 [477f73a3] + libaec_jll v1.1.4+0 [06c338fa] + libgeotiff_jll v100.702.400+0 [b53b4c65] + libpng_jll v1.6.50+0 [075b6546] + libsixel_jll v1.10.5+0 [c5f90fcd] + libwebp_jll v1.6.0+0 [337d8026] + libzip_jll v1.11.3+0 [888e69b1] + muparser_jll v2.3.5+0 [1317d2d5] + oneTBB_jll v2022.0.0+0 [fe1e1685] + snappy_jll v1.2.3+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.15.0+1 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.8.12 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.2+0 [efcefdf7] + PCRE2_jll v10.46.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.67.0+0 [3f19e933] + p7zip_jll v17.6.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log` Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Installation completed after 50.72s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 737.78s ################################################################################ # Testing # Testing IceFloeTracker Status `/tmp/jl_5iicWP/Project.toml` [c7e460c6] ArgParse v1.2.0 [336ed68f] CSV v0.10.15 [a93c6f00] DataFrames v1.7.1 [8bb1440f] DelimitedFiles v1.9.1 [04643c7a] IceFloeTracker v0.9.0 [916415d5] Images v0.26.2 [438e738f] PyCall v1.96.4 [5e47fb64] TestImages v1.9.0 [06e1c1a7] TiledIteration v0.5.0 [a5390f91] ZipFile v0.10.1 [ade2ca70] Dates v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_5iicWP/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [c9ce4bd3] ArchGDAL v0.10.10 [c7e460c6] ArgParse v1.2.0 [ec485272] ArnoldiMethod v0.4.0 [4fba245c] ArrayInterface v7.20.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.7 [0e736298] Bessels v0.2.8 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [fa961155] CEnum v0.5.0 [2a0fbf3d] CPUSummary v0.2.7 [336ed68f] CSV v0.10.15 [aafaddc9] CatIndices v0.2.2 [d360d2e6] ChainRulesCore v1.26.0 [fb6a15b2] CloseOpenIntervals v0.1.13 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.30.0 ⌅ [3da002f7] ColorTypes v0.11.5 ⌃ [c3611d14] ColorVectorSpace v0.10.0 [5ae59095] Colors v0.13.1 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.18.0 [ed09eef8] ComputationalResources v0.3.2 [8f4d0f93] Conda v1.10.2 [187b0558] ConstructionBase v1.6.0 [150eb455] CoordinateTransformations v0.6.4 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.1.1 [dc8bdbbb] CustomUnitRanges v1.0.2 [717857b8] DSP v0.8.4 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.1 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [3c3547ce] DiskArrays v0.4.15 [b4f34e82] Distances v0.10.12 [ffbed154] DocStringExtensions v0.9.5 [e2ba6199] ExprTools v0.1.10 [411431e0] Extents v0.1.6 [4f61f5a4] FFTViews v0.3.2 [7a1cc6ca] FFTW v1.9.0 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 [53c48c17] FixedPointNumbers v0.8.5 [add2ef01] GDAL v1.11.0 [68eda718] GeoFormatTypes v0.4.4 [cf35fbd7] GeoInterface v1.5.0 [a2bd30eb] Graphics v1.1.3 [86223c79] Graphs v1.13.1 [f67ccb44] HDF5 v0.17.2 [076d061b] HashArrayMappedTries v0.2.0 [2c695a8d] HistogramThresholding v0.3.1 [3e5b6fbb] HostCPUFeatures v0.1.17 [04643c7a] IceFloeTracker v0.9.0 [615f187c] IfElse v0.1.1 [2803e5a7] ImageAxes v0.6.12 [c817782e] ImageBase v0.1.7 [cbc4b850] ImageBinarization v0.3.1 [f332f351] ImageContrastAdjustment v0.3.12 [a09fc81d] ImageCore v0.10.5 [89d5987c] ImageCorners v0.1.3 [51556ac3] ImageDistances v0.2.17 [6a3955dd] ImageFiltering v0.7.12 [82e4d734] ImageIO v0.6.9 [6218d12a] ImageMagick v1.4.2 [bc367c6b] ImageMetadata v0.9.10 [787d08f9] ImageMorphology v0.4.6 [2996bd0c] ImageQualityIndexes v0.3.7 [80713f31] ImageSegmentation v1.9.0 [4e3cecfd] ImageShow v0.3.8 [02fcd773] ImageTransformations v0.10.2 [916415d5] Images v0.26.2 [9b13fd28] IndirectArrays v1.0.0 [d25df0c9] Inflate v0.1.5 [842dd82b] InlineStrings v1.4.5 [1d092043] IntegralArrays v0.1.6 [a98d9a8b] Interpolations v0.16.2 [8197267c] IntervalSets v0.7.11 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 ⌃ [033835bb] JLD2 v0.5.15 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v0.21.4 [b835a17e] JpegTurbo v0.1.6 [8ac3fa9e] LRUCache v1.6.2 [b964fa9f] LaTeXStrings v1.4.0 [10f19ff3] LayoutPointers v0.1.17 [8cdb02fc] LazyModules v0.3.1 [2ab3a3ac] LogExpFunctions v0.3.29 [bdcacae8] LoopVectorization v0.12.172 [3da0fdf6] MPIPreferences v0.1.11 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 [dbb5928d] MappedArrays v0.4.2 [626554b9] MetaGraphs v0.8.1 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [e94cdb99] MosaicViews v0.3.4 [77ba4419] NaNMath v1.1.3 [b8a86587] NearestNeighbors v0.4.22 [f09324ee] Netpbm v1.1.1 [6fe1bfb0] OffsetArrays v1.17.0 [52e1d378] OpenEXR v0.3.3 [bac558e1] OrderedCollections v1.8.1 [f57f5aa1] PNGFiles v0.4.4 [5432bcbf] PaddedViews v0.5.12 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [18e31ff7] Peaks v0.5.3 [eebad327] PkgVersion v0.3.3 [1d0040c9] PolyesterWeave v0.2.2 [f27b6e38] Polynomials v4.1.0 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 ⌅ [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.11.0 [c94c279d] Proj v1.9.0 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [4b34888f] QOI v1.0.1 [94ee1d12] Quaternions v0.7.6 [b3c3ace0] RangeArrays v0.3.2 [c84ed2f1] Ratios v0.4.5 [c1ae055f] RealDot v0.1.0 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [dee08c22] RegionTrees v0.3.2 [ae029012] Requires v1.3.1 [6038ab10] Rotations v1.7.1 [fdea26ae] SIMD v3.7.1 [94e857df] SIMDTypes v0.1.0 [476501e8] SLEEFPirates v0.6.43 [7e506255] ScopedValues v1.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] 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v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.15.0+1 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.8.12 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.2+0 [efcefdf7] PCRE2_jll v10.46.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.67.0+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... [ Info: Installing skimage.measure via the Conda scikit-image=0.25.1 package... [ Info: Running `conda install -q -y scikit-image=0.25.1` in root environment Channels: - conda-forge Platform: linux-64 Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done ## Package Plan ## environment location: /home/pkgeval/.julia/conda/3/x86_64 added / updated specs: - scikit-image=0.25.1 The following packages will be downloaded: package | build ---------------------------|----------------- aom-3.9.1 | hac33072_0 2.6 MB conda-forge blosc-1.21.6 | he440d0b_1 47 KB conda-forge brotli-1.1.0 | hb9d3cd8_3 19 KB conda-forge brotli-bin-1.1.0 | hb9d3cd8_3 19 KB conda-forge brunsli-0.1 | h9c3ff4c_0 200 KB conda-forge c-blosc2-2.21.1 | h4cfbee9_0 339 KB conda-forge charls-2.4.2 | h59595ed_0 147 KB conda-forge dav1d-1.2.1 | hd590300_0 742 KB conda-forge giflib-5.2.2 | hd590300_0 75 KB conda-forge imagecodecs-2025.8.2 | py312h8629487_3 1.8 MB conda-forge imageio-2.37.0 | pyhfb79c49_0 286 KB conda-forge importlib-metadata-8.7.0 | pyhe01879c_1 34 KB conda-forge jxrlib-1.1 | hd590300_3 234 KB conda-forge lazy-loader-0.4 | pyhd8ed1ab_2 16 KB conda-forge lazy_loader-0.4 | pyhd8ed1ab_2 7 KB conda-forge lcms2-2.17 | h717163a_0 242 KB conda-forge lerc-4.0.0 | h0aef613_1 258 KB conda-forge libaec-1.1.4 | h3f801dc_0 36 KB conda-forge libavif16-1.3.0 | h6395336_2 136 KB conda-forge libbrotlicommon-1.1.0 | hb9d3cd8_3 68 KB conda-forge libbrotlidec-1.1.0 | hb9d3cd8_3 32 KB conda-forge libbrotlienc-1.1.0 | hb9d3cd8_3 276 KB conda-forge libdeflate-1.24 | h86f0d12_0 71 KB conda-forge libfreetype-2.13.3 | ha770c72_1 8 KB conda-forge libfreetype6-2.13.3 | h48d6fc4_1 371 KB conda-forge libhwy-1.3.0 | h4c17acf_0 1.4 MB conda-forge libjpeg-turbo-3.1.0 | hb9d3cd8_0 614 KB conda-forge libjxl-0.11.1 | h0a47e8d_3 1.7 MB conda-forge libpng-1.6.50 | h421ea60_1 310 KB conda-forge libtiff-4.7.0 | h8261f1e_6 423 KB conda-forge libwebp-base-1.6.0 | hd42ef1d_0 419 KB conda-forge libxcb-1.17.0 | h8a09558_0 387 KB conda-forge libzopfli-1.0.3 | h9c3ff4c_0 164 KB conda-forge networkx-3.5 | pyhe01879c_0 1.5 MB conda-forge openjpeg-2.5.3 | h55fea9a_1 349 KB conda-forge pillow-11.3.0 | py312h0e488c8_1 40.3 MB conda-forge pthread-stubs-0.4 | hb9d3cd8_1002 8 KB conda-forge pywavelets-1.9.0 | py312h4f23490_1 3.5 MB conda-forge rav1e-0.7.1 | h8fae777_3 4.9 MB conda-forge scikit-image-0.25.1 | py312hf9745cd_0 10.7 MB conda-forge scipy-1.16.1 | py312h7a1785b_1 16.2 MB conda-forge snappy-1.2.2 | h03e3b7b_0 45 KB conda-forge svt-av1-3.1.2 | hecca717_0 2.6 MB conda-forge tifffile-2025.8.28 | pyhd8ed1ab_0 178 KB conda-forge xorg-libxau-1.0.12 | hb9d3cd8_0 14 KB conda-forge xorg-libxdmcp-1.1.5 | hb9d3cd8_0 19 KB conda-forge zfp-1.0.1 | h909a3a2_3 271 KB conda-forge zipp-3.23.0 | pyhd8ed1ab_0 22 KB conda-forge zlib-ng-2.2.5 | hde8ca8f_0 108 KB conda-forge ------------------------------------------------------------ Total: 94.0 MB The following NEW packages will be INSTALLED: aom conda-forge/linux-64::aom-3.9.1-hac33072_0 blosc conda-forge/linux-64::blosc-1.21.6-he440d0b_1 brotli conda-forge/linux-64::brotli-1.1.0-hb9d3cd8_3 brotli-bin conda-forge/linux-64::brotli-bin-1.1.0-hb9d3cd8_3 brunsli conda-forge/linux-64::brunsli-0.1-h9c3ff4c_0 c-blosc2 conda-forge/linux-64::c-blosc2-2.21.1-h4cfbee9_0 charls conda-forge/linux-64::charls-2.4.2-h59595ed_0 dav1d conda-forge/linux-64::dav1d-1.2.1-hd590300_0 giflib conda-forge/linux-64::giflib-5.2.2-hd590300_0 imagecodecs conda-forge/linux-64::imagecodecs-2025.8.2-py312h8629487_3 imageio conda-forge/noarch::imageio-2.37.0-pyhfb79c49_0 importlib-metadata conda-forge/noarch::importlib-metadata-8.7.0-pyhe01879c_1 jxrlib conda-forge/linux-64::jxrlib-1.1-hd590300_3 lazy-loader conda-forge/noarch::lazy-loader-0.4-pyhd8ed1ab_2 lazy_loader conda-forge/noarch::lazy_loader-0.4-pyhd8ed1ab_2 lcms2 conda-forge/linux-64::lcms2-2.17-h717163a_0 lerc conda-forge/linux-64::lerc-4.0.0-h0aef613_1 libaec conda-forge/linux-64::libaec-1.1.4-h3f801dc_0 libavif16 conda-forge/linux-64::libavif16-1.3.0-h6395336_2 libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.1.0-hb9d3cd8_3 libbrotlidec conda-forge/linux-64::libbrotlidec-1.1.0-hb9d3cd8_3 libbrotlienc conda-forge/linux-64::libbrotlienc-1.1.0-hb9d3cd8_3 libdeflate conda-forge/linux-64::libdeflate-1.24-h86f0d12_0 libfreetype conda-forge/linux-64::libfreetype-2.13.3-ha770c72_1 libfreetype6 conda-forge/linux-64::libfreetype6-2.13.3-h48d6fc4_1 libhwy conda-forge/linux-64::libhwy-1.3.0-h4c17acf_0 libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.1.0-hb9d3cd8_0 libjxl conda-forge/linux-64::libjxl-0.11.1-h0a47e8d_3 libpng conda-forge/linux-64::libpng-1.6.50-h421ea60_1 libtiff conda-forge/linux-64::libtiff-4.7.0-h8261f1e_6 libwebp-base conda-forge/linux-64::libwebp-base-1.6.0-hd42ef1d_0 libxcb conda-forge/linux-64::libxcb-1.17.0-h8a09558_0 libzopfli conda-forge/linux-64::libzopfli-1.0.3-h9c3ff4c_0 networkx conda-forge/noarch::networkx-3.5-pyhe01879c_0 openjpeg conda-forge/linux-64::openjpeg-2.5.3-h55fea9a_1 pillow conda-forge/linux-64::pillow-11.3.0-py312h0e488c8_1 pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-hb9d3cd8_1002 pywavelets conda-forge/linux-64::pywavelets-1.9.0-py312h4f23490_1 rav1e conda-forge/linux-64::rav1e-0.7.1-h8fae777_3 scikit-image conda-forge/linux-64::scikit-image-0.25.1-py312hf9745cd_0 scipy conda-forge/linux-64::scipy-1.16.1-py312h7a1785b_1 snappy conda-forge/linux-64::snappy-1.2.2-h03e3b7b_0 svt-av1 conda-forge/linux-64::svt-av1-3.1.2-hecca717_0 tifffile conda-forge/noarch::tifffile-2025.8.28-pyhd8ed1ab_0 xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.12-hb9d3cd8_0 xorg-libxdmcp conda-forge/linux-64::xorg-libxdmcp-1.1.5-hb9d3cd8_0 zfp conda-forge/linux-64::zfp-1.0.1-h909a3a2_3 zipp conda-forge/noarch::zipp-3.23.0-pyhd8ed1ab_0 zlib-ng conda-forge/linux-64::zlib-ng-2.2.5-hde8ca8f_0 Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done ------------------------------------------------- ---------------- bridge tests ------------------- ------------------------------------------------- ------------ bwareamaxfilt Tests -------------- ┌ Warning: connectivity mask is expected to be a centered bool array. Do you mean `centered(connectivity)`? │ caller = strel at strel.jl:43 [inlined] └ @ Core ~/.julia/packages/ImageMorphology/lktkj/src/StructuringElements/strel.jl:43 ┌ Warning: connectivity mask is expected to be a centered bool array. Do you mean `centered(connectivity)`? │ caller = strel(::Type{CartesianIndex}, se::BitMatrix) at strel.jl:43 └ @ Core ~/.julia/packages/ImageMorphology/lktkj/src/StructuringElements/strel.jl:43 ------------------------------------------------- ---------------- bwperim Tests ------------------ ------------------------------------------------- ------------ bwtraceboundary Tests -------------- ┌ Warning: Point at (0, 0) not found in any contour. Returning all found countours. └ @ IceFloeTracker ~/.julia/packages/IceFloeTracker/eJrS2/src/bwtraceboundary.jl:133 ┌ Warning: Point at (4, 4) not found in any contour. Returning all found countours. └ @ IceFloeTracker ~/.julia/packages/IceFloeTracker/eJrS2/src/bwtraceboundary.jl:133 Precompiling packages... 82486.2 ms ✓ TiffImages 1 dependency successfully precompiled in 83 seconds. 49 already precompiled. Precompiling packages... 4809.1 ms ✓ PNGFiles 1 dependency successfully precompiled in 5 seconds. 27 already precompiled. ------------------------------------------------- ------------ Create Cloudmask Test -------------- --------- Create and apply cloudmask -------- 2.153488 seconds (387.98 k allocations: 584.084 MiB, 0.87% gc time, 43.82% compilation time) 0.611908 seconds (259.48 k allocations: 300.876 MiB, 15.50% gc time, 41.41% compilation time) [ Info: Test image that loads as RGBA ------------------------------------------------ ------------ Create Landmask Test -------------- 15.897854 seconds (963.44 k allocations: 62.911 MiB, 16.56% compilation time) 0.021301 seconds (600 allocations: 31.141 KiB, 51.39% compilation time) 0.322455 seconds (374.87 k allocations: 24.319 MiB, 95.74% compilation time) 0.012187 seconds (16 allocations: 3.577 MiB) [ Info: Test passed with 8.916666666666666e-5 mismatch with threshold 0.005 [ Info: Test passed with 0.0 mismatch with threshold 0.005 0.012019 seconds (11 allocations: 3.434 MiB) 0.114566 seconds (58.04 k allocations: 3.485 MiB, 90.31% compilation time) [ Info: normal allocated: 3600088 [ Info: in-place allocated: 0 ┌ Info: Persisting image to ./test_outputs/matlab_landmask_test_2025-09-05-044525.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/landmask_test_2025-09-05-044526.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/landmask_test_no_dilate_2025-09-05-044527.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/landmasked_truecolor_test_image_2025-09-05-044527.