diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..80ee5975bdd4a4208f6288d579874e304246d72f --- /dev/null +++ b/.gitattributes @@ -0,0 +1,90 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.avro filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/1.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/10.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/2.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/3.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/4.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/5.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/6.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/7.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/8.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/gt/9.csv filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/1.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/10.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/2.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/3.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/4.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/5.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/6.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/7.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/8.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histgram/9.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/1.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/10.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/2.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/3.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/4.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/5.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/6.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/7.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/8.txt filter=lfs diff=lfs merge=lfs -text +DepthEstimate/Histogram/9.txt filter=lfs diff=lfs merge=lfs -text diff --git a/Annotations/p1/Sequence1.zip b/Annotations/p1/Sequence1.zip new file mode 100644 index 0000000000000000000000000000000000000000..5ae02933e1ef4cacb25c49e2a42ad7aafee457c6 --- /dev/null +++ b/Annotations/p1/Sequence1.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3bfe841279a11b99f5970454d819f4683e584f9551bdfd66b76446124a63df0 +size 100558372 diff --git a/Annotations/p1/Sequence10.zip b/Annotations/p1/Sequence10.zip new file mode 100644 index 0000000000000000000000000000000000000000..23e7a193dd0ca0122ba7117306dc2217f1d433fc --- /dev/null +++ b/Annotations/p1/Sequence10.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd49cd279e8125135fa8db04f598eb11c8cb33aebfa120a6a4a1f05d897269cc +size 6258310 diff --git a/Annotations/p1/Sequence11.zip b/Annotations/p1/Sequence11.zip new file mode 100644 index 0000000000000000000000000000000000000000..5bbc97b6baa3ce106086eedb0a1085045f909e92 --- /dev/null +++ b/Annotations/p1/Sequence11.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5c16c3a44fa8e7650c6d94c702dbfac1513335d8bfff850bb55c4fa834c26a8 +size 28765724 diff --git a/Annotations/p1/Sequence12.zip b/Annotations/p1/Sequence12.zip new file mode 100644 index 0000000000000000000000000000000000000000..851bd7f06152c055e7874993e67b2bd914345bdd --- /dev/null +++ b/Annotations/p1/Sequence12.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51e0d8f064fc707800a32d698d393633181004a24483e30e66df1b7cfc217a9c +size 23715989 diff --git a/Annotations/p1/Sequence13.zip b/Annotations/p1/Sequence13.zip new file mode 100644 index 0000000000000000000000000000000000000000..c144c3feb79ca775c29910ed6f8fc711879421eb --- /dev/null +++ b/Annotations/p1/Sequence13.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7db9de5fddde72a859c49fb8de647dde4214030a57ac1841a62205da47974e70 +size 12943298 diff --git a/Annotations/p1/Sequence14.zip b/Annotations/p1/Sequence14.zip new file mode 100644 index 0000000000000000000000000000000000000000..678b128f2edebaff0b9113abd8b16616ed1b18b3 --- /dev/null +++ b/Annotations/p1/Sequence14.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75b7882d96b5744be7b772f0011a2b14c40bc8731e00557a9add749520a0d6df +size 39590810 diff --git a/Annotations/p1/Sequence15.zip b/Annotations/p1/Sequence15.zip new file mode 100644 index 0000000000000000000000000000000000000000..3b08b818c27fdc1865af4310fb3d5846b7438260 --- /dev/null +++ b/Annotations/p1/Sequence15.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0392f78699a91e2cee9574ba41434f2b51668970b3ae8c8b840e409ee1388a30 +size 9621790 diff --git a/Annotations/p1/Sequence16.zip b/Annotations/p1/Sequence16.zip new file mode 100644 index 0000000000000000000000000000000000000000..a97101862406473ac715c5fa09e4b0052f1422d1 --- /dev/null +++ b/Annotations/p1/Sequence16.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd010509abf8212a43139355d6b96bbb325d50b97945ae4efa98922aff494be7 +size 28686108 diff --git a/Annotations/p1/Sequence17.zip b/Annotations/p1/Sequence17.zip new file mode 100644 index 0000000000000000000000000000000000000000..952da33ac051804c0c00816a85d36dc4b26cc227 --- /dev/null +++ b/Annotations/p1/Sequence17.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b8d4e60e7fe1ec2e90fb86f24731f8dc9ed17b6e55f8d1d6528a5a028380eec +size 67784474 diff --git a/Annotations/p1/Sequence18.zip b/Annotations/p1/Sequence18.zip new file mode 100644 index 0000000000000000000000000000000000000000..b5c47b2098e7fe7f760f73619d587630af15f713 --- /dev/null +++ b/Annotations/p1/Sequence18.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc86503d3eaee38cca64683c832085b57e96d621a09dcf75eafbee47e81ea23b +size 43123225 diff --git a/Annotations/p1/Sequence19.zip b/Annotations/p1/Sequence19.zip new file mode 100644 index 0000000000000000000000000000000000000000..cf5078bf22b61b6a79edec7996a21330be4a4337 --- /dev/null +++ b/Annotations/p1/Sequence19.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c101b11073f0f538532375bf49d2924b43cf80c2c79c1dcbf200d628556410fb +size 36482729 diff --git a/Annotations/p1/Sequence2.zip b/Annotations/p1/Sequence2.zip new file mode 100644 index 0000000000000000000000000000000000000000..468746a6aaa51fdc44b8fd86eba9d12b8fabf50f --- /dev/null +++ b/Annotations/p1/Sequence2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf089bbb2f183a20c6d40a7465706d66a3e2bf8239df1abf005dff020fc6168b +size 37333328 diff --git a/Annotations/p1/Sequence20.zip b/Annotations/p1/Sequence20.zip new file mode 100644 index 0000000000000000000000000000000000000000..793ed80a21fd59b0928bc4b6c5ff278fb3988c12 --- /dev/null +++ b/Annotations/p1/Sequence20.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7baaf302421e89cc498dbaf19aaefb792bd96c282192c1609f7dd1ca52460c44 +size 74292177 diff --git a/Annotations/p1/Sequence3.zip b/Annotations/p1/Sequence3.zip new file mode 100644 index 0000000000000000000000000000000000000000..a0a35ca70c4126ee19a89a44dc0b661df0f03a59 --- /dev/null +++ b/Annotations/p1/Sequence3.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a42395f07b940f99705d9c0b94734e607b0c80367a463498ba8e3b3a62d2c8e0 +size 99339250 diff --git a/Annotations/p1/Sequence4.zip b/Annotations/p1/Sequence4.zip new file mode 100644 index 0000000000000000000000000000000000000000..4b1420519e6970ab7bd1039d9d36bde8349852be --- /dev/null +++ b/Annotations/p1/Sequence4.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72358b710adc679d2698a0ba86cb79fb87a2e171560bf06ae92cad362d7a525e +size 58901782 diff --git a/Annotations/p1/Sequence5.zip b/Annotations/p1/Sequence5.zip new file mode 100644 index 0000000000000000000000000000000000000000..9eb2719a4b94613e4f0da0fd66c5cd0ef25398c1 --- /dev/null +++ b/Annotations/p1/Sequence5.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73f38573ed16405e1703c2d7883eace48900676951e1384b3e5d7db8733c74cf +size 37624737 diff --git a/Annotations/p1/Sequence6.zip b/Annotations/p1/Sequence6.zip new file mode 100644 index 0000000000000000000000000000000000000000..49394265151d1a82e2449bcaac79b820f162b2e7 --- /dev/null +++ b/Annotations/p1/Sequence6.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef92db8eb5d08b7637571caa4d4741624577f402c3f7926c1a45996b69a62c49 +size 42037304 diff --git a/Annotations/p1/Sequence7.zip b/Annotations/p1/Sequence7.zip new file mode 100644 index 0000000000000000000000000000000000000000..56c757d082ac32881195298d8d8e1a1b7186012e --- /dev/null +++ b/Annotations/p1/Sequence7.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6cd392feacdb4b8cbb9fa2d3b49c588ce7f24eef73c2772b8b015fb5b4e8ea8f +size 5610003 diff --git a/Annotations/p1/Sequence8.zip b/Annotations/p1/Sequence8.zip new file mode 100644 index 0000000000000000000000000000000000000000..01bd91f9113cbc3624fd244fc33108d0b9c44177 --- /dev/null +++ b/Annotations/p1/Sequence8.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2102ef977f2848580a18d7888e8a43e7eae544618ae760b0f7b0c6bebc2c51e +size 12882026 diff --git a/Annotations/p1/Sequence9.zip b/Annotations/p1/Sequence9.zip new file mode 100644 index 0000000000000000000000000000000000000000..91cff72af86c0e39434bb08f820d3894653a4c7c --- /dev/null +++ b/Annotations/p1/Sequence9.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1af67c8614fd14d8961bdf6e2c775d8f2b3610337793665108c0904f48cfc6c +size 21586380 diff --git a/Annotations/p2/Sequence21.zip b/Annotations/p2/Sequence21.zip new file mode 100644 index 0000000000000000000000000000000000000000..9eea31eb0769ba6bc723ae6fc0cc297a5b368566 --- /dev/null +++ b/Annotations/p2/Sequence21.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8706bd4abdac5e25fdf8445405179f4f98b5e86b506919051615d88dc1b7d13f +size 30602679 diff --git a/Annotations/p2/Sequence22.zip b/Annotations/p2/Sequence22.zip new file mode 100644 index 0000000000000000000000000000000000000000..fedb54dc27760a75b98dd599ddc12575549ca414 --- /dev/null +++ b/Annotations/p2/Sequence22.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9b0f9cd4213596c7d48fca7cf06ba7079e7c99b23ee33c38c3bada99e13ad76 +size 36242239 diff --git a/Annotations/p2/Sequence23.zip b/Annotations/p2/Sequence23.zip new file mode 100644 index 0000000000000000000000000000000000000000..0170792304aa47b520c3742f873f7f202451209f --- /dev/null +++ b/Annotations/p2/Sequence23.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e742560f128ceb7350a1d1d034cdb60fbbdc10fea954ee280e9c7d6d5e6ba63 +size 34957888 diff --git a/Annotations/p2/Sequence24.zip b/Annotations/p2/Sequence24.zip new file mode 100644 index 0000000000000000000000000000000000000000..f9907ea3bca4ff04f257f4566713a90bf9fcbbcb --- /dev/null +++ b/Annotations/p2/Sequence24.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:657f170ee27d81722beb35ddf99c3bde9091c42d419e6d32d6ca02aab1b486c4 +size 19578543 diff --git a/Annotations/p2/Sequence25.zip b/Annotations/p2/Sequence25.zip new file mode 100644 index 0000000000000000000000000000000000000000..dc97332f91c5590f3a30b9a0fd5d330a99d774de --- /dev/null +++ b/Annotations/p2/Sequence25.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f8ee492151314f510dd2b089a4b1be54de3415ba6a47bd6f6afe796ffb5236d +size 22739260 diff --git a/Annotations/p2/Sequence26.zip b/Annotations/p2/Sequence26.zip new file mode 100644 index 0000000000000000000000000000000000000000..cbf079db72cd0d7f7a0ce7a94bb5bf6c2bc2c28f --- /dev/null +++ b/Annotations/p2/Sequence26.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab6ab11289a4063fb90eb6088d49a074947b02f9eca0d768855ab6df378ba2ef +size 25316999 diff --git a/Annotations/p2/Sequence27.zip b/Annotations/p2/Sequence27.zip new file mode 100644 index 0000000000000000000000000000000000000000..d93f1b1e2e54be5fc8020caaf78b4fd527424c03 --- /dev/null +++ b/Annotations/p2/Sequence27.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0495c85fc39813b5986302c49c58ecce2c558fbd8608eac10c28679a9b45a47f +size 27873605 diff --git a/DepthEstimate/Histogram/1.txt b/DepthEstimate/Histogram/1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae25785a95bfc20b2dd39f4872af0f5348ac0259 --- /dev/null +++ b/DepthEstimate/Histogram/1.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4fc3a40fdda3e6ca77860eb307d96461d075e80e4b977709d533b0c9a03aa29 +size 67528872 diff --git a/DepthEstimate/Histogram/10.txt b/DepthEstimate/Histogram/10.txt new file mode 100644 index 0000000000000000000000000000000000000000..24f622ebea55cf9063f8c1d2ced897ebde172a39 --- /dev/null +++ b/DepthEstimate/Histogram/10.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19b4927aa3a542c5215216a15d7811fc44ab69228dba6a6cf4bd7d7d89da3977 +size 67423168 diff --git a/DepthEstimate/Histogram/2.txt b/DepthEstimate/Histogram/2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6fec151fde20c1fbd872ce79644741983ba48240 --- /dev/null +++ b/DepthEstimate/Histogram/2.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dcc0a24912cf58e90123e53a9d685e900fdfae665361809482c5f29de862789 +size 67331095 diff --git a/DepthEstimate/Histogram/3.txt b/DepthEstimate/Histogram/3.txt new file mode 100644 index 0000000000000000000000000000000000000000..8d03fa2b70ed62bd5e8e10fee912ebe1d2c6fc33 --- /dev/null +++ b/DepthEstimate/Histogram/3.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d260fe01848c868285460e85148b5794a735c6c837323113b450ff3ba688192 +size 67694262 diff --git a/DepthEstimate/Histogram/4.txt b/DepthEstimate/Histogram/4.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a5eaf31114993535a1a9b19bfd2564ff72c0a2e --- /dev/null +++ b/DepthEstimate/Histogram/4.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12b7834f7e88391801d2e98acc39a099d73fca47d7845129263f647a88e7a575 +size 67404653 diff --git a/DepthEstimate/Histogram/5.txt b/DepthEstimate/Histogram/5.txt new file mode 100644 index 0000000000000000000000000000000000000000..e049c9645a5c929fd062d7bff2fe8b021370fc95 --- /dev/null +++ b/DepthEstimate/Histogram/5.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d25217c682286fc5b2694dc9e6b67d603400c1ef430210751588876aa2a7c33e +size 67382170 diff --git a/DepthEstimate/Histogram/6.txt b/DepthEstimate/Histogram/6.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b49955bc27c2779dd614bcedb3b021cbb759682 --- /dev/null +++ b/DepthEstimate/Histogram/6.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79257fa0707fbe4bfd1eb3f26c256447f091ffd1a6229d5a2465fb3814dd8de9 +size 67424247 diff --git a/DepthEstimate/Histogram/7.txt b/DepthEstimate/Histogram/7.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8b7f08f06fe5b169e275351153580b9e5fa88a6 --- /dev/null +++ b/DepthEstimate/Histogram/7.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eace7b3d508e547507ca6143bdeed6dda5c0de6473f9d662a6d29e6f22950602 +size 67235083 diff --git a/DepthEstimate/Histogram/8.txt b/DepthEstimate/Histogram/8.txt new file mode 100644 index 0000000000000000000000000000000000000000..54916ab8eacd69a56b47044fd392d42f97735838 --- /dev/null +++ b/DepthEstimate/Histogram/8.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8270754d0befd2211a32c0bb53e9347e050090dd9c363d96dafdf215fdbda73d +size 67073014 diff --git a/DepthEstimate/Histogram/9.txt b/DepthEstimate/Histogram/9.txt new file mode 100644 index 0000000000000000000000000000000000000000..adcc8f3fb052443146b7b2dc3a8219bc48c0a986 --- /dev/null +++ b/DepthEstimate/Histogram/9.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c58c41b996d7d680c46a3e47dd01c026b44ce57145edd891a4f08586c10f069 +size 66719217 diff --git a/DepthEstimate/gt/1.csv b/DepthEstimate/gt/1.csv new file mode 100644 index 0000000000000000000000000000000000000000..3d0d8dcf4d6bbc9c136bdbda4defde7f4fdb5c2d --- /dev/null +++ b/DepthEstimate/gt/1.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97614b5c9a808492e8e95129b6e68fd4490cfb02941c6d717d2be6ce6fcc4567 +size 33754512 diff --git a/DepthEstimate/gt/10.csv b/DepthEstimate/gt/10.csv new file mode 100644 index 0000000000000000000000000000000000000000..f0130593fae11c978bc6e527e936e44ef31756b2 --- /dev/null +++ b/DepthEstimate/gt/10.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bbd89fa5f778e0e8b9f669b02e9cca737d9b825d41ea847d87e2369d63f1b11b +size 33384695 diff --git a/DepthEstimate/gt/2.csv b/DepthEstimate/gt/2.csv new file mode 100644 index 0000000000000000000000000000000000000000..5b8d149a2b44bdb9e6185f925a0c3e62637b965b --- /dev/null +++ b/DepthEstimate/gt/2.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4de9fe99b85255388cc8b6f5fa31f95d7f83e4881138ce3563d0811cbb910ecb +size 34367167 diff --git a/DepthEstimate/gt/3.csv b/DepthEstimate/gt/3.csv new file mode 100644 index 0000000000000000000000000000000000000000..66bc007d803c1e9e8d5ac1f740a7189c9b5e8d6f --- /dev/null +++ b/DepthEstimate/gt/3.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:409ec52efc08f91f54e1e79dceab9c27db5327868968edb7f1d17e0df9618d62 +size 34015535 diff --git a/DepthEstimate/gt/4.csv b/DepthEstimate/gt/4.csv new file mode 100644 index 0000000000000000000000000000000000000000..600304f4d3b569d0ddea36ad4cc859e9c21fb6fa --- /dev/null +++ b/DepthEstimate/gt/4.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:746dde8840f09bf87a68009953f9432165e2c903f5ff1e4e83fe6c82f575139c +size 33859363 diff --git a/DepthEstimate/gt/5.csv b/DepthEstimate/gt/5.csv new file mode 100644 index 0000000000000000000000000000000000000000..9b62ed4c79607944dd7c749774e8aa3fd6adc561 --- /dev/null +++ b/DepthEstimate/gt/5.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fe7ae606a51facf945c2a26eda40d574609435ac05ea96905b844497b6833fc +size 33825171 diff --git a/DepthEstimate/gt/6.csv b/DepthEstimate/gt/6.csv new file mode 100644 index 0000000000000000000000000000000000000000..76794ae2d039cd2d14708b7a641ef71c8a5c4219 --- /dev/null +++ b/DepthEstimate/gt/6.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50b1888a1c25072fee02df77c7bcf2161f1c029a6915f971d0dced149152283b +size 33809994 diff --git a/DepthEstimate/gt/7.csv b/DepthEstimate/gt/7.csv new file mode 100644 index 0000000000000000000000000000000000000000..389b8a4e56901f5a1d84e2e959e674a2e82c7fbb --- /dev/null +++ b/DepthEstimate/gt/7.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1db4e2ebc975366919390d206a03d6e0c173a93c711418c820c1084b315e663 +size 34007721 diff --git a/DepthEstimate/gt/8.csv b/DepthEstimate/gt/8.csv new file mode 100644 index 0000000000000000000000000000000000000000..ef7300a0bca0bfe6046c3dc67835814aa42ae64b --- /dev/null +++ b/DepthEstimate/gt/8.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2637ac551d39f97608f31a6dd5dd490093feb8e13cb99aaa4d3d26d283f315f6 +size 33529768 diff --git a/DepthEstimate/gt/9.csv b/DepthEstimate/gt/9.csv new file mode 100644 index 0000000000000000000000000000000000000000..2b800be8920300be7177ce08e167c568ce7518a0 --- /dev/null +++ b/DepthEstimate/gt/9.