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/landmasked_truecolor_test_no_dilate_image_2025-09-05-044527.png. └ To load the persisted object use `Images.load(img_path)` ------------------------------------------------- ----------- cross correlation tests ------------- ------------------------------------------------ ------------ Create Ice-Water Discrimination Test -------------- ┌ Info: Persisting image to ./test_outputs/ice_water_discrim_test_image_2025-09-05-044654.png. └ To load the persisted object use `Images.load(img_path)` ------------------------------------------------ ------------ Create Ice Labels Test -------------- 22.790025 seconds (2.64 M allocations: 149.706 MiB, 99.58% compilation time) Find_Ice_Labels: Error During Test at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-find-ice-labels.jl:1 Got exception outside of a @test SystemError: opening file "ice_labels_julia.csv": Permission denied Stacktrace: [1] systemerror(p::String, errno::Int32; extrainfo::Nothing) @ Base ./error.jl:186 [2] open(fname::String; lock::Bool, read::Nothing, write::Nothing, create::Nothing, truncate::Bool, append::Nothing) @ Base ./iostream.jl:327 [3] open @ ./iostream.jl:306 [inlined] [4] open(fname::String, mode::String; lock::Bool) @ Base ./iostream.jl:390 [5] open(fname::String, mode::String) @ Base ./iostream.jl:389 [6] open(::DelimitedFiles.var"#18#19"{@Kwargs{}, Vector{Int64}, Char}, ::String, ::Vararg{String}; kwargs::@Kwargs{}) @ Base ./io.jl:418 [7] open @ ./io.jl:417 [inlined] [8] #writedlm#16 @ ~/.julia/packages/DelimitedFiles/aGcsu/src/DelimitedFiles.jl:794 [inlined] [9] writedlm(fname::String, a::Vector{Int64}, dlm::Char) @ DelimitedFiles ~/.julia/packages/DelimitedFiles/aGcsu/src/DelimitedFiles.jl:793 [10] macro expansion @ ~/.julia/packages/IceFloeTracker/eJrS2/test/test-find-ice-labels.jl:14 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [12] top-level scope @ ~/.julia/packages/IceFloeTracker/eJrS2/test/test-find-ice-labels.jl:2 [13] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [14] macro expansion @ ~/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:79 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] [16] top-level scope @ ~/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:78 [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [18] top-level scope @ none:6 [19] eval(m::Module, e::Any) @ Core ./boot.jl:489 [20] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [21] _start() @ Base ./client.jl:563 Precompiling packages... 22614.8 ms ✓ CSV 1 dependency successfully precompiled in 23 seconds. 29 already precompiled. ------------------------------------------------- --------------test-long-tracker.jl--------------- ------------------------------------------------- --------------------Utilities-------------------- ------------------------------------------------- --------------get_trajectory_heads--------------- ------------------------------------------------- -------------------basic case-------------------- ------------------------------------------------- ------------no existing trajectories------------- ------------------------------------------------- -------wider range of numbers for ranking-------- ------------------------------------------------- -------------------Basic cases------------------- ------------------------------------------------- ---------------------Case 1---------------------- ------------------------------------------------- ---------------------Case 2---------------------- ------------------------------------------------- --------------------Test gaps-------------------- ------------------------------------------------- ---------------------Case 3---------------------- ------------------------------------------------- ---------------------Case 4---------------------- ------------------------------------------------- --------------------Ellipses--------------------- ------------------------------------------------- -----------10 observations of 2 floes------------ ------------------------------------------------- -----------10 observations of 40 floes----------- ------------------------------------------------- ------------some observations missing------------ ------------------------------------------------- ---------------exclude small floes--------------- ------------------------------------------------- ----------------test-matchcorr.jl---------------- ┌ Warning: correlation too low, c: 0.9109228896144568 └ @ IceFloeTracker ~/.julia/packages/IceFloeTracker/eJrS2/src/tracker/matchcorr.jl:46 ------------------------------------------------ ----------------- Misc. Tests ------------------ ------------------------------------------------ ----------- Create Morp Fill Test -------------- ------------------------------------------------ ---------------- MorphSE Tests ----------------- ------------------------------------------------- ---------- Create Normalization Test ------------ -------------- Process Image - Diffusion ---------------- 1.413775 seconds (52 allocations: 276.213 MiB) ┌ Info: Persisting image to ./test_outputs/diffused_test_image_2025-09-05-045246.png. └ To load the persisted object use `Images.load(img_path)` -------------- Process Image - Equalization ---------------- ┌ Info: Persisting image to ./test_outputs/equalized_test_image_2025-09-05-045259.png. └ To load the persisted object use `Images.load(img_path)` -------------- Process Image - Sharpening ---------------- 12.615706 seconds (632.76 k allocations: 3.561 GiB, 15.52% gc time) 0.263820 seconds (18 allocations: 184.141 MiB) ┌ Info: Persisting image to ./test_outputs/sharpened_test_image_2025-09-05-045315.png. └ To load the persisted object use `Images.load(img_path)` -------------- Process Image - Normalization ---------------- 2.284515 seconds (3.50 k allocations: 565.988 MiB, 5.34% gc time) ┌ Info: Persisting image to ./test_outputs/normalized_test_image_2025-09-05-045319.png. └ To load the persisted object use `Images.load(img_path)` ------------------------------------------------- ---------- Persist Image Tests ------------ ┌ Info: Persisting image to outimage1.tiff. └ To load the persisted object use `Images.load(img_path)` Precompiling packages... 8385.7 ms ✓ ImageMagick 1 dependency successfully precompiled in 9 seconds. 67 already precompiled. ┌ Info: Persisting image to outimage2.tiff. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to persisted_img-2025-09-05-045329.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to outimage1.tiff. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to outimage2.tiff. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to persisted_img-2025-09-05-045330.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to persistedimg.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to foo-2025-09-05-045319.png. └ To load the persisted object use `Images.load(img_path)` ------------------------------------------------------------ -------------- regionprops (labeled) Tests ---------------- [ Info: testing props with label [ Info: testing props without label [ Info: testing values with label ------------------------------------------------- -------------- regionprops Tests ---------------- ┌ Info: 289×6 DataFrame │ Row │ θ1 θ2 Δθ Δθ_measured minimum_absolute_error ok │ │ Int64 Int64 Float64 Float64 Float64 Bool │ ─────┼──────────────────────────────────────────────────────────────────────── │ 1 │ -90 -90 0.0 0.0 0.0 true │ 2 │ -90 -80 0.174533 -2.87979 0.0872665 true │ 3 │ -90 -75 0.261799 -2.79253 0.0872665 true │ 4 │ -90 -60 0.523599 -2.70526 0.0872665 true │ 5 │ -90 -55 0.610865 0.698132 0.0872665 true │ 6 │ -90 -45 0.785398 -2.53073 0.174533 true │ 7 │ -90 -30 1.0472 1.0472 4.80741e-16 true │ 8 │ -90 -15 1.309 1.309 1.60859e-16 true │ 9 │ -90 -10 1.39626 1.39626 2.18671e-16 true │ 10 │ -90 0 1.5708 1.48353 0.0872665 true │ 11 │ -90 10 1.74533 -1.39626 4.37342e-16 true │ 12 │ -90 25 2.00713 -1.13446 2.01241e-16 true │ 13 │ -90 30 2.0944 -0.959931 0.0872665 true │ 14 │ -90 45 2.35619 -0.872665 0.0872665 true │ 15 │ -90 60 2.61799 -0.610865 0.0872665 true │ 16 │ -90 80 2.96706 -0.174533 1.91337e-16 true │ 17 │ -90 90 3.14159 0.0 2.44929e-16 true │ 18 │ -80 -90 -0.174533 0.0 0.174533 false │ 19 │ -80 -80 0.0 0.0 0.0 true │ 20 │ -80 -75 0.0872665 0.0 0.0872665 true │ 21 │ -80 -60 0.349066 -2.70526 0.0872665 true │ 22 │ -80 -55 0.436332 -2.70526 2.01241e-16 true │ 23 │ -80 -45 0.610865 -2.53073 3.63777e-16 true │ 24 │ -80 -30 0.872665 0.785398 0.0872665 true │ 25 │ -80 -15 1.13446 1.13446 4.02482e-16 true │ 26 │ -80 -10 1.22173 1.39626 0.174533 false │ 27 │ -80 0 1.39626 1.39626 2.18671e-16 true │ 28 │ -80 10 1.5708 1.48353 0.0872665 true │ 29 │ -80 25 1.8326 -1.39626 0.0872665 true │ 30 │ -80 30 1.91986 -1.22173 6.78124e-16 true │ 31 │ -80 45 2.18166 -0.959931 7.27553e-16 true │ 32 │ -80 60 2.44346 -0.785398 0.0872665 true │ 33 │ -80 80 2.79253 -0.436332 0.0872665 true │ 34 │ -80 90 2.96706 0.0 0.174533 false │ 35 │ -75 -90 -0.261799 -0.261799 2.68098e-16 true │ 36 │ -75 -80 -0.0872665 0.0 0.0872665 true │ 37 │ -75 -75 5.36197e-17 0.0 5.36197e-17 true │ 38 │ -75 -60 0.261799 -2.70526 0.174533 false │ 39 │ -75 -55 0.349066 -2.70526 0.0872665 true │ 40 │ -75 -45 0.523599 -2.53073 0.0872665 true │ 41 │ -75 -30 0.785398 0.785398 1.57009e-16 true │ 42 │ -75 -15 1.0472 1.0472 4.80741e-16 true │ 43 │ -75 -10 1.13446 1.13446 4.02482e-16 true │ 44 │ -75 0 1.309 1.309 1.60859e-16 true │ 45 │ -75 10 1.48353 1.48353 0.0 true │ 46 │ -75 25 1.74533 -1.39626 4.37342e-16 true │ 47 │ -75 30 1.8326 -1.22173 0.0872665 true │ 48 │ -75 45 2.0944 -1.0472 2.88444e-16 true │ 49 │ -75 60 2.35619 -0.785398 3.14018e-16 true │ 50 │ -75 80 2.70526 -0.436332 1.50931e-16 true │ 51 │ -75 90 2.87979 -0.261799 2.68098e-16 true │ 52 │ -60 -90 -0.523599 -0.610865 0.0872665 true │ 53 │ -60 -80 -0.349066 -0.436332 0.0872665 true │ 54 │ -60 -75 -0.261799 -0.349066 0.0872665 true │ 55 │ -60 -60 0.0 0.0 0.0 true │ 56 │ -60 -55 0.0872665 0.0 0.0872665 true │ 57 │ -60 -45 0.261799 -2.87979 6.97056e-16 true │ 58 │ -60 -30 0.523599 -2.79253 0.174533 true │ 59 │ -60 -15 0.785398 0.698132 0.0872665 true │ 60 │ -60 -10 0.872665 0.872665 0.0 true │ 61 │ -60 0 1.0472 0.959931 0.0872665 true │ 62 │ -60 10 1.22173 1.22173 3.65144e-16 true │ 63 │ -60 25 1.48353 1.48353 2.212e-16 true │ 64 │ -60 30 1.5708 1.48353 0.0872665 true │ 65 │ -60 45 1.8326 -1.309 4.28957e-16 true │ 66 │ -60 60 2.0944 -1.0472 6.24963e-16 true │ 67 │ -60 80 2.44346 -0.698132 3.40192e-16 true │ 68 │ -60 90 2.61799 -0.610865 0.0872665 true │ 69 │ -55 -90 -0.610865 -0.610865 3.63777e-16 true │ 70 │ -55 -80 -0.436332 -0.523599 0.0872665 true │ 71 │ -55 -75 -0.349066 -0.523599 0.174533 true │ 72 │ -55 -60 -0.0872665 0.0 0.0872665 true │ 73 │ -55 -55 0.0 0.0 0.0 true │ 74 │ -55 -45 0.174533 -2.87979 0.0872665 true │ 75 │ -55 -30 0.436332 -2.79253 0.0872665 true │ 76 │ -55 -15 0.698132 0.698132 0.0 true │ 77 │ -55 -10 0.785398 0.698132 0.0872665 true │ 78 │ -55 0 0.959931 0.959931 2.72832e-16 true │ 79 │ -55 10 1.13446 1.0472 0.0872665 true │ 80 │ -55 25 1.39626 1.39626 0.0 true │ 81 │ -55 30 1.48353 1.48353 0.0 true │ 82 │ -55 45 1.74533 -1.39626 4.37342e-16 true │ 83 │ -55 60 2.00713 -1.22173 0.0872665 true │ 84 │ -55 80 2.35619 -0.872665 0.0872665 true │ 85 │ -55 90 2.53073 -0.610865 1.81888e-16 true │ 86 │ -45 -90 -0.785398 -0.785398 0.0 true │ 87 │ -45 -80 -0.610865 -0.698132 0.0872665 true │ 88 │ -45 -75 -0.523599 -0.523599 3.36518e-16 true │ 89 │ -45 -60 -0.261799 0.0 0.261799 false │ 90 │ -45 -55 -0.174533 0.0 0.174533 false │ 91 │ -45 -45 0.0 0.0 0.0 true │ 92 │ -45 -30 0.261799 0.0 0.261799 false │ 93 │ -45 -15 0.523599 0.523599 0.0 true │ 94 │ -45 -10 0.610865 0.698132 0.0872665 true │ 95 │ -45 0 0.785398 0.785398 0.0 true │ 96 │ -45 10 0.959931 0.959931 9.09441e-17 true │ 97 │ -45 25 1.22173 1.22173 3.65144e-16 true │ 98 │ -45 30 1.309 1.309 4.82577e-16 true │ 99 │ -45 45 1.5708 1.48353 0.0872665 true │ 100 │ -45 60 1.8326 -1.309 4.28957e-16 true │ 101 │ -45 80 2.18166 -0.959931 7.27553e-16 true │ 102 │ -45 90 2.35619 -0.785398 3.14018e-16 true │ 103 │ -30 -90 -1.0472 -0.959931 0.0872665 true │ 104 │ -30 -80 -0.872665 -0.872665 3.40192e-16 true │ 105 │ -30 -75 -0.785398 -0.698132 0.0872665 true │ 106 │ -30 -60 -0.523599 2.79253 0.174533 true │ 107 │ -30 -55 -0.436332 2.79253 0.0872665 true │ 108 │ -30 -45 -0.261799 2.87979 4.82577e-16 true │ 109 │ -30 -30 -4.80741e-17 0.0 4.80741e-17 true │ 110 │ -30 -15 0.261799 0.349066 0.0872665 true │ 111 │ -30 -10 0.349066 0.436332 0.0872665 true │ 112 │ -30 0 0.523599 0.610865 0.0872665 true │ 113 │ -30 10 0.698132 0.698132 2.55144e-16 true │ 114 │ -30 25 0.959931 0.959931 2.72832e-16 true │ 115 │ -30 30 1.0472 1.0472 9.61481e-17 true │ 116 │ -30 45 1.309 1.309 4.82577e-16 true │ 117 │ -30 60 1.5708 1.48353 0.0872665 true │ 118 │ -30 80 1.91986 -1.22173 6.78124e-16 true │ 119 │ -30 90 2.0944 -0.959931 0.0872665 true │ 120 │ -15 -90 -1.309 -1.309 5.36197e-17 true │ 121 │ -15 -80 -1.13446 -1.13446 0.0 true │ 122 │ -15 -75 -1.0472 -1.0472 0.0 true │ 123 │ -15 -60 -0.785398 -0.785398 7.85046e-17 true │ 124 │ -15 -55 -0.698132 2.53073 0.0872665 true │ 125 │ -15 -45 -0.523599 2.53073 0.0872665 true │ 126 │ -15 -30 -0.261799 2.70526 0.174533 false │ 127 │ -15 -15 -5.36197e-17 0.0 5.36197e-17 true │ 128 │ -15 -10 0.0872665 0.0 0.0872665 true │ 129 │ -15 0 0.261799 0.261799 2.68098e-16 true │ 130 │ -15 10 0.436332 0.436332 5.03102e-17 true │ 131 │ -15 25 0.698132 0.785398 0.0872665 true │ 132 │ -15 30 0.785398 0.785398 0.0 true │ 133 │ -15 45 1.0472 1.0472 1.92296e-16 true │ 134 │ -15 60 1.309 1.22173 0.0872665 true │ 135 │ -15 80 1.65806 1.48353 0.174533 false │ 136 │ -15 90 1.8326 -1.309 4.28957e-16 true │ 137 │ -10 -90 -1.39626 -1.39626 0.0 true │ 138 │ -10 -80 -1.22173 -1.39626 0.174533 false │ 139 │ -10 -75 -1.13446 -1.13446 0.0 true │ 140 │ -10 -60 -0.872665 -0.785398 0.0872665 true │ 141 │ -10 -55 -0.785398 -0.872665 0.0872665 true │ 142 │ -10 -45 -0.610865 2.53073 0.0 true │ 143 │ -10 -30 -0.349066 2.70526 0.0872665 true │ 144 │ -10 -15 -0.0872665 0.0 0.0872665 true │ 145 │ -10 -10 0.0 0.0 0.0 true │ 146 │ -10 0 0.174533 0.0 0.174533 false │ 147 │ -10 10 0.349066 0.436332 0.0872665 true │ 148 │ -10 25 0.610865 0.610865 3.63777e-16 true │ 149 │ -10 30 0.698132 0.785398 0.0872665 true │ 150 │ -10 45 0.959931 0.959931 9.09441e-17 true │ 151 │ -10 60 1.22173 1.22173 3.65144e-16 true │ 152 │ -10 80 1.5708 1.48353 0.0872665 true │ 153 │ -10 90 1.74533 -1.39626 4.37342e-16 true │ 154 │ 0 -90 -1.5708 1.48353 0.0872665 true │ 155 │ 0 -80 -1.39626 -1.39626 0.0 true │ 156 │ 0 -75 -1.309 -1.309 5.36197e-17 true │ 157 │ 0 -60 -1.0472 -1.0472 2.88444e-16 true │ 158 │ 0 -55 -0.959931 -0.959931 1.81888e-16 true │ 159 │ 0 -45 -0.785398 2.53073 0.174533 true │ 160 │ 0 -30 -0.523599 2.70526 0.0872665 true │ 161 │ 0 -15 -0.261799 2.79253 0.0872665 true │ 162 │ 0 -10 -0.174533 2.87979 0.0872665 true │ 163 │ 0 0 0.0 0.0 0.0 true │ 164 │ 0 10 0.174533 0.174533 2.18671e-16 true │ 165 │ 0 25 0.436332 0.436332 5.03102e-17 true │ 166 │ 0 30 0.523599 0.610865 0.0872665 true │ 167 │ 0 45 0.785398 -2.35619 2.35514e-16 true │ 168 │ 0 60 1.0472 0.959931 0.0872665 true │ 169 │ 0 80 1.39626 1.39626 2.18671e-16 true │ 170 │ 0 90 1.5708 1.48353 0.0872665 true │ 171 │ 10 -90 -1.74533 1.39626 2.18671e-16 true │ 172 │ 10 -80 -1.5708 1.48353 0.0872665 true │ 173 │ 10 -75 -1.48353 1.48353 0.174533 true │ 174 │ 10 -60 -1.22173 -1.309 0.0872665 true │ 175 │ 10 -55 -1.13446 -1.13446 0.0 true │ 176 │ 10 -45 -0.959931 -0.872665 0.0872665 true │ 177 │ 10 -30 -0.698132 -0.872665 0.174533 false │ 178 │ 10 -15 -0.436332 -0.610865 0.174533 false │ 179 │ 10 -10 -0.349066 2.87979 0.0872665 true │ 180 │ 10 0 -0.174533 0.0 0.174533 false │ 181 │ 10 10 0.0 0.0 0.0 true │ 182 │ 10 25 0.261799 0.349066 0.0872665 true │ 183 │ 10 30 0.349066 0.349066 1.5649e-16 true │ 184 │ 10 45 0.610865 0.698132 0.0872665 true │ 185 │ 10 60 0.872665 0.959931 0.0872665 true │ 186 │ 10 80 1.22173 1.39626 0.174533 false │ 187 │ 10 90 1.39626 1.39626 2.18671e-16 true │ 188 │ 25 -90 -2.00713 1.13446 0.0 true │ 189 │ 25 -80 -1.8326 1.309 4.82577e-16 true │ 190 │ 25 -75 -1.74533 1.309 0.0872665 true │ 191 │ 25 -60 -1.48353 1.48353 0.174533 false │ 192 │ 25 -55 -1.39626 -1.309 0.0872665 true │ 193 │ 25 -45 -1.22173 -1.22173 0.0 true │ 194 │ 25 -30 -0.959931 -1.13446 0.174533 true │ 195 │ 25 -15 -0.698132 -0.610865 0.0872665 true │ 196 │ 25 -10 -0.610865 -0.610865 1.81888e-16 true │ 197 │ 25 0 -0.436332 -0.610865 0.174533 false │ 198 │ 25 10 -0.261799 2.96706 0.0872665 true │ 199 │ 25 25 0.0 0.0 0.0 true │ 200 │ 25 30 0.0872665 0.0 0.0872665 true │ 201 │ 25 45 0.349066 0.349066 1.5649e-16 true │ 202 │ 25 