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e254316a59f14a4b6d92deb192f0cbd0f288a2ff8ea276370f50a09c2869192 +size 34795524 diff --git a/Histogram/depth_maps/P1_Sequence10_depth.png b/Histogram/depth_maps/P1_Sequence10_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..ed4e8cd225726d5adbd668512e0fe59e13a0f7d2 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence10_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49b22c66735f5022e82c13a4a65e56e06ce9a4374ee2ea40ed0b638a4ca6a60d +size 156034 diff --git a/Histogram/depth_maps/P1_Sequence11_depth.png b/Histogram/depth_maps/P1_Sequence11_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..d6e44925870903000942ad101dab8bef0545a55f --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence11_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e6280db40b56aedf1a422d30400f373fc309ef66053c48ea43bee7f6da3cc22 +size 176228 diff --git a/Histogram/depth_maps/P1_Sequence12_depth.png b/Histogram/depth_maps/P1_Sequence12_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..4d51afa539662a487d6f4a4914e1f75e65f56d89 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence12_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f47b3223de40c29d45882d20c134e3f057e5c2f135c8c3cc8f2a5d5a514547b5 +size 210781 diff --git a/Histogram/depth_maps/P1_Sequence13_depth.png b/Histogram/depth_maps/P1_Sequence13_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..5332aa239dfd77ad90cef30028c7724b9300d0d8 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence13_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8292a1ced7abd348f4f95c978930736d04587491da5e97fba54d4b16489cd2 +size 185332 diff --git a/Histogram/depth_maps/P1_Sequence14_depth.png b/Histogram/depth_maps/P1_Sequence14_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..dc787083c68f177d8a1ad42e4c8393a6949f24ac --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence14_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9edd9cbf2eda5ec51c705417b2c16302c8007c586e0b1eb709c17efea1efe4bc +size 126098 diff --git a/Histogram/depth_maps/P1_Sequence15_depth.png b/Histogram/depth_maps/P1_Sequence15_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..fceacf16157b8c1dc2f918ad687a82d1751f3e7c --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence15_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f952e204b6be0ed35c6a636096846786aaf486e335ddf3f851e53b902420a7e3 +size 119762 diff --git a/Histogram/depth_maps/P1_Sequence16_depth.png b/Histogram/depth_maps/P1_Sequence16_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..ad4a7bc067b9ab3829ea610863f55b1b492288de --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence16_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:521fb3121f9ce26717b382076eb476a93ea30750312ca397c0fb26cd0a161c09 +size 99073 diff --git a/Histogram/depth_maps/P1_Sequence17_depth.png b/Histogram/depth_maps/P1_Sequence17_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..daf3934f41198ea502df6279e17ab63d5f11e07a --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence17_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d811e52ae34b56d6609fd2e26cc513165dc3d84130994483ece470b41eabb9d5 +size 112435 diff --git a/Histogram/depth_maps/P1_Sequence18_depth.png b/Histogram/depth_maps/P1_Sequence18_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..4437d918192f8f5a0d4633a70ba076d9692535a7 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence18_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afdabdd4e80f199bbf48cd93bf33b20b21f1482b344daeaee78ab56286a1ec3d +size 174502 diff --git a/Histogram/depth_maps/P1_Sequence19_depth.png b/Histogram/depth_maps/P1_Sequence19_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..28545835f6c8ac33ef902cffa2e69c88163dddab --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence19_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59ed67826c8749c9ed9b35fd4a7319e95d29b070f741dd4bdca427319b82a2ae +size 197455 diff --git a/Histogram/depth_maps/P1_Sequence1_depth.png b/Histogram/depth_maps/P1_Sequence1_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..541e699c39c53aa7dc3ebb950c7bab30251c2d28 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence1_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d67baf5534680b66cbee839780e2bb0b4483446674555816a74720b6fed6777b +size 154004 diff --git a/Histogram/depth_maps/P1_Sequence20_depth.png b/Histogram/depth_maps/P1_Sequence20_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..a36a882a749f6e741bc867e9ef5b3785a48f6195 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence20_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a63c955c4227201960d6ba91859ac4c5d45e35f76091b0781691884256fd984c +size 130079 diff --git a/Histogram/depth_maps/P1_Sequence2_depth.png b/Histogram/depth_maps/P1_Sequence2_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..d4e242d109c1c5f85850ae511040b50487c613fc --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence2_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2f1b58ad7efb3b5368f2d8fc467956adecbcd491bf5bec1303db6b30c77c060 +size 144804 diff --git a/Histogram/depth_maps/P1_Sequence3_depth.png b/Histogram/depth_maps/P1_Sequence3_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..101930816ed8d08f8d60ed886cdeb798d6595f23 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence3_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eaf953bba979009206d4a18918bf8a138c825cefbd09c5dd80715f2ecdfe8c00 +size 150377 diff --git a/Histogram/depth_maps/P1_Sequence4_depth.png b/Histogram/depth_maps/P1_Sequence4_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..fedb8a7ec216c8aab7bda7109a68155b5589cb05 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence4_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b79dd3c67982a4538687145c289132aae6b82d0e69674c4939d6f6284a2cc1ca +size 136071 diff --git a/Histogram/depth_maps/P1_Sequence5_depth.png b/Histogram/depth_maps/P1_Sequence5_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..cdb768ab08a1818fbde235916af15fb8d900f6c0 --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence5_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65c75d67fc26914ce0b8b19e0061d03fe1f164c442c9ae88e4a6cf25cab32b6e +size 167192 diff --git a/Histogram/depth_maps/P1_Sequence6_depth.png b/Histogram/depth_maps/P1_Sequence6_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..3d0ef2bd88d4727808e31a4d4fc93fb8fbe52d1c --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence6_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0766e14dbf80961ae82225e1d052aaa10a1a69240da6f8fc7e422ad767e7fde +size 208224 diff --git a/Histogram/depth_maps/P1_Sequence7_depth.png b/Histogram/depth_maps/P1_Sequence7_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..b03d9cd600c6fa12ef0bdbe8a0c81cc89017a68e --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence7_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6e632ea45abbc3ddcc0e7b41dab8de22199419363feb7d63e0eba86438e6910 +size 106549 diff --git a/Histogram/depth_maps/P1_Sequence8_depth.png b/Histogram/depth_maps/P1_Sequence8_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..c05d452ca25f9b1542f03cb4d772d73e02c5832f --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence8_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e50822a99732c761c88b817dfa3ece31133f9755418b19830fba5bd50fad0fe +size 93282 diff --git a/Histogram/depth_maps/P1_Sequence9_depth.png b/Histogram/depth_maps/P1_Sequence9_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..16f48f7259d6357e2192238a52faca98f20e699c --- /dev/null +++ b/Histogram/depth_maps/P1_Sequence9_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4371a44bc45cc0f16a9b39418cf99430f5f6f7dd589f0a11466232833c2f7b6 +size 173097 diff --git a/Histogram/depth_maps/P2_Sequence21_depth.png b/Histogram/depth_maps/P2_Sequence21_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..490553cbf5daeba0d3462f59602224e86dd6f798 --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence21_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ebefdbb765857ff4c3ca7bef95cc78c16d05ef28f7b3afa01e844b5465ab3031 +size 167954 diff --git a/Histogram/depth_maps/P2_Sequence22_depth.png b/Histogram/depth_maps/P2_Sequence22_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..11a7939e4d7b094df79d276623ecbf91c5b49da6 --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence22_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55d9c7381713aedd2992a55df47063996d3c052c68df4255d7d54c83fd31fd2 +size 110619 diff --git a/Histogram/depth_maps/P2_Sequence23_depth.png b/Histogram/depth_maps/P2_Sequence23_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..28a8cfcb941f05c3463e2d44e61403d693c0bcf2 --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence23_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f16d049d76c3961dfcd248f9ff69114c92567154e399b2e17288949d4cda1e2 +size 91747 diff --git a/Histogram/depth_maps/P2_Sequence24_depth.png b/Histogram/depth_maps/P2_Sequence24_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..ee8866ebaee9fa344531ccf79d9e47383f31a190 --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence24_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21e0ecf9142b0acc3d050c28b7892433d7f140bc5bf7d2323d152b0796b33398 +size 92608 diff --git a/Histogram/depth_maps/P2_Sequence25_depth.png b/Histogram/depth_maps/P2_Sequence25_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..d50dd4f4f8d16f1fa1c3e760da57841822d4da9b --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence25_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71a9ecc66ed1d6ac6a374235d8f8f44daf6642c55247d013b4162849ab262390 +size 106507 diff --git a/Histogram/depth_maps/P2_Sequence26_depth.png b/Histogram/depth_maps/P2_Sequence26_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..932077da028530fff4ad030bcb96932e57f5ebb3 --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence26_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b88d1b099223f062faef0bcdebc12c0f7277b37cea3a8eab70e57efa98154f1 +size 97023 diff --git a/Histogram/depth_maps/P2_Sequence27_depth.png b/Histogram/depth_maps/P2_Sequence27_depth.png new file mode 100644 index 0000000000000000000000000000000000000000..a959722063fd8fd68579c5370ac548d6b29c16df --- /dev/null +++ b/Histogram/depth_maps/P2_Sequence27_depth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e59ea67ce91d5001ddea5859447ca76888ec29b5ebb5909c70faa46383213404 +size 102165 diff --git a/Histogram/p1.zip b/Histogram/p1.zip new file mode 100644 index 0000000000000000000000000000000000000000..4db3c81a6c5f1af6fbad364b5b7eac990bcc160b --- /dev/null +++ b/Histogram/p1.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d915b4fa327ae3371001a567c958d1f7b44350acdcf1463b1b771fa3c18070cd +size 156925687435 diff --git a/Histogram/p2.zip b/Histogram/p2.zip new file mode 100644 index 0000000000000000000000000000000000000000..ba28ad1e563213136b89d1b2d9d80f91fca99ada --- /dev/null +++ b/Histogram/p2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db910eb7c756d3afa7bcbc93df18c0650a25a827d4d88d52597954379dba8290 +size 7079798249 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4671583195846d773a35bb1851071f453307e2a8 --- /dev/null +++ b/README.md @@ -0,0 +1,246 @@ +--- +license: cc-by-nc-sa-4.0 +task_categories: +- depth-estimation +- image-to-3d +- image-segmentation +tags: +- single-photon-lidar +- spad +- transient-imaging +- time-of-flight +- 3d-vision +- multi-view-reconstruction +- semantic-segmentation +pretty_name: SP-TransientBench +size_categories: +- 10K Dong, H., Zhang, Z., Wen, Z., Qiang, Y., Deng, R., Dong, W., Jiang, Z., Li, X., Lu, R., Sun, S., Wang, W., Xia, Z., Zheng, H., Shi, G., & Ren, X. SP-TransientBench: A Real-Captured Single Photon Perception Benchmark. arXiv:2606.18952, 2026. + +**SP-TransientBench (STB)** is a real-captured benchmark for single-photon LiDAR (SPL) perception in photon-starved 3D scenes. It provides full per-pixel time-of-flight histograms, calibrated metadata, task-specific supervision, and official splits for depth estimation, multi-view 3D reconstruction, and 3D semantic segmentation. + +STB contains `10,297` views captured with a solid-state single-photon LiDAR at `256 x 192` spatial resolution. Each view stores a full transient waveform with `672` temporal bins, preserving photon sparsity, background noise, and multi-return structures that are often lost in depth-only releases. + +## Capture Setup + +STB is captured with an Adaps ADS6311 Hawk solid-state SPL device operating under Direct Time-of-Flight (DToF) and Time-Correlated Single Photon Counting (TCSPC). The transmitter uses a `940 nm` VCSEL array, and the receiver records photon arrival timestamps with a SPAD array. + +| Item | Value | +| --- | --- | +| SPL device | Adaps ADS6311 Hawk | +| Acquisition mode | Solid-state flash SPL | +| Raw SPAD resolution | `768 x 576` | +| Released output resolution | `256 x 192` after `3 x 3` on-chip binning | +| Histogram bins | `672` | +| Bin width | `750 ps` | +| Field of view | `128 deg x 96 deg` | +| Frame rate | `10-20 Hz` | +| Detection range | Up to `30 m` | +| Range accuracy | `< 5 cm` | + +An auxiliary Livox Avia LiDAR is mounted with the SPL device during collection. It is used for pose estimation, SPL-Livox calibration, and depth-reference generation where required by the benchmark track. + +## Benchmark Tracks + +STB is organized around three complementary tasks: + +| Track | Scale | Purpose | +| --- | ---: | --- | +| Depth estimation | `10` samples | Recover single-view depth directly from raw transient histograms | +| Multi-view 3D reconstruction | `10` scenes, `20-40` views per scene | Reconstruct geometry and render novel views from calibrated SPL captures | +| 3D semantic segmentation | `27` sequences, `10,297` frames | Segment SPAD-derived 3D observations with histogram-domain semantic labels | + +Every track includes raw SPAD histograms. Depending on the task, the release also includes SPL intrinsics, SPL-Livox extrinsics, reference Livox point clouds, camera poses, ambient illumination metadata, pile-up metadata, and semantic annotations. + +## Layout + +```text +SP-TransientBench/ +|-- README.md +|-- fig/ figures used in this dataset card +|-- DepthEstimate/ depth-estimation histograms and Livox references +|-- Reconstruction/ multi-view reconstruction scene archives +|-- Annotations/ semantic annotation archives +|-- Histogram/ semantic-track raw histograms and depth maps +|-- codes/ annotation, point-cloud conversion, simulation, and reconstruction tools +`-- config/ SPL calibration, extrinsics, parsing settings, and IRF data +``` + +The full release is approximately `168.7 GB`. Large files are stored in the Hugging Face dataset repository and should be downloaded from the dataset files page or with Git LFS/Xet-compatible tooling. + +```bash +git lfs install +git clone https://huggingface.co/datasets/shuinb/SP-TransientBench +``` + +## Data + +Depth estimation samples pair raw SPL histograms with Livox reference point clouds: + +```text +DepthEstimate/ +|-- Histogram/ +| `-- 1.txt ... 10.txt +`-- gt/ + `-- 1.csv ... 10.csv +``` + +`DepthEstimate/Histogram/{id}.txt` stores one flattened `256 x 192` SPL histogram grid, with `672` photon-count bins per valid row. `DepthEstimate/gt/{id}.csv` stores the corresponding Livox reference point cloud with metric `X,Y,Z` coordinates and capture metadata. + +Multi-view reconstruction scenes are released as compressed scene packages: + +```text +Reconstruction/ +|-- AI_floor2.zip +|-- artbuilding_floor2.zip +|-- c4floor2.zip +|-- design_floor1.zip +|-- library_floor2.zip +|-- material_building.zip +|-- parking.zip +|-- physics_building2.zip +`-- physics_building3.zip +``` + +After decompression, each scene follows the same structure as `AI_floor2`: + +```text +AI_floor2/ +|-- RawDataHistogramMap_frame_0_.txt +|-- 1.csv ... 26.csv +|-- sp_pose_results.csv +|-- sp_merged_map.ply +`-- json/ + |-- three_views/ + | |-- train.json + | `-- test.json + |-- five_views/ + | |-- train.json + | `-- test.json + `-- ten_views/ + |-- train.json + `-- test.json +``` + +The `RawDataHistogramMap_frame_0_.txt` files store SPL views, `{view_id}.csv` files store matched Livox point clouds, `sp_pose_results.csv` records Livox and SPL poses as flattened `4 x 4` transforms, and `sp_merged_map.ply` provides the registered scene-level reference map. The `json/` folders define official `3`, `5`, and `10` input-view reconstruction splits in a NeRF-style format. + +Semantic segmentation data are split by SPL device: + +```text +Annotations/ +|-- p1/ +| `-- Sequence1.zip ... Sequence20.zip +`-- p2/ + `-- Sequence21.zip ... Sequence27.zip + +Histogram/ +|-- p1.zip +|-- p2.zip +`-- depth_maps/ + |-- P1_Sequence1_depth.png ... P1_Sequence20_depth.png + `-- P2_Sequence21_depth.png ... P2_Sequence27_depth.png +``` + +Inside each sequence archive, semantic labels are stored as `.npy` arrays named like `RawDataHistogramMap_frame_*_semantic.npy`. Each array has shape `(49152, 672)`, where `49152 = 256 x 192`, and stores `uint8` semantic ids with `0` for unlabeled/background bins and `1-13` for semantic classes. + +## Code + +The `codes/` folder contains utility code released with STB: + +- `codes/annotation tool/labelLidarwave.py`: a single-photon LiDAR waveform annotation tool. +- `codes/pointcloud_extract/raw2pc.py`: converts raw histogram `.txt` files into point clouds using the maximum-bin histogram method. +- `codes/pointcloud_extract/ann2pc.py`: converts annotated `.npy` semantic data into corresponding semantic point clouds. +- `codes/simulator/`: simulation tools used for STB experiments. +- `codes/reconstruction/`: STB reconstruction code based on the TransientNeRF method. + +## Task Details + +### Depth Estimation + +![Depth estimation qualitative results](fig/Depth%20Estimation.png) + +This track evaluates depth recovery directly from raw photon time-of-flight histograms. Predictions are back-projected to 3D with calibrated SPL intrinsics and compared against Livox references with Chamfer Distance (CD, meters) and Recall under `1`, `3`, and `5` temporal-bin tolerances. + +### Multi-view 3D Reconstruction + +![Multi-view reconstruction qualitative results](fig/Multi-view%20reconstruction.png) + +This track evaluates scene reconstruction and novel-view rendering from multiple calibrated SPL views. Each scene provides sparse-view settings with `3`, `5`, or `10` input views for training and reserves the remaining views for evaluation. Reported metrics cover intensity rendering (SSIM, LPIPS), depth rendering (`L1` error), and histogram rendering (PSNR). + +### 3D Semantic Segmentation + +![Semantic segmentation qualitative results](fig/Semantic%20Segmentation.png) + +This track evaluates semantic understanding from SPAD time-resolved measurements. Histograms are preprocessed, converted into single-photon point clouds through histogram-to-range projection, and segmented with point-cloud backbones. + +| Split | Samples | +| --- | ---: | +| Train | `8,297` | +| Test | `2,000` | + +The semantic track uses `13` foreground classes. Evaluation reports Overall Accuracy (OA) and mean Intersection-over-Union (mIoU), averaged over three random seeds in the paper protocol. + +## Semantic Labels + +![Annotation pipeline](fig/annotation_pipeline.png) + +STB uses histogram-domain semantic annotation to handle multi-return SPL measurements. Instead of assigning a single label to each pixel, annotations are defined over temporal bins: + +```text +S in {0, ..., C}^{N x B} +N = H x W +B = number of temporal bins +``` + +The annotation pipeline identifies dominant peaks, assigns semantic labels to peak-support intervals, peels the labeled signal, and repeats the process to reveal weaker returns. This lets a single pixel ray contain multiple semantic entities at different ranges. + +## Contents + +| File or folder | Purpose | +| --- | --- | +| `DepthEstimate/` | Single-view depth-estimation samples with raw histograms and Livox point-cloud references | +| `Reconstruction/` | Scene packages for sparse-view SPL reconstruction and novel-view evaluation | +| `Annotations/` | Histogram-domain semantic label packages for `27` sequences | +| `Histogram/` | Raw histogram packages and sequence-level depth maps for the semantic track | +| `codes/` | Annotation, point-cloud conversion, simulation, and TransientNeRF-based reconstruction code | +| `config/config.yaml` | SPL intrinsics, distortion coefficients, temporal bin width, image size, and parsing settings | +| `config/IRF_global.csv` | Global instrument response function (IRF) measurements for transient calibration and analysis | +| `config/final_extrinsic_sp_to_livox.txt` | SPL-to-Livox extrinsic calibration used for cross-sensor alignment | +| `config/offset.txt` | Additional extrinsic offset parameters used for SPL/Livox alignment | +| `fig/` | Dataset-card figures, qualitative examples, annotation diagrams, and statistics | + +## Statistics + +![Semantic category distribution](fig/semantic_category_distribution.png) + +![Light intensity distribution](fig/Light%20Intensity%20Distribution.png) + +STB records sensing-condition metadata such as ambient illumination and pile-up indicators. These metadata are intended for dataset analysis and robustness studies rather than model input. + +## License + +The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0). + +## Citation + +If you use SP-TransientBench, please cite: + +```bibtex +@misc{dong2026sptransientbench, + title = {SP-TransientBench: A Real-Captured Single Photon Perception Benchmark}, + author = {Dong, Hongzhou and Zhang, Zili and Wen, Ziting and Qiang, Yiheng and Deng, Runrong and Dong, Wenle and Jiang, Ziwen and Li, Xinyang and Lu, Rui and Sun, Shuoyao and Wang, Wenyu and Xia, Ziyi and Zheng, Haitao and Shi, Guodong and Ren, Xiaoqiang}, + year = {2026}, + eprint = {2606.18952}, + archivePrefix = {arXiv}, + primaryClass = {cs.CV}, + doi = {10.48550/arXiv.2606.18952} +} +``` diff --git a/Reconstruction/AI_floor2.zip b/Reconstruction/AI_floor2.zip new file mode 100644 index 0000000000000000000000000000000000000000..231ca255181d6b52947071459bc0aa10340103af --- /dev/null +++ b/Reconstruction/AI_floor2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64c3ef16897a4d932df10489829013bbf2de5c4e08e7c09540ac7a3db0e34dbf +size 240714713 diff --git a/Reconstruction/artbuilding_floor2.zip b/Reconstruction/artbuilding_floor2.zip new file mode 100644 index 0000000000000000000000000000000000000000..e2bf0c3bb2123a7020ccb4c64e30d13f2a1ec31d --- /dev/null +++ b/Reconstruction/artbuilding_floor2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4afe38bf9407967b737af8378a47de4306b60482c687be3b0a4e04d026afa97f +size 293660830 diff --git a/Reconstruction/c4floor2.zip b/Reconstruction/c4floor2.zip new file mode 100644 index 0000000000000000000000000000000000000000..042b5f90ebdb7fb9713ef73e1ca4ec3284ca0358 --- /dev/null +++ b/Reconstruction/c4floor2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3268b99d79e18526eb3702899077efbb64578da1ebff0098004884e87cdc227e +size 207575257 diff --git a/Reconstruction/design_floor1.zip b/Reconstruction/design_floor1.zip new file mode 100644 index 0000000000000000000000000000000000000000..dc0de954ba274426706cbfde1ebd332f98aa348a --- /dev/null +++ b/Reconstruction/design_floor1.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c84f8a53eb0c75b0c72ce5c0cbf4e57f6ae46cff0fd95c6b83a02be5fbaf6b7d +size 239884294 diff --git a/Reconstruction/library_floor2.zip b/Reconstruction/library_floor2.zip new file mode 100644 index 0000000000000000000000000000000000000000..8b86ec976e0aa012ef8158897cf93775f6eba081 --- /dev/null +++ b/Reconstruction/library_floor2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:309336bd72a700a4e09693aec3282373505fe44179b7dcb043da44841e11196c +size 156838326 diff --git a/Reconstruction/material_building.zip b/Reconstruction/material_building.zip new file mode 100644 index 0000000000000000000000000000000000000000..f4fc2d83bb4e8f6454d14046a09cb23330df5ee6 --- /dev/null +++ b/Reconstruction/material_building.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49442812c551d2752c86ca3a6bb50ec6be2c3ef420d7c477f04815ffc26ed985 +size 169490412 diff --git a/Reconstruction/parking.zip b/Reconstruction/parking.zip new file mode 100644 index 0000000000000000000000000000000000000000..a8f5911983be66e82b78de5d5c814ef253220dac --- /dev/null +++ b/Reconstruction/parking.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fe5cfb11d41a8d03ecb1ce3589fe4f4f35206ab60084191df51ee42855a78b0 +size 316850291 diff --git a/Reconstruction/physics_building2.zip b/Reconstruction/physics_building2.zip new file mode 100644 index 0000000000000000000000000000000000000000..f47f127696b6789949f781b18e2eb01955ec9bf0 --- /dev/null +++ b/Reconstruction/physics_building2.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc156abef7f717c5284872cc75144d34a2fb5968615b5f8563d69ffb05c2b546 +size 339765768 diff --git a/Reconstruction/physics_building3.zip b/Reconstruction/physics_building3.zip new file mode 100644 index 0000000000000000000000000000000000000000..50920299233b78fbc4418246e55a4d99628f391c --- /dev/null +++ b/Reconstruction/physics_building3.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:01b93e9e8786dae0ab4acf8e88dab5d49ebbb6185acc599e0a6df203e66fa715 +size 736031340 diff --git a/codes/annotation tool/labelLidarwave.py b/codes/annotation tool/labelLidarwave.py new file mode 100644 index 0000000000000000000000000000000000000000..f28ceaa7ff0ab4b0708f67bc5e7363df5002cbb6 --- /dev/null +++ b/codes/annotation tool/labelLidarwave.py @@ -0,0 +1,1780 @@ +# visualize_semantic_labels_v3.py +# ------------------------------------------------------------ +# Sequential peak annotation workflow + Multi-layer semantic 3D +# +# Added: Traditional Magic Wand UI & Edge-aware region growing +# +# MOD (New Features Request - V3): +# ✅ [Restored] 3D Bins Visualization button and logic. +# ✅ [Modified] Pixel Histogram now works in a dedicated "Inspect" tool mode. +# - Pick/Brush/Eraser: Perform labeling ONLY. +# - Inspect: Performs histogram visualization ONLY (Safe mode). +# ------------------------------------------------------------ + +import sys +import os +import traceback +import argparse +import numpy as np +import cv2 +from glob import glob +from collections import deque +import matplotlib +from concurrent.futures import ProcessPoolExecutor, as_completed + +# Set backend before importing pyplot +matplotlib.use("Qt5Agg") +import matplotlib.pyplot as plt +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure + +try: + import pandas as pd + HAS_PANDAS = True +except ImportError: + HAS_PANDAS = False + print("[Warning] Pandas not installed. TXT->NPY conversion will be disabled.") + +from PyQt5.QtWidgets import ( + QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, + QLabel, QPushButton, QSlider, QRadioButton, QGroupBox, + QSplitter, QSizePolicy, QTextEdit, QScrollArea, QCheckBox, + QButtonGroup, QShortcut, QListWidget, QListWidgetItem, + QProgressBar, QMessageBox, QLineEdit, QFileDialog, QDialog +) +from PyQt5.QtCore import Qt, QPoint, QRect, QThread, pyqtSignal +from PyQt5.QtGui import QImage, QPixmap, QPainter, QColor, QPen, QKeySequence + +try: + import open3d as o3d + HAS_OPEN3D = True +except ImportError: + HAS_OPEN3D = False + print("[Warning] Open3D not installed. 3D visualization will be disabled.") + + +# ========================================== +# Config +# ========================================== +class AppConfig: + IMG_H = 192 + IMG_W = 256 + NUM_LAYERS = 30 + BIN_UNIT = 297 * 1e-12 * 299792458 / 2.0 + DEFAULT_SIGNAL_THRESHOLD = 5 + DEFAULT_SNR_THRESHOLD = 2.0 + LABEL_FWHM_RATIO = 0.5 + + CLASS_LABELS = [ + "Tree (树)", "Road (路)", "Fence (围栏)", "Person (人)", + "Non-motor (非机动车)", "Car (汽车)", "Street Light (路灯)", + "Signage (指示牌)", "Traffic Light (信号灯)", "Door (门)", + "Building (建筑)", "Wall (墙壁)", "Indoor Roof (室内屋顶)", + "Unknown (未知)" + ] + + CUSTOM_COLORS = { + 1: (0, 255, 0), 2: (128, 128, 128), 3: (255, 165, 0), + 4: (255, 0, 0), 5: (255, 20, 147), 6: (30, 144, 255), + 7: (218, 165, 32), 8: (0, 255, 255), 9: (255, 69, 0), + 10: (165, 42, 42), 11: (160, 32, 240), 12: (189, 183, 107), + 13: (0, 128, 128), 14: (255, 255, 0) + } + + CAM_K = np.array([[120.94, 0.0, 130.41], [0.0, 121.12, 97.12], [0.0, 0.0, 1.0]], dtype=np.float64) + CAM_D = np.array([-0.276, 0.062, 0.0, 0.0, 0.0], dtype=np.float64) + + DEFAULT_WAND_TOLERANCE = 15 + DEFAULT_WAND_EDGE_HIGH = 80 + DEFAULT_WAND_CONNECTIVITY_8 = True + DEFAULT_WAND_EDGE_AWARE = True + + +def get_class_colors_dict(): + colors = {0: (0, 0, 0)} + colors.update(AppConfig.CUSTOM_COLORS) + return colors + + +CLASS_COLORS = get_class_colors_dict() + + +def _cv2_cmap(name: str, fallback): + return getattr(cv2, name, fallback) + + +PEAK_CMAPS = [ + ("VIRIDIS", _cv2_cmap("COLORMAP_VIRIDIS", cv2.COLORMAP_JET)), + ("PLASMA", _cv2_cmap("COLORMAP_PLASMA", cv2.COLORMAP_JET)), + ("INFERNO", _cv2_cmap("COLORMAP_INFERNO", cv2.COLORMAP_JET)), + ("MAGMA", _cv2_cmap("COLORMAP_MAGMA", cv2.COLORMAP_JET)), + ("TURBO", _cv2_cmap("COLORMAP_TURBO", cv2.COLORMAP_JET)), + ("JET", cv2.COLORMAP_JET), + ("HOT", cv2.COLORMAP_HOT), + ("OCEAN", cv2.COLORMAP_OCEAN), +] + + +# ========================================== +# Helpers (General) +# ========================================== +def load_hist_npy(path: str) -> np.ndarray: + try: + print(f"[Log] Loading NPY: {path}") + data = np.load(path, mmap_mode="r") + if data.ndim == 3: + H, W, B = data.shape + data = data.reshape(-1, B) + elif data.ndim == 1: + data = data.reshape(1, -1) + return np.ascontiguousarray(data, dtype=np.float32) + except Exception as e: + print(f"[Error] Loading {path} failed: {e}", flush=True) + return None + + +def analyze_peak_structure(vector, peak_idx, noise_floor): + B = len(vector) + peak_val = vector[peak_idx] + if peak_val <= noise_floor: + return (peak_idx, peak_idx), (peak_idx, peak_idx) + + label_thresh = peak_val * AppConfig.LABEL_FWHM_RATIO + l_lab, r_lab = peak_idx, peak_idx + while l_lab > 0 and vector[l_lab] > label_thresh: + l_lab -= 1 + while r_lab < B - 1 and vector[r_lab] > label_thresh: + r_lab += 1 + + l_rem, r_rem = peak_idx, peak_idx + while l_rem > 0: + if vector[l_rem] < noise_floor or vector[l_rem - 1] > vector[l_rem]: + break + l_rem -= 1 + while r_rem < B - 1: + if vector[r_rem] < noise_floor or vector[r_rem + 1] > vector[r_rem]: + break + r_rem += 1 + + l_rem = min(l_rem, l_lab) + r_rem = max(r_rem, r_lab) + return (l_lab, r_lab), (l_rem, r_rem) + + +def safe_numpy_to_pixmap(img_data): + if img_data is None: + return None + h, w = img_data.shape[:2] + try: + if not img_data.flags["C_CONTIGUOUS"]: + img_data = np.ascontiguousarray(img_data) + + if img_data.ndim == 2: + qimg = QImage(img_data.data, w, h, w, QImage.Format_Grayscale8).copy() + elif img_data.ndim == 3: + if img_data.shape[2] == 3: + img_rgba = cv2.cvtColor(img_data, cv2.COLOR_BGR2RGBA) + qimg = QImage(img_rgba.data, w, h, w * 4, QImage.Format_RGBA8888).copy() + elif img_data.shape[2] == 4: + qimg = QImage(img_data.data, w, h, w * 4, QImage.Format_RGBA8888).copy() + else: + return None + else: + return None + return QPixmap.fromImage(qimg) + except Exception: + return None + + +def get_robust_colors(values, colormap_name="jet", p_min=2, p_max=99.5): + vmin, vmax = np.percentile(values, [p_min, p_max]) + denom = max(vmax - vmin, 1e-6) + norm_values = np.clip((values - vmin) / denom, 0, 1) + cmap = plt.get_cmap(colormap_name) + colors = cmap(norm_values)[:, :3] + return colors + + +def sem_bins_to_layers(sem_bins: np.ndarray, H: int, W: int, num_layers: int): + N, B = sem_bins.shape + masks = [np.zeros((H, W), dtype=np.uint8) for _ in range(num_layers)] + flat = sem_bins + + for i in range(N): + row = flat[i] + nz = np.flatnonzero(row) + if nz.size == 0: + continue + breaks = np.flatnonzero(np.diff(nz) != 1) + run_starts = np.concatenate(([0], breaks + 1)) + run_ends = np.concatenate((breaks, [nz.size - 1])) + segments = [] + for rs, re in zip(run_starts, run_ends): + b0 = int(nz[rs]) + b1 = int(nz[re]) + cid = int(row[b0]) + if cid <= 0: continue + segments.append((b0, b1, cid)) + segments.sort(key=lambda t: t[0]) + y = i // W + x = i % W + for k, (_, __, cid) in enumerate(segments[:num_layers]): + masks[k][y, x] = cid + return masks + + +def save_iterative_peeling_layers(out_path, raw_data, manual_masks, layer_thresholds, H, W, fallback_thresh): + try: + folder = os.path.dirname(out_path) + if folder and not os.path.exists(folder): + os.makedirs(folder, exist_ok=True) + + N, B = raw_data.shape + sem_bins = np.zeros((N, B), dtype=np.uint8) + working_data = raw_data.copy() + saved_count = 0 + + for l, mask2d in enumerate(manual_masks): + thr = layer_thresholds[l] if layer_thresholds[l] is not None else fallback_thresh + mask_flat = mask2d.reshape(-1) + labeled_idx = np.flatnonzero(mask_flat > 0) + if labeled_idx.size == 0: + continue + for idx in labeled_idx: + cid = int(mask_flat[idx]) + hist = working_data[idx] + if np.max(hist) <= thr: + continue + peak_idx = int(np.argmax(hist)) + (l_lab, r_lab), (l_rem, r_rem) = analyze_peak_structure(hist, peak_idx, thr) + if r_rem <= l_rem: + continue + sem_bins[idx, l_lab:r_lab + 1] = cid + working_data[idx, l_rem:r_rem + 1] = 0 + saved_count += 1 + + np.save(out_path, sem_bins) + return True, f"Saved: {os.path.basename(out_path)} [Pts: {saved_count}]", saved_count + except Exception as e: + traceback.print_exc() + return False, f"Error saving: {str(e)}", 0 + + +# ========================================== +# Batch Conversion +# ========================================== +def convert_one_file(file_path, output_root): + try: + import pandas as pd + basename = os.path.basename(file_path) + npy_name = os.path.splitext(basename)[0] + '.npy' + out_path = os.path.join(output_root, npy_name) + if os.path.exists(out_path): + return "Skipped (Exists)" + df = pd.read_csv(file_path, sep=r'\s+', header=None, dtype=np.float32, engine='c', memory_map=True) + data = df.values + if data.ndim == 1: + data = data.reshape(1, -1) + np.save(out_path, data) + return "Success" + except Exception as e: + return f"Error: {str(e)}" + +class BatchConverterThread(QThread): + progress_signal = pyqtSignal(int, int) + log_signal = pyqtSignal(str) + finished_signal = pyqtSignal(int, int, int) + + def __init__(self, root_search_dir, output_dir): + super().__init__() + self.root_search_dir = root_search_dir + self.output_dir = output_dir + self.is_running = True + + def run(self): + if not HAS_PANDAS: + self.log_signal.emit("[Error] Pandas not found.") + self.finished_signal.emit(0, 0, 0) + return + + self.log_signal.emit(f"Scanning for TXT files in: {self.root_search_dir}") + search_pattern = os.path.join(self.root_search_dir, "**", "RawDataHistogramMap_frame_*.txt") + files = glob(search_pattern, recursive=True) + if not files: + search_pattern_alt = os.path.join(self.root_search_dir, "**", "*.txt") + files = glob(search_pattern_alt, recursive=True) + + total_files = len(files) + self.log_signal.emit(f"Found {total_files} files.") + if total_files == 0: + self.finished_signal.emit(0, 0, 0) + return + + try: + os.makedirs(self.output_dir, exist_ok=True) + except Exception as e: + self.finished_signal.emit(0, 0, 0) + return + + success_count = 0 + skip_count = 0 + error_count = 0 + processed = 0 + max_workers = min(os.cpu_count() or 4, 8) + + with ProcessPoolExecutor(max_workers=max_workers) as executor: + future_to_file = {executor.submit(convert_one_file, f, self.output_dir): f for f in files} + for future in as_completed(future_to_file): + if not self.is_running: + executor.shutdown(wait=False, cancel_futures=True) + break + result = future.result() + processed += 1 + if result == "Success": success_count += 1 + elif result == "Skipped (Exists)": skip_count += 1 + else: error_count += 1 + self.progress_signal.emit(processed, total_files) + + self.finished_signal.emit(success_count, skip_count, error_count) + + def stop(self): + self.is_running = False + + +# ========================================== +# GUI widgets +# ========================================== +class CenteredCanvas(QWidget): + def __init__(self, parent=None): + super().__init__(parent) + self.setMouseTracking(True) + self.setFocusPolicy(Qt.StrongFocus) + self.img_pixmap = None + self.zoom = 1.0 + self.offset = QPoint(0, 0) + self.last_mouse_pos = QPoint() + self.mode = "pick" + self.panning = False + self.drawing = False + self.start_pos = QPoint() + self.curr_pos = QPoint() + self.action_callback = None + self.mask_overlay = None + self.ghost_overlay = None + self.hide_labels = False + self.ctrl_pressed = False + + def set_content(self, pixmap, mask_pixmap, ghost_pixmap=None): + self.img_pixmap = pixmap + self.mask_overlay = mask_pixmap + self.ghost_overlay = ghost_pixmap + self.update() + + def fit_to_window(self): + if self.img_pixmap is None: + return + w_view, h_view = self.width(), self.height() + w_img, h_img = self.img_pixmap.width(), self.img_pixmap.height() + if w_img > 0 and h_img > 0: + self.zoom = min(w_view / w_img, h_view / h_img) * 0.98 + self.offset = QPoint(0, 0) + self.update() + + def paintEvent(self, event): + painter = QPainter(self) + painter.fillRect(self.rect(), QColor(40, 40, 40)) + if self.img_pixmap is None: + return + ww, wh = self.width(), self.height() + cx, cy = ww // 2, wh // 2 + painter.translate(cx + self.offset.x(), cy + self.offset.y()) + painter.scale(self.zoom, self.zoom) + iw, ih = self.img_pixmap.width(), self.img_pixmap.height() + painter.translate(-iw // 2, -ih // 2) + painter.setRenderHint(QPainter.SmoothPixmapTransform, False) + painter.drawPixmap(0, 0, self.img_pixmap) + if not self.hide_labels: + if self.ghost_overlay is not None: + painter.setOpacity(0.30) + painter.drawPixmap(0, 0, self.ghost_overlay) + painter.setOpacity(1.0) + if self.mask_overlay is not None: + painter.setOpacity(1.0) + painter.drawPixmap(0, 0, self.mask_overlay) + + # Only draw selection box if NOT in inspect mode or pure panning + if self.drawing and self.mode == "pick": + pen = QPen(Qt.green, 2) if self.ctrl_pressed else QPen(Qt.yellow, 1) + pen.setStyle(Qt.SolidLine if self.ctrl_pressed else Qt.DashLine) + painter.setPen(pen) + painter.drawRect(QRect(self.start_pos, self.curr_pos).normalized()) + + def get_img_pos(self, widget_pos): + ww, wh = self.width(), self.height() + cx, cy = ww // 2, wh // 2 + if self.img_pixmap: + dx = widget_pos.x() - (cx + self.offset.x()) + dy = widget_pos.y() - (cy + self.offset.y()) + return int(dx / self.zoom + self.img_pixmap.width() / 2), int(dy / self.zoom + self.img_pixmap.height() / 2) + return 0, 0 + + def wheelEvent(self, event): + self.zoom *= 1.1 if event.angleDelta().y() > 0 else 0.9 + self.zoom = max(0.1, min(self.zoom, 50.0)) + self.update() + + def mousePressEvent(self, event): + if event.button() == Qt.MiddleButton: + self.panning = True + self.last_mouse_pos = event.pos() + self.setCursor(Qt.ClosedHandCursor) + elif event.button() == Qt.LeftButton: + x, y = self.get_img_pos(event.pos()) + self.start_pos = QPoint(x, y) + self.curr_pos = QPoint(x, y) + self.drawing = True + if self.mode == "draw" and self.action_callback: + self.action_callback("draw_start", self.start_pos, None) + + def mouseMoveEvent(self, event): + if self.panning: + self.offset += event.pos() - self.last_mouse_pos + self.last_mouse_pos = event.pos() + self.update() + else: + x, y = self.get_img_pos(event.pos()) + self.curr_pos = QPoint(x, y) + if self.drawing: + if self.mode == "draw" and self.action_callback: + self.action_callback("draw_drag", self.start_pos, self.curr_pos) + self.start_pos = self.curr_pos + elif self.mode == "pick": + self.update() + + def mouseReleaseEvent(self, event): + if self.panning: + self.panning = False + self.setCursor(Qt.ArrowCursor) + return + if event.button() == Qt.LeftButton and self.drawing: + self.drawing = False + end_pos = self.curr_pos if not self.curr_pos.isNull() else self.start_pos + if self.mode == "pick" and self.action_callback: + self.action_callback("pick_end_ctrl" if self.ctrl_pressed else "pick_end", self.start_pos, end_pos) + elif self.mode == "draw" and self.action_callback: + self.action_callback("draw_end", end_pos, None) + self.update() + +class PopupImageDialog(QDialog): + def __init__(self, pixmap, title, parent=None): + super().__init__(parent) + self.setWindowTitle(title + " (Double click to close)") + self.resize(800, 600) + self.setWindowFlags(Qt.Window) + layout = QVBoxLayout(self) + layout.setContentsMargins(0, 0, 0, 0) + self.viewer = CenteredCanvas() + self.viewer.set_content(pixmap, None) + self.viewer.fit_to_window() + layout.addWidget(self.viewer) + + def showEvent(self, event): + self.viewer.fit_to_window() + super().showEvent(event) + +class ResizableImage(QWidget): + def __init__(self, parent=None, title=""): + super().__init__(parent) + self.pixmap = None + self.title = title + self.setSizePolicy(QSizePolicy.Ignored, QSizePolicy.Ignored) + self.setMinimumHeight(150) + self.setCursor(Qt.PointingHandCursor) + self.setToolTip("Double click to enlarge (Popup)") + + def set_image(self, pixmap): + self.pixmap = pixmap + self.update() + + def set_title(self, title: str): + self.title = title + self.update() + + def paintEvent(self, event): + painter = QPainter(self) + painter.fillRect(self.rect(), QColor(25, 25, 25)) + painter.setPen(Qt.white) + painter.drawText(10, 20, self.title) + if self.pixmap: + target = self.rect().adjusted(0, 25, 0, 0) + scaled = self.pixmap.scaled(target.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation) + x = target.x() + (target.width() - scaled.width()) // 2 + y = target.y() + (target.height() - scaled.height()) // 2 + painter.drawPixmap(x, y, scaled) + + def mouseDoubleClickEvent(self, event): + if self.pixmap: + self.pop = PopupImageDialog(self.pixmap, self.title, self) + self.pop.show() + +class PixelHistogramDialog(QDialog): + def __init__(self, parent=None): + super().__init__(parent) + self.setWindowTitle("Pixel Histogram Inspector") + self.resize(600, 450) + self.setWindowFlags(Qt.Window) + layout = QVBoxLayout(self) + self.fig = Figure(figsize=(5, 4), dpi=100) + self.canvas = FigureCanvas(self.fig) + self.canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) + layout.addWidget(self.canvas) + self.ax = self.fig.add_subplot(111) + self.status_label = QLabel("Switch tool to 'Inspect' and click pixel.") + layout.addWidget(self.status_label) + + def update_plot(self, x, y, raw_vec, labels_info, noise_floor=0): + self.ax.clear() + self.ax.plot(raw_vec, color='black', linewidth=1.0, label='Photon Counts') + max_val = np.max(raw_vec) if raw_vec.size > 0 else 1.0 + drawn_labels = set() + for item in labels_info: + cid = item['cid'] + l_range, r_range = item['range'] + rgb = item['color'] + c_norm = (rgb[0]/255.0, rgb[1]/255.0, rgb[2]/255.0) + x_vals = np.arange(l_range, r_range + 1) + if x_vals.size > 0: + label_text = f"Class {cid}" + if cid not in drawn_labels: + self.ax.fill_between(x_vals, 0, max_val * 1.1, color=c_norm, alpha=0.3, label=label_text) + drawn_labels.add(cid) + else: + self.ax.fill_between(x_vals, 0, max_val * 1.1, color=c_norm, alpha=0.3) + if noise_floor > 0: + self.ax.axhline(y=noise_floor, color='gray', linestyle='--', alpha=0.5, label='Noise Level') + self.ax.set_title(f"Pixel ({x}, {y}) | Max: {max_val:.1f}") + self.ax.set_xlabel("Time Bins") + self.ax.set_ylabel("Counts") + self.ax.grid(True, linestyle=':', alpha=0.6) + if drawn_labels: + self.ax.legend(loc='upper right') + self.canvas.draw() + self.status_label.setText(f"Inspecting Pixel: x={x}, y={y}") + + +# ========================================== +# Main window +# ========================================== +class SPADLabelerPixel(QMainWindow): + def __init__(self, args): + super().__init__() + self.args = args + self.setWindowTitle("SPAD Labeler - Sequential Peak Annotation") + self.resize(1780, 1040) + os.makedirs(self.args.cache_dir, exist_ok=True) + + self.H, self.W = AppConfig.IMG_H, AppConfig.IMG_W + self.num_layers = AppConfig.NUM_LAYERS + self.class_names = {i + 1: name for i, name in enumerate(AppConfig.CLASS_LABELS)} + self.UNKNOWN_CID = int(max(self.class_names.keys())) + self.class_colors_rgb = CLASS_COLORS + + self.raw_data = None + self.noise_map = None + self.layer_view_cache = {} + + self.manual_masks = [np.zeros((self.H, self.W), dtype=np.uint8) for _ in range(self.num_layers)] + self.layer_thresholds = [None for _ in range(self.num_layers)] + + self.undo_stack = deque(maxlen=20) + self.is_dirty = False + self.mask_revision = 0 + self.brush_size = 5 + self.tool_mode = "pick" + self.current_class = 1 + self.peel_depth = 0 + self.edit_layer = 0 + self.region_step = 0 + self.peel_by_class = True + self._region_cache_layer = None + self._region_cache_revision = -1 + self._region_cache_mode = None + self._region_cache_regions = [] + + self.wand_tolerance = int(AppConfig.DEFAULT_WAND_TOLERANCE) + self.wand_connectivity_8 = bool(AppConfig.DEFAULT_WAND_CONNECTIVITY_8) + self.wand_edge_aware = bool(AppConfig.DEFAULT_WAND_EDGE_AWARE) + self.wand_edge_high = int(AppConfig.DEFAULT_WAND_EDGE_HIGH) + + self.converter_thread = None + self.hist_inspector = None + + self.init_ui() + + self.file_list = sorted(glob(os.path.join(args.in_dir, args.pattern))) + self.current_file_idx = 0 + self.update_file_list_ui() + if self.file_list: + self.load_file(self.file_list[0]) + + # ---------------- UI ---------------- + def init_ui(self): + self.splitter = QSplitter(Qt.Horizontal) + self.setCentralWidget(self.splitter) + + self.left_panel = QSplitter(Qt.Vertical) + self.lbl_global = ResizableImage(title="Layer 0 (Raw Peak)") + self.left_panel.addWidget(self.lbl_global) + self.lbl_result = ResizableImage(title="Annotation Preview (Unpeeled + Current Region State)") + self.left_panel.addWidget(self.lbl_result) + self.lbl_info = ResizableImage(title="Peeling View") + self.left_panel.addWidget(self.lbl_info) + self.lbl_depth = ResizableImage(title="Depth Map (Visible Peak)") + self.left_panel.addWidget(self.lbl_depth) + + self.left_ctrl = QWidget() + l_lay = QVBoxLayout(self.left_ctrl) + l_lay.addWidget(QLabel("Peel / Edit / Region Control")) + l_lay.addWidget(QLabel("Tip: Double-click images above to enlarge.")) + + l_lay.addWidget(QLabel("Peel Depth (display stage):")) + self.sl_peel = QSlider(Qt.Horizontal) + self.sl_peel.setRange(0, self.num_layers) + self.sl_peel.setValue(0) + self.sl_peel.valueChanged.connect(self.change_peel_depth) + l_lay.addWidget(self.sl_peel) + + self.lbl_region = QLabel("Semantic Step (current peeled layer): 0 / 0") + l_lay.addWidget(self.lbl_region) + self.sl_region = QSlider(Qt.Horizontal) + self.sl_region.setRange(0, 0) + self.sl_region.setValue(0) + self.sl_region.valueChanged.connect(self.change_region_step) + l_lay.addWidget(self.sl_region) + + self.lbl_next_region = QLabel("Selected Peel Target: -") + self.lbl_next_region.setWordWrap(True) + l_lay.addWidget(self.lbl_next_region) + + l_lay.addWidget(QLabel("Edit Layer (write target):")) + self.sl_edit = QSlider(Qt.Horizontal) + self.sl_edit.setRange(0, self.num_layers - 1) + self.sl_edit.setValue(0) + self.sl_edit.valueChanged.connect(self.change_edit_layer) + l_lay.addWidget(self.sl_edit) + + self.chk_auto_edit = QCheckBox("Auto Edit Layer = Peel Depth (recommended)") + self.chk_auto_edit.setChecked(True) + self.chk_auto_edit.toggled.connect(self.on_auto_edit_toggled) + l_lay.addWidget(self.chk_auto_edit) + + self.chk_lock_to_visible = QCheckBox("Lock labeling to visible peak") + self.chk_lock_to_visible.setChecked(True) + self.chk_lock_to_visible.toggled.connect(self.update_all_views) + l_lay.addWidget(self.chk_lock_to_visible) + + self.chk_focus_edit = QCheckBox("Focus view to Edit Layer") + self.chk_focus_edit.setChecked(True) + self.chk_focus_edit.toggled.connect(self.update_all_views) + l_lay.addWidget(self.chk_focus_edit) + + self.chk_peel_class = QCheckBox("Region Step by Semantic") + self.chk_peel_class.setChecked(True) + self.chk_peel_class.toggled.connect(self.on_peel_mode_changed) + l_lay.addWidget(self.chk_peel_class) + + self.lbl_state = QLabel("Peel Depth: 0 | Region Step: 0 | Edit Layer: 0") + l_lay.addWidget(self.lbl_state) + self.lbl_peeled_ids = QLabel("Fully Peeled Layers: None") + self.lbl_peeled_ids.setStyleSheet("color: #FFA500; font-weight: bold;") + l_lay.addWidget(self.lbl_peeled_ids) + self.btn_clear_layer_thr = QPushButton("Clear Layer Threshold Locks") + self.btn_clear_layer_thr.clicked.connect(self.clear_layer_threshold_locks) + l_lay.addWidget(self.btn_clear_layer_thr) + + l_lay.addStretch() + self.left_panel.addWidget(self.left_ctrl) + self.left_panel.setSizes([220, 220, 220, 220, 300]) + self.splitter.addWidget(self.left_panel) + + self.canvas = CenteredCanvas() + self.canvas.action_callback = self.handle_canvas_action + self.splitter.addWidget(self.canvas) + + self.right_panel = QWidget() + r_lay = QVBoxLayout(self.right_panel) + + r_lay.addWidget(QLabel("File List:")) + self.file_list_widget = QListWidget() + self.file_list_widget.setFixedHeight(200) + self.file_list_widget.currentRowChanged.connect(self.on_file_list_clicked) + r_lay.addWidget(self.file_list_widget) + + r_lay.addWidget(QLabel("Batch Convert Config:")) + h_src = QHBoxLayout() + self.txt_source_edit = QLineEdit() + self.txt_source_edit.setText(self.args.in_dir) + self.txt_source_edit.setPlaceholderText("Path to folder with TXT files...") + btn_browse_src = QPushButton("...") + btn_browse_src.setFixedWidth(30) + btn_browse_src.clicked.connect(self.browse_txt_source) + h_src.addWidget(self.txt_source_edit) + h_src.addWidget(btn_browse_src) + r_lay.addLayout(h_src) + + self.btn_convert = QPushButton("Batch Convert TXT -> NPY") + self.btn_convert.setStyleSheet("background-color: #3d3d3d; color: #aaffaa;") + self.btn_convert.clicked.connect(self.start_batch_conversion) + r_lay.addWidget(self.btn_convert) + self.pbar = QProgressBar() + self.pbar.setVisible(False) + r_lay.addWidget(self.pbar) + + h_nav = QHBoxLayout() + btn_pl = QPushButton("Peel -1 (A)") + btn_pl.clicked.connect(self.apply_peel_prev) + btn_nl = QPushButton("Peel +1 (D)") + btn_nl.clicked.connect(self.apply_peel_next) + h_nav.addWidget(btn_pl) + h_nav.addWidget(btn_nl) + r_lay.addLayout(h_nav) + + h_reg = QHBoxLayout() + btn_rprev = QPushButton("Region -1 (Z)") + btn_rprev.clicked.connect(self.region_prev) + btn_rnext = QPushButton("Region +1 (X)") + btn_rnext.clicked.connect(self.region_next) + h_reg.addWidget(btn_rprev) + h_reg.addWidget(btn_rnext) + r_lay.addLayout(h_reg) + + h_edit = QHBoxLayout() + btn_el = QPushButton("Edit -1 (Shift+A)") + btn_el.clicked.connect(self.edit_prev) + btn_er = QPushButton("Edit +1 (Shift+D)") + btn_er.clicked.connect(self.edit_next) + h_edit.addWidget(btn_el) + h_edit.addWidget(btn_er) + r_lay.addLayout(h_edit) + + h_file = QHBoxLayout() + btn_pf = QPushButton("<< File") + btn_pf.clicked.connect(self.prev_file) + btn_nf = QPushButton("File >>") + btn_nf.clicked.connect(self.next_file) + h_file.addWidget(btn_pf) + h_file.addWidget(btn_nf) + r_lay.addLayout(h_file) + + self.chk_autosave = QCheckBox("Auto Save") + self.chk_autosave.setChecked(True) + r_lay.addWidget(self.chk_autosave) + + h_viz = QHBoxLayout() + self.chk_ghost = QCheckBox("Ghost (G)") + self.chk_ghost.toggled.connect(self.update_all_views) + self.chk_hide = QCheckBox("Hide (H)") + self.chk_hide.toggled.connect(self.update_all_views) + btn_viz = QPushButton("3D Depth") + btn_viz.clicked.connect(self.visualize_3d_point_cloud) + btn_sem = QPushButton("3D Semantic") + btn_sem.clicked.connect(self.visualize_3d_semantic_point_cloud) + + # [RESTORED] 3D Bins Button + btn_bins = QPushButton("3D Bins") + btn_bins.clicked.connect(self.visualize_3d_semantic_bins_point_cloud) + + btn_hist = QPushButton("Pixel Hist") + btn_hist.clicked.connect(self.open_pixel_inspector) + + h_viz.addWidget(self.chk_ghost) + h_viz.addWidget(self.chk_hide) + h_viz.addWidget(btn_viz) + h_viz.addWidget(btn_sem) + h_viz.addWidget(btn_bins) # Add back + h_viz.addWidget(btn_hist) + r_lay.addLayout(h_viz) + + g_filt = QGroupBox("Filter (Threshold & SNR)") + f_lay = QVBoxLayout(g_filt) + self.sl_thresh = QSlider(Qt.Horizontal) + self.sl_thresh.setRange(0, 2000) + self.sl_thresh.setValue(AppConfig.DEFAULT_SIGNAL_THRESHOLD) + self.lbl_thr = QLabel(f"Int Thresh: {AppConfig.DEFAULT_SIGNAL_THRESHOLD}") + self.sl_thresh.valueChanged.connect(self.on_thresh_changed) + h_t = QHBoxLayout() + h_t.addWidget(self.sl_thresh) + h_t.addWidget(self.lbl_thr) + f_lay.addLayout(h_t) + self.sl_snr = QSlider(Qt.Horizontal) + self.sl_snr.setRange(10, 200) + self.sl_snr.setValue(int(AppConfig.DEFAULT_SNR_THRESHOLD * 10)) + self.lbl_snr = QLabel(f"SNR (Peak/Mean) > {AppConfig.DEFAULT_SNR_THRESHOLD:.1f}") + self.sl_snr.valueChanged.connect(self.on_snr_changed) + h_s = QHBoxLayout() + h_s.addWidget(self.sl_snr) + h_s.addWidget(self.lbl_snr) + f_lay.addLayout(h_s) + r_lay.addWidget(g_filt) + + g_wand = QGroupBox("Magic Wand (传统魔棒)") + w_lay = QVBoxLayout(g_wand) + self.chk_wand_edge = QCheckBox("Edge-aware") + self.chk_wand_edge.setChecked(self.wand_edge_aware) + self.chk_wand_edge.toggled.connect(self.on_wand_params_changed) + w_lay.addWidget(self.chk_wand_edge) + self.chk_wand_conn8 = QCheckBox("8-way connectivity") + self.chk_wand_conn8.setChecked(self.wand_connectivity_8) + self.chk_wand_conn8.toggled.connect(self.on_wand_params_changed) + w_lay.addWidget(self.chk_wand_conn8) + w_lay.addWidget(QLabel("Tolerance:")) + self.sl_wand_tol = QSlider(Qt.Horizontal) + self.sl_wand_tol.setRange(0, 255) + self.sl_wand_tol.setValue(self.wand_tolerance) + self.sl_wand_tol.valueChanged.connect(self.on_wand_tol_changed) + self.lbl_wand_tol = QLabel(str(self.wand_tolerance)) + h_tol = QHBoxLayout() + h_tol.addWidget(self.sl_wand_tol) + h_tol.addWidget(self.lbl_wand_tol) + w_lay.addLayout(h_tol) + w_lay.addWidget(QLabel("Edge strength:")) + self.sl_wand_edge = QSlider(Qt.Horizontal) + self.sl_wand_edge.setRange(0, 255) + self.sl_wand_edge.setValue(self.wand_edge_high) + self.sl_wand_edge.valueChanged.connect(self.on_wand_edge_changed) + self.lbl_wand_edge = QLabel(str(self.wand_edge_high)) + h_ed = QHBoxLayout() + h_ed.addWidget(self.sl_wand_edge) + h_ed.addWidget(self.lbl_wand_edge) + w_lay.addLayout(h_ed) + r_lay.addWidget(g_wand) + + g_tool = QGroupBox("Tools") + t_lay = QVBoxLayout(g_tool) + self.rb_pick = QRadioButton("Pick (Q)") + self.rb_pick.setChecked(True) + self.rb_pick.toggled.connect(self.update_tool_mode) + self.rb_brush = QRadioButton("Brush (W)") + self.rb_brush.toggled.connect(self.update_tool_mode) + self.rb_eraser = QRadioButton("Eraser (E)") + self.rb_eraser.toggled.connect(self.update_tool_mode) + + # [NEW] Inspect Mode + self.rb_inspect = QRadioButton("Inspect (I)") + self.rb_inspect.toggled.connect(self.update_tool_mode) + + t_lay.addWidget(self.rb_pick) + t_lay.addWidget(self.rb_brush) + t_lay.addWidget(self.rb_eraser) + t_lay.addWidget(self.rb_inspect) + + h_sz = QHBoxLayout() + h_sz.addWidget(QLabel("Brush Size:")) + self.sl_size = QSlider(Qt.Horizontal) + self.sl_size.setRange(1, 30) + self.sl_size.setValue(self.brush_size) + self.lbl_size = QLabel(str(self.brush_size)) + self.sl_size.valueChanged.connect(self.update_brush_size) + h_sz.addWidget(self.sl_size) + h_sz.addWidget(self.lbl_size) + t_lay.addLayout(h_sz) + self.chk_overwrite = QCheckBox("Allow Overwrite (允许覆盖)") + self.chk_overwrite.setChecked(True) + t_lay.addWidget(self.chk_overwrite) + r_lay.addWidget(g_tool) + + g_cls = QGroupBox("Classes") + scroll = QScrollArea() + scroll.setWidgetResizable(True) + content = QWidget() + sc_lay = QVBoxLayout(content) + self.cls_bg = QButtonGroup(self) + self.cls_radios = {} + for cid, name in self.class_names.items(): + rgb = self.class_colors_rgb.get(cid, (0, 0, 0)) + rb = QRadioButton(f"■ {name}") + rb.setStyleSheet(f"color: rgb{rgb}; font-weight: bold;") + self.cls_bg.addButton(rb, cid) + self.cls_radios[cid] = rb + sc_lay.addWidget(rb) + scroll.setWidget(content) + c_lay = QVBoxLayout(g_cls) + c_lay.addWidget(scroll) + r_lay.addWidget(g_cls) + self.cls_bg.buttonClicked[int].connect(lambda i: setattr(self, "current_class", int(i))) + + self.log_win = QTextEdit() + self.log_win.setReadOnly(True) + self.log_win.setFixedHeight(100) + r_lay.addWidget(self.log_win) + + btn_unk = QPushButton("Fill Unknown (Layer U)") + btn_unk.clicked.connect(self.fill_unknown_current_layer) + r_lay.addWidget(btn_unk) + btn_save = QPushButton("SAVE (Ctrl+S)") + btn_save.clicked.connect(self.save_current) + r_lay.addWidget(btn_save) + + self.splitter.addWidget(self.right_panel) + self.splitter.setSizes([430, 980, 340]) + + self.init_shortcuts() + self.hist_inspector = PixelHistogramDialog(self) + + def init_shortcuts(self): + QShortcut(QKeySequence("A"), self, self.apply_peel_prev) + QShortcut(QKeySequence("D"), self, self.apply_peel_next) + QShortcut(QKeySequence("Z"), self, self.region_prev) + QShortcut(QKeySequence("X"), self, self.region_next) + QShortcut(QKeySequence("Shift+A"), self, self.edit_prev) + QShortcut(QKeySequence("Shift+D"), self, self.edit_next) + QShortcut(QKeySequence("Left"), self, self.prev_file) + QShortcut(QKeySequence("Right"), self, self.next_file) + QShortcut(QKeySequence("Q"), self, lambda: self.rb_pick.setChecked(True)) + QShortcut(QKeySequence("W"), self, lambda: self.rb_brush.setChecked(True)) + QShortcut(QKeySequence("E"), self, lambda: self.rb_eraser.setChecked(True)) + # [NEW] Shortcut for Inspect + QShortcut(QKeySequence("I"), self, lambda: self.rb_inspect.setChecked(True)) + + QShortcut(QKeySequence("Ctrl+S"), self, self.save_current) + QShortcut(QKeySequence("Ctrl+Z"), self, self.undo) + QShortcut(QKeySequence("U"), self, self.fill_unknown_current_layer) + QShortcut(QKeySequence("H"), self, lambda: self.chk_hide.setChecked(not self.chk_hide.isChecked())) + QShortcut(QKeySequence("G"), self, lambda: self.chk_ghost.setChecked(not self.chk_ghost.isChecked())) + QShortcut(QKeySequence("F"), self, lambda: self.chk_focus_edit.setChecked(not self.chk_focus_edit.isChecked())) + QShortcut(QKeySequence("T"), self, lambda: self.chk_lock_to_visible.setChecked(not self.chk_lock_to_visible.isChecked())) + QShortcut(QKeySequence("R"), self, lambda: self.chk_peel_class.setChecked(not self.chk_peel_class.isChecked())) + QShortcut(QKeySequence("["), self, self.cycle_class_prev) + QShortcut(QKeySequence("]"), self, self.cycle_class_next) + QShortcut(QKeySequence("="), self, lambda: self.morph_current_mask("dilate")) + QShortcut(QKeySequence("-"), self, lambda: self.morph_current_mask("erode")) + + keys = [Qt.Key_1, Qt.Key_2, Qt.Key_3, Qt.Key_4, Qt.Key_5, + Qt.Key_6, Qt.Key_7, Qt.Key_8, Qt.Key_9, Qt.Key_0] + for i, key in enumerate(keys): + cls_idx = i + 1 + if cls_idx in self.class_names: + QShortcut(QKeySequence(key), self, lambda idx=cls_idx: self.set_class_by_key(idx)) + + # ---------------- UI Helper Methods ---------------- + def browse_txt_source(self): + directory = QFileDialog.getExistingDirectory( + self, "Select TXT Source Directory", self.txt_source_edit.text() + ) + if directory: + self.txt_source_edit.setText(directory) + + # ---------------- Pixel Inspector ---------------- + def open_pixel_inspector(self): + if self.hist_inspector is None: + self.hist_inspector = PixelHistogramDialog(self) + self.hist_inspector.show() + self.hist_inspector.raise_() + self.hist_inspector.activateWindow() + # Auto switch to inspect mode for convenience? Optional. + # self.rb_inspect.setChecked(True) + + def update_histogram_inspector(self, x, y): + if self.hist_inspector is None or not self.hist_inspector.isVisible(): + # If closed, re-open + self.open_pixel_inspector() + + if self.raw_data is None: return + idx = y * self.W + x + if idx >= self.raw_data.shape[0]: return + raw_vec = self.raw_data[idx] + labels_info = self.simulate_pixel_peeling(x, y, raw_vec) + noise = self.noise_map[y, x] if self.noise_map is not None else 0 + self.hist_inspector.update_plot(x, y, raw_vec, labels_info, noise) + + def simulate_pixel_peeling(self, x, y, raw_vec): + labels_info = [] + working_vec = raw_vec.copy() + curr_thr = int(self.sl_thresh.value()) + for l in range(self.num_layers): + m = self.manual_masks[l] + cid = int(m[y, x]) + if cid > 0: + thr = self.layer_thresholds[l] if self.layer_thresholds[l] is not None else curr_thr + if np.max(working_vec) > thr: + peak_idx = int(np.argmax(working_vec)) + (l_lab, r_lab), (l_rem, r_rem) = analyze_peak_structure(working_vec, peak_idx, thr) + if r_lab >= l_lab: + color = self.class_colors_rgb.get(cid, (128, 128, 128)) + labels_info.append({'cid': cid, 'range': (l_lab, r_lab), 'color': color}) + if r_rem > l_rem: + working_vec[l_rem:r_rem + 1] = 0 + return labels_info + + # ... [Batch Conversion methods kept same] ... + def start_batch_conversion(self): + if self.converter_thread is not None and self.converter_thread.isRunning(): return + target_dir = self.txt_source_edit.text().strip() + if not target_dir or not os.path.isdir(target_dir): + QMessageBox.warning(self, "Invalid Path", "Please select a valid directory containing TXT files.") + return + script_dir = os.path.dirname(os.path.abspath(__file__)) + output_npy_dir = os.path.join(script_dir, "npy") + reply = QMessageBox.question(self, 'Batch Convert', + f"Convert TXT files from:\n{target_dir}\n\nTo NPY files in:\n{output_npy_dir} ?", + QMessageBox.Yes | QMessageBox.No, QMessageBox.No) + if reply == QMessageBox.No: return + self.btn_convert.setEnabled(False) + self.pbar.setVisible(True) + self.pbar.setValue(0) + self.log_win.append(f"Starting batch conversion...") + self.converter_thread = BatchConverterThread(target_dir, output_npy_dir) + self.converter_thread.progress_signal.connect(self.on_conversion_progress) + self.converter_thread.log_signal.connect(self.log_win.append) + self.converter_thread.finished_signal.connect(self.on_conversion_finished) + self.converter_thread.start() + + def on_conversion_progress(self, current, total): + self.pbar.setMaximum(total) + self.pbar.setValue(current) + + def on_conversion_finished(self, success, skip, error): + self.log_win.append(f"Conversion Done! Success: {success}, Skipped: {skip}, Errors: {error}") + self.pbar.setVisible(False) + self.btn_convert.setEnabled(True) + source_dir = self.txt_source_edit.text().strip() + output_npy_dir = os.path.join(source_dir, "npy") + if os.path.abspath(self.args.in_dir) == os.path.abspath(output_npy_dir): + self.file_list = sorted(glob(os.path.join(self.args.in_dir, self.args.pattern))) + self.update_file_list_ui() + + # ---------------- Magic Wand/Tools ---------------- + def on_wand_params_changed(self, _=None): + self.wand_edge_aware = bool(self.chk_wand_edge.isChecked()) + self.wand_connectivity_8 = bool(self.chk_wand_conn8.isChecked()) + + def on_wand_tol_changed(self, v): + self.wand_tolerance = int(v) + self.lbl_wand_tol.setText(str(int(v))) + + def on_wand_edge_changed(self, v): + self.wand_edge_high = int(v) + self.lbl_wand_edge.setText(str(int(v))) + + # ... [State helpers kept same] ... + def clear_layer_cache(self): self.layer_view_cache.clear() + def set_dirty(self): + self.clear_layer_cache() + self.is_dirty = True + self.mask_revision += 1 + if " *" not in self.windowTitle(): self.setWindowTitle(self.windowTitle() + " *") + def push_undo(self): + stack = np.stack(self.manual_masks, axis=0).copy() + thr = list(self.layer_thresholds) + self.undo_stack.append((stack, thr, self.peel_depth, self.region_step, self.edit_layer, self.peel_by_class)) + def clear_layer_threshold_locks(self): + self.layer_thresholds = [None for _ in range(self.num_layers)] + self.clear_layer_cache() + self.update_all_views() + def _clamp_xy(self, x, y): + x = max(0, min(int(x), self.W - 1)) + y = max(0, min(int(y), self.H - 1)) + return x, y + def _ensure_layer_threshold(self, layer_idx: int): + if self.layer_thresholds[layer_idx] is None: + self.layer_thresholds[layer_idx] = int(self.sl_thresh.value()) + + def update_brush_size(self, val): + self.brush_size = int(val) + self.lbl_size.setText(str(self.brush_size)) + + def on_thresh_changed(self, v): + self.clear_layer_cache() + self.update_all_views() + self.lbl_thr.setText(f"Int Thresh: {int(v)}") + + def on_snr_changed(self, v): + val = float(v) / 10.0 + self.lbl_snr.setText(f"SNR > {val:.1f}") + self.clear_layer_cache() + self.update_all_views() + + def update_tool_mode(self): + # Determine mode + if self.rb_pick.isChecked(): self.tool_mode = "pick" + elif self.rb_brush.isChecked(): self.tool_mode = "brush" + elif self.rb_eraser.isChecked(): self.tool_mode = "eraser" + elif self.rb_inspect.isChecked(): self.tool_mode = "inspect" + + # Set canvas visual mode (inspect shares pick cursor logic, but handled differently in click) + if self.tool_mode in ["brush", "eraser"]: + self.canvas.mode = "draw" + else: + self.canvas.mode = "pick" + + # ... [Peel/Edit logic kept same] ... + def on_auto_edit_toggled(self, checked): + if checked: self._sync_edit_with_peel() + self.update_all_views() + def on_peel_mode_changed(self, checked): + self.peel_by_class = bool(checked) + self.region_step = 0 + self.clear_layer_cache() + self._region_cache_layer = None + self._region_cache_revision = -1 + self._region_cache_mode = None + self._region_cache_regions = [] + self._sync_region_slider() + self.update_all_views() + def _sync_edit_with_peel(self): + target = min(int(self.peel_depth), self.num_layers - 1) + if int(self.edit_layer) != target: + self.edit_layer = target + self.sl_edit.blockSignals(True) + self.sl_edit.setValue(self.edit_layer) + self.sl_edit.blockSignals(False) + def change_peel_depth(self, idx): + self.peel_depth = int(idx) + self.region_step = 0 + if self.chk_auto_edit.isChecked(): self._sync_edit_with_peel() + self._sync_region_slider() + self.update_all_views() + def change_region_step(self, idx): + self.region_step = int(idx) + self.clear_layer_cache() + self.update_all_views() + def change_edit_layer(self, idx): + self.edit_layer = int(idx) + self.update_all_views() + def apply_peel_next(self): + self.peel_depth = min(self.num_layers, self.peel_depth + 1) + self.region_step = 0 + self.sl_peel.blockSignals(True) + self.sl_peel.setValue(self.peel_depth) + self.sl_peel.blockSignals(False) + if self.chk_auto_edit.isChecked(): self._sync_edit_with_peel() + self._sync_region_slider() + self.update_all_views() + def apply_peel_prev(self): + self.peel_depth = max(0, self.peel_depth - 1) + self.region_step = 0 + self.sl_peel.blockSignals(True) + self.sl_peel.setValue(self.peel_depth) + self.sl_peel.blockSignals(False) + if self.chk_auto_edit.isChecked(): self._sync_edit_with_peel() + self._sync_region_slider() + self.update_all_views() + def region_next(self): + maxv = self.sl_region.maximum() + self.region_step = min(maxv, self.region_step + 1) + self.sl_region.blockSignals(True) + self.sl_region.setValue(self.region_step) + self.sl_region.blockSignals(False) + self.clear_layer_cache() + self.update_all_views() + def region_prev(self): + self.region_step = max(0, self.region_step - 1) + self.sl_region.blockSignals(True) + self.sl_region.setValue(self.region_step) + self.sl_region.blockSignals(False) + self.clear_layer_cache() + self.update_all_views() + def edit_next(self): + self.edit_layer = min(self.num_layers - 1, self.edit_layer + 1) + self.sl_edit.blockSignals(True) + self.sl_edit.setValue(self.edit_layer) + self.sl_edit.blockSignals(False) + self.update_all_views() + def edit_prev(self): + self.edit_layer = max(0, self.edit_layer - 1) + self.sl_edit.blockSignals(True) + self.sl_edit.setValue(self.edit_layer) + self.sl_edit.blockSignals(False) + self.update_all_views() + def _get_peak_cmap_for_depth(self, peel_depth: int): + name, cmap = PEAK_CMAPS[int(peel_depth) % len(PEAK_CMAPS)] + return name, cmap + + # ... [File list / Load Save ... same] ... + def update_file_list_ui(self): + self.file_list_widget.clear() + for f in self.file_list: + base = os.path.splitext(os.path.basename(f))[0] + is_lbl = os.path.exists(os.path.join(self.args.out_root, f"{base}_semantic.npy")) + item = QListWidgetItem(os.path.basename(f)) + if is_lbl: + item.setForeground(QColor(0, 150, 0)) + font = item.font() + font.setBold(True) + item.setFont(font) + item.setText(f"✔ {os.path.basename(f)}") + self.file_list_widget.addItem(item) + if 0 <= self.current_file_idx < len(self.file_list): + self.file_list_widget.setCurrentRow(self.current_file_idx) + def on_file_list_clicked(self, row): + if row < 0 or row >= len(self.file_list): return + if self.chk_autosave.isChecked() and self.is_dirty: self.save_current(silent=True) + self.current_file_idx = int(row) + self.load_file(self.file_list[self.current_file_idx]) + def prev_file(self): self.on_file_list_clicked(max(0, self.current_file_idx - 1)) + def next_file(self): self.on_file_list_clicked(min(len(self.file_list) - 1, self.current_file_idx + 1)) + + def load_file(self, path): + QApplication.setOverrideCursor(Qt.WaitCursor) + self.is_dirty = False + try: + self.undo_stack.clear() + self.current_file_path = path + self.setWindowTitle(f"SPAD Labeler - {os.path.basename(path)}") + self.raw_data = load_hist_npy(path) + if self.raw_data is None: return + self.noise_map = np.mean(self.raw_data, axis=1).reshape(self.H, self.W) + self.manual_masks = [np.zeros((self.H, self.W), dtype=np.uint8) for _ in range(self.num_layers)] + self.layer_thresholds = [None for _ in range(self.num_layers)] + self.layer_view_cache.clear() + base = os.path.splitext(os.path.basename(path))[0] + npy = os.path.join(self.args.out_root, f"{base}_semantic.npy") + if os.path.exists(npy): + try: + d = np.load(npy) + if d.ndim == 2 and d.shape[0] == self.H * self.W: + self.manual_masks = sem_bins_to_layers(d.astype(np.uint8), self.H, self.W, self.num_layers) + self.log_win.append("Loaded existing semantic.npy.") + except Exception: pass + self.peel_depth = 0 + self.edit_layer = 0 + self.region_step = 0 + self.peel_by_class = bool(self.chk_peel_class.isChecked()) + self.sl_peel.setValue(0) + self.sl_edit.setValue(0) + self._sync_region_slider() + self.update_all_views() + if self.hist_inspector and self.hist_inspector.isVisible(): + self.hist_inspector.ax.clear() + self.hist_inspector.canvas.draw() + except Exception as e: + traceback.print_exc() + finally: + QApplication.restoreOverrideCursor() + + def save_current(self, silent=False): + if not self.is_dirty and not silent: return + base = os.path.splitext(os.path.basename(self.current_file_path))[0] + out = os.path.join(self.args.out_root, f"{base}_semantic.npy") + ok, msg, _ = save_iterative_peeling_layers(out, self.raw_data, self.manual_masks, self.layer_thresholds, self.H, self.W, int(self.sl_thresh.value())) + if ok: + self.is_dirty = False + self.setWindowTitle(f"SPAD Labeler - {os.path.basename(self.current_file_path)}") + if not silent: self.log_win.append(msg) + self.update_file_list_ui() + + def closeEvent(self, event): + if self.chk_autosave.isChecked() and self.is_dirty: self.save_current(silent=True) + if self.hist_inspector: self.hist_inspector.close() + event.accept() + + # ... [Region calc/Peel calc/Display state... same] ... + def _get_current_peeled_layer(self): + if self.peel_depth <= 0: return None + return self.peel_depth - 1 + def _compute_regions_for_layer(self, layer_idx: int, peel_by_class: bool): + mask2d = self.manual_masks[layer_idx] + regions = [] + if peel_by_class: + flat = mask2d.reshape(-1) + for cid in sorted(self.class_names.keys()): + pix = np.flatnonzero(flat == cid) + if pix.size == 0: continue + regions.append({"cid": int(cid), "pixels": pix, "area": int(pix.size)}) + regions.sort(key=lambda r: (r["cid"],)) + return regions + for cid in sorted(self.class_names.keys()): + binary = (mask2d == cid).astype(np.uint8) + if binary.sum() == 0: continue + n, cc = cv2.connectedComponents(binary, connectivity=8) + for k in range(1, n): + pix = np.flatnonzero(cc.reshape(-1) == k) + if pix.size == 0: continue + regions.append({"cid": int(cid), "pixels": pix, "area": int(pix.size)}) + regions.sort(key=lambda r: (r["cid"], -r["area"])) + return regions + def _get_regions_cached(self, layer_idx: int): + if layer_idx is None: return [] + mode = bool(self.peel_by_class) + if (self._region_cache_layer == layer_idx and self._region_cache_revision == self.mask_revision and self._region_cache_mode == mode): + return self._region_cache_regions + regions = self._compute_regions_for_layer(layer_idx, peel_by_class=mode) + self._region_cache_layer = layer_idx + self._region_cache_revision = self.mask_revision + self._region_cache_mode = mode + self._region_cache_regions = regions + return regions + def _sync_region_slider(self): + L = self._get_current_peeled_layer() + if L is None: + self.sl_region.blockSignals(True) + self.sl_region.setRange(0, 0) + self.sl_region.setValue(0) + self.sl_region.blockSignals(False) + self.lbl_region.setText("Semantic Step: 0 / 0") + self.lbl_next_region.setText("Selected Peel Target: -") + return + regions = self._get_regions_cached(L) + n = len(regions) + self.region_step = max(0, min(self.region_step, n)) + self.sl_region.blockSignals(True) + self.sl_region.setRange(0, n) + self.sl_region.setValue(self.region_step) + self.sl_region.blockSignals(False) + self.lbl_region.setText(f"Semantic Step (Layer {L}): {self.region_step} / {n}") + if n == 0: self.lbl_next_region.setText("Target: (no regions)") + elif self.region_step == 0: self.lbl_next_region.setText("Target: (none)") + else: + rid = min(max(0, self.region_step - 1), n - 1) + r = regions[rid] + cname = self.class_names.get(int(r["cid"]), f"Class {r['cid']}") + self.lbl_next_region.setText(f"Target: #{rid+1} | {cname} | area={r['area']}") + + def _peel_pixels_peak_segment(self, working_data, pixel_indices, thr, peel_count=None): + if pixel_indices is None or pixel_indices.size == 0: return + for idx in pixel_indices: + hist = working_data[idx] + if np.max(hist) > thr: + p_idx = int(np.argmax(hist)) + _, (l_rem, r_rem) = analyze_peak_structure(hist, p_idx, thr) + if r_rem > l_rem: + working_data[idx, l_rem:r_rem + 1] = 0 + if peel_count is not None: peel_count[idx] += 1 + def _build_working_hist_for_display(self, return_peel_count=False): + working = self.raw_data.copy() + curr_thr = int(self.sl_thresh.value()) + peel_count = None + if return_peel_count: peel_count = np.zeros((self.H * self.W,), dtype=np.int16) + if self.peel_depth <= 0: return (working, peel_count) if return_peel_count else working + full_end = self.peel_depth - 1 + for l in range(0, max(0, full_end)): + mask_flat = self.manual_masks[l].reshape(-1) + pix = np.flatnonzero(mask_flat > 0) + if pix.size == 0: continue + thr = self.layer_thresholds[l] if self.layer_thresholds[l] is not None else curr_thr + self._peel_pixels_peak_segment(working, pix, thr, peel_count=peel_count) + L = self.peel_depth - 1 + thrL = self.layer_thresholds[L] if self.layer_thresholds[L] is not None else curr_thr + regions = self._get_regions_cached(L) + if len(regions) > 0 and self.region_step > 0: + rid = min(max(0, self.region_step - 1), len(regions) - 1) + self._peel_pixels_peak_segment(working, regions[rid]["pixels"], thrL, peel_count=peel_count) + return (working, peel_count) if return_peel_count else working + def get_display_state(self, return_peak_idx=False): + key_thr = int(self.sl_thresh.value()) + lock_sig = tuple([(-1 if t is None else int(t)) for t in self.layer_thresholds[:max(0, self.peel_depth)]]) + key = (int(self.peel_depth), int(self.region_step), int(self.peel_by_class), key_thr, lock_sig, int(self.mask_revision), int(return_peak_idx)) + if key in self.layer_view_cache: return self.layer_view_cache[key] + working, peel_count = self._build_working_hist_for_display(return_peel_count=True) + raw2d = np.max(working, axis=1).reshape(self.H, self.W) + peel2d = peel_count.reshape(self.H, self.W) + if return_peak_idx: + peak_idx2d = np.argmax(working, axis=1).reshape(self.H, self.W) + self.layer_view_cache[key] = (raw2d, peel2d, peak_idx2d) + return raw2d, peel2d, peak_idx2d + self.layer_view_cache[key] = (raw2d, peel2d) + return raw2d, peel2d + def _get_valid_edit_mask(self, peel2d): + if not self.chk_lock_to_visible.isChecked(): return np.ones((self.H, self.W), dtype=bool) + return peel2d == int(self.edit_layer) + def _get_snr_mask(self, current_intensity): + if self.noise_map is None: return np.ones((self.H, self.W), dtype=bool) + snr_map = current_intensity / (self.noise_map + 1e-6) + return snr_map > (float(self.sl_snr.value()) / 10.0) + + # ... [Combine layers/Split layers... same] ... + def _combine_layers(self, l_start, l_end_exclusive): + combined = np.zeros((self.H, self.W), dtype=np.uint8) + for l in range(max(0, int(l_start)), min(self.num_layers, int(l_end_exclusive))): + m = self.manual_masks[l] + combined[m > 0] = m[m > 0] + return combined + def _split_partial_layer_by_region_step(self, layer_idx): + peeled = np.zeros(self.H * self.W, dtype=np.uint8) + unpeeled = np.zeros(self.H * self.W, dtype=np.uint8) + if layer_idx is None: return peeled, unpeeled + regions = self._get_regions_cached(layer_idx) + if len(regions) == 0: return peeled, unpeeled + if self.region_step <= 0: + for r in regions: unpeeled[r["pixels"]] = np.uint8(r["cid"]) + return peeled, unpeeled + sel = min(max(0, self.region_step - 1), len(regions) - 1) + for rid, r in enumerate(regions): + if rid == sel: peeled[r["pixels"]] = np.uint8(r["cid"]) + else: unpeeled[r["pixels"]] = np.uint8(r["cid"]) + return peeled, unpeeled + + # ... [update_all_views ... same] ... + def update_all_views(self): + if self.raw_data is None: return + self._sync_region_slider() + L = self._get_current_peeled_layer() + raw, peel2d, peak_idx2d = self.get_display_state(return_peak_idx=True) + display_raw = raw.copy() + snr_mask = self._get_snr_mask(display_raw) + if self.chk_focus_edit.isChecked(): + display_raw[~self._get_valid_edit_mask(peel2d)] = 0 + display_raw[display_raw < int(self.sl_thresh.value())] = 0 + display_raw[~snr_mask] = 0 + norm = cv2.normalize(np.log1p(display_raw), None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) + cmap_name, cmap = self._get_peak_cmap_for_depth(self.peel_depth) + self.lbl_info.set_image(safe_numpy_to_pixmap(cv2.applyColorMap(norm, cmap))) + + depth_vis = peak_idx2d.astype(np.float32) * float(AppConfig.BIN_UNIT) + valid = display_raw > 0 + depth_vis[~valid] = 0.0 + if np.any(valid): + d = depth_vis[valid] + lo, hi = float(np.percentile(d, 2)), float(np.percentile(d, 98)) + if hi <= lo: hi = lo + 1e-6 + dn = np.clip((depth_vis - lo) / (hi - lo), 0.0, 1.0) + depth_col = cv2.applyColorMap((dn * 255.0).astype(np.uint8), _cv2_cmap("COLORMAP_TURBO", cv2.COLORMAP_JET)) + depth_col[~valid] = 0 + else: depth_col = np.zeros((self.H, self.W, 3), dtype=np.uint8) + self.lbl_depth.set_image(safe_numpy_to_pixmap(depth_col)) + + l0_raw = np.max(self.raw_data, axis=1).reshape(self.H, self.W) + n0 = cv2.normalize(np.log1p(l0_raw), None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) + self.lbl_global.set_image(safe_numpy_to_pixmap(cv2.applyColorMap(n0, _cv2_cmap("COLORMAP_MAGMA", cv2.COLORMAP_JET)))) + + unpeeled_mask = self._combine_layers(self.peel_depth, self.num_layers) + ghost_mask = self._combine_layers(0, max(0, self.peel_depth - 1)) if self.chk_ghost.isChecked() else None + pL, uL = self._split_partial_layer_by_region_step(L) + if L is not None: unpeeled_mask[uL.reshape(self.H, self.W) > 0] = uL.reshape(self.H, self.W)[uL.reshape(self.H, self.W) > 0] + if self.chk_ghost.isChecked() and L is not None and ghost_mask is not None: + ghost_mask[pL.reshape(self.H, self.W) > 0] = pL.reshape(self.H, self.W)[pL.reshape(self.H, self.W) > 0] + + mask_rgba = np.zeros((self.H, self.W, 4), dtype=np.uint8) + ghost_rgba = np.zeros((self.H, self.W, 4), dtype=np.uint8) if ghost_mask is not None else None + if not self.chk_hide.isChecked(): + for cid, rgb in self.class_colors_rgb.items(): + if cid <= 0: continue + mask_rgba[unpeeled_mask == cid] = [rgb[0], rgb[1], rgb[2], 180] + if ghost_rgba is not None: ghost_rgba[ghost_mask == cid] = [rgb[0], rgb[1], rgb[2], 180] + self.canvas.hide_labels = self.chk_hide.isChecked() + self.canvas.set_content(safe_numpy_to_pixmap(depth_col), safe_numpy_to_pixmap(mask_rgba), safe_numpy_to_pixmap(ghost_rgba)) + + res_rgb = np.zeros((self.H, self.W, 3), dtype=np.uint8) + for cid, rgb in self.class_colors_rgb.items(): + if cid > 0: res_rgb[unpeeled_mask == cid] = rgb + self.lbl_result.set_image(safe_numpy_to_pixmap(res_rgb)) + + # ... [Assign/Erase... same] ... + def _assign_to_current_layer(self, region, cid, layer, overwrite=True): + if cid <= 0 or region is None or not np.any(region): return 0 + m = self.manual_masks[int(layer)] + target = region if overwrite else (region & (m == 0)) + if not np.count_nonzero(target): return 0 + m[target] = np.uint8(cid) + self._ensure_layer_threshold(int(layer)) + return np.count_nonzero(target) + def _erase_current_layer(self, region, layer): + if region is None or not np.any(region): return 0 + m = self.manual_masks[int(layer)] + hit = region & (m > 0) + if not np.count_nonzero(hit): return 0 + m[hit] = 0 + return np.count_nonzero(hit) + def _erase_topmost_per_pixel(self, region): return self._erase_current_layer(region, self.edit_layer) + def _make_brush_region_circle(self, x, y, radius): + tmp = np.zeros((self.H, self.W), dtype=np.uint8) + cv2.circle(tmp, (x, y), int(radius), 1, -1) + return tmp > 0 + def _make_brush_region_line(self, x1, y1, x2, y2, thickness): + tmp = np.zeros((self.H, self.W), dtype=np.uint8) + cv2.line(tmp, (x1, y1), (x2, y2), 1, int(thickness)) + return tmp > 0 + + # ... [Magic Wand ... same] ... + def _compute_edge_barrier(self, raw_u8): + if not self.wand_edge_aware: return np.zeros_like(raw_u8, dtype=bool) + high = int(self.wand_edge_high) + return cv2.Canny(raw_u8, max(0, min(255, int(high * 0.5))), high).astype(bool) + def _magic_wand_grow(self, seed_x, seed_y, raw2d, valid_mask, thr): + if seed_x < 0 or seed_x >= self.W or seed_y < 0 or seed_y >= self.H: return np.zeros((self.H, self.W), dtype=bool) + if not valid_mask[seed_y, seed_x] or raw2d[seed_y, seed_x] <= thr: return np.zeros((self.H, self.W), dtype=bool) + raw_u8 = cv2.normalize(np.log1p(np.clip(raw2d, 0, None)), None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) + seed_I, tol, barrier = int(raw_u8[seed_y, seed_x]), int(self.wand_tolerance), self._compute_edge_barrier(raw_u8) + neigh = [(-1, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-1, 1), (0, 1), (1, 1)] if self.wand_connectivity_8 else [(0, -1), (-1, 0), (1, 0), (0, 1)] + visited, region = np.zeros((self.H, self.W), dtype=bool), np.zeros((self.H, self.W), dtype=bool) + dq = deque([(seed_x, seed_y)]) + visited[seed_y, seed_x] = True + while dq: + x, y = dq.popleft() + if barrier[y, x] or not valid_mask[y, x] or raw2d[y, x] <= thr or abs(int(raw_u8[y, x]) - seed_I) > tol: continue + region[y, x] = True + for dx, dy in neigh: + nx, ny = x + dx, y + dy + if 0 <= nx < self.W and 0 <= ny < self.H and not visited[ny, nx]: + visited[ny, nx] = True + if not barrier[ny, nx] and valid_mask[ny, nx] and raw2d[ny, nx] > thr: dq.append((nx, ny)) + return region + + # ========================================== + # Canvas actions (Modified for Inspect Mode) + # ========================================== + def handle_canvas_action(self, action, p1, p2): + if self.raw_data is None or p1 is None: return + if action == "draw_end": return + + x1, y1 = self._clamp_xy(p1.x(), p1.y()) + x2, y2 = (x1, y1) if p2 is None else self._clamp_xy(p2.x(), p2.y()) + + # [NEW LOGIC] If in Inspect Mode, only update histogram and exit. + if self.tool_mode == "inspect": + if action in ["pick_end", "pick_end_ctrl"]: + # Only react to single clicks to avoid confusion with dragging + if (p1 - p2).manhattanLength() <= 5: + self.update_histogram_inspector(x1, y1) + return # <--- STOP HERE, do NOT proceed to labeling logic + + # --- Labeling Logic Below (Pick/Brush/Eraser) --- + raw, peel2d, peak_idx2d = self.get_display_state(return_peak_idx=True) + curr_thresh = int(self.sl_thresh.value()) + is_erase_action = (self.tool_mode == "eraser") or (action == "pick_end_ctrl") + valid_mask = np.ones((self.H, self.W), dtype=bool) if is_erase_action else self._get_valid_edit_mask(peel2d) + valid_mask = valid_mask & self._get_snr_mask(raw) + + if action in ["draw_start", "pick_end", "pick_end_ctrl"]: self.push_undo() + changed = 0 + allow_overwrite = self.chk_overwrite.isChecked() + + if action == "draw_start": + region = self._make_brush_region_circle(x1, y1, self.brush_size) + if not is_erase_action: region = region & valid_mask + changed = self._erase_topmost_per_pixel(region) if self.tool_mode == "eraser" else self._assign_to_current_layer(region, int(self.current_class), int(self.edit_layer), allow_overwrite) + elif action == "draw_drag": + region = self._make_brush_region_line(x1, y1, x2, y2, self.brush_size * 2) + if not is_erase_action: region = region & valid_mask + changed = self._erase_topmost_per_pixel(region) if self.tool_mode == "eraser" else self._assign_to_current_layer(region, int(self.current_class), int(self.edit_layer), allow_overwrite) + elif action in ["pick_end", "pick_end_ctrl"]: + if (p1 - p2).manhattanLength() > 5: + xs, xe, ys, ye = sorted([x1, x2])[0], sorted([x1, x2])[1], sorted([y1, y2])[0], sorted([y1, y2])[1] + xe, ye = min(xe + 1, self.W), min(ye + 1, self.H) + region = np.zeros((self.H, self.W), dtype=bool) + roi = raw[ys:ye, xs:xe] + region[ys:ye, xs:xe] = (roi > curr_thresh) & valid_mask[ys:ye, xs:xe] + else: + region = self._magic_wand_grow(x1, y1, raw, valid_mask, curr_thresh) + + if not np.any(region): + if not is_erase_action and self.chk_lock_to_visible.isChecked(): + self.log_win.append("Pick ignored: invalid area.") + else: + ctrl_erase = (action == "pick_end_ctrl") + changed = self._erase_topmost_per_pixel(region) if (ctrl_erase or self.tool_mode == "eraser") else self._assign_to_current_layer(region, int(self.current_class), int(self.edit_layer), allow_overwrite) + + if changed > 0: + self.set_dirty() + self.update_all_views() + + # ... [Undo/Morph/Key events ... same] ... + def undo(self): + if not self.undo_stack: return + stack, thr, pd, rs, el, peel_mode = self.undo_stack.pop() + self.manual_masks = [stack[l].copy() for l in range(self.num_layers)] + self.layer_thresholds = list(thr) + self.peel_depth, self.region_step, self.edit_layer, self.peel_by_class = int(pd), int(rs), int(el), bool(peel_mode) + self.chk_peel_class.blockSignals(True) + self.chk_peel_class.setChecked(self.peel_by_class) + self.chk_peel_class.blockSignals(False) + self.sl_peel.blockSignals(True) + self.sl_peel.setValue(self.peel_depth) + self.sl_peel.blockSignals(False) + self.sl_edit.blockSignals(True) + self.sl_edit.setValue(self.edit_layer) + self.sl_edit.blockSignals(False) + self.clear_layer_cache() + self.is_dirty = True + self.mask_revision += 1 + self._sync_region_slider() + self.update_all_views() + self.log_win.append("Undo.") + if self.hist_inspector and self.hist_inspector.isVisible(): + self.hist_inspector.ax.clear() + self.hist_inspector.canvas.draw() + def keyPressEvent(self, event): + if event.key() == Qt.Key_Up: self.sl_thresh.setValue(self.sl_thresh.value() + (10 if event.modifiers() & Qt.ShiftModifier else 1)) + elif event.key() == Qt.Key_Down: self.sl_thresh.setValue(max(0, self.sl_thresh.value() - (10 if event.modifiers() & Qt.ShiftModifier else 1))) + elif event.key() == Qt.Key_Control: + self.canvas.ctrl_pressed = True + self.canvas.update() + else: super().keyPressEvent(event) + def keyReleaseEvent(self, event): + if event.key() == Qt.Key_Control: + self.canvas.ctrl_pressed = False + self.canvas.update() + super().keyReleaseEvent(event) + def set_class_by_key(self, idx): + if int(idx) in self.cls_radios: + self.cls_radios[int(idx)].setChecked(True) + self.current_class = int(idx) + def cycle_class_prev(self): self.set_class_by_key(self.current_class - 1 if self.current_class > 1 else len(self.class_names)) + def cycle_class_next(self): self.set_class_by_key(self.current_class + 1 if self.current_class < len(self.class_names) else 1) + def morph_current_mask(self, op): + mask = self.manual_masks[0] + if not np.any(mask): return + self.push_undo() + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) + for cid in self.class_names: + c_mask = (mask == cid).astype(np.uint8) + if not np.any(c_mask): continue + proc = cv2.dilate(c_mask, kernel) if op == "dilate" else cv2.erode(c_mask, kernel) + mask[proc > 0] = cid + self.set_dirty() + self.update_all_views() + def fill_unknown_current_layer(self): + if self.raw_data is None: return + layer = int(self.edit_layer) + raw2d, peel2d = self.get_display_state() + valid = self._get_valid_edit_mask(peel2d) & self._get_snr_mask(raw2d) + region = (raw2d > int(self.sl_thresh.value())) & valid + m = self.manual_masks[layer] + fill = region & (m == 0) + if not np.count_nonzero(fill): return + self.push_undo() + m[fill] = np.uint8(self.UNKNOWN_CID) + self._ensure_layer_threshold(layer) + self.set_dirty() + self.update_all_views() + + # ========================================== + # 3D + # ========================================== + def visualize_3d_point_cloud(self): + if not HAS_OPEN3D or self.raw_data is None: return + QApplication.setOverrideCursor(Qt.WaitCursor) + try: + raw, _ = self.get_display_state() + snr_mask = self._get_snr_mask(raw) + valid = (raw > int(self.sl_thresh.value())) & snr_mask + rows, cols = np.where(valid) + working_data = self._build_working_hist_for_display(return_peel_count=False) + zs = [int(np.argmax(working_data[r * self.W + c])) for r, c in zip(rows, cols)] + Z = np.array(zs, dtype=np.float32) * AppConfig.BIN_UNIT + u, v = cols.astype(float), rows.astype(float) + pts_uv = np.stack([u, v], axis=1).reshape(-1, 1, 2) + pts_undist = cv2.undistortPoints(pts_uv, AppConfig.CAM_K, AppConfig.CAM_D).reshape(-1, 2) + xyz = np.stack([pts_undist[:, 0] * Z, pts_undist[:, 1] * Z, Z], axis=1) + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(xyz) + pcd.colors = o3d.utility.Vector3dVector(get_robust_colors(raw[rows, cols])) + o3d.visualization.draw_geometries([pcd], window_name=f"3D Depth (PeelDepth {self.peel_depth})") + finally: QApplication.restoreOverrideCursor() + + def visualize_3d_semantic_point_cloud(self): + if not HAS_OPEN3D or self.raw_data is None: return + QApplication.setOverrideCursor(Qt.WaitCursor) + try: + curr_thr = int(self.sl_thresh.value()) + working = self.raw_data.copy() + all_rows, all_cols, all_Z, all_cids = [], [], [], [] + for l in range(self.num_layers): + m = self.manual_masks[l] + flat = m.reshape(-1) + labeled_idx = np.flatnonzero(flat > 0) + if labeled_idx.size == 0: continue + thr_l = self.layer_thresholds[l] if self.layer_thresholds[l] is not None else curr_thr + for idx in labeled_idx: + cid = int(flat[idx]) + hist = working[idx] + if np.max(hist) <= thr_l: continue + peak_idx = int(np.argmax(hist)) + (__, ___), (l_rem, r_rem) = analyze_peak_structure(hist, peak_idx, thr_l) + if r_rem <= l_rem: continue + all_rows.append(int(idx // self.W)) + all_cols.append(int(idx % self.W)) + all_Z.append(float(peak_idx) * AppConfig.BIN_UNIT) + all_cids.append(cid) + working[idx, l_rem:r_rem + 1] = 0 + if len(all_rows) == 0: return + rows, cols, Z, cids = np.asarray(all_rows), np.asarray(all_cols), np.asarray(all_Z), np.asarray(all_cids) + u, v = cols.astype(np.float32), rows.astype(np.float32) + pts_uv = np.stack([u, v], axis=1).reshape(-1, 1, 2) + pts_undist = cv2.undistortPoints(pts_uv, AppConfig.CAM_K, AppConfig.CAM_D).reshape(-1, 2) + xyz = np.stack([pts_undist[:, 0] * Z, pts_undist[:, 1] * Z, Z], axis=1) + colors = np.zeros((cids.size, 3), dtype=np.float32) + for i, cid in enumerate(cids): + rgb = self.class_colors_rgb.get(int(cid), (255, 255, 255)) + colors[i] = [rgb[0]/255.0, rgb[1]/255.0, rgb[2]/255.0] + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(xyz) + pcd.colors = o3d.utility.Vector3dVector(colors) + o3d.visualization.draw_geometries([pcd], window_name="3D Semantic (Multi-layer)") + finally: QApplication.restoreOverrideCursor() + + # [RESTORED METHOD] + def visualize_3d_semantic_bins_point_cloud(self): + if not HAS_OPEN3D or self.raw_data is None: return + QApplication.setOverrideCursor(Qt.WaitCursor) + try: + thr_global = int(self.sl_thresh.value()) + H, W = self.H, self.W + BIN_STRIDE = 1 + PIX_STRIDE = 1 + MAX_POINTS = 2_000_000 + working = self.raw_data.copy() + all_xyz, all_rgb = [], [] + total_pts = 0 + + for layer in range(self.num_layers): + m = self.manual_masks[layer] + flat = m.reshape(-1) + pix = np.flatnonzero(flat > 0) + if pix.size == 0: continue + if PIX_STRIDE > 1: pix = pix[::PIX_STRIDE] + thr = self.layer_thresholds[layer] if self.layer_thresholds[layer] is not None else thr_global + thr = int(thr) + rows, cols = (pix // W).astype(np.int32), (pix % W).astype(np.int32) + pts_uv = np.stack([cols.astype(np.float32), rows.astype(np.float32)], axis=1).reshape(-1, 1, 2) + pts_undist = cv2.undistortPoints(pts_uv, AppConfig.CAM_K, AppConfig.CAM_D).reshape(-1, 2) + xnd, ynd = pts_undist[:, 0], pts_undist[:, 1] + cids = flat[pix].astype(np.int32) + + for i, idx in enumerate(pix): + hist = working[idx] + if hist.max() <= thr: continue + p_idx = int(np.argmax(hist)) + (l_lab, r_lab), (l_rem, r_rem) = analyze_peak_structure(hist, p_idx, thr) + if r_lab >= l_lab: + bins = np.arange(l_lab, r_lab + 1, BIN_STRIDE, dtype=np.int32) + if bins.size > 0: + Z = bins.astype(np.float32) * float(AppConfig.BIN_UNIT) + xyz = np.stack([xnd[i] * Z, ynd[i] * Z, Z], axis=1) + cid = int(cids[i]) + rgb = self.class_colors_rgb.get(cid, (64, 64, 64)) + rgb_f = (np.array(rgb, dtype=np.float32) / 255.0).reshape(1, 3) + rgb_arr = np.repeat(rgb_f, xyz.shape[0], axis=0) + all_xyz.append(xyz) + all_rgb.append(rgb_arr) + total_pts += xyz.shape[0] + if total_pts >= MAX_POINTS: break + if r_rem > l_rem: working[idx, l_rem:r_rem + 1] = 0 + if total_pts >= MAX_POINTS: break + + if total_pts == 0: + self.log_win.append("3D Labeled Bins: no labeled bin segments to draw.") + return + xyz = np.concatenate(all_xyz, axis=0) + rgb = np.concatenate(all_rgb, axis=0) + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(xyz) + pcd.colors = o3d.utility.Vector3dVector(rgb) + o3d.visualization.draw_geometries([pcd], window_name="3D Labeled Bins (per-pixel labeled segments)") + finally: QApplication.restoreOverrideCursor() + + +if __name__ == "__main__": + import multiprocessing as mp + mp.freeze_support() + ap = argparse.ArgumentParser() + ap.add_argument("--in_dir", default="npy") + ap.add_argument("--out_root", default="output") + ap.add_argument("--pattern", default="*.npy") + ap.add_argument("--cache_dir", default="cache") + args = ap.parse_args() + + app = QApplication(sys.argv) + win = SPADLabelerPixel(args) + win.show() + sys.exit(app.exec_()) diff --git a/codes/pointcloud_extract/ann2pc.py b/codes/pointcloud_extract/ann2pc.py new file mode 100644 index 0000000000000000000000000000000000000000..089f1a156cc76b8bb1fd30722dfe4229eb1cdf96 --- /dev/null +++ b/codes/pointcloud_extract/ann2pc.py @@ -0,0 +1,185 @@ +# -*- coding: utf-8 -*- +""" +将标注文件 (semantic.npy) 转换为带语义标签的PLY点云真值 +基于 visualize_semantic_labels.py 的标注逻辑 +""" + +import os +import glob +import numpy as np +import cv2 +import yaml +from tqdm import tqdm +from concurrent.futures import ProcessPoolExecutor, as_completed +import multiprocessing + +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) + +def load_config(config_path=None): + if config_path is None: + config_path = os.path.join(SCRIPT_DIR, 'config.yaml') + with open(config_path, 'r', encoding='utf-8') as f: + return yaml.safe_load(f) + +CONFIG = load_config() + +# 相机和深度参数 +IMG_H, IMG_W = 192, 256 +BIN_TO_M = 299_792_458.0 * CONFIG['common']['dt_ps'] * 1e-12 / 2.0 + + +def save_ply_with_label(pts, path): + """保存带语义标签的PLY文件: x y z label""" + with open(path, "wb") as f: + header = f"ply\nformat ascii 1.0\nelement vertex {len(pts)}\n" + header += "property float x\nproperty float y\nproperty float z\nproperty int label\nend_header\n" + f.write(header.encode()) + np.savetxt(f, pts, fmt='%.6f %.6f %.6f %d') + + +def process_single_ann(args): + """处理单个标注文件，转换为PLY点云""" + npy_path, cam_config, out_dir = args + + # 从文件名提取基础名 (去掉 _semantic.npy) + basename = os.path.basename(npy_path) + if basename.endswith('_semantic.npy'): + basename = basename[:-13] # 去掉 '_semantic.npy' + + out_path = os.path.join(out_dir, f"{basename}_gt.ply") + + try: + # 加载标注数据 (H*W, num_bins) + sem_bins = np.load(npy_path) + num_pos, num_bins = sem_bins.shape + + # 预计算相机参数 + K = np.array([[cam_config['fx'], 0, cam_config['cx']], + [0, cam_config['fy'], cam_config['cy']], + [0, 0, 1]], dtype=np.float64) + D = np.array([cam_config['k1'], cam_config['k2'], cam_config['p1'], cam_config['p2']], dtype=np.float64) + + points = [] + + # 遍历每个像素，提取标注的峰值 + for idx in range(num_pos): + row = sem_bins[idx] + nz = np.flatnonzero(row > 0) # 找到有标注的bin + if