60 0.610865 0.523599 0.0872665 true │ 203 │ 25 80 0.959931 0.872665 0.0872665 true │ 204 │ 25 90 1.13446 1.13446 4.02482e-16 true │ 205 │ 30 -90 -2.0944 1.0472 9.61481e-17 true │ 206 │ 30 -80 -1.91986 1.22173 3.65144e-16 true │ 207 │ 30 -75 -1.8326 1.22173 0.0872665 true │ 208 │ 30 -60 -1.5708 1.48353 0.0872665 true │ 209 │ 30 -55 -1.48353 1.48353 0.174533 true │ 210 │ 30 -45 -1.309 -1.309 2.68098e-16 true │ 211 │ 30 -30 -1.0472 -1.13446 0.0872665 true │ 212 │ 30 -15 -0.785398 -0.959931 0.174533 false │ 213 │ 30 -10 -0.698132 -0.785398 0.0872665 true │ 214 │ 30 0 -0.523599 -0.698132 0.174533 false │ 215 │ 30 10 -0.349066 2.96706 0.174533 false │ 216 │ 30 25 -0.0872665 0.0 0.0872665 true │ 217 │ 30 30 4.80741e-17 0.0 4.80741e-17 true │ 218 │ 30 45 0.261799 0.0 0.261799 false │ 219 │ 30 60 0.523599 0.349066 0.174533 true │ 220 │ 30 80 0.872665 0.872665 3.40192e-16 true │ 221 │ 30 90 1.0472 1.0472 4.80741e-16 true │ 222 │ 45 -90 -2.35619 0.872665 0.0872665 true │ 223 │ 45 -80 -2.18166 0.959931 3.63777e-16 true │ 224 │ 45 -75 -2.0944 1.0472 9.61481e-17 true │ 225 │ 45 -60 -1.8326 1.39626 0.0872665 true │ 226 │ 45 -55 -1.74533 1.39626 2.18671e-16 true │ 227 │ 45 -45 -1.5708 1.48353 0.0872665 true │ 228 │ 45 -30 -1.309 -1.39626 0.0872665 true │ 229 │ 45 -15 -1.0472 -1.0472 2.88444e-16 true │ 230 │ 45 -10 -0.959931 -0.959931 2.72832e-16 true │ 231 │ 45 0 -0.785398 -0.872665 0.0872665 true │ 232 │ 45 10 -0.610865 -0.698132 0.0872665 true │ 233 │ 45 25 -0.349066 -0.523599 0.174533 false │ 234 │ 45 30 -0.261799 0.0 0.261799 false │ 235 │ 45 45 0.0 0.0 0.0 true │ 236 │ 45 60 0.261799 0.0 0.261799 false │ 237 │ 45 80 0.610865 0.698132 0.0872665 true │ 238 │ 45 90 0.785398 0.872665 0.0872665 true │ 239 │ 60 -90 -2.61799 0.698132 0.174533 true │ 240 │ 60 -80 -2.44346 0.785398 0.0872665 true │ 241 │ 60 -75 -2.35619 0.959931 0.174533 true │ 242 │ 60 -60 -2.0944 1.13446 0.0872665 true │ 243 │ 60 -55 -2.00713 1.13446 4.02482e-16 true │ 244 │ 60 -45 -1.8326 1.309 1.60859e-16 true │ 245 │ 60 -30 -1.5708 1.48353 0.0872665 true │ 246 │ 60 -15 -1.309 -1.22173 0.0872665 true │ 247 │ 60 -10 -1.22173 -1.22173 0.0 true │ 248 │ 60 0 -1.0472 -1.0472 2.88444e-16 true │ 249 │ 60 10 -0.872665 -0.872665 0.0 true │ 250 │ 60 25 -0.610865 -0.610865 3.63777e-16 true │ 251 │ 60 30 -0.523599 -0.349066 0.174533 true │ 252 │ 60 45 -0.261799 0.0 0.261799 false │ 253 │ 60 60 0.0 0.0 0.0 true │ 254 │ 60 80 0.349066 -2.96706 0.174533 true │ 255 │ 60 90 0.523599 0.698132 0.174533 true │ 256 │ 80 -90 -2.96706 0.0 0.174533 true │ 257 │ 80 -80 -2.79253 -2.87979 0.0872665 true │ 258 │ 80 -75 -2.70526 0.610865 0.174533 true │ 259 │ 80 -60 -2.44346 0.872665 0.174533 true │ 260 │ 80 -55 -2.35619 0.872665 0.0872665 true │ 261 │ 80 -45 -2.18166 0.872665 0.0872665 true │ 262 │ 80 -30 -1.91986 1.309 0.0872665 true │ 263 │ 80 -15 -1.65806 1.48353 2.212e-16 true │ 264 │ 80 -10 -1.5708 1.48353 0.0872665 true │ 265 │ 80 0 -1.39626 -1.39626 0.0 true │ 266 │ 80 10 -1.22173 -1.39626 0.174533 false │ 267 │ 80 25 -0.959931 -0.959931 1.81888e-16 true │ 268 │ 80 30 -0.872665 -0.959931 0.0872665 true │ 269 │ 80 45 -0.610865 -0.698132 0.0872665 true │ 270 │ 80 60 -0.349066 -0.349066 1.04327e-16 true │ 271 │ 80 80 0.0 0.0 0.0 true │ 272 │ 80 90 0.174533 0.0 0.174533 false │ 273 │ 90 -90 -3.14159 0.0 0.0 true │ 274 │ 90 -80 -2.96706 -2.87979 0.0872665 true │ 275 │ 90 -75 -2.87979 -2.79253 0.0872665 true │ 276 │ 90 -60 -2.61799 -2.70526 0.0872665 true │ 277 │ 90 -55 -2.53073 0.698132 0.0872665 true │ 278 │ 90 -45 -2.35619 -2.53073 0.174533 true │ 279 │ 90 -30 -2.0944 1.0472 9.61481e-17 true │ 280 │ 90 -15 -1.8326 1.309 4.82577e-16 true │ 281 │ 90 -10 -1.74533 1.39626 2.18671e-16 true │ 282 │ 90 0 -1.5708 1.48353 0.0872665 true │ 283 │ 90 10 -1.39626 -1.39626 0.0 true │ 284 │ 90 25 -1.13446 -1.13446 0.0 true │ 285 │ 90 30 -1.0472 -0.959931 0.0872665 true │ 286 │ 90 45 -0.785398 -0.872665 0.0872665 true │ 287 │ 90 60 -0.523599 -0.610865 0.0872665 true │ 288 │ 90 80 -0.174533 -0.174533 2.18671e-16 true └ 289 │ 90 90 0.0 0.0 0.0 true [ Info: fraction_ok 0.9065743944636678 >= 0.9? ┌ Info: 441×6 DataFrame │ Row │ θ1 θ2 Δθ Δθ_measured minimum_absolute_error ok │ │ Int64 Int64 Float64 Float64 Float64 Bool │ ─────┼──────────────────────────────────────────────────────────────────────── │ 1 │ -165 -165 -5.36197e-17 0.0 5.36197e-17 true │ 2 │ -165 -150 0.261799 0.261799 5.36197e-16 true │ 3 │ -165 -135 0.523599 0.523599 0.0 true │ 4 │ -165 -120 0.785398 0.872665 0.0872665 true │ 5 │ -165 -90 1.309 1.309 4.82577e-16 true │ 6 │ -165 -80 1.48353 1.48353 2.212e-16 true │ 7 │ -165 -60 1.8326 1.8326 2.14479e-16 true │ 8 │ -165 -45 2.0944 2.0944 0.0 true │ 9 │ -165 -30 2.35619 2.44346 0.0872665 true │ 10 │ -165 0 2.87979 2.87979 4.82577e-16 true │ 11 │ -165 15 3.14159 -3.14159 2.44929e-16 true │ 12 │ -165 30 -2.87979 -2.79253 0.0872665 true │ 13 │ -165 45 -2.61799 -2.61799 4.32667e-16 true │ 14 │ -165 60 -2.35619 -2.35619 4.71028e-16 true │ 15 │ -165 80 -2.00713 -2.00713 4.52792e-16 true │ 16 │ -165 90 -1.8326 -1.8326 6.43436e-16 true │ 17 │ -165 120 -1.309 -1.22173 0.0872665 true │ 18 │ -165 135 -1.0472 -1.0472 2.4037e-16 true │ 19 │ -165 150 -0.785398 -0.785398 1.57009e-16 true │ 20 │ -165 165 -0.523599 -0.436332 0.0872665 true │ 21 │ -165 180 -0.261799 -0.261799 3.21718e-16 true │ 22 │ -150 -165 -0.261799 -0.174533 0.0872665 true │ 23 │ -150 -150 4.80741e-17 0.0 4.80741e-17 true │ 24 │ -150 -135 0.261799 0.261799 1.07239e-16 true │ 25 │ -150 -120 0.523599 0.523599 4.32667e-16 true │ 26 │ -150 -90 1.0472 1.0472 4.80741e-16 true │ 27 │ -150 -80 1.22173 1.13446 0.0872665 true │ 28 │ -150 -60 1.5708 1.5708 4.44089e-16 true │ 29 │ -150 -45 1.8326 1.8326 7.50675e-16 true │ 30 │ -150 -30 2.0944 2.0944 0.0 true │ 31 │ -150 0 2.61799 2.61799 4.80741e-16 true │ 32 │ -150 15 2.87979 2.96706 0.0872665 true │ 33 │ -150 30 -3.14159 -3.14159 0.0 true │ 34 │ -150 45 -2.87979 -2.87979 4.82577e-16 true │ 35 │ -150 60 -2.61799 -2.53073 0.0872665 true │ 36 │ -150 80 -2.26893 -2.26893 0.0 true │ 37 │ -150 90 -2.0944 -2.0944 0.0 true │ 38 │ -150 120 -1.5708 -1.5708 2.22045e-16 true │ 39 │ -150 135 -1.309 -1.309 5.36197e-17 true │ 40 │ -150 150 -1.0472 -0.959931 0.0872665 true │ 41 │ -150 165 -0.785398 -0.785398 1.57009e-16 true │ 42 │ -150 180 -0.523599 -0.523599 2.4037e-16 true │ 43 │ -135 -165 -0.523599 -0.523599 0.0 true │ 44 │ -135 -150 -0.261799 -0.261799 1.07239e-16 true │ 45 │ -135 -135 0.0 0.0 0.0 true │ 46 │ -135 -120 0.261799 0.349066 0.0872665 true │ 47 │ -135 -90 0.785398 0.698132 0.0872665 true │ 48 │ -135 -80 0.959931 1.0472 0.0872665 true │ 49 │ -135 -60 1.309 1.309 1.60859e-16 true │ 50 │ -135 -45 1.5708 1.5708 0.0 true │ 51 │ -135 -30 1.8326 1.91986 0.0872665 true │ 52 │ -135 0 2.35619 2.26893 0.0872665 true │ 53 │ -135 15 2.61799 2.61799 9.61481e-17 true │ 54 │ -135 30 2.87979 2.87979 4.82577e-16 true │ 55 │ -135 45 3.14159 -3.14159 2.44929e-16 true │ 56 │ -135 60 -2.87979 -2.79253 0.0872665 true │ 57 │ -135 80 -2.53073 -2.53073 5.45665e-16 true │ 58 │ -135 90 -2.35619 -2.44346 0.0872665 true │ 59 │ -135 120 -1.8326 -1.8326 4.82577e-16 true │ 60 │ -135 135 -1.5708 -1.5708 4.44089e-16 true │ 61 │ -135 150 -1.309 -1.22173 0.0872665 true │ 62 │ -135 165 -1.0472 -1.0472 2.4037e-16 true │ 63 │ -135 180 -0.785398 -0.785398 2.35514e-16 true │ 64 │ -120 -165 -0.785398 -0.785398 0.0 true │ 65 │ -120 -150 -0.523599 -0.610865 0.0872665 true │ 66 │ -120 -135 -0.261799 -0.261799 1.07239e-16 true │ 67 │ -120 -120 4.80741e-17 0.0 4.80741e-17 true │ 68 │ -120 -90 0.523599 0.523599 0.0 true │ 69 │ -120 -80 0.698132 0.610865 0.0872665 true │ 70 │ -120 -60 1.0472 0.959931 0.0872665 true │ 71 │ -120 -45 1.309 1.22173 0.0872665 true │ 72 │ -120 -30 1.5708 1.5708 0.0 true │ 73 │ -120 0 2.0944 2.0944 0.0 true │ 74 │ -120 15 2.35619 2.35619 0.0 true │ 75 │ -120 30 2.61799 2.53073 0.0872665 true │ 76 │ -120 45 2.87979 2.79253 0.0872665 true │ 77 │ -120 60 3.14159 -3.14159 2.44929e-16 true │ 78 │ -120 80 -2.79253 -2.70526 0.0872665 true │ 79 │ -120 90 -2.61799 -2.61799 4.32667e-16 true │ 80 │ -120 120 -2.0944 -2.18166 0.0872665 true │ 81 │ -120 135 -1.8326 -1.8326 4.82577e-16 true │ 82 │ -120 150 -1.5708 -1.5708 2.22045e-16 true │ 83 │ -120 165 -1.309 -1.39626 0.0872665 true │ 84 │ -120 180 -1.0472 -1.0472 2.4037e-16 true │ 85 │ -90 -165 -1.309 -1.309 2.68098e-16 true │ 86 │ -90 -150 -1.0472 -1.0472 0.0 true │ 87 │ -90 -135 -0.785398 -0.785398 0.0 true │ 88 │ -90 -120 -0.523599 -0.523599 0.0 true │ 89 │ -90 -90 0.0 0.0 0.0 true │ 90 │ -90 -80 0.174533 0.174533 2.46005e-16 true │ 91 │ -90 -60 0.523599 0.523599 3.36518e-16 true │ 92 │ -90 -45 0.785398 0.785398 1.57009e-16 true │ 93 │ -90 -30 1.0472 1.0472 4.80741e-16 true │ 94 │ -90 0 1.5708 1.5708 0.0 true │ 95 │ -90 15 1.8326 1.8326 4.28957e-16 true │ 96 │ -90 30 2.0944 2.0944 0.0 true │ 97 │ -90 45 2.35619 2.35619 0.0 true │ 98 │ -90 60 2.61799 2.61799 9.61481e-17 true │ 99 │ -90 80 2.96706 2.96706 0.0 true │ 100 │ -90 90 3.14159 -3.14159 2.44929e-16 true │ 101 │ -90 120 -2.61799 -2.61799 4.32667e-16 true │ 102 │ -90 135 -2.35619 -2.35619 0.0 true │ 103 │ -90 150 -2.0944 -2.0944 0.0 true │ 104 │ -90 165 -1.8326 -1.8326 6.43436e-16 true │ 105 │ -90 180 -1.5708 -1.5708 4.44089e-16 true │ 106 │ -80 -165 -1.48353 -1.48353 2.212e-16 true │ 107 │ -80 -150 -1.22173 -1.22173 2.60817e-16 true │ 108 │ -80 -135 -0.959931 -0.959931 1.81888e-16 true │ 109 │ -80 -120 -0.698132 -0.610865 0.0872665 true │ 110 │ -80 -90 -0.174533 -0.174533 2.18671e-16 true │ 111 │ -80 -80 0.0 0.0 0.0 true │ 112 │ -80 -60 0.349066 0.349066 1.04327e-16 true │ 113 │ -80 -45 0.610865 0.610865 2.72832e-16 true │ 114 │ -80 -30 0.872665 0.872665 1.70096e-16 true │ 115 │ -80 0 1.39626 1.39626 2.18671e-16 true │ 116 │ -80 15 1.65806 1.65806 2.212e-16 true │ 117 │ -80 30 1.91986 1.91986 4.17307e-16 true │ 118 │ -80 45 2.18166 2.18166 5.45665e-16 true │ 119 │ -80 60 2.44346 2.44346 0.0 true │ 120 │ -80 80 2.79253 2.79253 4.17307e-16 true │ 121 │ -80 90 2.96706 2.96706 0.0 true │ 122 │ -80 120 -2.79253 -2.79253 4.69471e-16 true │ 123 │ -80 135 -2.53073 -2.53073 5.45665e-16 true │ 124 │ -80 150 -2.26893 -2.18166 0.0872665 true │ 125 │ -80 165 -2.00713 -2.00713 4.52792e-16 true │ 126 │ -80 180 -1.74533 -1.74533 2.18671e-16 true │ 127 │ -60 -165 -1.8326 -2.00713 0.174533 false │ 128 │ -60 -150 -1.5708 -1.5708 4.44089e-16 true │ 129 │ -60 -135 -1.309 -1.309 5.36197e-17 true │ 130 │ -60 -120 -1.0472 -0.959931 0.0872665 true │ 131 │ -60 -90 -0.523599 -0.523599 2.4037e-16 true │ 132 │ -60 -80 -0.349066 -0.261799 0.0872665 true │ 133 │ -60 -60 0.0 0.0 0.0 true │ 134 │ -60 -45 0.261799 0.261799 2.68098e-16 true │ 135 │ -60 -30 0.523599 0.523599 0.0 true │ 136 │ -60 0 1.0472 1.0472 1.92296e-16 true │ 137 │ -60 15 1.309 1.13446 0.174533 false │ 138 │ -60 30 1.5708 1.5708 2.22045e-16 true │ 139 │ -60 45 1.8326 1.8326 2.14479e-16 true │ 140 │ -60 60 2.0944 2.0944 4.32667e-16 true │ 141 │ -60 80 2.44346 2.44346 0.0 true │ 142 │ -60 90 2.61799 2.61799 9.61481e-17 true │ 143 │ -60 120 3.14159 -3.14159 6.89019e-16 true │ 144 │ -60 135 -2.87979 -2.87979 4.82577e-16 true │ 145 │ -60 150 -2.61799 -2.53073 0.0872665 true │ 146 │ -60 165 -2.35619 -2.35619 4.71028e-16 true │ 147 │ -60 180 -2.0944 -2.0944 0.0 true │ 148 │ -45 -165 -2.0944 -2.00713 0.0872665 true │ 149 │ -45 -150 -1.8326 -1.8326 4.82577e-16 true │ 150 │ -45 -135 -1.5708 -1.5708 0.0 true │ 151 │ -45 -120 -1.309 -1.309 2.68098e-16 true │ 152 │ -45 -90 -0.785398 -0.872665 0.0872665 true │ 153 │ -45 -80 -0.610865 -0.523599 0.0872665 true │ 154 │ -45 -60 -0.261799 -0.261799 2.14479e-16 true │ 155 │ -45 -45 0.0 0.0 0.0 true │ 156 │ -45 -30 0.261799 0.261799 2.68098e-16 true │ 157 │ -45 0 0.785398 0.785398 0.0 true │ 158 │ -45 15 1.0472 1.13446 0.0872665 true │ 159 │ -45 30 1.309 1.309 4.82577e-16 true │ 160 │ -45 45 1.5708 1.5708 2.22045e-16 true │ 161 │ -45 60 1.8326 1.8326 2.14479e-16 true │ 162 │ -45 80 2.18166 2.18166 5.45665e-16 true │ 163 │ -45 90 2.35619 2.35619 0.0 true │ 164 │ -45 120 2.87979 2.87979 4.82577e-16 true │ 165 │ -45 135 3.14159 -3.14159 2.44929e-16 true │ 166 │ -45 150 -2.87979 -2.87979 4.82577e-16 true │ 167 │ -45 165 -2.61799 -2.70526 0.0872665 true │ 168 │ -45 180 -2.35619 -2.44346 0.0872665 true │ 169 │ -30 -165 -2.35619 -2.35619 0.0 true │ 170 │ -30 -150 -2.0944 -2.18166 0.0872665 true │ 171 │ -30 -135 -1.8326 -1.8326 1.60859e-16 true │ 172 │ -30 -120 -1.5708 -1.5708 0.0 true │ 173 │ -30 -90 -1.0472 -1.0472 0.0 true │ 174 │ -30 -80 -0.872665 -0.959931 0.0872665 true │ 175 │ -30 -60 -0.523599 -0.610865 0.0872665 true │ 176 │ -30 -45 -0.261799 -0.261799 1.60859e-16 true │ 177 │ -30 -30 -4.80741e-17 0.0 4.80741e-17 true │ 178 │ -30 0 0.523599 0.523599 0.0 true │ 179 │ -30 15 0.785398 0.785398 0.0 true │ 180 │ -30 30 1.0472 0.959931 0.0872665 true │ 181 │ -30 45 1.309 1.22173 0.0872665 true │ 182 │ -30 60 1.5708 1.5708 2.22045e-16 true │ 183 │ -30 80 1.91986 2.00713 0.0872665 true │ 184 │ -30 90 2.0944 2.0944 0.0 true │ 185 │ -30 120 2.61799 2.53073 0.0872665 true │ 186 │ -30 135 2.87979 2.79253 0.0872665 true │ 187 │ -30 150 3.14159 -3.14159 2.44929e-16 true │ 188 │ -30 165 -2.87979 -2.87979 4.82577e-16 true │ 189 │ -30 180 -2.61799 -2.61799 4.80741e-17 true │ 190 │ 0 -165 -2.87979 -2.87979 5.36197e-17 true │ 191 │ 0 -150 -2.61799 -2.61799 4.80741e-17 true │ 192 │ 0 -135 -2.35619 -2.35619 0.0 true │ 193 │ 0 -120 -2.0944 -2.0944 0.0 true │ 194 │ 0 -90 -1.5708 -1.5708 0.0 true │ 195 │ 0 -80 -1.39626 -1.39626 0.0 true │ 196 │ 0 -60 -1.0472 -1.0472 2.88444e-16 true │ 197 │ 0 -45 -0.785398 -0.785398 0.0 true │ 198 │ 0 -30 -0.523599 -0.523599 0.0 true │ 199 │ 0 0 0.0 0.0 0.0 true │ 200 │ 0 15 0.261799 0.261799 2.14479e-16 true │ 201 │ 0 30 0.523599 0.523599 0.0 true │ 202 │ 0 45 0.785398 0.785398 0.0 true │ 203 │ 0 60 1.0472 1.0472 1.92296e-16 true │ 204 │ 0 80 1.39626 1.39626 2.18671e-16 true │ 205 │ 0 90 1.5708 1.5708 0.0 true │ 206 │ 0 120 2.0944 2.0944 0.0 true │ 207 │ 0 135 2.35619 2.35619 0.0 true │ 208 │ 0 150 2.61799 2.61799 4.80741e-16 true │ 209 │ 0 165 2.87979 2.87979 4.82577e-16 true │ 210 │ 0 180 3.14159 -3.14159 2.44929e-16 true │ 211 │ 15 -165 -3.14159 -3.14159 0.0 true │ 212 │ 15 -150 -2.87979 -2.79253 0.0872665 true │ 213 │ 15 -135 -2.61799 -2.61799 4.32667e-16 true │ 214 │ 15 -120 -2.35619 -2.35619 0.0 true │ 215 │ 15 -90 -1.8326 -1.8326 1.60859e-16 true │ 216 │ 15 -80 -1.65806 -1.65806 0.0 true │ 217 │ 15 -60 -1.309 -1.22173 0.0872665 true │ 218 │ 15 -45 -1.0472 -1.0472 2.88444e-16 true │ 219 │ 15 -30 -0.785398 -0.785398 0.0 true │ 220 │ 15 0 -0.261799 -0.261799 2.68098e-16 true │ 221 │ 15 15 5.36197e-17 0.0 5.36197e-17 true │ 222 │ 15 30 0.261799 0.261799 2.14479e-16 true │ 223 │ 15 45 0.523599 0.523599 0.0 true │ 224 │ 15 60 0.785398 0.785398 7.85046e-17 true │ 225 │ 15 80 1.13446 1.13446 4.02482e-16 true │ 226 │ 15 90 1.309 1.309 1.60859e-16 true │ 227 │ 15 120 1.8326 1.8326 2.14479e-16 true │ 228 │ 15 135 2.0944 2.0944 0.0 true │ 229 │ 15 150 2.35619 2.35619 0.0 true │ 230 │ 15 165 2.61799 2.61799 9.61481e-17 true │ 231 │ 15 180 2.87979 2.87979 5.36197e-17 true │ 232 │ 30 -165 2.87979 2.70526 0.174533 false │ 233 │ 30 -150 -3.14159 -3.14159 0.0 true │ 234 │ 30 -135 -2.87979 -2.87979 5.36197e-17 true │ 235 │ 30 -120 -2.61799 -2.53073 0.0872665 true │ 236 │ 30 -90 -2.0944 -2.0944 0.0 true │ 237 │ 30 -80 -1.91986 -1.8326 0.0872665 true │ 238 │ 30 -60 -1.5708 -1.5708 2.22045e-16 true │ 239 │ 30 -45 -1.309 -1.309 2.68098e-16 true │ 240 │ 30 -30 -1.0472 -0.959931 0.0872665 true │ 241 │ 30 0 -0.523599 -0.523599 0.0 true │ 242 │ 30 15 -0.261799 -0.436332 0.174533 false │ 243 │ 30 30 4.80741e-17 0.0 4.80741e-17 true │ 244 │ 30 45 0.261799 0.261799 1.60859e-16 true │ 245 │ 30 60 0.523599 