nz.size == 0: + continue + + # 找到连续的标注段 + breaks = np.flatnonzero(np.diff(nz) != 1) + run_starts = np.concatenate(([0], breaks + 1)) + run_ends = np.concatenate((breaks, [nz.size - 1])) + + for rs, re in zip(run_starts, run_ends): + b0 = int(nz[rs]) + b1 = int(nz[re]) + cid = int(row[b0]) # 语义类别 + if cid <= 0: + continue + + # 使用标注段的中心bin作为深度 + peak_bin = (b0 + b1) // 2 + depth = peak_bin * BIN_TO_M + + # 深度范围过滤 + if depth < CONFIG['common']['min_range_m'] or depth > CONFIG['common']['max_range_m']: + continue + + # 计算3D坐标 + v, u = idx // IMG_W, idx % IMG_W + + if CONFIG['common']['undistort']: + uv = np.array([[[u, v]]], dtype=np.float32) + uv_norm = cv2.undistortPoints(uv, K, D) + x_n, y_n = uv_norm[0, 0, 0], uv_norm[0, 0, 1] + else: + x_n = (u - cam_config['cx']) / cam_config['fx'] + y_n = (v - cam_config['cy']) / cam_config['fy'] + + if CONFIG['common']['depth_is_range']: + ray = np.array([x_n, y_n, 1.0]) + ray_unit = ray / np.linalg.norm(ray) + xyz = ray_unit * depth + else: + xyz = np.array([x_n * depth, y_n * depth, depth]) + + points.append([xyz[0], xyz[1], xyz[2], cid]) + + if len(points) == 0: + return basename, False, 0 + + pts = np.array(points, dtype=np.float32) + pts[:, 3] = pts[:, 3].astype(np.int32) # 确保label是整数 + save_ply_with_label(pts, out_path) + + return basename, True, len(pts) + + except Exception as e: + print(f"Error processing {npy_path}: {e}") + return basename, False, 0 + + +def main(): + config = load_config() + datasets = config['datasets'] + + ann_root = os.path.join(SCRIPT_DIR, 'ann') + output_root = os.path.join(SCRIPT_DIR, 'output_denoised', 'gt') + + num_workers = min(config['common'].get('num_workers', 8), multiprocessing.cpu_count()) + + print(f"[INFO] Converting annotations to PLY ground truth") + print(f"[INFO] Ann root: {ann_root}") + print(f"[INFO] Output root: {output_root}") + print(f"[INFO] Workers: {num_workers}") + + # 遍历数据集 (p1, p2) + for dataset_name, cam_config in datasets.items(): + dataset_ann_path = os.path.join(ann_root, dataset_name) + if not os.path.isdir(dataset_ann_path): + print(f"[Skip] {dataset_name} not found in ann/") + continue + + print(f"\n[Dataset] {dataset_name}") + + # 遍历序列目录 + seq_dirs = [d for d in os.listdir(dataset_ann_path) + if os.path.isdir(os.path.join(dataset_ann_path, d))] + + for seq_name in seq_dirs: + seq_path = os.path.join(dataset_ann_path, seq_name) + out_dir = os.path.join(output_root, seq_name) + os.makedirs(out_dir, exist_ok=True) + + # 查找所有标注文件 + npy_files = sorted(glob.glob(os.path.join(seq_path, "*_semantic.npy"))) + if not npy_files: + continue + + # 准备任务 + tasks = [(f, cam_config, out_dir) for f in npy_files] + + success_count = 0 + total_pts = 0 + + with ProcessPoolExecutor(max_workers=num_workers) as executor: + futures = [executor.submit(process_single_ann, t) for t in tasks] + + pbar = tqdm(as_completed(futures), total=len(npy_files), + desc=f" {seq_name}", leave=False) + for future in pbar: + basename, ok, pts_count = future.result() + if ok: + success_count += 1 + total_pts += pts_count + + print(f" {seq_name}: {success_count}/{len(npy_files)} files, {total_pts:,} pts") + + print(f"\n[Done] Ground truth PLY saved to: {output_root}") + + +if __name__ == "__main__": + main() diff --git a/codes/pointcloud_extract/raw2pc.py b/codes/pointcloud_extract/raw2pc.py new file mode 100644 index 0000000000000000000000000000000000000000..3af31b219a98bc0f1b84604e12f9b3a228065ffd --- /dev/null +++ b/codes/pointcloud_extract/raw2pc.py @@ -0,0 +1,533 @@ +import argparse +import glob +import os +import re + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +C = 299_792_458.0 # m/s +COMMON_SIZES = ((192, 256), (256, 192)) +TARGET_PIXELS = 192 * 256 + +OUTPUT_COLUMNS = ["Timestamp", "X", "Y", "Z", "Reflectivity"] + + +def _str2bool(value): + if isinstance(value, bool): + return value + text = str(value).strip().lower() + return text in {"1", "true", "yes", "y", "on"} + + +def _extract_timestamp_from_name(path): + name = os.path.basename(path) + m = re.search(r"(\d+)(?=\.txt$)", name) + if m: + return int(m.group(1)) + + nums = re.findall(r"\d+", name) + if nums: + return int(nums[-1]) + return -1 + + +def _infer_shape(num_pos): + for h, w in COMMON_SIZES: + if h * w == num_pos: + return h, w, num_pos + + if num_pos >= TARGET_PIXELS: + return 192, 256, TARGET_PIXELS + + h = int(np.sqrt(num_pos)) + while h > 1 and num_pos % h != 0: + h -= 1 + w = num_pos // h + return h, w, num_pos + + +def _build_intrinsics(fx, fy, cx, cy, k1, k2, p1, p2): + K = np.array( + [ + [fx, 0, cx], + [0, fy, cy], + [0, 0, 1], + ], + dtype=np.float64, + ) + D = np.array([k1, k2, p1, p2], dtype=np.float64) + return K, D + + +def _top2_counts_per_row(data): + top2 = np.partition(data, -2, axis=1)[:, -2:] + second = top2[:, 0].astype(np.float32) + peak = top2[:, 1].astype(np.float32) + return peak, second + + +def _load_hist_intensity_depth(txt_path, dt_ps): + data = np.loadtxt(txt_path, dtype=np.int64) + if data.ndim == 1: + data = data.reshape(1, -1) + + num_pos, _ = data.shape + intensity_1d, _ = _top2_counts_per_row(data) + peak_bin_1d = data.argmax(axis=1).astype(np.int32) + + dt = dt_ps * 1e-12 + bin_to_m = C * dt / 2.0 + # depth_m_1d = peak_bin_1d.astype(np.float32) * bin_to_m + depth_m_1d = ( peak_bin_1d.astype(np.float32) - 17) * bin_to_m#25 18 + + ### + offset = np.loadtxt("./offset.txt", dtype=float) + idx_offset = np.argwhere( offset < 10 )[:,0] + depth_m_1d[idx_offset] -= offset[idx_offset] + + height, width, keep_n = _infer_shape(num_pos) + if keep_n < num_pos: + intensity_1d = intensity_1d[:keep_n] + depth_m_1d = depth_m_1d[:keep_n] + + intensity = intensity_1d.reshape(height, width) + depth_m = depth_m_1d.reshape(height, width) + + return intensity, depth_m, height, width + + +def _compute_points_undistort_points( + intensity, + depth_m, + K, + D, + depth_is_range, + min_range_m, + max_range_m, + intensity_min, + intensity_max, + undistort_intensity, +): + if undistort_intensity: + intensity_u = cv2.undistort(intensity, K, D) + else: + intensity_u = intensity + + depth_u = depth_m + + h, w = depth_u.shape + u, v = np.meshgrid(np.arange(w), np.arange(h)) + + valid = ( + np.isfinite(depth_u) + & (depth_u > float(min_range_m)) + & (depth_u < float(max_range_m)) + & (intensity_u >= float(intensity_min)) + ) + if intensity_max is not None: + valid &= intensity_u <= float(intensity_max) + + offset = np.loadtxt("offset.txt", dtype=float) + offset_m = offset.reshape(h, w) + valid_offset = offset_m < 10 + valid = valid & valid_offset + + u_valid = u[valid].astype(np.float32) + v_valid = v[valid].astype(np.float32) + d_valid = depth_u[valid].astype(np.float32) + i_valid = intensity_u[valid].astype(np.float32) + + uv = np.stack([u_valid, v_valid], axis=1).reshape(-1, 1, 2).astype(np.float32) + xy = cv2.undistortPoints(uv, K, D) + x_n = xy[:, 0, 0] + y_n = xy[:, 0, 1] + + ray = np.stack([x_n, y_n, np.ones_like(x_n)], axis=1) + ray_norm = np.linalg.norm(ray, axis=1, keepdims=True) + ray_unit = ray / np.maximum(ray_norm, 1e-12) + + if depth_is_range: + pts = ray_unit * d_valid.reshape(-1, 1) + else: + pts = ray_unit * (d_valid.reshape(-1, 1) / np.maximum(ray_unit[:, 2:3], 1e-12)) + + reflectivity = np.rint(i_valid).astype(np.int32) + return pts.astype(np.float32), reflectivity, intensity_u, depth_u + + +def _normalize_to_u8(values): + arr = values.astype(np.float32) + if arr.size == 0: + return np.array([], dtype=np.uint8) + lo = float(np.min(arr)) + hi = float(np.max(arr)) + if hi <= lo: + return np.zeros(arr.shape, dtype=np.uint8) + return ((arr - lo) * (255.0 / (hi - lo))).clip(0, 255).astype(np.uint8) + + +def _write_ply_ascii(path, pts, reflectivity): + i_255 = _normalize_to_u8(reflectivity.astype(np.float32)) + with open(path, "w", encoding="utf-8") as f: + f.write("ply\n") + f.write("format ascii 1.0\n") + f.write(f"element vertex {pts.shape[0]}\n") + f.write("property float x\n") + f.write("property float y\n") + f.write("property float z\n") + f.write("property uchar red\n") + f.write("property uchar green\n") + f.write("property uchar blue\n") + f.write("end_header\n") + for idx in range(pts.shape[0]): + x, y, z = pts[idx] + ii = int(i_255[idx]) if idx < i_255.shape[0] else 0 + f.write(f"{x} {y} {z} {ii} {ii} {ii}\n") + + +def _save_maps_png(path, intensity_u, depth_u): + plt.figure(figsize=(12, 5)) + plt.subplot(1, 2, 1) + plt.imshow(intensity_u, origin="upper") + plt.title("Intensity (max count)") + plt.colorbar() + plt.subplot(1, 2, 2) + plt.imshow(depth_u, origin="upper") + plt.title("Depth (m) from peak bin") + plt.colorbar() + plt.tight_layout() + plt.savefig(path, dpi=150) + plt.close() + + +def _points_to_simple_csv(pts, reflectivity, timestamp): + n = pts.shape[0] + df = pd.DataFrame( + { + "Timestamp": np.full(n, int(timestamp), dtype=np.int64), + "X": pts[:, 0], + "Y": pts[:, 1], + "Z": pts[:, 2], + "Reflectivity": reflectivity.astype(np.int32), + }, + columns=OUTPUT_COLUMNS, + ) + return df + + +def _maybe_visualize_open3d(pts, reflectivity, point_size=1.5): + try: + import open3d as o3d + import matplotlib.cm as cm + except Exception: + print("[warn] open3d/matplotlib colormap not available, skip visualization") + return + + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(pts.astype(np.float64)) + + if reflectivity.size > 0: + refl = reflectivity.astype(np.float32) + refl_norm = (refl - refl.min()) / (refl.max() - refl.min() + 1e-12) + colors = cm.get_cmap("turbo")(refl_norm)[:, :3] + pcd.colors = o3d.utility.Vector3dVector(colors.astype(np.float64)) + + axis = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.2, origin=[0, 0, 0]) + + vis = o3d.visualization.Visualizer() + vis.create_window(window_name="SP Point Cloud (undistortPoints)", width=1200, height=800) + vis.add_geometry(pcd) + vis.add_geometry(axis) + opt = vis.get_render_option() + if opt is not None: + opt.point_size = float(point_size) + opt.background_color = np.array([0.02, 0.02, 0.02], dtype=np.float64) + vis.run() + vis.destroy_window() + + +def convert_one_txt( + txt_path, + output_base, + dt_ps, + depth_is_range, + undistort_intensity, + K, + D, + min_range_m, + max_range_m, + intensity_min, + intensity_max, + output_mode, + save_maps, + show, + show_point_size=1.5, +): + timestamp = _extract_timestamp_from_name(txt_path) + if timestamp < 0: + raise ValueError(f"No timestamp found in filename: {txt_path}") + + intensity, depth_m, _, _ = _load_hist_intensity_depth(txt_path=txt_path, dt_ps=dt_ps) + pts, refl, intensity_u, depth_u = _compute_points_undistort_points( + intensity=intensity, + depth_m=depth_m, + K=K, + D=D, + depth_is_range=depth_is_range, + min_range_m=min_range_m, + max_range_m=max_range_m, + intensity_min=intensity_min, + intensity_max=intensity_max, + undistort_intensity=undistort_intensity, + ) + + csv_path = "" + ply_path = "" + map_path = "" + + if output_mode in ("csv", "both"): + csv_path = output_base + ".csv" + _points_to_simple_csv(pts, refl, timestamp).to_csv(csv_path, index=False) + + if output_mode in ("ply", "both"): + ply_path = output_base + ".ply" + _write_ply_ascii(ply_path, pts, refl) + + if save_maps: + map_path = output_base + "_maps.png" + _save_maps_png(map_path, intensity_u, depth_u) + + if show: + _maybe_visualize_open3d(pts, refl, point_size=show_point_size) + + return { + "txt": txt_path, + "timestamp": timestamp, + "points": int(pts.shape[0]), + "csv": csv_path, + "ply": ply_path, + "maps": map_path, + } + + +def batch_convert( + input_dir, + output_dir, + pattern, + prefix, + start_index, + dt_ps, + depth_is_range, + undistort_intensity, + K, + D, + min_range_m, + max_range_m, + intensity_min, + intensity_max, + output_mode, + save_maps, +): + os.makedirs(output_dir, exist_ok=True) + + txt_files = glob.glob(os.path.join(input_dir, pattern)) + if not txt_files: + raise FileNotFoundError(f"No files matched: {os.path.join(input_dir, pattern)}") + + txt_files.sort(key=lambda p: (_extract_timestamp_from_name(p), os.path.basename(p))) + + results = [] + out_idx = int(start_index) + for txt_path in txt_files: + out_base = os.path.join(output_dir, f"{prefix}{out_idx}") + info = convert_one_txt( + txt_path=txt_path, + output_base=out_base, + dt_ps=dt_ps, + depth_is_range=depth_is_range, + undistort_intensity=undistort_intensity, + K=K, + D=D, + min_range_m=min_range_m, + max_range_m=max_range_m, + intensity_min=intensity_min, + intensity_max=intensity_max, + output_mode=output_mode, + save_maps=save_maps, + show=False, + ) + results.append(info) + + outputs = [] + if info["csv"]: + outputs.append(os.path.basename(info["csv"])) + if info["ply"]: + outputs.append(os.path.basename(info["ply"])) + if info["maps"]: + outputs.append(os.path.basename(info["maps"])) + out_text = ", ".join(outputs) if outputs else "" + + print( + f"[{out_idx}] {os.path.basename(txt_path)} -> {out_text}, " + f"ts={info['timestamp']}, points={info['points']}" + ) + out_idx += 1 + + return results + + +def build_parser(): + parser = argparse.ArgumentParser( + description="Convert single-photon histogram txt to point cloud with cv2.undistortPoints." + ) + + parser.add_argument("--input-dir", type=str, default="./imaging") + parser.add_argument("--output-dir", type=str, default="./imaging") + parser.add_argument( + "--pattern", + type=str, + default="RawDataHistogramMap_frame_0_*.txt", + help="glob pattern under input-dir", + ) + parser.add_argument("--prefix", type=str, default="") + parser.add_argument("--start-index", type=int, default=1) + + parser.add_argument("--single-txt", type=str, default="", help="optional: convert one txt only") + parser.add_argument( + "--single-out-base", + type=str, + default="", + help="optional: output base path without extension for --single-txt", + ) + + parser.add_argument("--dt-ps", type=float, default=750.0) + parser.add_argument("--depth-is-range", type=_str2bool, default=True) + parser.add_argument("--undistort-intensity", type=_str2bool, default=True) + + parser.add_argument("--fx", type=float, default=118.6514575329715) + parser.add_argument("--fy", type=float, default=118.7964934010577) + parser.add_argument("--cx", type=float, default=130.6802784645003) + parser.add_argument("--cy", type=float, default=100.3605702468140) + + parser.add_argument("--k1", type=float, default=-0.257910069121181) + parser.add_argument("--k2", type=float, default=0.053237073644331) + parser.add_argument("--p1", type=float, default=0.0) + parser.add_argument("--p2", type=float, default=0.0) + + parser.add_argument("--min-range-m", type=float, default=0.0) + parser.add_argument("--max-range-m", type=float, default=20.0) + parser.add_argument("--intensity-min", type=float, default=1.0) + parser.add_argument( + "--intensity-max", + type=float, + default=None, + help="optional max photon-count threshold", + ) + + parser.add_argument( + "--output-mode", + type=str, + choices=["ply", "csv", "both"], + default="csv", + help="export file mode", + ) + parser.add_argument("--save-maps", type=_str2bool, default=True) + parser.add_argument("--show", type=_str2bool, default=False) + parser.add_argument("--show-point-size", type=float, default=1.5) + + return parser + + +def main(): + args = build_parser().parse_args() + + K, D = _build_intrinsics( + fx=args.fx, + fy=args.fy, + cx=args.cx, + cy=args.cy, + k1=args.k1, + k2=args.k2, + p1=args.p1, + p2=args.p2, + ) + + if args.single_txt: + if args.single_out_base: + out_base = args.single_out_base + else: + base = os.path.splitext(os.path.basename(args.single_txt))[0] + out_base = os.path.join(args.output_dir, base) + + os.makedirs(os.path.dirname(out_base) or ".", exist_ok=True) + info = convert_one_txt( + txt_path=args.single_txt, + output_base=out_base, + dt_ps=args.dt_ps, + depth_is_range=args.depth_is_range, + undistort_intensity=args.undistort_intensity, + K=K, + D=D, + min_range_m=args.min_range_m, + max_range_m=args.max_range_m, + intensity_min=args.intensity_min, + intensity_max=args.intensity_max, + output_mode=args.output_mode, + save_maps=args.save_maps, + show=args.show, + show_point_size=args.show_point_size, + ) + print( + f"done: {os.path.basename(info['txt'])}, points={info['points']}, " + f"csv={info['csv'] or '-'}, ply={info['ply'] or '-'}, maps={info['maps'] or '-'}" + ) + return + + results = batch_convert( + input_dir=args.input_dir, + output_dir=args.output_dir, + pattern=args.pattern, + prefix=args.prefix, + start_index=args.start_index, + dt_ps=args.dt_ps, + depth_is_range=args.depth_is_range, + undistort_intensity=args.undistort_intensity, + K=K, + D=D, + min_range_m=args.min_range_m, + max_range_m=args.max_range_m, + intensity_min=args.intensity_min, + intensity_max=args.intensity_max, + output_mode=args.output_mode, + save_maps=args.save_maps, + ) + print(f"done: converted {len(results)} files") + + if args.show and results: + target = results[0]["txt"] + out_base = os.path.join(args.output_dir, "_preview_first") + info = convert_one_txt( + txt_path=target, + output_base=out_base, + dt_ps=args.dt_ps, + depth_is_range=args.depth_is_range, + undistort_intensity=args.undistort_intensity, + K=K, + D=D, + min_range_m=args.min_range_m, + max_range_m=args.max_range_m, + intensity_min=args.intensity_min, + intensity_max=args.intensity_max, + output_mode="ply", + save_maps=False, + show=True, + show_point_size=args.show_point_size, + ) + print(f"preview shown for: {os.path.basename(info['txt'])}") + + +if __name__ == "__main__": + main() diff --git a/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_five_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_five_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..1ad9cc26cde411b08be889d6bb46dee904fc3b96 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_five_views_quantitative.ini @@ -0,0 +1,47 @@ +# eval job +scene = "artbuilding3_five_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/artbuilding3_five_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/five_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-25.379819,-15.216244,-2.908998,29.218790,32.190764,16.977430]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_ten_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_ten_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..588aa16e889e83fe8ba7439dc0ebb5d530533958 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/artbuilding3_ten_views_quantitative.ini @@ -0,0 +1,47 @@ +# eval job +scene = "artbuilding3_ten_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/artbuilding3_ten_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-25.379819,-15.216244,-2.908998,29.218790,32.190764,16.977430]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_fiveviews.ini b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_fiveviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..bca494792dd560f4a6ff0d7bed8ddb57103c8d83 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_fiveviews.ini @@ -0,0 +1,47 @@ +# eval job +scene = "balldesk_fiveviews" +step = 300000 +rep_number = 30 +split = "test" +checkpoint_dir = "./results/ours_balldesk_five_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/five_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_tenviews.ini b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_tenviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..41d0f87aa8a01f946db1d4a24270dca2ec179872 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_tenviews.ini @@ -0,0 +1,47 @@ +# eval job +scene = "balldesk_tenviews" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "./results/ours_balldesk" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_threeviews.ini b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_threeviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..f5fdce4fd5e5b7a9533e7809d8e762416fa10f99 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/balldesk_quantitative_threeviews.ini @@ -0,0 +1,47 @@ +# eval job +scene = "balldesk_threeviews" +step = 170000 +rep_number = 30 +split = "test" +checkpoint_dir = "./results/ours_balldesk_three_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/three_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/three_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_five_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_five_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..f0e14714c2c8db9d924173a0492ab8ee880897f8 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_five_views_quantitative.ini @@ -0,0 +1,47 @@ +# eval job +scene = "designbuilding1_five_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "./results/designbuilding1_five_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/five_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.723363,-32.531209,-1.819858,30.377352,12.024207,9.260004]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_quantitative_tenviews.ini b/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_quantitative_tenviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..1a78e036c7e5b39ed3dd3767dfae6966c5db1209 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/designbuilding1_quantitative_tenviews.ini @@ -0,0 +1,47 @@ +# eval job +scene = "designbuilding1_ten_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "./results/designbuilding1_ten_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.723363,-32.531209,-1.819858,30.377352,12.024207,9.260004]" +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/parking_five_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/parking_five_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..c3dc25145824d4a93f0054bdca657a62365f970a --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/parking_five_views_quantitative.ini @@ -0,0 +1,48 @@ +# eval job +scene = "parking_five_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/parking_five_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/five_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.503840,-28.637896,-2.067505,30.827200,12.873783,5.960705]" +near_plane = 0.100000 +far_plane = 30.