0.523599 4.80741e-17 true │ 246 │ 30 80 0.872665 0.872665 3.40192e-16 true │ 247 │ 30 90 1.0472 1.0472 4.80741e-16 true │ 248 │ 30 120 1.5708 1.5708 0.0 true │ 249 │ 30 135 1.8326 1.8326 4.28957e-16 true │ 250 │ 30 150 2.0944 2.0944 0.0 true │ 251 │ 30 165 2.35619 2.35619 0.0 true │ 252 │ 30 180 2.61799 2.61799 4.80741e-16 true │ 253 │ 45 -165 2.61799 2.61799 9.13407e-16 true │ 254 │ 45 -150 2.87979 2.70526 0.174533 false │ 255 │ 45 -135 -3.14159 -3.14159 0.0 true │ 256 │ 45 -120 -2.87979 -2.79253 0.0872665 true │ 257 │ 45 -90 -2.35619 -2.26893 0.0872665 true │ 258 │ 45 -80 -2.18166 -2.18166 5.45665e-16 true │ 259 │ 45 -60 -1.8326 -1.74533 0.0872665 true │ 260 │ 45 -45 -1.5708 -1.5708 2.22045e-16 true │ 261 │ 45 -30 -1.309 -1.22173 0.0872665 true │ 262 │ 45 0 -0.785398 -0.785398 0.0 true │ 263 │ 45 15 -0.523599 -0.523599 0.0 true │ 264 │ 45 30 -0.261799 -0.174533 0.0872665 true │ 265 │ 45 45 0.0 0.0 0.0 true │ 266 │ 45 60 0.261799 0.261799 2.14479e-16 true │ 267 │ 45 80 0.610865 0.523599 0.0872665 true │ 268 │ 45 90 0.785398 0.785398 0.0 true │ 269 │ 45 120 1.309 1.13446 0.174533 false │ 270 │ 45 135 1.5708 1.5708 0.0 true │ 271 │ 45 150 1.8326 1.8326 7.50675e-16 true │ 272 │ 45 165 2.0944 2.0944 0.0 true │ 273 │ 45 180 2.35619 2.35619 0.0 true │ 274 │ 60 -165 2.35619 2.35619 4.71028e-16 true │ 275 │ 60 -150 2.61799 2.53073 0.0872665 true │ 276 │ 60 -135 2.87979 2.79253 0.0872665 true │ 277 │ 60 -120 -3.14159 -3.14159 4.44089e-16 true │ 278 │ 60 -90 -2.61799 -2.61799 4.32667e-16 true │ 279 │ 60 -80 -2.44346 -2.53073 0.0872665 true │ 280 │ 60 -60 -2.0944 -2.18166 0.0872665 true │ 281 │ 60 -45 -1.8326 -1.8326 5.36197e-17 true │ 282 │ 60 -30 -1.5708 -1.5708 2.22045e-16 true │ 283 │ 60 0 -1.0472 -1.0472 2.88444e-16 true │ 284 │ 60 15 -0.785398 -0.785398 7.85046e-17 true │ 285 │ 60 30 -0.523599 -0.610865 0.0872665 true │ 286 │ 60 45 -0.261799 -0.261799 2.68098e-16 true │ 287 │ 60 60 0.0 0.0 0.0 true │ 288 │ 60 80 0.349066 0.436332 0.0872665 true │ 289 │ 60 90 0.523599 0.523599 2.4037e-16 true │ 290 │ 60 120 1.0472 0.959931 0.0872665 true │ 291 │ 60 135 1.309 1.22173 0.0872665 true │ 292 │ 60 150 1.5708 1.5708 4.44089e-16 true │ 293 │ 60 165 1.8326 1.74533 0.0872665 true │ 294 │ 60 180 2.0944 2.0944 0.0 true │ 295 │ 80 -165 2.00713 2.00713 8.55273e-16 true │ 296 │ 80 -150 2.26893 2.26893 0.0 true │ 297 │ 80 -135 2.53073 2.53073 5.45665e-16 true │ 298 │ 80 -120 2.79253 2.79253 9.38942e-16 true │ 299 │ 80 -90 -2.96706 -2.96706 0.0 true │ 300 │ 80 -80 -2.79253 -2.79253 0.0 true │ 301 │ 80 -60 -2.44346 -2.44346 0.0 true │ 302 │ 80 -45 -2.18166 -2.18166 5.45665e-16 true │ 303 │ 80 -30 -1.91986 -1.91986 5.21634e-17 true │ 304 │ 80 0 -1.39626 -1.39626 0.0 true │ 305 │ 80 15 -1.13446 -1.13446 0.0 true │ 306 │ 80 30 -0.872665 -0.872665 1.70096e-16 true │ 307 │ 80 45 -0.610865 -0.610865 2.72832e-16 true │ 308 │ 80 60 -0.349066 -0.349066 1.04327e-16 true │ 309 │ 80 80 0.0 0.0 0.0 true │ 310 │ 80 90 0.174533 0.174533 2.46005e-16 true │ 311 │ 80 120 0.698132 0.698132 2.55144e-16 true │ 312 │ 80 135 0.959931 0.959931 2.72832e-16 true │ 313 │ 80 150 1.22173 1.22173 6.78124e-16 true │ 314 │ 80 165 1.48353 1.48353 2.212e-16 true │ 315 │ 80 180 1.74533 1.74533 2.46005e-16 true │ 316 │ 90 -165 1.8326 1.8326 9.11534e-16 true │ 317 │ 90 -150 2.0944 2.0944 0.0 true │ 318 │ 90 -135 2.35619 2.35619 0.0 true │ 319 │ 90 -120 2.61799 2.61799 9.13407e-16 true │ 320 │ 90 -90 -3.14159 -3.14159 0.0 true │ 321 │ 90 -80 -2.96706 -2.96706 0.0 true │ 322 │ 90 -60 -2.61799 -2.61799 4.32667e-16 true │ 323 │ 90 -45 -2.35619 -2.26893 0.0872665 true │ 324 │ 90 -30 -2.0944 -2.0944 0.0 true │ 325 │ 90 0 -1.5708 -1.5708 0.0 true │ 326 │ 90 15 -1.309 -1.309 5.36197e-17 true │ 327 │ 90 30 -1.0472 -1.0472 0.0 true │ 328 │ 90 45 -0.785398 -0.785398 1.57009e-16 true │ 329 │ 90 60 -0.523599 -0.523599 3.36518e-16 true │ 330 │ 90 80 -0.174533 -0.174533 2.18671e-16 true │ 331 │ 90 90 0.0 0.0 0.0 true │ 332 │ 90 120 0.523599 0.523599 0.0 true │ 333 │ 90 135 0.785398 0.785398 0.0 true │ 334 │ 90 150 1.0472 1.0472 4.80741e-16 true │ 335 │ 90 165 1.309 1.309 4.82577e-16 true │ 336 │ 90 180 1.5708 1.5708 0.0 true │ 337 │ 120 -165 1.309 1.39626 0.0872665 true │ 338 │ 120 -150 1.5708 1.5708 2.22045e-16 true │ 339 │ 120 -135 1.8326 1.8326 7.50675e-16 true │ 340 │ 120 -120 2.0944 2.0944 0.0 true │ 341 │ 120 -90 2.61799 2.61799 9.13407e-16 true │ 342 │ 120 -80 2.79253 2.70526 0.0872665 true │ 343 │ 120 -60 -3.14159 -3.14159 0.0 true │ 344 │ 120 -45 -2.87979 -2.87979 5.36197e-17 true │ 345 │ 120 -30 -2.61799 -2.53073 0.0872665 true │ 346 │ 120 0 -2.0944 -2.0944 0.0 true │ 347 │ 120 15 -1.8326 -1.74533 0.0872665 true │ 348 │ 120 30 -1.5708 -1.5708 0.0 true │ 349 │ 120 45 -1.309 -1.309 2.68098e-16 true │ 350 │ 120 60 -1.0472 -0.959931 0.0872665 true │ 351 │ 120 80 -0.698132 -0.698132 2.55144e-16 true │ 352 │ 120 90 -0.523599 -0.523599 0.0 true │ 353 │ 120 120 -4.80741e-17 0.0 4.80741e-17 true │ 354 │ 120 135 0.261799 0.261799 1.07239e-16 true │ 355 │ 120 150 0.523599 0.523599 4.32667e-16 true │ 356 │ 120 165 0.785398 0.785398 0.0 true │ 357 │ 120 180 1.0472 1.0472 4.80741e-16 true │ 358 │ 135 -165 1.0472 0.872665 0.174533 false │ 359 │ 135 -150 1.309 1.13446 0.174533 false │ 360 │ 135 -135 1.5708 1.5708 4.44089e-16 true │ 361 │ 135 -120 1.8326 1.8326 7.50675e-16 true │ 362 │ 135 -90 2.35619 2.44346 0.0872665 true │ 363 │ 135 -80 2.53073 2.53073 5.45665e-16 true │ 364 │ 135 -60 2.87979 2.70526 0.174533 false │ 365 │ 135 -45 -3.14159 -3.14159 0.0 true │ 366 │ 135 -30 -2.87979 -2.87979 5.36197e-17 true │ 367 │ 135 0 -2.35619 -2.35619 0.0 true │ 368 │ 135 15 -2.0944 -2.26893 0.174533 true │ 369 │ 135 30 -1.8326 -1.74533 0.0872665 true │ 370 │ 135 45 -1.5708 -1.5708 0.0 true │ 371 │ 135 60 -1.309 -1.309 5.36197e-17 true │ 372 │ 135 80 -0.959931 -1.0472 0.0872665 true │ 373 │ 135 90 -0.785398 -0.785398 0.0 true │ 374 │ 135 120 -0.261799 -0.174533 0.0872665 true │ 375 │ 135 135 0.0 0.0 0.0 true │ 376 │ 135 150 0.261799 0.261799 1.07239e-16 true │ 377 │ 135 165 0.523599 0.698132 0.174533 false │ 378 │ 135 180 0.785398 0.872665 0.0872665 true │ 379 │ 150 -165 0.785398 0.785398 1.57009e-16 true │ 380 │ 150 -150 1.0472 0.959931 0.0872665 true │ 381 │ 150 -135 1.309 1.22173 0.0872665 true │ 382 │ 150 -120 1.5708 1.5708 2.22045e-16 true │ 383 │ 150 -90 2.0944 2.0944 0.0 true │ 384 │ 150 -80 2.26893 2.18166 0.0872665 true │ 385 │ 150 -60 2.61799 2.53073 0.0872665 true │ 386 │ 150 -45 2.87979 2.79253 0.0872665 true │ 387 │ 150 -30 3.14159 -3.14159 2.44929e-16 true │ 388 │ 150 0 -2.61799 -2.61799 4.80741e-17 true │ 389 │ 150 15 -2.35619 -2.35619 0.0 true │ 390 │ 150 30 -2.0944 -2.18166 0.0872665 true │ 391 │ 150 45 -1.8326 -1.8326 4.82577e-16 true │ 392 │ 150 60 -1.5708 -1.5708 4.44089e-16 true │ 393 │ 150 80 -1.22173 -1.13446 0.0872665 true │ 394 │ 150 90 -1.0472 -1.0472 0.0 true │ 395 │ 150 120 -0.523599 -0.610865 0.0872665 true │ 396 │ 150 135 -0.261799 -0.261799 1.07239e-16 true │ 397 │ 150 150 -4.80741e-17 0.0 4.80741e-17 true │ 398 │ 150 165 0.261799 0.174533 0.0872665 true │ 399 │ 150 180 0.523599 0.523599 0.0 true │ 400 │ 165 -165 0.523599 0.523599 8.17259e-16 true │ 401 │ 165 -150 0.785398 0.785398 1.57009e-16 true │ 402 │ 165 -135 1.0472 0.959931 0.0872665 true │ 403 │ 165 -120 1.309 1.39626 0.0872665 true │ 404 │ 165 -90 1.8326 1.8326 9.11534e-16 true │ 405 │ 165 -80 2.00713 2.00713 8.55273e-16 true │ 406 │ 165 -60 2.35619 2.35619 4.71028e-16 true │ 407 │ 165 -45 2.61799 2.53073 0.0872665 true │ 408 │ 165 -30 2.87979 2.96706 0.0872665 true │ 409 │ 165 0 -2.87979 -2.87979 5.36197e-17 true │ 410 │ 165 15 -2.61799 -2.61799 4.32667e-16 true │ 411 │ 165 30 -2.35619 -2.35619 0.0 true │ 412 │ 165 45 -2.0944 -2.00713 0.0872665 true │ 413 │ 165 60 -1.8326 -1.74533 0.0872665 true │ 414 │ 165 80 -1.48353 -1.48353 2.212e-16 true │ 415 │ 165 90 -1.309 -1.309 2.68098e-16 true │ 416 │ 165 120 -0.785398 -0.785398 0.0 true │ 417 │ 165 135 -0.523599 -0.436332 0.0872665 true │ 418 │ 165 150 -0.261799 -0.174533 0.0872665 true │ 419 │ 165 165 5.36197e-17 0.0 5.36197e-17 true │ 420 │ 165 180 0.261799 0.261799 1.07239e-16 true │ 421 │ 180 -165 0.261799 0.261799 2.68098e-16 true │ 422 │ 180 -150 0.523599 0.523599 2.4037e-16 true │ 423 │ 180 -135 0.785398 0.785398 2.35514e-16 true │ 424 │ 180 -120 1.0472 1.0472 7.21111e-16 true │ 425 │ 180 -90 1.5708 1.5708 4.44089e-16 true │ 426 │ 180 -80 1.74533 1.74533 4.64676e-16 true │ 427 │ 180 -60 2.0944 2.0944 0.0 true │ 428 │ 180 -45 2.35619 2.35619 4.71028e-16 true │ 429 │ 180 -30 2.61799 2.61799 4.80741e-16 true │ 430 │ 180 0 -3.14159 -3.14159 0.0 true │ 431 │ 180 15 -2.87979 -2.87979 5.36197e-17 true │ 432 │ 180 30 -2.61799 -2.61799 4.80741e-17 true │ 433 │ 180 45 -2.35619 -2.26893 0.0872665 true │ 434 │ 180 60 -2.0944 -2.0944 0.0 true │ 435 │ 180 80 -1.74533 -1.74533 0.0 true │ 436 │ 180 90 -1.5708 -1.5708 0.0 true │ 437 │ 180 120 -1.0472 -1.0472 0.0 true │ 438 │ 180 135 -0.785398 -0.698132 0.0872665 true │ 439 │ 180 150 -0.523599 -0.523599 0.0 true │ 440 │ 180 165 -0.261799 -0.261799 0.0 true └ 441 │ 180 180 0.0 0.0 0.0 true [ Info: fraction_ok 0.9773242630385488 >= 0.975? ┌ Info: 625×6 DataFrame │ Row │ θ1 θ2 Δθ Δθ_measured minimum_absolute_error ok │ │ Int64 Int64 Float64 Float64 Float64 Bool │ ─────┼──────────────────────────────────────────────────────────────────────── │ 1 │ 0 0 0.0 0.0 0.0 true │ 2 │ 0 15 0.261799 0.261799 2.14479e-16 true │ 3 │ 0 30 0.523599 0.523599 0.0 true │ 4 │ 0 45 0.785398 0.785398 0.0 true │ 5 │ 0 60 1.0472 1.0472 1.92296e-16 true │ 6 │ 0 75 1.309 1.309 1.60859e-16 true │ 7 │ 0 90 1.5708 1.5708 0.0 true │ 8 │ 0 105 1.8326 1.8326 4.28957e-16 true │ 9 │ 0 120 2.0944 2.0944 0.0 true │ 10 │ 0 135 2.35619 2.35619 0.0 true │ 11 │ 0 150 2.61799 2.61799 4.80741e-16 true │ 12 │ 0 165 2.87979 2.87979 4.82577e-16 true │ 13 │ 0 180 3.14159 -3.14159 2.44929e-16 true │ 14 │ 0 195 -2.87979 -2.87979 4.82577e-16 true │ 15 │ 0 210 -2.61799 -2.61799 4.32667e-16 true │ 16 │ 0 225 -2.35619 -2.35619 0.0 true │ 17 │ 0 240 -2.0944 -2.0944 4.80741e-16 true │ 18 │ 0 255 -1.8326 -1.8326 5.36197e-17 true │ 19 │ 0 270 -1.5708 -1.5708 4.44089e-16 true │ 20 │ 0 285 -1.309 -1.309 4.28957e-16 true │ 21 │ 0 300 -1.0472 -1.0472 2.88444e-16 true │ 22 │ 0 315 -0.785398 -0.785398 2.35514e-16 true │ 23 │ 0 330 -0.523599 -0.523599 7.21111e-16 true │ 24 │ 0 345 -0.261799 -0.261799 2.68098e-16 true │ 25 │ 0 360 -2.44929e-16 0.0 2.44929e-16 true │ 26 │ 15 0 -0.261799 -0.261799 2.68098e-16 true │ 27 │ 15 15 5.36197e-17 0.0 5.36197e-17 true │ 28 │ 15 30 0.261799 0.174533 0.0872665 true │ 29 │ 15 45 0.523599 0.610865 0.0872665 true │ 30 │ 15 60 0.785398 0.785398 7.85046e-17 true │ 31 │ 15 75 1.0472 0.959931 0.0872665 true │ 32 │ 15 90 1.309 1.309 1.60859e-16 true │ 33 │ 15 105 1.5708 1.5708 2.22045e-16 true │ 34 │ 15 120 1.8326 1.74533 0.0872665 true │ 35 │ 15 135 2.0944 2.18166 0.0872665 true │ 36 │ 15 150 2.35619 2.35619 0.0 true │ 37 │ 15 165 2.61799 2.53073 0.0872665 true │ 38 │ 15 180 2.87979 2.87979 5.36197e-17 true │ 39 │ 15 195 -3.14159 -3.14159 0.0 true │ 40 │ 15 210 -2.87979 -2.96706 0.0872665 true │ 41 │ 15 225 -2.61799 -2.53073 0.0872665 true │ 42 │ 15 240 -2.35619 -2.35619 4.71028e-16 true │ 43 │ 15 255 -2.0944 -2.0944 0.0 true │ 44 │ 15 270 -1.8326 -1.8326 4.82577e-16 true │ 45 │ 15 285 -1.5708 -1.5708 6.66134e-16 true │ 46 │ 15 300 -1.309 -1.39626 0.0872665 true │ 47 │ 15 315 -1.0472 -0.959931 0.0872665 true │ 48 │ 15 330 -0.785398 -0.785398 6.28037e-16 true │ 49 │ 15 345 -0.523599 -0.523599 0.0 true │ 50 │ 15 360 -0.261799 -0.261799 0.0 true │ 51 │ 30 0 -0.523599 -0.523599 0.0 true │ 52 │ 30 15 -0.261799 -0.174533 0.0872665 true │ 53 │ 30 30 4.80741e-17 0.0 4.80741e-17 true │ 54 │ 30 45 0.261799 0.349066 0.0872665 true │ 55 │ 30 60 0.523599 0.610865 0.0872665 true │ 56 │ 30 75 0.785398 0.785398 1.57009e-16 true │ 57 │ 30 90 1.0472 1.0472 4.80741e-16 true │ 58 │ 30 105 1.309 1.39626 0.0872665 true │ 59 │ 30 120 1.5708 1.5708 0.0 true │ 60 │ 30 135 1.8326 1.91986 0.0872665 true │ 61 │ 30 150 2.0944 2.18166 0.0872665 true │ 62 │ 30 165 2.35619 2.35619 0.0 true │ 63 │ 30 180 2.61799 2.61799 4.80741e-16 true │ 64 │ 30 195 2.87979 2.96706 0.0872665 true │ 65 │ 30 210 -3.14159 -3.14159 0.0 true │ 66 │ 30 225 -2.87979 -2.79253 0.0872665 true │ 67 │ 30 240 -2.61799 -2.53073 0.0872665 true │ 68 │ 30 255 -2.35619 -2.35619 0.0 true │ 69 │ 30 270 -2.0944 -2.0944 0.0 true │ 70 │ 30 285 -1.8326 -1.74533 0.0872665 true │ 71 │ 30 300 -1.5708 -1.5708 2.22045e-16 true │ 72 │ 30 315 -1.309 -1.13446 0.174533 false │ 73 │ 30 330 -1.0472 -0.959931 0.0872665 true │ 74 │ 30 345 -0.785398 -0.785398 1.57009e-16 true │ 75 │ 30 360 -0.523599 -0.523599 3.36518e-16 true │ 76 │ 45 0 -0.785398 -0.785398 0.0 true │ 77 │ 45 15 -0.523599 -0.523599 0.0 true │ 78 │ 45 30 -0.261799 -0.261799 2.68098e-16 true │ 79 │ 45 45 0.0 0.0 0.0 true │ 80 │ 45 60 0.261799 0.261799 2.14479e-16 true │ 81 │ 45 75 0.523599 0.523599 3.36518e-16 true │ 82 │ 45 90 0.785398 0.785398 0.0 true │ 83 │ 45 105 1.0472 1.0472 4.80741e-16 true │ 84 │ 45 120 1.309 1.309 4.82577e-16 true │ 85 │ 45 135 1.5708 1.5708 0.0 true │ 86 │ 45 150 1.8326 1.8326 7.50675e-16 true │ 87 │ 45 165 2.0944 2.0944 0.0 true │ 88 │ 45 180 2.35619 2.35619 0.0 true │ 89 │ 45 195 2.61799 2.61799 4.80741e-16 true │ 90 │ 45 210 2.87979 2.87979 5.36197e-17 true │ 91 │ 45 225 3.14159 -3.14159 2.44929e-16 true │ 92 │ 45 240 -2.87979 -2.87979 4.82577e-16 true │ 93 │ 45 255 -2.61799 -2.61799 4.32667e-16 true │ 94 │ 45 270 -2.35619 -2.35619 0.0 true │ 95 │ 45 285 -2.0944 -2.0944 4.80741e-16 true │ 96 │ 45 300 -1.8326 -1.8326 5.36197e-17 true │ 97 │ 45 315 -1.5708 -1.5708 4.44089e-16 true │ 98 │ 45 330 -1.309 -1.309 4.28957e-16 true │ 99 │ 45 345 -1.0472 -1.0472 4.80741e-16 true │ 100 │ 45 360 -0.785398 -0.785398 1.57009e-16 true │ 101 │ 60 0 -1.0472 -1.0472 2.88444e-16 true │ 102 │ 60 15 -0.785398 -0.785398 7.85046e-17 true │ 103 │ 60 30 -0.523599 -0.523599 0.0 true │ 104 │ 60 45 -0.261799 -0.261799 2.68098e-16 true │ 105 │ 60 60 0.0 0.0 0.0 true │ 106 │ 60 75 0.261799 0.174533 0.0872665 true │ 107 │ 60 90 0.523599 0.523599 2.4037e-16 true │ 108 │ 60 105 0.785398 0.698132 0.0872665 true │ 109 │ 60 120 1.0472 1.0472 1.92296e-16 true │ 110 │ 60 135 1.309 1.22173 0.0872665 true │ 111 │ 60 150 1.5708 1.5708 4.44089e-16 true │ 112 │ 60 165 1.8326 1.74533 0.0872665 true │ 113 │ 60 180 2.0944 2.0944 0.0 true │ 114 │ 60 195 2.35619 2.26893 0.0872665 true │ 115 │ 60 210 2.61799 2.61799 9.13407e-16 true │ 116 │ 60 225 2.87979 2.79253 0.0872665 true │ 117 │ 60 240 3.14159 -3.14159 2.44929e-16 true │ 118 │ 60 255 -2.87979 -2.96706 0.0872665 true │ 119 │ 60 270 -2.61799 -2.61799 4.80741e-17 true │ 120 │ 60 285 -2.35619 -2.35619 4.71028e-16 true │ 121 │ 60 300 -2.0944 -2.0944 4.32667e-16 true │ 122 │ 60 315 -1.8326 -1.91986 0.0872665 true │ 123 │ 60 330 -1.5708 -1.5708 4.44089e-16 true │ 124 │ 60 345 -1.309 -1.39626 0.0872665 true │ 125 │ 60 360 -1.0472 -1.0472 0.0 true │ 126 │ 75 0 -1.309 -1.309 5.36197e-17 true │ 127 │ 75 15 -1.0472 -0.959931 0.0872665 true │ 128 │ 75 30 -0.785398 -0.785398 1.57009e-16 true │ 129 │ 75 45 -0.523599 -0.523599 4.32667e-16 true │ 130 │ 75 60 -0.261799 -0.174533 0.0872665 true │ 131 │ 75 75 -5.36197e-17 0.0 5.36197e-17 true │ 132 │ 75 90 0.261799 0.261799 2.68098e-16 true │ 133 │ 75 105 0.523599 0.523599 0.0 true │ 134 │ 75 120 0.785398 0.698132 0.0872665 true │ 135 │ 75 135 1.0472 1.0472 1.92296e-16 true │ 136 │ 75 150 1.309 1.39626 0.0872665 true │ 137 │ 75 165 1.5708 1.5708 2.22045e-16 true │ 138 │ 75 180 1.8326 1.8326 2.14479e-16 true │ 139 │ 75 195 2.0944 2.0944 0.0 true │ 140 │ 75 210 2.35619 2.26893 0.0872665 true │ 141 │ 75 225 2.61799 2.61799 9.61481e-17 true │ 142 │ 75 240 2.87979 2.96706 0.0872665 true │ 143 │ 75 255 -3.14159 -3.14159 0.0 true │ 144 │ 75 270 -2.87979 -2.87979 4.82577e-16 true │ 145 │ 75 285 -2.61799 -2.53073 0.0872665 true │ 146 │ 75 300 -2.35619 -2.35619 0.0 true │ 147 │ 75 315 -2.0944 -2.0944 0.0 true │ 148 │ 75 330 -1.8326 -1.74533 0.0872665 true │ 149 │ 75 345 -1.5708 -1.5708 0.0 true │ 150 │ 75 360 -1.309 -1.309 2.14479e-16 true │ 151 │ 90 0 -1.5708 -1.5708 0.0 true │ 152 │ 90 15 -1.309 -1.309 5.36197e-17 true │ 153 │ 90 30 -1.0472 -1.0472 0.0 true │ 154 │ 90 45 -0.785398 -0.785398 1.57009e-16 true │ 155 │ 90 60 -0.523599 -0.523599 3.36518e-16 true │ 156 │ 90 75 -0.261799 -0.261799 2.68098e-16 true │ 157 │ 90 90 0.0 0.0 0.0 true │ 158 │ 90 105 0.261799 0.261799 0.0 true │ 159 │ 90 120 0.523599 0.523599 0.0 true │ 160 │ 90 135 0.785398 0.872665 0.0872665 true │ 161 │ 90 150 1.0472 1.0472 4.80741e-16 true │ 162 │ 90 165 1.309 1.309 4.82577e-16 true │ 163 │ 90 180 1.5708 1.5708 0.0 true │ 164 │ 90 195 1.8326 1.8326 4.28957e-16 true │ 165 │ 90 210 2.0944 2.0944 0.0 true │ 166 │ 90 225 2.35619 2.44346 0.0872665 true │ 167 │ 90 240 2.61799 2.61799 9.61481e-17 true │ 168 │ 90 255 2.87979 2.87979 5.36197e-17 true │ 169 │ 90 270 3.14159 -3.14159 2.44929e-16 true │ 170 │ 90 285 -2.87979 -2.87979 4.82577e-16 true │ 171 │ 90 300 -2.61799 -2.61799 4.32667e-16 true │ 172 │ 90 315 -2.35619 -2.35619 4.71028e-16 true │ 173 │ 90 330 -2.0944 -2.0944 4.80741e-16 true │ 174 │ 90 345 -1.8326 -1.8326 5.36197e-17 true │ 175 │ 90 360 -1.5708 -1.5708 4.44089e-16 true │ 176 │ 105 0 -1.8326 -1.8326 1.60859e-16 true │ 177 │ 105 15 -1.5708 -1.5708 2.22045e-16 true │ 178 │ 105 30 -1.309 -1.39626 0.0872665 true │ 179 │ 105 45 -1.0472 -0.959931 0.0872665 true │ 180 │ 105 60 -0.785398 -0.785398 2.35514e-16 true │ 181 │ 105 75 -0.523599 -0.523599 0.0 true │ 182 │ 105 90 -0.261799 -0.261799 1.07239e-16 true │ 183 │ 105 105 5.36197e-17 0.0 5.36197e-17 true │ 184 │ 105 120 0.261799 0.174533 0.0872665 true │ 185 │ 105 135 0.523599 0.610865 0.0872665 true │ 186 │ 105 150 0.785398 0.785398 1.57009e-16 true │ 187 │ 105 165 1.0472 0.959931 0.0872665 true │ 188 │ 105 180 1.309 1.309 4.82577e-16 true │ 189 │ 105 195 1.5708 1.5708 0.0 true │ 190 │ 105 210 1.8326 1.74533 0.0872665 true │ 191 │ 105 225 2.0944 2.00713 0.0872665 true │ 192 │ 105 240 2.35619 2.35619 4.71028e-16 true │ 193 │ 105 255 2.61799 2.53073 0.0872665 true │ 194 │ 105 270 2.87979 2.87979 4.82577e-16 true │ 195 │ 105 285 3.14159 -3.14159 6.89019e-16 true │ 196 │ 105 300 -2.87979 -2.96706 0.0872665 true │ 197 │ 105 315 -2.61799 -2.53073 0.0872665 true │ 198 │ 105 330 -2.35619 -2.35619 9.42055e-16 true │ 199 │ 105 345 -2.0944 -2.0944 0.0 true │ 200 │ 105 360 -1.8326 -1.8326 6.43436e-16 true │ 201 │ 120 0 -2.0944 -2.0944 0.0 true │ 202 │ 120 15 -1.8326 -1.74533 0.0872665 true │ 203 │ 120 30 -1.5708 -1.5708 0.0 true │ 204 │ 120 45 -1.309 -1.22173 0.0872665 true │ 205 │ 120 60 -1.0472 -0.959931 0.0872665 true │ 206 │ 120 75 -0.785398 -0.785398 1.57009e-16 true │ 207 │ 120 90 -0.523599 -0.523599 0.0 true │ 208 │ 120 105 -0.261799 -0.174533 0.0872665 true │ 209 │ 120 120 -4.80741e-17 0.0 4.80741e-17 true │ 210 │ 120 135 0.261799 0.349066 0.0872665 true │ 211 │ 120 150 0.523599 0.610865 0.0872665 true │ 212 │ 120 165 0.785398 0.785398 0.0 true │ 213 │ 120 180 1.0472 1.0472 4.80741e-16 true │ 214 │ 120 195 1.309 1.39626 0.0872665 true │ 215 │ 120 210 1.5708 1.5708 4.44089e-16 true │ 216 │ 120 225 1.8326 1.91986 0.0872665 true │ 217 │ 120 240 2.0944 2.18166 0.0872665 true │ 218 │ 120 255 2.35619 2.35619 4.71028e-16 true │ 219 │ 120 270 2.61799 2.61799 4.80741e-16 true │ 220 │ 120 285 2.87979 2.96706 0.0872665 true │ 221 │ 120 300 -3.14159 -3.14159 0.0 true │ 222 │ 120 315 -2.87979 -2.70526 0.174533 false │ 223 │ 120 330 -2.61799 -2.53073 0.0872665 true │ 224 │ 120 345 -2.35619 -2.35619 0.0 true │ 225 │ 120 360 -2.0944 -2.0944 0.0 true │ 226 │ 135 0 -2.35619 -2.35619 0.0 true │ 227 │ 135 15 -2.0944 -2.0944 0.0 true │ 228 │ 135 30 -1.8326 -1.8326 1.60859e-16 true │ 229 │ 135 45 -1.5708 -1.5708 0.0 true │ 230 │ 135 60 -1.309 -1.309 5.36197e-17 true │ 231 │ 135 75 -1.0472 -1.0472 2.88444e-16 true │ 232 │ 135 90 -0.785398 -0.785398 0.0 true │ 233 │ 135 105 -0.523599 -0.523599 0.0 true │ 234 │ 135 120 -0.261799 -0.261799 1.07239e-16 true │ 235 │ 135 135 0.0 0.0 0.0 true │ 236 │ 135 150 0.261799 0.261799 1.07239e-16 true │ 237 │ 135 165 0.523599 0.436332 0.0872665 true │ 238 │ 135 180 0.785398 0.785398 0.0 true │ 239 │ 135 195 1.0472 1.0472 4.80741e-16 true │ 240 │ 135 210 1.309 1.309 0.0 true │ 241 │ 135 225 1.5708 1.5708 0.0 true │ 242 │ 135 240 1.8326 1.8326 2.14479e-16 true │ 243 │ 135 255 2.0944 2.00713 0.0872665 true │ 244 │ 135 270 2.35619 2.35619 0.0 true │ 245 │ 135 285 2.61799 2.61799 9.61481e-17 true │ 246 │ 135 300 2.87979 2.87979 5.36197e-17 true │ 247 │ 135 315 3.14159 -3.14159 2.44929e-16 true │ 248 │ 135 330 -2.87979 -2.87979 4.82577e-16 true │ 249 │ 135 345 -2.61799 -2.61799 4.32667e-16 true │ 250 │ 135 360 -2.35619 -2.35619 0.0 true │ 251 │ 150 0 -2.61799 -2.61799 4.80741e-17 true │ 252 │ 150 15 -2.35619 -2.18166 0.174533 true │ 253 │ 150 30 -2.0944 -2.00713 0.0872665 true │ 254 │ 150 45 -1.8326 -1.74533 0.0872665 true │ 255 │ 150 60 -1.5708 -1.5708 4.44089e-16 true │ 256 │ 150 75 -1.309 -1.309 5.36197e-17 true │ 257 │ 150 90 -1.0472 -1.0472 0.0 true │ 258 │ 150 105 -0.785398 -0.610865 0.174533 true │ 259 │ 150 120 -0.523599 -0.436332 0.0872665 true │ 260 │ 150 135 -0.261799 -0.174533 0.0872665 true │ 261 │ 150 150 -4.80741e-17 0.0 4.80741e-17 true │ 262 │ 150 165 0.261799 0.261799 5.36197e-16 true │ 263 │ 150 180 0.523599 0.523599 0.0 true │ 264 │ 150 195 0.785398 0.872665 0.0872665 true │ 265 │ 150 210 1.0472 1.13446 0.0872665 true │ 266 │ 150 225 1.309 1.39626 0.0872665 true │ 267 │ 150 240 1.5708 1.5708 4.44089e-16 true │ 268 │ 150 255 1.8326 1.8326 7.50675e-16 true │ 269 │ 150 270 2.0944 2.0944 0.0 true │ 270 │ 150 285 2.35619 2.44346 0.0872665 true │ 271 │ 150 300 2.61799 2.70526 0.0872665 true │ 272 │ 150 315 2.87979 2.87979 4.82577e-16 true │ 273 │ 150 330 3.14159 -3.14159 6.89019e-16 true │ 274 │ 150 345 -2.87979 -2.87979 4.82577e-16 true │ 275 │ 150 360 -2.61799 -2.61799 4.32667e-16 true │ 276 │ 165 0 -2.87979 -2.87979 5.36197e-17 true │ 277 │ 165 15 -2.61799 -2.53073 0.0872665 true │ 278 │ 165 30 -2.35619 -2.35619 0.0 true │ 279 │ 165 45 -2.0944 -2.0944 0.0 true │ 280 │ 165 60 -1.8326 -1.74533 0.0872665 true │ 281 │ 165 75 -1.5708 -1.5708 2.22045e-16 true │ 282 │ 165 90 -1.309 -1.309 2.68098e-16 true │ 283 │ 165 105 -1.0472 -0.959931 0.0872665 true │ 284 │ 165 120 -0.785398 -0.785398 0.0 true │ 285 │ 165 135 -0.523599 -0.523599 0.0 true │ 286 │ 165 150 -0.261799 -0.174533 0.0872665 true │ 287 │ 165 165 5.36197e-17 0.0 5.36197e-17 true │ 288 │ 165 180 0.261799 0.261799 1.07239e-16 true │ 289 │ 165 195 0.523599 0.523599 4.32667e-16 true │ 290 │ 165 210 0.785398 0.698132 0.0872665 true │ 291 │ 165 225 1.0472 1.0472 4.80741e-16 true │ 292 │ 165 240 1.309 1.39626 0.0872665 true │ 293 │ 165 255 1.5708 1.5708 6.66134e-16 true │ 294 │ 165 270 1.8326 1.8326 7.50675e-16 true │ 295 │ 165 285 2.0944 2.0944 0.0 true │ 296 │ 165 300 2.35619 2.26893 0.0872665 true │ 297 │ 165 315 2.61799 2.61799 4.80741e-16 true │ 298 │ 165 330 2.87979 2.96706 0.0872665 true │ 299 │ 165 345 -3.14159 -3.14159 4.44089e-16 true │ 300 │ 165 360 -2.87979 -2.87979 5.36197e-17 true │ 301 │ 180 0 -3.14159 -3.14159 0.0 true │ 302 │ 180 15 -2.87979 -2.87979 5.36197e-17 true │ 303 │ 180 30 -2.61799 -2.61799 4.80741e-17 true │ 304 │ 180 45 -2.35619 -2.35619 0.0 true │ 305 │ 180 60 -2.0944 -2.0944 0.0 true │ 306 │ 180 75 -1.8326 -1.8326 5.36197e-17 true │ 307 │ 180 90 -1.5708 -1.5708 0.0 true │ 308 │ 180 105 -1.309 -1.309 2.68098e-16 true │ 309 │ 180 120 -1.0472 -1.0472 0.0 true │ 310 │ 180 135 -0.785398 -0.785398 0.0 true │ 311 │ 180 150 -0.523599 -0.523599 0.0 true │ 312 │ 180 165 -0.261799 -0.261799 0.0 true │ 313 │ 180 180 0.0 0.0 0.0 true │ 314 │ 180 195 0.261799 0.261799 0.0 true │ 315 │ 180 210 0.523599 0.523599 4.32667e-16 true │ 316 │ 180 225 0.785398 0.872665 0.0872665 true │ 317 │ 180 240 1.0472 1.0472 9.61481e-17 true │ 318 │ 180 255 1.309 1.309 0.0 true │ 319 │ 180 270 1.5708 1.5708 0.0 true │ 320 │ 180 285 1.8326 1.8326 2.14479e-16 true │ 321 │ 180 300 2.0944 2.0944 0.0 true │ 322 │ 180 315 2.35619 2.35619 0.0 true │ 323 │ 180 330 2.61799 2.61799 9.61481e-17 true │ 324 │ 180 345 2.87979 2.87979 5.36197e-17 true │ 325 │ 180 360 3.14159 -3.14159 2.44929e-16 true │ 326 │ 195 0 2.87979 2.87979 5.36197e-17 true │ 327 │ 195 15 3.14159 -3.14159 2.44929e-16 true │ 328 │ 195 30 -2.87979 -2.96706 0.0872665 true │ 329 │ 195 45 -2.61799 -2.53073 0.0872665 true │ 330 │ 195 60 -2.35619 -2.35619 0.0 true │ 331 │ 195 75 -2.0944 -2.0944 0.0 true │ 332 │ 195 90 -1.8326 -1.8326 1.60859e-16 true │ 333 │ 195 105 -1.5708 -1.5708 0.0 true │ 334 │ 195 120 -1.309 -1.39626 0.0872665 true │ 335 │ 195 135 -1.0472 -0.959931 0.0872665 true │ 336 │ 195 150 -0.785398 -0.785398 0.0 true │ 337 │ 195 165 -0.523599 -0.523599 4.32667e-16 true │ 338 │ 195 180 -0.261799 -0.261799 1.07239e-16 true │ 339 │ 195 195 5.36197e-17 0.0 5.36197e-17 true │ 340 │ 195 210 0.261799 0.174533 0.0872665 true │ 341 │ 195 225 0.523599 0.436332 0.0872665 true │ 342 │ 195 240 0.785398 0.785398 3.14018e-16 true │ 343 │ 195 255 1.0472 0.959931 0.0872665 true │ 344 │ 195 270 1.309 1.309 4.82577e-16 true │ 345 │ 195 285 1.5708 1.5708 4.44089e-16 true │ 346 │ 195 300 1.8326 1.74533 0.0872665 true │ 347 │ 195 315 2.0944 2.18166 0.0872665 true │ 348 │ 195 330 2.35619 2.35619 4.71028e-16 true │ 349 │ 195 345 2.61799 2.53073 0.0872665 true │ 350 │ 195 360 2.87979 2.87979 4.82577e-16 true │ 351 │ 210 0 2.61799 2.61799 9.61481e-17 true │ 352 │ 210 15 2.87979 2.96706 0.0872665 true │ 353 │ 210 30 3.14159 -3.14159 2.44929e-16 true │ 354 │ 210 45 -2.87979 -2.79253 0.0872665 true │ 355 │ 210 60 -2.61799 -2.53073 0.0872665 true │ 356 │ 210 75 -2.35619 -2.26893 0.0872665 true │ 357 │ 210 90 -2.0944 -2.0944 0.0 true │ 358 │ 210 105 -1.8326 -1.74533 0.0872665 true │ 359 │ 210 120 -1.5708 -1.5708 4.44089e-16 true │ 360 │ 210 135 -1.309 -1.22173 0.0872665 true │ 361 │ 210 150 -1.0472 -0.959931 0.0872665 true │ 362 │ 210 165 -0.785398 -0.785398 4.71028e-16 true │ 363 │ 210 180 -0.523599 -0.523599 4.32667e-16 true │ 364 │ 210 195 -0.261799 -0.174533 0.0872665 true │ 365 │ 210 210 0.0 0.0 0.0 true │ 366 │ 210 225 0.261799 0.349066 0.0872665 true │ 367 │ 210 240 0.523599 0.610865 0.0872665 true │ 368 │ 210 255 0.785398 0.785398 0.0 true │ 369 │ 210 270 1.0472 1.0472 9.61481e-17 true │ 370 │ 210 285 1.309 1.39626 0.0872665 true │ 371 │ 210 300 1.5708 1.5708 0.0 true │ 372 │ 210 315 1.8326 1.91986 0.0872665 true │ 373 │ 210 330 2.0944 2.18166 0.0872665 true │ 374 │ 210 345 2.35619 2.35619 0.0 true │ 375 │ 210 360 2.61799 2.61799 9.61481e-17 true │ 376 │ 225 0 2.35619 2.35619 0.0 true │ 377 │ 225 15 2.61799 2.61799 4.80741e-16 true │ 378 │ 225 30 2.87979 2.87979 5.36197e-17 true │ 379 │ 225 45 -3.14159 -3.14159 0.0 true │ 380 │ 225 60 -2.87979 -2.87979 4.82577e-16 true │ 381 │ 225 75 -2.61799 -2.61799 4.32667e-16 true │ 382 │ 225 90 -2.35619 -2.35619 0.0 true │ 383 │ 225 105 -2.0944 -2.00713 0.0872665 true │ 384 │ 225 120 -1.8326 -1.8326 1.60859e-16 true │ 385 │ 225 135 -1.5708 -1.5708 0.0 true │ 386 │ 225 150 -1.309 -1.309 2.68098e-16 true │ 387 │ 225 165 -1.0472 -1.0472 0.0 true │ 388 │ 225 180 -0.785398 -0.785398 0.0 true │ 389 │ 225 195 -0.523599 -0.436332 0.0872665 true │ 390 │ 225 210 -0.261799 -0.261799 4.82577e-16 true │ 391 │ 225 225 0.0 0.0 0.0 true │ 392 │ 225 240 0.261799 0.261799 4.28957e-16 true │ 393 │ 225 255 0.523599 0.523599 4.32667e-16 true │ 394 │ 225 270 0.785398 0.785398 0.0 true │ 395 │ 225 285 1.0472 1.0472 1.92296e-16 true │ 396 │ 225 300 1.309 1.309 0.0 true │ 397 │ 225 315 1.5708 1.5708 0.0 true │ 398 │ 225 330 1.8326 1.8326 2.14479e-16 true │ 399 │ 225 345 2.0944 2.0944 0.0 true │ 400 │ 225 360 2.35619 2.35619 0.0 true │ 401 │ 240 0 2.0944 2.0944 4.80741e-16 true │ 402 │ 240 15 2.35619 2.44346 0.0872665 true │ 403 │ 240 30 2.61799 2.70526 0.0872665 true │ 404 │ 240 45 2.87979 2.96706 0.0872665 true │ 405 │ 240 60 -3.14159 -3.14159 4.44089e-16 true │ 406 │ 240 75 -2.87979 -2.87979 4.28957e-16 true │ 407 │ 240 90 -2.61799 -2.61799 4.32667e-16 true │ 408 │ 240 105 -2.35619 -2.18166 0.174533 true │ 409 │ 240 120 -2.0944 -2.00713 0.0872665 true │ 410 │ 240 135 -1.8326 -1.74533 0.0872665 true │ 411 │ 240 150 -1.5708 -1.5708 4.44089e-16 true │ 412 │ 240 165 -1.309 -1.309 2.68098e-16 true │ 413 │ 240 180 -1.0472 -1.0472 4.80741e-16 true │ 414 │ 240 195 -0.785398 -0.610865 0.174533 true │ 415 │ 240 210 -0.523599 -0.436332 0.0872665 true │ 416 │ 240 225 -0.261799 -0.174533 0.0872665 true │ 417 │ 240 240 0.0 0.0 0.0 true │ 418 │ 240 255 0.261799 0.261799 4.82577e-16 true │ 419 │ 240 270 0.523599 0.523599 4.32667e-16 true │ 420 │ 240 285 0.785398 0.872665 0.0872665 true │ 421 │ 240 300 1.0472 1.13446 0.0872665 true │ 422 │ 240 315 1.309 1.309 0.0 true │ 423 │ 240 330 1.5708 1.5708 0.0 true │ 424 │ 240 345 1.8326 1.8326 1.07239e-15 true │ 425 │ 240 360 2.0944 2.0944 0.0 true │ 426 │ 255 0 1.8326 1.8326 2.14479e-16 true │ 427 │ 255 15 2.0944 2.0944 0.0 true │ 428 │ 255 30 2.35619 2.35619 0.0 true │ 429 │ 255 45 2.61799 2.61799 9.61481e-17 true │ 430 │ 255 60 2.87979 2.96706 0.0872665 true │ 431 │ 255 75 3.14159 -3.14159 2.44929e-16 true │ 432 │ 255 90 -2.87979 -2.87979 4.82577e-16 true │ 433 │ 255 105 -2.61799 -2.53073 0.0872665 true │ 434 │ 255 120 -2.35619 -2.35619 4.71028e-16 true │ 435 │ 255 135 -2.0944 -2.0944 0.0 true │ 436 │ 255 150 -1.8326 -1.74533 0.0872665 true │ 437 │ 255 165 -1.5708 -1.5708 6.66134e-16 true │ 438 │ 255 180 -1.309 -1.309 2.14479e-16 true │ 439 │ 255 195 -1.0472 -0.959931 0.0872665 true │ 440 │ 255 210 -0.785398 -0.785398 1.57009e-16 true │ 441 │ 255 225 -0.523599 -0.523599 4.32667e-16 true │ 442 │ 255 240 -0.261799 -0.174533 0.0872665 true │ 443 │ 255 255 -5.36197e-17 0.0 5.36197e-17 true │ 444 │ 255 270 0.261799 0.261799 4.82577e-16 true │ 445 │ 255 285 0.523599 0.523599 5.28815e-16 true │ 446 │ 255 300 0.785398 0.785398 0.0 true │ 447 │ 255 315 1.0472 1.0472 9.61481e-17 true │ 448 │ 255 330 1.309 1.39626 0.0872665 true │ 449 │ 255 345 1.5708 1.5708 0.0 true │ 450 │ 255 360 1.8326 1.8326 2.14479e-16 true │ 451 │ 270 0 1.5708 1.5708 4.44089e-16 true │ 452 │ 270 15 1.8326 1.8326 7.50675e-16 true │ 453 │ 270 30 2.0944 2.0944 0.0 true │ 454 │ 270 45 2.35619 2.44346 0.0872665 true │ 455 │ 270 60 2.61799 2.61799 4.80741e-16 true │ 456 │ 270 75 2.87979 2.87979 5.36197e-17 true │ 457 │ 270 90 -3.14159 -3.14159 0.0 true │ 458 │ 270 105 -2.87979 -2.87979 5.36197e-17 true │ 459 │ 270 120 -2.61799 -2.61799 4.80741e-17 true │ 460 │ 270 135 -2.35619 -2.35619 0.0 true │ 461 │ 270 150 -2.0944 -2.0944 4.32667e-16 true │ 462 │ 270 165 -1.8326 -1.8326 4.82577e-16 true │ 463 │ 270 180 -1.5708 -1.5708 0.0 true │ 464 │ 270 195 -1.309 -1.309 2.68098e-16 true │ 465 │ 270 210 -1.0472 -1.0472 4.80741e-16 true │ 466 │ 270 225 -0.785398 -0.698132 0.0872665 true │ 467 │ 270 240 -0.523599 -0.523599 4.32667e-16 true │ 468 │ 270 255 -0.261799 -0.261799 4.82577e-16 true │ 469 │ 270 270 0.0 0.0 0.0 true │ 470 │ 270 285 0.261799 0.261799 4.82577e-16 true │ 471 │ 270 300 0.523599 0.523599 4.32667e-16 true │ 472 │ 270 315 0.785398 0.785398 0.0 true │ 473 │ 270 330 1.0472 1.0472 9.61481e-17 true │ 474 │ 270 345 1.309 1.309 0.0 true │ 475 │ 270 360 1.5708 1.5708 0.0 true │ 476 │ 285 0 1.309 1.309 2.14479e-16 true │ 477 │ 285 15 1.5708 1.5708 6.66134e-16 true │ 478 │ 285 30 1.8326 1.74533 0.0872665 true │ 479 │ 285 45 2.0944 2.18166 0.0872665 true │ 480 │ 285 60 2.35619 2.35619 4.71028e-16 true │ 481 │ 285 75 2.61799 2.53073 0.0872665 true │ 482 │ 285 90 2.87979 2.87979 5.36197e-17 true │ 483 │ 285 105 -3.14159 -3.14159 4.44089e-16 true │ 484 │ 285 120 -2.87979 -2.96706 0.0872665 true │ 485 │ 285 135 -2.61799 -2.53073 0.0872665 true │ 486 │ 285 150 -2.35619 -2.35619 4.71028e-16 true │ 487 │ 285 165 -2.0944 -2.0944 0.0 true │ 488 │ 285 180 -1.8326 -1.8326 5.36197e-17 true │ 489 │ 285 195 -1.5708 -1.5708 4.44089e-16 true │ 490 │ 285 210 -1.309 -1.39626 0.0872665 true │ 491 │ 285 225 -1.0472 -0.959931 0.0872665 true │ 492 │ 285 240 -0.785398 -0.785398 0.0 true │ 493 │ 285 255 -0.523599 -0.523599 4.32667e-16 true │ 494 │ 285 270 -0.261799 -0.261799 4.82577e-16 true │ 495 │ 285 285 0.0 0.0 0.0 true │ 496 │ 285 300 0.261799 0.174533 0.0872665 true │ 497 │ 285 315 0.523599 0.610865 0.0872665 true │ 498 │ 285 330 0.785398 0.785398 0.0 true │ 499 │ 285 345 1.0472 0.959931 0.0872665 true │ 500 │ 285 360 1.309 1.309 0.0 true │ 501 │ 300 0 1.0472 1.0472 1.92296e-16 true │ 502 │ 300 15 1.309 1.39626 0.0872665 true │ 503 │ 300 30 1.5708 1.5708 2.22045e-16 true │ 504 │ 300 45 1.8326 1.91986 0.0872665 true │ 505 │ 300 60 2.0944 2.18166 0.0872665 true │ 506 │ 300 75 2.35619 2.35619 0.0 true │ 507 │ 300 90 2.61799 2.61799 9.61481e-17 true │ 508 │ 300 105 2.87979 2.96706 0.0872665 true │ 509 │ 300 120 3.14159 -3.14159 6.89019e-16 true │ 510 │ 300 135 -2.87979 -2.79253 0.0872665 true │ 511 │ 300 150 -2.61799 -2.53073 0.0872665 true │ 512 │ 300 165 -2.35619 -2.35619 4.71028e-16 true │ 513 │ 300 180 -2.0944 -2.0944 0.0 true │ 514 │ 300 195 -1.8326 -1.74533 0.0872665 true │ 515 │ 300 210 -1.5708 -1.5708 0.0 true │ 516 │ 300 225 -1.309 -1.22173 0.0872665 true │ 517 │ 300 240 -1.0472 -0.959931 0.0872665 true │ 518 │ 300 255 -0.785398 -0.785398 1.57009e-16 true │ 519 │ 300 270 -0.523599 -0.523599 5.28815e-16 true │ 520 │ 300 285 -0.261799 -0.174533 0.0872665 true │ 521 │ 300 300 0.0 0.0 0.0 true │ 522 │ 300 315 0.261799 0.349066 0.0872665 true │ 523 │ 300 330 0.523599 0.610865 0.0872665 true │ 524 │ 300 345 0.785398 0.785398 0.0 true │ 525 │ 300 360 1.0472 1.0472 9.61481e-17 true │ 526 │ 315 0 0.785398 0.785398 2.35514e-16 true │ 527 │ 315 15 1.0472 1.0472 7.21111e-16 true │ 528 │ 315 30 1.309 1.309 1.60859e-16 true │ 529 │ 315 45 1.5708 1.5708 4.44089e-16 true │ 530 │ 315 60 1.8326 1.8326 4.28957e-16 true │ 531 │ 315 75 2.0944 2.00713 0.0872665 true │ 532 │ 315 90 2.35619 2.35619 0.0 true │ 533 │ 315 105 2.61799 2.61799 9.13407e-16 true │ 534 │ 315 120 2.87979 2.87979 5.36197e-17 true │ 535 │ 315 135 -3.14159 -3.14159 0.0 true │ 536 │ 315 150 -2.87979 -2.87979 5.36197e-17 true │ 537 │ 315 165 -2.61799 -2.61799 4.80741e-17 true │ 538 │ 315 180 -2.35619 -2.35619 0.0 true │ 539 │ 315 195 -2.0944 -2.0944 4.32667e-16 true │ 540 │ 315 210 -1.8326 -1.8326 5.36197e-17 true │ 541 │ 315 225 -1.5708 -1.5708 0.0 true │ 542 │ 315 240 -1.309 -1.309 2.14479e-16 true │ 543 │ 315 255 -1.0472 -1.0472 4.80741e-16 true │ 544 │ 315 270 -0.785398 -0.785398 0.0 true │ 545 │ 315 285 -0.523599 -0.523599 4.32667e-16 true │ 546 │ 315 300 -0.261799 -0.261799 4.82577e-16 true │ 547 │ 315 315 0.0 0.0 0.0 true │ 548 │ 315 330 0.261799 0.261799 4.82577e-16 true │ 549 │ 315 345 0.523599 0.436332 0.0872665 true │ 550 │ 315 360 0.785398 0.785398 0.0 true │ 551 │ 330 0 0.523599 0.523599 7.21111e-16 true │ 552 │ 330 15 0.785398 0.698132 0.0872665 true │ 553 │ 330 30 1.0472 0.959931 0.0872665 true │ 554 │ 330 45 1.309 1.309 2.14479e-16 true │ 555 │ 330 60 1.5708 1.5708 4.44089e-16 true │ 556 │ 330 75 1.8326 1.74533 0.0872665 true │ 557 │ 330 90 2.0944 2.0944 4.80741e-16 true │ 558 │ 330 105 2.35619 2.26893 0.0872665 true │ 559 │ 330 120 2.61799 2.53073 0.0872665 true │ 560 │ 330 135 2.87979 2.79253 0.0872665 true │ 561 │ 330 150 -3.14159 -3.14159 4.44089e-16 true │ 562 │ 330 165 -2.87979 -2.96706 0.0872665 true │ 563 │ 330 180 -2.61799 -2.61799 4.32667e-16 true │ 564 │ 330 195 -2.35619 -2.35619 4.71028e-16 true │ 565 │ 330 210 -2.0944 -2.18166 0.0872665 true │ 566 │ 330 225 -1.8326 -1.8326 5.36197e-17 true │ 567 │ 330 240 -1.5708 -1.5708 0.0 true │ 568 │ 330 255 -1.309 -1.39626 0.0872665 true │ 569 │ 330 270 -1.0472 -1.0472 4.80741e-16 true │ 570 │ 330 285 -0.785398 -0.785398 0.0 true │ 571 │ 330 300 -0.523599 -0.610865 0.0872665 true │ 572 │ 330 315 -0.261799 -0.349066 0.0872665 true │ 573 │ 330 330 0.0 0.0 0.0 true │ 574 │ 330 345 0.261799 0.174533 0.0872665 true │ 575 │ 330 360 0.523599 0.523599 5.28815e-16 true │ 576 │ 345 0 0.261799 0.261799 2.68098e-16 true │ 577 │ 345 15 0.523599 0.523599 0.0 true │ 578 │ 345 30 0.785398 0.785398 7.85046e-17 true │ 579 │ 345 45 1.0472 1.0472 1.92296e-16 true │ 580 │ 345 60 1.309 1.39626 0.0872665 true │ 581 │ 345 75 1.5708 1.5708 0.0 true │ 582 │ 345 90 1.8326 1.8326 2.14479e-16 true │ 583 │ 345 105 2.0944 2.0944 0.0 true │ 584 │ 345 120 2.35619 2.35619 0.0 true │ 585 │ 345 135 2.61799 2.61799 9.61481e-17 true │ 586 │ 345 150 2.87979 2.96706 0.0872665 true │ 587 │ 345 165 3.14159 -3.14159 6.89019e-16 true │ 588 │ 345 180 -2.87979 -2.87979 4.82577e-16 true │ 589 │ 345 195 -2.61799 -2.53073 0.0872665 true │ 590 │ 345 210 -2.35619 -2.35619 0.0 true │ 591 │ 345 225 -2.0944 -2.0944 0.0 true │ 592 │ 345 240 -1.8326 -1.74533 0.0872665 true │ 593 │ 345 255 -1.5708 -1.5708 0.0 true │ 594 │ 345 270 -1.309 -1.309 2.14479e-16 true │ 595 │ 345 285 -1.0472 -0.959931 0.0872665 true │ 596 │ 345 300 -0.785398 -0.785398 0.0 true │ 597 │ 345 315 -0.523599 -0.523599 5.28815e-16 true │ 598 │ 345 330 -0.261799 -0.174533 0.0872665 true │ 599 │ 345 345 -5.36197e-17 0.0 5.36197e-17 true │ 600 │ 345 360 0.261799 0.261799 4.82577e-16 true │ 601 │ 360 0 2.44929e-16 0.0 2.44929e-16 true │ 602 │ 360 15 0.261799 0.261799 1.07239e-16 true │ 603 │ 360 30 0.523599 0.523599 3.36518e-16 true │ 604 │ 360 45 0.785398 0.785398 2.35514e-16 true │ 605 │ 360 60 1.0472 1.0472 4.80741e-16 true │ 606 │ 360 75 1.309 1.309 0.0 true │ 607 │ 360 90 1.5708 1.5708 4.44089e-16 true │ 608 │ 360 105 1.8326 1.8326 9.11534e-16 true │ 609 │ 360 120 2.0944 2.0944 0.0 true │ 610 │ 360 135 2.35619 2.35619 4.71028e-16 true │ 611 │ 360 150 2.61799 2.61799 9.13407e-16 true │ 612 │ 360 165 2.87979 2.87979 5.36197e-17 true │ 613 │ 360 180 -3.14159 -3.14159 0.0 true │ 614 │ 360 195 -2.87979 -2.87979 5.36197e-17 true │ 615 │ 360 210 -2.61799 -2.61799 4.32667e-16 true │ 616 │ 360 225 -2.35619 -2.35619 0.0 true │ 617 │ 360 240 -2.0944 -2.0944 0.0 true │ 618 │ 360 255 -1.8326 -1.8326 5.36197e-17 true │ 619 │ 360 270 -1.5708 -1.5708 0.0 true │ 620 │ 360 285 -1.309 -1.309 2.14479e-16 true │ 621 │ 360 300 -1.0472 -1.0472 4.80741e-16 true │ 622 │ 360 315 -0.785398 -0.785398 0.0 true │ 623 │ 360 330 -0.523599 -0.523599 4.32667e-16 true │ 624 │ 360 345 -0.261799 -0.261799 4.82577e-16 true └ 625 │ 360 360 0.0 0.0 0.0 true [ Info: fraction_ok 0.9968 >= 0.99? ┌ Info: 625×6 DataFrame │ Row │ θ1 θ2 Δθ Δθ_measured minimum_absolute_error ok │ │ Int64 Int64 Float64 Float64 Float64 Bool │ ─────┼──────────────────────────────────────────────────────────────────────── │ 1 │ 0 0 0.0 0.0 0.0 true │ 2 │ 0 15 0.261799 0.261799 2.14479e-16 true │ 3 │ 0 30 0.523599 0.523599 0.0 true │ 4 │ 0 45 0.785398 0.785398 0.0 true │ 5 │ 0 60 1.0472 1.0472 1.92296e-16 true │ 6 │ 0 75 1.309 1.309 1.60859e-16 true │ 7 │ 0 90 1.5708 1.5708 0.0 true │ 8 │ 0 105 1.8326 1.8326 4.28957e-16 true │ 9 │ 0 120 2.0944 2.0944 0.0 true │ 10 │ 0 135 2.35619 2.35619 0.0 true │ 11 │ 0 150 2.61799 2.61799 4.80741e-16 true │ 12 │ 0 165 2.87979 2.87979 4.82577e-16 true │ 13 │ 0 180 3.14159 -3.14159 2.44929e-16 true │ 14 │ 0 195 -2.87979 -2.87979 4.82577e-16 true │ 15 │ 0 210 -2.61799 -2.61799 4.32667e-16 true │ 16 │ 0 225 -2.35619 -2.35619 0.0 true │ 17 │ 0 240 -2.0944 -2.0944 4.80741e-16 true │ 18 │ 0 255 -1.8326 -1.8326 5.36197e-17 true │ 19 │ 0 270 -1.5708 -1.5708 4.44089e-16 true │ 20 │ 0 285 -1.309 -1.309 4.28957e-16 true │ 21 │ 0 300 -1.0472 -1.0472 2.88444e-16 true │ 22 │ 0 315 -0.785398 -0.785398 2.35514e-16 true │ 23 │ 0 330 -0.523599 -0.523599 7.21111e-16 true │ 24 │ 0 345 -0.261799 -0.261799 2.68098e-16 true │ 25 │ 0 360 -2.44929e-16 0.0 2.44929e-16 true │ 26 │ 15 0 -0.261799 -0.261799 2.68098e-16 true │ 27 │ 15 15 5.36197e-17 0.0 5.36197e-17 true │ 28 │ 15 30 0.261799 0.261799 2.14479e-16 true │ 29 │ 15 45 0.523599 0.436332 0.0872665 true │ 30 │ 15 60 0.785398 0.785398 7.85046e-17 true │ 31 │ 15 75 1.0472 1.0472 4.80741e-16 true │ 32 │ 15 90 1.309 1.309 1.60859e-16 true │ 33 │ 15 105 1.5708 1.5708 2.22045e-16 true │ 34 │ 15 120 1.8326 1.8326 2.14479e-16 true │ 35 │ 15 135 2.0944 2.00713 0.0872665 false │ 36 │ 15 150 2.35619 2.35619 0.0 true │ 37 │ 15 165 2.61799 2.61799 9.61481e-17 true │ 38 │ 15 180 2.87979 2.87979 5.36197e-17 true │ 39 │ 15 195 -3.14159 -3.14159 0.0 true │ 40 │ 15 210 -2.87979 -2.87979 5.36197e-17 true │ 41 │ 15 225 -2.61799 -2.70526 0.0872665 true │ 42 │ 15 240 -2.35619 -2.35619 4.71028e-16 true │ 43 │ 15 255 -2.0944 -2.0944 0.0 true │ 44 │ 15 270 -1.8326 -1.8326 4.82577e-16 true │ 45 │ 15 285 -1.5708 -1.5708 6.66134e-16 true │ 46 │ 15 300 -1.309 -1.309 2.68098e-16 true │ 47 │ 15 315 -1.0472 -1.13446 0.0872665 true │ 48 │ 15 330 -0.785398 -0.785398 6.28037e-16 true │ 49 │ 15 345 -0.523599 -0.523599 0.0 true │ 50 │ 15 360 -0.261799 -0.261799 0.0 true │ 51 │ 30 0 -0.523599 -0.523599 0.0 true │ 52 │ 30 15 -0.261799 -0.261799 2.14479e-16 true │ 53 │ 30 30 4.80741e-17 0.0 4.80741e-17 true │ 54 │ 30 45 0.261799 0.261799 1.60859e-16 true │ 55 │ 30 60 0.523599 0.523599 4.80741e-17 true │ 56 │ 30 75 0.785398 0.785398 1.57009e-16 true │ 57 │ 30 90 1.0472 1.0472 4.80741e-16 true │ 58 │ 30 105 1.309 1.309 0.0 true │ 59 │ 30 120 1.5708 1.5708 0.0 true │ 60 │ 30 135 1.8326 1.8326 4.28957e-16 true │ 61 │ 30 150 2.0944 2.0944 0.0 true │ 62 │ 30 165 2.35619 2.35619 0.0 true │ 63 │ 30 180 2.61799 2.61799 4.80741e-16 true │ 64 │ 30 195 2.87979 2.87979 5.36197e-17 true │ 65 │ 30 210 -3.14159 -3.14159 0.0 true │ 66 │ 30 225 -2.87979 -2.87979 4.82577e-16 true │ 67 │ 30 240 -2.61799 -2.61799 4.32667e-16 true │ 68 │ 30 255 -2.35619 -2.35619 0.0 true │ 69 │ 30 270 -2.0944 -2.0944 0.0 true │ 70 │ 30 285 -1.8326 -1.8326 6.43436e-16 true │ 71 │ 30 300 -1.5708 -1.5708 2.22045e-16 true │ 72 │ 30 315 -1.309 -1.309 2.14479e-16 true │ 73 │ 30 330 -1.0472 -1.0472 3.84593e-16 true │ 74 │ 30 345 -0.785398 -0.785398 1.57009e-16 true │ 75 │ 30 360 -0.523599 -0.523599 3.36518e-16 true │ 76 │ 45 0 -0.785398 -0.785398 0.0 true │ 77 │ 45 15 -0.523599 -0.523599 0.0 true │ 78 │ 45 30 -0.261799 -0.261799 2.68098e-16 true │ 79 │ 45 45 0.0 0.0 0.0 true │ 80 │ 45 60 0.261799 0.261799 2.14479e-16 true │ 81 │ 45 75 0.523599 0.523599 3.36518e-16 true │ 82 │ 45 90 0.785398 0.785398 0.0 true │ 83 │ 45 105 1.0472 1.0472 4.80741e-16 true │ 84 │ 45 120 1.309 1.309 4.82577e-16 true │ 85 │ 45 135 1.5708 1.5708 0.0 true │ 86 │ 45 150 1.8326 1.8326 7.50675e-16 true │ 87 │ 45 165 2.0944 2.0944 0.0 true │ 88 │ 45 180 2.35619 2.35619 0.0 true │ 89 │ 45 195 2.61799 2.61799 4.80741e-16 true │ 90 │ 45 210 2.87979 2.87979 5.36197e-17 true │ 91 │ 45 225 3.14159 -3.14159 2.44929e-16 true │ 92 │ 45 240 -2.87979 -2.87979 4.82577e-16 true │ 93 │ 45 255 -2.61799 -2.61799 4.32667e-16 true │ 94 │ 45 270 -2.35619 -2.35619 0.0 true │ 95 │ 45 285 -2.0944 -2.0944 4.80741e-16 true │ 96 │ 45 300 -1.8326 -1.8326 5.36197e-17 true │ 97 │ 45 315 -1.5708 -1.5708 4.44089e-16 true │ 98 │ 45 330 -1.309 -1.309 4.28957e-16 true │ 99 │ 45 345 -1.0472 -1.0472 4.80741e-16 true │ 100 │ 45 360 -0.785398 -0.785398 1.57009e-16 true │ 101 │ 60 0 -1.0472 -1.0472 2.88444e-16 true │ 102 │ 60 15 -0.785398 -0.785398 7.85046e-17 true │ 103 │ 60 30 -0.523599 -0.523599 0.0 true │ 104 │ 60 45 -0.261799 -0.261799 2.68098e-16 true │ 105 │ 60 60 0.0 0.0 0.0 true │ 106 │ 60 75 0.261799 0.261799 1.07239e-16 true │ 107 │ 60 90 0.523599 0.523599 2.4037e-16 true │ 108 │ 60 105 0.785398 0.785398 3.14018e-16 true │ 109 │ 60 120 1.0472 1.0472 1.92296e-16 true │ 110 │ 60 135 1.309 1.309 1.60859e-16 true │ 111 │ 60 150 1.5708 1.5708 4.44089e-16 true │ 112 │ 60 165 1.8326 1.8326 2.14479e-16 true │ 113 │ 60 180 2.0944 2.0944 0.0 true │ 114 │ 60 195 2.35619 2.35619 0.0 true │ 115 │ 60 210 2.61799 2.61799 9.13407e-16 true │ 116 │ 60 225 2.87979 2.87979 5.36197e-17 true │ 117 │ 60 240 3.14159 -3.14159 2.44929e-16 true │ 118 │ 60 255 -2.87979 -2.87979 5.36197e-17 true │ 119 │ 60 270 -2.61799 -2.61799 4.80741e-17 true │ 120 │ 60 285 -2.35619 -2.35619 4.71028e-16 true │ 121 │ 60 300 -2.0944 -2.0944 4.32667e-16 true │ 122 │ 60 315 -1.8326 -1.8326 1.60859e-16 true │ 123 │ 60 330 -1.5708 -1.5708 4.44089e-16 true │ 124 │ 60 345 -1.309 -1.309 2.68098e-16 true │ 125 │ 60 360 -1.0472 -1.0472 0.0 true │ 126 │ 75 0 -1.309 -1.309 5.36197e-17 true │ 127 │ 75 15 -1.0472 -1.0472 0.0 true │ 128 │ 75 30 -0.785398 -0.785398 1.57009e-16 true │ 129 │ 75 45 -0.523599 -0.523599 4.32667e-16 true │ 130 │ 75 60 -0.261799 -0.261799 1.07239e-16 true │ 131 │ 75 75 -5.36197e-17 0.0 5.36197e-17 true │ 132 │ 75 90 0.261799 0.261799 2.68098e-16 true │ 133 │ 75 105 0.523599 0.523599 0.0 true │ 134 │ 75 120 0.785398 0.785398 3.14018e-16 true │ 135 │ 75 135 1.0472 1.0472 1.92296e-16 true │ 136 │ 75 150 1.309 1.309 1.60859e-16 true │ 137 │ 75 165 1.5708 1.5708 2.22045e-16 true │ 138 │ 75 180 1.8326 1.8326 2.14479e-16 true │ 139 │ 75 195 2.0944 2.0944 0.0 true │ 140 │ 75 210 2.35619 2.35619 0.0 true │ 141 │ 75 225 2.61799 2.61799 9.61481e-17 true │ 142 │ 75 240 2.87979 2.87979 9.11534e-16 true │ 143 │ 75 255 -3.14159 -3.14159 0.0 true │ 144 │ 75 270 -2.87979 -2.87979 4.82577e-16 true │ 145 │ 75 285 -2.61799 -2.61799 4.32667e-16 true │ 146 │ 75 300 -2.35619 -2.35619 0.0 true │ 147 │ 75 315 -2.0944 -2.0944 0.0 true │ 148 │ 75 330 -1.8326 -1.8326 8.57914e-16 true │ 149 │ 75 345 -1.5708 -1.5708 0.0 true │ 150 │ 75 360 -1.309 -1.309 2.14479e-16 true │ 151 │ 90 0 -1.5708 -1.5708 0.0 true │ 152 │ 90 15 -1.309 -1.309 5.36197e-17 true │ 153 │ 90 30 -1.0472 -1.0472 0.0 true │ 154 │ 90 45 -0.785398 -0.785398 1.57009e-16 true │ 155 │ 90 60 -0.523599 -0.523599 3.36518e-16 true │ 156 │ 90 75 -0.261799 -0.261799 2.68098e-16 true │ 157 │ 90 90 0.0 0.0 0.0 true │ 158 │ 90 105 0.261799 0.261799 0.0 true │ 159 │ 90 120 0.523599 0.523599 0.0 true │ 160 │ 90 135 0.785398 0.785398 0.0 true │ 161 │ 90 150 1.0472 1.0472 4.80741e-16 true │ 162 │ 90 165 1.309 1.309 4.82577e-16 true │ 163 │ 90 180 1.5708 1.5708 0.0 true │ 164 │ 90 195 1.8326 1.8326 4.28957e-16 true │ 165 │ 90 210 2.0944 2.0944 0.0 true │ 166 │ 90 225 2.35619 2.35619 0.0 true │ 167 │ 90 240 2.61799 2.61799 9.61481e-17 true │ 168 │ 90 255 2.87979 2.87979 5.36197e-17 true │ 169 │ 90 270 3.14159 -3.14159 2.44929e-16 true │ 170 │ 90 285 -2.87979 -2.87979 4.82577e-16 true │ 171 │ 90 300 -2.61799 -2.61799 4.32667e-16 true │ 172 │ 90 315 -2.35619 -2.35619 4.71028e-16 true │ 173 │ 90 330 -2.0944 -2.0944 4.80741e-16 true │ 174 │ 90 345 -1.8326 -1.8326 5.36197e-17 true │ 175 │ 90 360 -1.5708 -1.5708 4.44089e-16 true │ 176 │ 105 0 -1.8326 -1.8326 1.60859e-16 true │ 177 │ 105 15 -1.5708 -1.5708 2.22045e-16 true │ 178 │ 105 30 -1.309 -1.309 2.14479e-16 true │ 179 │ 105 45 -1.0472 -1.13446 0.0872665 true │ 180 │ 105 60 -0.785398 -0.785398 2.35514e-16 true │ 181 │ 105 75 -0.523599 -0.523599 0.0 true │ 182 │ 105 90 -0.261799 -0.261799 1.07239e-16 true │ 183 │ 105 105 5.36197e-17 0.0 5.36197e-17 true │ 184 │ 105 120 0.261799 0.261799 4.28957e-16 true │ 185 │ 105 135 0.523599 0.436332 0.0872665 true │ 186 │ 105 150 0.785398 0.785398 1.57009e-16 true │ 187 │ 105 165 1.0472 1.0472 9.61481e-17 true │ 188 │ 105 180 1.309 1.309 4.82577e-16 true │ 189 │ 105 195 1.5708 1.5708 0.0 true │ 190 │ 105 210 1.8326 1.8326 4.28957e-16 true │ 191 │ 105 225 2.0944 2.00713 0.0872665 false │ 192 │ 105 240 2.35619 2.35619 4.71028e-16 true │ 193 │ 105 255 2.61799 2.61799 4.80741e-16 true │ 194 │ 105 270 2.87979 2.87979 4.82577e-16 true │ 195 │ 105 285 3.14159 -3.14159 6.89019e-16 true │ 196 │ 105 300 -2.87979 -2.87979 5.36197e-17 true │ 197 │ 105 315 -2.61799 -2.70526 0.0872665 true │ 198 │ 105 330 -2.35619 -2.35619 9.42055e-16 true │ 199 │ 105 345 -2.0944 -2.0944 0.0 true │ 200 │ 105 360 -1.8326 -1.8326 6.43436e-16 true │ 201 │ 120 0 -2.0944 -2.0944 0.0 true │ 202 │ 120 15 -1.8326 -1.8326 5.36197e-17 true │ 203 │ 120 30 -1.5708 -1.5708 0.0 true │ 204 │ 120 45 -1.309 -1.309 2.68098e-16 true │ 205 │ 120 60 -1.0472 -1.0472 2.88444e-16 true │ 206 │ 120 75 -0.785398 -0.785398 1.57009e-16 true │ 207 │ 120 90 -0.523599 -0.523599 0.0 true │ 208 │ 120 105 -0.261799 -0.261799 4.28957e-16 true │ 209 │ 120 120 -4.80741e-17 0.0 4.80741e-17 true │ 210 │ 120 135 0.261799 0.261799 1.07239e-16 true │ 211 │ 120 150 0.523599 0.523599 4.32667e-16 true │ 212 │ 120 165 0.785398 0.785398 0.0 true │ 213 │ 120 180 1.0472 1.0472 4.80741e-16 true │ 214 │ 120 195 1.309 1.309 0.0 true │ 215 │ 120 210 1.5708 1.5708 4.44089e-16 true │ 216 │ 120 225 1.8326 1.8326 4.28957e-16 true │ 217 │ 120 240 2.0944 2.0944 0.0 true │ 218 │ 120 255 2.35619 2.35619 4.71028e-16 true │ 219 │ 120 270 2.61799 2.61799 4.80741e-16 true │ 220 │ 120 285 2.87979 2.87979 4.82577e-16 true │ 221 │ 120 300 -3.14159 -3.14159 0.0 true │ 222 │ 120 315 -2.87979 -2.87979 4.82577e-16 true │ 223 │ 120 330 -2.61799 -2.61799 4.32667e-16 true │ 224 │ 120 345 -2.35619 -2.35619 0.0 true │ 225 │ 120 360 -2.0944 -2.0944 0.0 true │ 226 │ 135 0 -2.35619 -2.35619 0.0 true │ 227 │ 135 15 -2.0944 -2.0944 0.0 true │ 228 │ 135 30 -1.8326 -1.8326 1.60859e-16 true │ 229 │ 135 45 -1.5708 -1.5708 0.0 true │ 230 │ 135 60 -1.309 -1.309 5.36197e-17 true │ 231 │ 135 75 -1.0472 -1.0472 2.88444e-16 true │ 232 │ 135 90 -0.785398 -0.785398 0.0 true │ 233 │ 135 105 -0.523599 -0.523599 0.0 true │ 234 │ 135 120 -0.261799 -0.261799 1.07239e-16 true │ 235 │ 135 135 0.0 0.0 0.0 true │ 236 │ 135 150 0.261799 0.261799 1.07239e-16 true │ 237 │ 135 165 0.523599 0.523599 0.0 true │ 238 │ 135 180 0.785398 0.785398 0.0 true │ 239 │ 135 195 1.0472 1.0472 4.80741e-16 true │ 240 │ 135 210 1.309 1.309 0.0 true │ 241 │ 135 225 1.5708 1.5708 0.0 true │ 242 │ 135 240 1.8326 1.8326 2.14479e-16 true │ 243 │ 135 255 2.0944 2.0944 0.0 true │ 244 │ 135 270 2.35619 2.35619 0.0 true │ 245 │ 135 285 2.61799 2.61799 9.61481e-17 true │ 246 │ 135 300 2.87979 2.87979 5.36197e-17 true │ 247 │ 135 315 3.14159 -3.14159 2.44929e-16 true │ 248 │ 135 330 -2.87979 -2.87979 4.82577e-16 true │ 249 │ 135 345 -2.61799 -2.61799 4.32667e-16 true │ 250 │ 135 360 -2.35619 -2.35619 0.0 true │ 251 │ 150 0 -2.61799 -2.61799 4.80741e-17 true │ 252 │ 150 15 -2.35619 -2.35619 0.0 true │ 253 │ 150 30 -2.0944 -2.0944 0.0 true │ 254 │ 150 45 -1.8326 -1.8326 4.82577e-16 true │ 255 │ 150 60 -1.5708 -1.5708 4.44089e-16 true │ 256 │ 150 75 -1.309 -1.309 5.36197e-17 true │ 257 │ 150 90 -1.0472 -1.0472 0.0 true │ 258 │ 150 105 -0.785398 -0.785398 0.0 true │ 259 │ 150 120 -0.523599 -0.523599 4.32667e-16 true │ 260 │ 150 135 -0.261799 -0.261799 1.07239e-16 true │ 261 │ 150 150 -4.80741e-17 0.0 4.80741e-17 true │ 262 │ 150 165 0.261799 0.261799 5.36197e-16 true │ 263 │ 150 180 0.523599 0.523599 0.0 true │ 264 │ 150 195 0.785398 0.785398 0.0 true │ 265 │ 150 210 1.0472 1.0472 4.80741e-16 true │ 266 │ 150 225 1.309 1.309 4.82577e-16 true │ 267 │ 150 240 1.5708 1.5708 4.44089e-16 true │ 268 │ 150 255 1.8326 1.8326 7.50675e-16 true │ 269 │ 150 270 2.0944 2.0944 0.0 true │ 270 │ 150 285 2.35619 2.35619 4.71028e-16 true │ 271 │ 150 300 2.61799 2.61799 4.80741e-16 true │ 272 │ 150 315 2.87979 2.87979 4.82577e-16 true │ 273 │ 150 330 3.14159 -3.14159 6.89019e-16 true │ 274 │ 150 345 -2.87979 -2.87979 4.82577e-16 true │ 275 │ 150 360 -2.61799 -2.61799 4.32667e-16 true │ 276 │ 165 0 -2.87979 -2.87979 5.36197e-17 true │ 277 │ 165 15 -2.61799 -2.61799 4.32667e-16 true │ 278 │ 165 30 -2.35619 -2.35619 0.0 true │ 279 │ 165 45 -2.0944 -2.0944 0.0 true │ 280 │ 165 60 -1.8326 -1.8326 5.36197e-17 true │ 281 │ 165 75 -1.5708 -1.5708 2.22045e-16 true │ 282 │ 165 90 -1.309 -1.309 2.68098e-16 true │ 283 │ 165 105 -1.0472 -1.0472 4.80741e-16 true │ 284 │ 165 120 -0.785398 -0.785398 0.0 true │ 285 │ 165 135 -0.523599 -0.523599 0.0 true │ 286 │ 165 150 -0.261799 -0.261799 5.36197e-16 true │ 287 │ 165 165 5.36197e-17 0.0 5.36197e-17 true │ 288 │ 165 180 0.261799 0.261799 1.07239e-16 true │ 289 │ 165 195 0.523599 0.523599 4.32667e-16 true │ 290 │ 165 210 0.785398 0.785398 4.71028e-16 true │ 291 │ 165 225 1.0472 1.0472 4.80741e-16 true │ 292 │ 165 240 1.309 1.309 4.82577e-16 true │ 293 │ 165 255 1.5708 1.5708 6.66134e-16 true │ 294 │ 165 270 1.8326 1.8326 7.50675e-16 true │ 295 │ 165 285 2.0944 2.0944 0.0 true │ 296 │ 165 300 2.35619 2.35619 4.71028e-16 true │ 297 │ 165 315 2.61799 2.61799 4.80741e-16 true │ 298 │ 165 330 2.87979 2.87979 4.82577e-16 true │ 299 │ 165 345 -3.14159 -3.14159 4.44089e-16 true │ 300 │ 165 360 -2.87979 -2.87979 5.36197e-17 true │ 301 │ 180 0 -3.14159 -3.14159 0.0 true │ 302 │ 180 15 -2.87979 -2.87979 5.36197e-17 true │ 303 │ 180 30 -2.61799 -2.61799 4.80741e-17 true │ 304 │ 180 45 -2.35619 -2.35619 0.0 true │ 305 │ 180 60 -2.0944 -2.0944 0.0 true │ 306 │ 180 75 -1.8326 -1.8326 5.36197e-17 true │ 307 │ 180 90 -1.5708 -1.5708 0.0 true │ 308 │ 180 105 -1.309 -1.309 2.68098e-16 true │ 309 │ 180 120 -1.0472 -1.0472 0.0 true │ 310 │ 180 135 -0.785398 -0.785398 0.0 true │ 311 │ 180 150 -0.523599 -0.523599 0.0 true │ 312 │ 180 165 -0.261799 -0.261799 0.0 true │ 313 │ 180 180 0.0 0.0 0.0 true │ 314 │ 180 195 0.261799 0.261799 0.0 true │ 315 │ 180 210 0.523599 0.523599 4.32667e-16 true │ 316 │ 180 225 0.785398 0.785398 0.0 true │ 317 │ 180 240 1.0472 1.0472 9.61481e-17 true │ 318 │ 180 255 1.309 1.309 0.0 true │ 319 │ 180 270 1.5708 1.5708 0.0 true │ 320 │ 180 285 1.8326 1.8326 2.14479e-16 true │ 321 │ 180 300 2.0944 2.0944 0.0 true │ 322 │ 180 315 2.35619 2.35619 0.0 true │ 323 │ 180 330 2.61799 2.61799 9.61481e-17 true │ 324 │ 180 345 2.87979 2.87979 5.36197e-17 true │ 325 │ 180 360 3.14159 -3.14159 2.44929e-16 true │ 326 │ 195 0 2.87979 2.87979 5.36197e-17 true │ 327 │ 195 15 3.14159 -3.14159 2.44929e-16 true │ 328 │ 195 30 -2.87979 -2.87979 4.82577e-16 true │ 329 │ 195 45 -2.61799 -2.70526 0.0872665 true │ 330 │ 195 60 -2.35619 -2.35619 0.0 true │ 331 │ 195 75 -2.0944 -2.0944 0.0 true │ 332 │ 195 90 -1.8326 -1.8326 1.60859e-16 true │ 333 │ 195 105 -1.5708 -1.5708 0.0 true │ 334 │ 195 120 -1.309 -1.309 2.14479e-16 true │ 335 │ 195 135 -1.0472 -1.13446 0.0872665 true │ 336 │ 195 150 -0.785398 -0.785398 0.0 true │ 337 │ 195 165 -0.523599 -0.523599 4.32667e-16 true │ 338 │ 195 180 -0.261799 -0.261799 1.07239e-16 true │ 339 │ 195 195 5.36197e-17 0.0 5.36197e-17 true │ 340 │ 195 210 0.261799 0.261799 1.07239e-16 true │ 341 │ 195 225 0.523599 0.436332 0.0872665 true │ 342 │ 195 240 0.785398 0.785398 3.14018e-16 true │ 343 │ 195 255 1.0472 1.0472 4.80741e-16 true │ 344 │ 195 270 1.309 1.309 4.82577e-16 true │ 345 │ 195 285 1.5708 1.5708 4.44089e-16 true │ 346 │ 195 300 1.8326 1.8326 4.28957e-16 true │ 347 │ 195 315 2.0944 2.00713 0.0872665 false │ 348 │ 195 330 2.35619 2.35619 4.71028e-16 true │ 349 │ 195 345 2.61799 2.61799 4.80741e-16 true │ 350 │ 195 360 2.87979 2.87979 4.82577e-16 true │ 351 │ 210 0 2.61799 2.61799 9.61481e-17 true │ 352 │ 210 15 2.87979 2.87979 4.82577e-16 true │ 353 │ 210 30 3.14159 -3.14159 2.44929e-16 true │ 354 │ 210 45 -2.87979 -2.87979 4.82577e-16 true │ 355 │ 210 60 -2.61799 -2.61799 4.32667e-16 true │ 356 │ 210 75 -2.35619 -2.35619 0.0 true │ 357 │ 210 90 -2.0944 -2.0944 0.0 true │ 358 │ 210 105 -1.8326 -1.8326 1.60859e-16 true │ 359 │ 210 120 -1.5708 -1.5708 4.44089e-16 true │ 360 │ 210 135 -1.309 -1.309 2.14479e-16 true │ 361 │ 210 150 -1.0472 -1.0472 0.0 true │ 362 │ 210 165 -0.785398 -0.785398 4.71028e-16 true │ 363 │ 210 180 -0.523599 -0.523599 4.32667e-16 true │ 364 │ 210 195 -0.261799 -0.261799 0.0 true │ 365 │ 210 210 0.0 0.0 0.0 true │ 366 │ 210 225 0.261799 0.261799 4.28957e-16 true │ 367 │ 210 240 0.523599 0.523599 4.32667e-16 true │ 368 │ 210 255 0.785398 0.785398 0.0 true │ 369 │ 210 270 1.0472 1.0472 9.61481e-17 true │ 370 │ 210 285 1.309 1.309 9.11534e-16 true │ 371 │ 210 300 1.5708 1.5708 0.0 true │ 372 │ 210 315 1.8326 1.8326 2.14479e-16 true │ 373 │ 210 330 2.0944 2.0944 9.13407e-16 true │ 374 │ 210 345 2.35619 2.35619 0.0 true │ 375 │ 210 360 2.61799 2.61799 9.61481e-17 true │ 376 │ 225 0 2.35619 2.35619 0.0 true │ 377 │ 225 15 2.61799 2.61799 4.80741e-16 true │ 378 │ 225 30 2.87979 2.87979 5.36197e-17 true │ 379 │ 225 45 -3.14159 -3.14159 0.0 true │ 380 │ 225 60 -2.87979 -2.87979 4.82577e-16 true │ 381 │ 225 75 -2.61799 -2.61799 4.32667e-16 true │ 382 │ 225 90 -2.35619 -2.35619 0.0 true │ 383 │ 225 105 -2.0944 -2.0944 0.0 true │ 384 │ 225 120 -1.8326 -1.8326 1.60859e-16 true │ 385 │ 225 135 -1.5708 -1.5708 0.0 true │ 386 │ 225 150 -1.309 -1.309 2.68098e-16 true │ 387 │ 225 165 -1.0472 -1.0472 0.0 true │ 388 │ 225 180 -0.785398 -0.785398 0.0 true │ 389 │ 225 195 -0.523599 -0.523599 0.0 true │ 390 │ 225 210 -0.261799 -0.261799 4.82577e-16 true │ 391 │ 225 225 0.0 0.0 0.0 true │ 392 │ 225 240 0.261799 0.261799 4.28957e-16 true │ 393 │ 225 255 0.523599 0.523599 4.32667e-16 true │ 394 │ 225 270 0.785398 0.785398 0.0 true │ 395 │ 225 285 1.0472 1.0472 1.92296e-16 true │ 396 │ 225 300 1.309 1.309 0.0 true │ 397 │ 225 315 1.5708 1.5708 0.0 true │ 398 │ 225 330 1.8326 1.8326 2.14479e-16 true │ 399 │ 225 345 2.0944 2.0944 0.0 true │ 400 │ 225 360 2.35619 2.35619 0.0 true │ 401 │ 240 0 2.0944 2.0944 4.80741e-16 true │ 402 │ 240 15 2.35619 2.35619 4.71028e-16 true │ 403 │ 240 30 2.61799 2.61799 9.13407e-16 true │ 404 │ 240 45 2.87979 2.87979 5.36197e-17 true │ 405 │ 240 60 -3.14159 -3.14159 4.44089e-16 true │ 406 │ 240 75 -2.87979 -2.87979 4.28957e-16 true │ 407 │ 240 90 -2.61799 -2.61799 4.32667e-16 true │ 408 │ 240 105 -2.35619 -2.35619 4.71028e-16 true │ 409 │ 240 120 -2.0944 -2.0944 4.32667e-16 true │ 410 │ 240 135 -1.8326 -1.8326 2.14479e-16 true │ 411 │ 240 150 -1.5708 -1.5708 4.44089e-16 true │ 412 │ 240 165 -1.309 -1.309 2.68098e-16 true │ 413 │ 240 180 -1.0472 -1.0472 4.80741e-16 true │ 414 │ 240 195 -0.785398 -0.785398 3.14018e-16 true │ 415 │ 240 210 -0.523599 -0.523599 4.32667e-16 true │ 416 │ 240 225 -0.261799 -0.261799 4.82577e-16 true │ 417 │ 240 240 0.0 0.0 0.0 true │ 418 │ 240 255 0.261799 0.261799 4.82577e-16 true │ 419 │ 240 270 0.523599 0.523599 4.32667e-16 true │ 420 │ 240 285 0.785398 0.785398 0.0 true │ 421 │ 240 300 1.0472 1.0472 9.13407e-16 true │ 422 │ 240 315 1.309 1.309 0.0 true │ 423 │ 240 330 1.5708 1.5708 0.0 true │ 424 │ 240 345 1.8326 1.8326 1.07239e-15 true │ 425 │ 240 360 2.0944 2.0944 0.0 true │ 426 │ 255 0 1.8326 1.8326 2.14479e-16 true │ 427 │ 255 15 2.0944 2.0944 0.0 true │ 428 │ 255 30 2.35619 2.35619 0.0 true │ 429 │ 255 45 2.61799 2.61799 9.61481e-17 true │ 430 │ 255 60 2.87979 2.87979 4.82577e-16 true │ 431 │ 255 75 3.14159 -3.14159 2.44929e-16 true │ 432 │ 255 90 -2.87979 -2.87979 4.82577e-16 true │ 433 │ 255 105 -2.61799 -2.61799 4.80741e-17 true │ 434 │ 255 120 -2.35619 -2.35619 4.71028e-16 true │ 435 │ 255 135 -2.0944 -2.0944 0.0 true │ 436 │ 255 150 -1.8326 -1.8326 1.60859e-16 true │ 437 │ 255 165 -1.5708 -1.5708 6.66134e-16 true │ 438 │ 255 180 -1.309 -1.309 2.14479e-16 true │ 439 │ 255 195 -1.0472 -1.0472 0.0 true │ 440 │ 255 210 -0.785398 -0.785398 1.57009e-16 true │ 441 │ 255 225 -0.523599 -0.523599 4.32667e-16 true │ 442 │ 255 240 -0.261799 -0.261799 4.82577e-16 true │ 443 │ 255 255 -5.36197e-17 0.0 5.36197e-17 true │ 444 │ 255 270 0.261799 0.261799 4.82577e-16 true │ 445 │ 255 285 0.523599 0.523599 5.28815e-16 true │ 446 │ 255 300 0.785398 0.785398 0.0 true │ 447 │ 255 315 1.0472 1.0472 9.61481e-17 true │ 448 │ 255 330 1.309 1.309 9.11534e-16 true │ 449 │ 255 345 1.5708 1.5708 0.0 true │ 450 │ 255 360 1.8326 1.8326 2.14479e-16 true │ 451 │ 270 0 1.5708 1.5708 4.44089e-16 true │ 452 │ 270 15 1.8326 1.8326 7.50675e-16 true │ 453 │ 270 30 2.0944 2.0944 0.0 true │ 454 │ 270 45 2.35619 2.35619 0.0 true │ 455 │ 270 60 2.61799 2.61799 4.80741e-16 true │ 456 │ 270 75 2.87979 2.87979 5.36197e-17 true │ 457 │ 270 90 -3.14159 -3.14159 0.0 true │ 458 │ 270 105 -2.87979 -2.87979 5.36197e-17 true │ 459 │ 270 120 -2.61799 -2.61799 4.80741e-17 true │ 460 │ 270 135 -2.35619 -2.35619 0.0 true │ 461 │ 270 150 -2.0944 -2.0944 4.32667e-16 true │ 462 │ 270 165 -1.8326 -1.8326 4.82577e-16 true │ 463 │ 270 180 -1.5708 -1.5708 0.0 true │ 464 │ 270 195 -1.309 -1.309 2.68098e-16 true │ 465 │ 270 210 -1.0472 -1.0472 4.80741e-16 true │ 466 │ 270 225 -0.785398 -0.785398 0.0 true │ 467 │ 270 240 -0.523599 -0.523599 4.32667e-16 true │ 468 │ 270 255 -0.261799 -0.261799 4.82577e-16 true │ 469 │ 270 270 0.0 0.0 0.0 true │ 470 │ 270 285 0.261799 0.261799 4.82577e-16 true │ 471 │ 270 300 0.523599 0.523599 4.32667e-16 true │ 472 │ 270 315 0.785398 0.785398 0.0 true │ 473 │ 270 330 1.0472 1.0472 9.61481e-17 true │ 474 │ 270 345 1.309 1.309 0.0 true │ 475 │ 270 360 1.5708 1.5708 0.0 true │ 476 │ 285 0 1.309 1.309 2.14479e-16 true │ 477 │ 285 15 1.5708 1.5708 6.66134e-16 true │ 478 │ 285 30 1.8326 1.8326 9.11534e-16 true │ 479 │ 285 45 2.0944 2.00713 0.0872665 false │ 480 │ 285 60 2.35619 2.35619 4.71028e-16 true │ 481 │ 285 75 2.61799 2.61799 9.13407e-16 true │ 482 │ 285 90 2.87979 2.87979 5.36197e-17 true │ 483 │ 285 105 -3.14159 -3.14159 4.44089e-16 true │ 484 │ 285 120 -2.87979 -2.87979 5.36197e-17 true │ 485 │ 285 135 -2.61799 -2.70526 0.0872665 false │ 486 │ 285 150 -2.35619 -2.35619 4.71028e-16 true │ 487 │ 285 165 -2.0944 -2.0944 0.0 true │ 488 │ 285 180 -1.8326 -1.8326 5.36197e-17 true │ 489 │ 285 195 -1.5708 -1.5708 4.44089e-16 true │ 490 │ 285 210 -1.309 -1.309 6.97056e-16 true │ 491 │ 285 225 -1.0472 -1.13446 0.0872665 false │ 492 │ 285 240 -0.785398 -0.785398 0.0 true │ 493 │ 285 255 -0.523599 -0.523599 4.32667e-16 true │ 494 │ 285 270 -0.261799 -0.261799 4.82577e-16 true │ 495 │ 285 285 0.0 0.0 0.0 true │ 496 │ 285 300 0.261799 0.261799 4.82577e-16 true │ 497 │ 285 315 0.523599 0.436332 0.0872665 false │ 498 │ 285 330 0.785398 0.785398 0.0 true │ 499 │ 285 345 1.0472 1.0472 9.13407e-16 true │ 500 │ 285 360 1.309 1.309 0.0 true │ 501 │ 300 0 1.0472 1.0472 1.92296e-16 true │ 502 │ 300 15 1.309 1.309 4.82577e-16 true │ 503 │ 300 30 1.5708 1.5708 2.22045e-16 true │ 504 │ 300 45 1.8326 1.8326 2.14479e-16 true │ 505 │ 300 60 2.0944 2.0944 4.32667e-16 true │ 506 │ 300 75 2.35619 2.35619 0.0 true │ 507 │ 300 90 2.61799 2.61799 9.61481e-17 true │ 508 │ 300 105 2.87979 2.87979 4.82577e-16 true │ 509 │ 300 120 3.14159 -3.14159 6.89019e-16 true │ 510 │ 300 135 -2.87979 -2.87979 4.82577e-16 true │ 511 │ 300 150 -2.61799 -2.61799 4.80741e-17 true │ 512 │ 300 165 -2.35619 -2.35619 4.71028e-16 true │ 513 │ 300 180 -2.0944 -2.0944 0.0 true │ 514 │ 300 195 -1.8326 -1.8326 1.60859e-16 true │ 515 │ 300 210 -1.5708 -1.5708 0.0 true │ 516 │ 300 225 -1.309 -1.309 2.14479e-16 true │ 517 │ 300 240 -1.0472 -1.0472 3.84593e-16 true │ 518 │ 300 255 -0.785398 -0.785398 1.57009e-16 true │ 519 │ 300 270 -0.523599 -0.523599 5.28815e-16 true │ 520 │ 300 285 -0.261799 -0.261799 4.82577e-16 true │ 521 │ 300 300 0.0 0.0 0.0 true │ 522 │ 300 315 0.261799 0.261799 5.36197e-16 true │ 523 │ 300 330 0.523599 0.523599 5.28815e-16 true │ 524 │ 300 345 0.785398 0.785398 0.0 true │ 525 │ 300 360 1.0472 1.0472 9.61481e-17 true │ 526 │ 315 0 0.785398 0.785398 2.35514e-16 true │ 527 │ 315 15 1.0472 1.0472 7.21111e-16 true │ 528 │ 315 30 1.309 1.309 1.60859e-16 true │ 529 │ 315 45 1.5708 1.5708 4.44089e-16 true │ 530 │ 315 60 1.8326 1.8326 4.28957e-16 true │ 531 │ 315 75 2.0944 2.0944 0.0 true │ 532 │ 315 90 2.35619 2.35619 0.0 true │ 533 │ 315 105 2.61799 2.61799 9.13407e-16 true │ 534 │ 315 120 2.87979 2.87979 5.36197e-17 true │ 535 │ 315 135 -3.14159 -3.14159 0.0 true │ 536 │ 315 150 -2.87979 -2.87979 5.36197e-17 true │ 537 │ 315 165 -2.61799 -2.61799 4.80741e-17 true │ 538 │ 315 180 -2.35619 -2.35619 0.0 true │ 539 │ 315 195 -2.0944 -2.0944 4.32667e-16 true │ 540 │ 315 210 -1.8326 -1.8326 5.36197e-17 true │ 541 │ 315 225 -1.5708 -1.5708 0.0 true │ 542 │ 315 240 -1.309 -1.309 2.14479e-16 true │ 543 │ 315 255 -1.0472 -1.0472 4.80741e-16 true │ 544 │ 315 270 -0.785398 -0.785398 0.0 true │ 545 │ 315 285 -0.523599 -0.523599 4.32667e-16 true │ 546 │ 315 300 -0.261799 -0.261799 4.82577e-16 true │ 547 │ 315 315 0.0 0.0 0.0 true │ 548 │ 315 330 0.261799 0.261799 4.82577e-16 true │ 549 │ 315 345 0.523599 0.523599 4.32667e-16 true │ 550 │ 315 360 0.785398 0.785398 0.0 true │ 551 │ 330 0 0.523599 0.523599 7.21111e-16 true │ 552 │ 330 15 0.785398 0.785398 6.28037e-16 true │ 553 │ 330 30 1.0472 1.0472 9.13407e-16 true │ 554 │ 330 45 1.309 1.309 2.14479e-16 true │ 555 │ 330 60 1.5708 1.5708 4.44089e-16 true │ 556 │ 330 75 1.8326 1.8326 1.07239e-15 true │ 557 │ 330 90 2.0944 2.0944 4.80741e-16 true │ 558 │ 330 105 2.35619 2.35619 9.42055e-16 true │ 559 │ 330 120 2.61799 2.61799 9.13407e-16 true │ 560 │ 330 135 2.87979 2.87979 4.28957e-16 true │ 561 │ 330 150 -3.14159 -3.14159 4.44089e-16 true │ 562 │ 330 165 -2.87979 -2.87979 5.36197e-17 true │ 563 │ 330 180 -2.61799 -2.61799 4.32667e-16 true │ 564 │ 330 195 -2.35619 -2.44346 0.0872665 false │ 565 │ 330 210 -2.0944 -2.0944 9.13407e-16 true │ 566 │ 330 225 -1.8326 -1.8326 5.36197e-17 true │ 567 │ 330 240 -1.5708 -1.5708 0.0 true │ 568 │ 330 255 -1.309 -1.309 6.97056e-16 true │ 569 │ 330 270 -1.0472 -1.0472 4.80741e-16 true │ 570 │ 330 285 -0.785398 -0.785398 0.0 true │ 571 │ 330 300 -0.523599 -0.523599 5.28815e-16 true │ 572 │ 330 315 -0.261799 -0.261799 5.36197e-16 true │ 573 │ 330 330 0.0 0.0 0.0 true │ 574 │ 330 345 0.261799 0.261799 4.82577e-16 true │ 575 │ 330 360 0.523599 0.523599 5.28815e-16 true │ 576 │ 345 0 0.261799 0.261799 2.68098e-16 true │ 577 │ 345 15 0.523599 0.523599 0.0 true │ 578 │ 345 30 0.785398 0.785398 7.85046e-17 true │ 579 │ 345 45 1.0472 1.0472 1.92296e-16 true │ 580 │ 345 60 1.309 1.309 4.82577e-16 true │ 581 │ 345 75 1.5708 1.5708 0.0 true │ 582 │ 345 90 1.8326 1.8326 2.14479e-16 true │ 583 │ 345 105 2.0944 2.0944 0.0 true │ 584 │ 345 120 2.35619 2.35619 0.0 true │ 585 │ 345 135 2.61799 2.61799 9.61481e-17 true │ 586 │ 345 150 2.87979 2.87979 5.36197e-17 true │ 587 │ 345 165 3.14159 -3.14159 6.89019e-16 true │ 588 │ 345 180 -2.87979 -2.87979 4.82577e-16 true │ 589 │ 345 195 -2.61799 -2.61799 4.80741e-17 true │ 590 │ 345 210 -2.35619 -2.35619 0.0 true │ 591 │ 345 225 -2.0944 -2.0944 0.0 true │ 592 │ 345 240 -1.8326 -1.8326 8.57914e-16 true │ 593 │ 345 255 -1.5708 -1.5708 0.0 true │ 594 │ 345 270 -1.309 -1.309 2.14479e-16 true │ 595 │ 345 285 -1.0472 -1.0472 3.84593e-16 true │ 596 │ 345 300 -0.785398 -0.785398 0.0 true │ 597 │ 345 315 -0.523599 -0.523599 5.28815e-16 true │ 598 │ 345 330 -0.261799 -0.261799 4.82577e-16 true │ 599 │ 345 345 -5.36197e-17 0.0 5.36197e-17 true │ 600 │ 345 360 0.261799 0.261799 4.82577e-16 true │ 601 │ 360 0 2.44929e-16 0.0 2.44929e-16 true │ 602 │ 360 15 0.261799 0.261799 1.07239e-16 true │ 603 │ 360 30 0.523599 0.523599 3.36518e-16 true │ 604 │ 360 45 0.785398 0.785398 2.35514e-16 true │ 605 │ 360 60 1.0472 1.0472 4.80741e-16 true │ 606 │ 360 75 1.309 1.309 0.0 true │ 607 │ 360 90 1.5708 1.5708 4.44089e-16 true │ 608 │ 360 105 1.8326 1.8326 9.11534e-16 true │ 609 │ 360 120 2.0944 2.0944 0.0 true │ 610 │ 360 135 2.35619 2.35619 4.71028e-16 true │ 611 │ 360 150 2.61799 2.61799 9.13407e-16 true │ 612 │ 360 165 2.87979 2.87979 5.36197e-17 true │ 613 │ 360 180 -3.14159 -3.14159 0.0 true │ 614 │ 360 195 -2.87979 -2.87979 5.36197e-17 true │ 615 │ 360 210 -2.61799 -2.61799 4.32667e-16 true │ 616 │ 360 225 -2.35619 -2.35619 0.0 true │ 617 │ 360 240 -2.0944 -2.0944 0.0 true │ 618 │ 360 255 -1.8326 -1.8326 5.36197e-17 true │ 619 │ 360 270 -1.5708 -1.5708 0.0 true │ 620 │ 360 285 -1.309 -1.309 2.14479e-16 true │ 621 │ 360 300 -1.0472 -1.0472 4.80741e-16 true │ 622 │ 360 315 -0.785398 -0.785398 0.0 true │ 623 │ 360 330 -0.523599 -0.523599 4.32667e-16 true │ 624 │ 360 345 -0.261799 -0.261799 4.82577e-16 true └ 625 │ 360 360 0.0 0.0 0.0 true [ Info: fraction_ok 0.9872 >= 0.98? ------------------------------------------------- -----------registration mismatch tests----------- ------------------------------------------------- --------------------defaults--------------------- ------------------------------------------------- -------------------test_angles------------------- ------------------------------------------------- -------------------mxrot, step------------------- ------------------------------------------------- ------------ resample_boundary Tests -------------- ------------------------------------------------ ------------ Create Segmentation-A Test -------------- ---------- Segment Image - Direct Method ------------ 18.467502 seconds (136.15 k allocations: 1.999 GiB, 9.21% gc time, 3.46% compilation time) 16.290514 seconds (40.34 M allocations: 2.998 GiB, 3.87% gc time, 13.62% compilation time) 15.974989 seconds (189 allocations: 1.979 GiB, 10.25% gc time, 0.04% compilation time) [ Info: Test passed with 0.03881857232375131 mismatch with threshold 0.039 [ Info: Test passed with 0.0500808514820341 mismatch with threshold 0.051 [ Info: Test passed with 8.808560561878439e-5 mismatch with threshold 0.001 ------------------------------------------------ ------------ Create Segmentation-B Test -------------- 18.084139 seconds (3.07 M allocations: 879.984 MiB, 1.64% gc time, 86.31% compilation time) ┌ Info: Persisting image to ./test_outputs/segB_not_ice_mask-2025-09-05-050200.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/segB_ice_mask-2025-09-05-050200.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/matlab_not_ice_mask-2025-09-05-050200.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/matlab_ice_intersect-2025-09-05-050200.png. └ To load the persisted object use `Images.load(img_path)` [ Info: Test passed with 0.0 mismatch with threshold 0.001 [ Info: Test passed with 0.004319757874795137 mismatch with threshold 0.005 ------------------------------------------------ --------- Create Segmentation-F Test ----------- [ Info: Done with k-means segmentation 21.778986 seconds (6.95 M allocations: 454.263 MiB, 96.56% compilation time) ┌ Info: Persisting image to ./test_outputs/isolated_floes-2025-09-05-050223.png. └ To load the persisted object use `Images.load(img_path)` ┌ Info: Persisting image to ./test_outputs/matlab_isolated_floes-2025-09-05-050223.png. └ To load the persisted object use `Images.load(img_path)` [ Info: Test passed with 0.012415417473402662 mismatch with threshold 0.013 ------------------------------------------------- -----test-segmentation-lopez-acosta-2019.jl------ ------------------------------------------------- ----------------Lopez-Acosta 2019---------------- ------------------------------------------------- -------------------Smoke test-------------------- [ Info: Image type: n0f8 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: n6f10 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: n4f12 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: n2f14 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: n0f16 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: float32 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 [ Info: Image type: float64 [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 201×201 Matrix{Int64} number of labels: 10 ------------------------------------------------- --------------------Full size-------------------- [ Info: building landmask [ Info: Building cloudmask [ Info: Finding ice labels [ Info: Sharpening truecolor image [ Info: Normalizing truecolor image [ Info: Discriminating ice/water [ Info: Segmenting floes part 1/3 [ Info: Segmenting floes part 2/3 [ Info: Building watersheds [ Info: Segmenting floes part 3/3 [ Info: Done with k-means segmentation [ Info: Labeling floes segments = Segmented Image with: labels map: 403×1052 Matrix{Int64} number of labels: 44 ------------------------------------------------ ------------ Create Segmentation-Watershed Test -------------- ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 653 running 1 of 1 signal (10): User defined signal 1 __munmap at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) __libc_free at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) jl_free_aligned at /source/src/gc-common.h:132 [inlined] jl_gc_free_memory at /source/src/gc-stock.c:637 [inlined] sweep_malloced_memory at /source/src/gc-stock.c:664 [inlined] gc_sweep_other at /source/src/gc-stock.c:995 [inlined] _jl_gc_collect at /source/src/gc-stock.c:3193 ijl_gc_collect at /source/src/gc-stock.c:3489 maybe_collect at /source/src/gc-stock.c:349 [inlined] jl_gc_small_alloc_inner at /source/src/gc-stock.c:725 jl_gc_small_alloc_noinline at /source/src/gc-stock.c:783 [inlined] jl_gc_alloc_ at /source/src/gc-stock.c:797 jl_alloc_genericmemory_unchecked at /source/src/genericmemory.c:41 GenericMemory at ./boot.jl:588 [inlined] new_as_memoryref at ./boot.jl:605 [inlined] Array at ./boot.jl:652 [inlined] similar at ./array.jl:378 [inlined] #erode#37 at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:55 [inlined] erode at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:55 [inlined] hmin_transform at /home/pkgeval/.julia/packages/ImageSegmentation/T47s5/src/watershed.jl:155 watershed_ice_floes at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/src/segmentation_watershed.jl:10 unknown function (ip: 0x70482b3ddd82) at (unknown file) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 macro expansion at ./timing.jl:645 [inlined] macro expansion at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-segmentation-watershed.jl:17 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] top-level scope at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-segmentation-watershed.jl:2 _jl_invoke at /source/src/gf.c:4001 [inlined] ijl_invoke at /source/src/gf.c:4008 jl_toplevel_eval_flex at /source/src/toplevel.c:761 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2865 _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 _include at ./loading.jl:2925 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 unknown function (ip: 0x704844a99022) at (unknown file) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 macro expansion at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:79 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] top-level scope at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:78 _jl_invoke at /source/src/gf.c:4001 [inlined] ijl_invoke at /source/src/gf.c:4008 jl_toplevel_eval_flex at /source/src/toplevel.c:761 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2865 _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 _include at ./loading.jl:2925 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 jfptr_IncludeInto_36079.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 exec_options at ./client.jl:296 _start at ./client.jl:563 jfptr__start_63312.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x704846391249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1200 wait_forever at ./task.jl:1137 jfptr_wait_forever_44650.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] start_task at /source/src/task.c:1253 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007a4cddcc0a60 Total snapshots: 583. Utilization: 0% ╎583 @Base/task.jl:1137 wait_forever() 582╎ 583 @Base/task.jl:1200 wait() [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1200 wait_forever at ./task.jl:1137 jfptr_wait_forever_44650.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] start_task at /source/src/task.c:1253 unknown function (ip: (nil)) at (unknown file) Allocations: 24997400 (Pool: 24996759; Big: 641); GC: 22 [653] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-segmentation-watershed.jl:1 min at ./math.jl:856 [inlined] _extreme_filter_generic! at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/extreme_filter.jl:161 unknown function (ip: 0x7048285696fe) at (unknown file) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 _extreme_filter! at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/extreme_filter.jl:98 extreme_filter! at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/extreme_filter.jl:95 erode! at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:67 [inlined] #erode!#38 at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:65 [inlined] erode! at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:64 [inlined] #erode#37 at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:55 [inlined] erode at /home/pkgeval/.julia/packages/ImageMorphology/lktkj/src/ops/erode.jl:55 [inlined] hmin_transform at /home/pkgeval/.julia/packages/ImageSegmentation/T47s5/src/watershed.jl:155 watershed_ice_floes at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/src/segmentation_watershed.jl:10 unknown function (ip: 0x70482b3ddd82) at (unknown file) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 macro expansion at ./timing.jl:645 [inlined] macro expansion at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-segmentation-watershed.jl:17 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] top-level scope at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/test-segmentation-watershed.jl:2 _jl_invoke at /source/src/gf.c:4001 [inlined] ijl_invoke at /source/src/gf.c:4008 jl_toplevel_eval_flex at /source/src/toplevel.c:761 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2865 _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 _include at ./loading.jl:2925 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 unknown function (ip: 0x704844a99022) at (unknown file) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 macro expansion at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:79 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.13/Test/src/Test.jl:1952 [inlined] top-level scope at /home/pkgeval/.julia/packages/IceFloeTracker/eJrS2/test/runtests.jl:78 _jl_invoke at /source/src/gf.c:4001 [inlined] ijl_invoke at /source/src/gf.c:4008 jl_toplevel_eval_flex at /source/src/toplevel.c:761 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 include_string at ./loading.jl:2865 _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 _include at ./loading.jl:2925 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 jfptr_IncludeInto_36079.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:690 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:772 jl_toplevel_eval_flex at /source/src/toplevel.c:712 ijl_toplevel_eval at /source/src/toplevel.c:784 ijl_toplevel_eval_in at /source/src/toplevel.c:829 eval at ./boot.jl:489 exec_options at ./client.jl:296 _start at ./client.jl:563 jfptr__start_63312.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3985 [inlined] ijl_apply_generic at /source/src/gf.c:4198 jl_apply at /source/src/julia.h:2382 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x704846391249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S unknown function (ip: (nil)) at (unknown file) Allocations: 731307872 (Pool: 731212230; Big: 95642); GC: 1047 PkgEval terminated after 2723.75s: test duration exceeded the time limit