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 +meas_peak_min = 20 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/parking_ten_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/parking_ten_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..83ca9b44402755ed0e72dd0c3767ea65c99831e0 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/parking_ten_views_quantitative.ini @@ -0,0 +1,48 @@ +# eval job +scene = "parking_ten_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/parking_ten_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.503840,-28.637896,-2.067505,30.827200,12.873783,5.960705]" +near_plane = 0.100000 +far_plane = 30.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 +meas_peak_min = 20 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative.ini b/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative.ini new file mode 100644 index 0000000000000000000000000000000000000000..736a6fe17473c370d5060f1b5b7b2dbea3b34abb --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative.ini @@ -0,0 +1,48 @@ +# eval job +scene = "physics_building2_ten_views" +step = 290000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/physics_building2_ten_views" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.008253,-20.426221,-1.811843,20.122535,20.849413,10.086755]" +near_plane = 0.100000 +far_plane = 38.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 +meas_peak_min = 10 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative1.ini b/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative1.ini new file mode 100644 index 0000000000000000000000000000000000000000..fdd18fa8abe3cac41f225c280edec89981fef61f --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/test/captured/physics_building2_ten_views_quantitative1.ini @@ -0,0 +1,48 @@ +# eval job +scene = "physics_building2_ten_views" +step = 100000 +rep_number = 30 +split = "test" +checkpoint_dir = "results/physics_building2_ten_views0" +test_folder_path = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/poses/ten_views" + +# model / rendering +version = captured +n_bins = 672 +aabb = "[-30.008253,-20.426221,-1.811843,20.122535,20.849413,10.086755]" +near_plane = 0.100000 +far_plane = 38.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +render_n_samples = 1024 +rfilter_sigma = 0.15 +sample_as_per_distribution = False +grid_resolution = 128 +grid_nlvl = 1 +meas_peak_min = 10 + +# timing / sensor +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 + +# data +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +meas_peak_min = 100 + +# image size +img_height_test = 192 +img_width_test = 256 +img_shape_test = 256 + +# misc +num_views = 10 +device = "cuda:0" +seed = 42 diff --git a/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_five_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_five_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..318150681c452695aa5e90e660a1284617abf9a5 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_five_views.ini @@ -0,0 +1,50 @@ +exp_name = "artbuilding3_five_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-25.379819,-15.216244,-2.908998,29.218790,32.190764,16.977430]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_ten_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_ten_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..7cfcbadb5ee90808f4a1b86babf8fb60274a937c --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/artbuilding3_ten_views.ini @@ -0,0 +1,50 @@ +exp_name = "artbuilding3_ten_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-25.379819,-15.216244,-2.908998,29.218790,32.190764,16.977430]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/artbuilding3/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_five_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_five_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..9bfef5bf47815247109e190d0adf9af300c28455 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_five_views.ini @@ -0,0 +1,50 @@ +exp_name = "designbuilding1_five_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-30.723363,-32.531209,-1.819858,30.377352,12.024207,9.260004]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_tenviews.ini b/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_tenviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..1003314292b6dc6e2b6df79b5a6faf2d87db6252 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/designbuilding1_tenviews.ini @@ -0,0 +1,50 @@ +exp_name = "designbuilding1_ten_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-30.723363,-32.531209,-1.819858,30.377352,12.024207,9.260004]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/designbuilding1/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/material_building_five_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/material_building_five_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..64ab54e1420547b785b2955a5f47f3747e9dc3da --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/material_building_five_views.ini @@ -0,0 +1,51 @@ +exp_name = "material_building_five_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-14.996998,-4.556170,-3.767088,54.273853,33.048301,29.515029]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/material_building/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/material_building/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +meas_peak_min = 20 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 55.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/material_building_ten_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/material_building_ten_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..01d6c299f97818cebf9e99b512e71b15c15f9ee2 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/material_building_ten_views.ini @@ -0,0 +1,51 @@ +exp_name = "material_building_ten_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-14.996998,-4.556170,-3.767088,54.273853,33.048301,29.515029]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/material_building/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/material_building/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +meas_peak_min = 20 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 55.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/parking_five_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/parking_five_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..d37bb3e5546ebb8ea383ae9f08126b2ef0300221 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/parking_five_views.ini @@ -0,0 +1,51 @@ +exp_name = "parking_five_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-30.503840,-28.637896,-2.067505,30.827200,12.873783,5.960705]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +meas_peak_min = 20 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 30.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/parking_ten_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/parking_ten_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..0e04b16173d3750d784f0c758f5e7c32a2d0bc48 --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/parking_ten_views.ini @@ -0,0 +1,51 @@ +exp_name = "parking_ten_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-30.503840,-28.637896,-2.067505,30.827200,12.873783,5.960705]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/parking/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +meas_peak_min = 20 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 30.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/physics_building2_ten_views.ini b/codes/reconstruction/transientnerf/configs/train/captured/physics_building2_ten_views.ini new file mode 100644 index 0000000000000000000000000000000000000000..c2cd4fb1354f59df9aaa7728950dc37edfff08ec --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/physics_building2_ten_views.ini @@ -0,0 +1,51 @@ +exp_name = "physics_building2_ten_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-30.008253,-20.426221,-1.811843,20.122535,20.849413,10.086755]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/360/physics_building2/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 +meas_peak_min = 10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 38.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_fiveviews.ini b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_fiveviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..16d76c6a1ccd1aa51d7e8b3dde634bdde02253ef --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_fiveviews.ini @@ -0,0 +1,50 @@ +exp_name = "ours_balldesk_five_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/five_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_tenviews.ini b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_tenviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..230dec5f9ee8e857b716d5d8cf5f2eca10bd666f --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_tenviews.ini @@ -0,0 +1,50 @@ +exp_name = "ours_balldesk" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/ten_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_threeviews.ini b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_threeviews.ini new file mode 100644 index 0000000000000000000000000000000000000000..f250d05ab82a01624f5cca9facd292521cbd66ee --- /dev/null +++ b/codes/reconstruction/transientnerf/configs/train/captured/tfnerf_balldesk_threeviews.ini @@ -0,0 +1,50 @@ +exp_name = "ours_balldesk_three_views" +test_chunk_size = 256 +num_rays_per_batch = 512 +starting_rays_per_pixel = 1 +tfilter_sigma = 3 +rfilter_sigma = 0.15 +space_carving = 0.0 +lr = 0.0001 +num_views = 10 + +n_bins = 672 +aabb = "[-20.685711,-15.981851,-1.508756,19.605109,8.297641,5.438520]" +version = "captured" +data_root_fp = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/poses/three_views" +measurement_root = "/work/sdim-lemons/wzt/data/spad_3d/balldesk/sp" +data_exts = ".npz,.txt,.pt,.h5,.hdf5" +irf_path = "/work/sdim-lemons/wzt/data/transient_nets/IRF_global.csv" +irf_column = "irf" +irf_half_window = 50 +intrinsics = "/work/sdim-lemons/wzt/data/transient_nets/intrinsics.npy" +invalid_mask_path = "/work/sdim-lemons/wzt/data/transient_nets/offset.txt" +invalid_mask_invalid_gt = 10.0 +exposure_time = 0.224844343500 +bin_width_s_loader = 7.500000000000e-10 + +thold_warmup = 80000 +max_steps = 300001 +img_height = 192 +img_width = 256 +img_height_test = 192 +img_width_test = 256 +img_shape = 256 +img_shape_test = 256 +near_plane = 0.100000 +far_plane = 28.000000 +alpha_thre = 0 +occ_thre = 0.0000001 +sample_as_per_distribution = False +render_n_samples = 1024 +exp = True +final = True +steps_til_checkpoint = 10000 +grid_resolution = 128 +grid_nlvl = 1 +outpath = "./results" +pixels_to_plot = ["(40, 60)", "(60, 100)", "(80, 60)"] +img_scale = 100 +seed = 42 +device = "cuda:0" + diff --git a/codes/reconstruction/transientnerf/eval.py b/codes/reconstruction/transientnerf/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..99262878aac93b9e1c39763ef09a0a92fec6abb8 --- /dev/null +++ b/codes/reconstruction/transientnerf/eval.py @@ -0,0 +1,556 @@ +import csv +import json +import math +import os + +import imageio +import lpips +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import scipy.io as sio +import torch +import tqdm +from nerfacc import OccGridEstimator +from skimage.metrics import structural_similarity + +from misc.dataset_utils import read_h5 +from misc.eval_utils import load_eval_args, read_json +from misc.transient_volrend import torch_laser_kernel +from radiance_fields.ngp import NGPRadianceField +from utils import render_transient + + +def _to_numpy(x): + if isinstance(x, np.ndarray): + return x + if isinstance(x, torch.Tensor): + return x.detach().cpu().numpy() + return np.asarray(x) + + +def get_gt_depth(frame, camtoworld, data_root_fp): + depth_folder = os.path.join(data_root_fp, "test") + number = int(frame["file_path"].split("_")[-1]) + ax_flip = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) + + try: + fname = os.path.join(depth_folder, f"test_{number:03d}_depth_gt.npy") + pos3d = np.load(fname) + except Exception: + fname = os.path.join(depth_folder, f"test_{number:03d}_depth_gt.h5") + pos3d = read_h5(fname) + + cam_pos = (ax_flip @ camtoworld)[:3, -1] + depth = np.sqrt(((pos3d - cam_pos[None, None, :]) ** 2).sum(-1)) + return depth.astype(np.float32) + + +def _safe_psnr(gt, pred, mask=None): + gt = np.asarray(gt, dtype=np.float64) + pred = np.asarray(pred, dtype=np.float64) + + if mask is not None: + mask = np.asarray(mask, dtype=bool) + if gt.ndim == 3: + if not np.any(mask): + return float("nan") + gt_eval = gt[mask] + pred_eval = pred[mask] + else: + if not np.any(mask): + return float("nan") + gt_eval = gt[mask] + pred_eval = pred[mask] + else: + gt_eval = gt.reshape(-1) + pred_eval = pred.reshape(-1) + + if gt_eval.size == 0: + return float("nan") + + mse = np.mean((gt_eval - pred_eval) ** 2) + max_val = max(float(np.max(gt_eval)), float(np.max(pred_eval)), 1e-8) + return float(20.0 * np.log10(max_val / np.sqrt(mse + 1e-12))) + + +def _save_metrics_csv(path, rows): + if not rows: + return + fieldnames = list(rows[0].keys()) + with open(path, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def _to_lpips_input(img_01): + img_rgb = np.repeat(img_01[..., None], 3, axis=2).astype(np.float32) + ten = torch.from_numpy(img_rgb).permute(2, 0, 1).unsqueeze(0) + ten = ten * 2.0 - 1.0 + return ten + + +def _frame_token(frame_dict): + raw = str(frame_dict.get("file_path", frame_dict.get("filepath", ""))) + return os.path.splitext(os.path.basename(raw))[0] + + +def _normalize_for_vis(img: np.ndarray, mask: np.ndarray = None, q_low: float = 1.0, q_high: float = 99.5): + arr = np.asarray(img, dtype=np.float32) + if mask is not None: + m = np.asarray(mask, dtype=bool) + vals = arr[m] + else: + vals = arr.reshape(-1) + vals = vals[np.isfinite(vals)] + if vals.size == 0: + return np.zeros_like(arr, dtype=np.float32) + + lo = float(np.percentile(vals, q_low)) + hi = float(np.percentile(vals, q_high)) + if not np.isfinite(lo) or not np.isfinite(hi) or hi <= lo: + hi = lo + 1e-6 + out = (arr - lo) / (hi - lo) + return np.clip(out, 0.0, 1.0) + + +def _extract_intensity_from_hist(hist: np.ndarray) -> np.ndarray: + hist = np.asarray(hist, dtype=np.float32) + if hist.ndim != 4: + raise ValueError(f"Expected histogram with shape [H, W, n_bins, C], got {hist.shape}") + + # Use peak intensity over time bins instead of temporal sum. + peak_rgb = hist.max(axis=-2) + return peak_rgb[..., 0].astype(np.float32) + + +def _to_gamma_domain(img_01: np.ndarray, gamma: float = 2.2) -> np.ndarray: + img_01 = np.asarray(img_01, dtype=np.float32) + return np.clip(img_01, 0.0, 1.0) ** (1.0 / gamma) + + +def _load_irf_series(path: str, column: str) -> np.ndarray: + ext = os.path.splitext(path)[1].lower() + if ext == ".csv": + df = pd.read_csv(path, sep=",") + if column in df.columns: + arr = df[column].to_numpy(dtype=np.float64) + else: + numeric_cols = [c for c in df.columns if np.issubdtype(df[c].dtype, np.number)] + if not numeric_cols: + raise ValueError(f"No numeric columns found in IRF CSV: {path}") + arr = df[numeric_cols[0]].to_numpy(dtype=np.float64) + return arr.squeeze() + if ext == ".npy": + return np.load(path).astype(np.float64).squeeze() + if ext == ".mat": + mat = sio.loadmat(path) + if "out" in mat: + return _to_numpy(mat["out"]).astype(np.float64).squeeze() + for value in mat.values(): + if isinstance(value, np.ndarray) and value.ndim >= 1 and value.size > 1: + return _to_numpy(value).astype(np.float64).squeeze() + raise ValueError(f"Cannot find valid IRF series in mat file: {path}") + if ext == ".pt": + return _to_numpy(torch.load(path, map_location="cpu")).astype(np.float64).squeeze() + raise ValueError(f"Unsupported IRF extension: {ext}") + + +def build_irf_kernel(args, device): + irf_path = getattr(args, "irf_path", "") or args.pulse_path + if not irf_path: + raise ValueError("IRF path is empty. Set --irf_path or --pulse_path.") + + irf_column = getattr(args, "irf_column", "irf") + irf_half_window = int(getattr(args, "irf_half_window", 50)) + no_irf_reverse = bool(getattr(args, "no_irf_reverse", False)) + + irf = _load_irf_series(irf_path, irf_column) + if irf.ndim != 1: + irf = irf.reshape(-1) + if irf.size == 0: + raise ValueError(f"Loaded empty IRF from: {irf_path}") + + peak_idx = int(np.argmax(irf)) + if irf_half_window > 0: + lo = max(0, peak_idx - irf_half_window) + hi = min(len(irf), peak_idx + irf_half_window + 1) + irf = irf[lo:hi] + + irf = irf / (irf.sum() + 1e-8) + if not no_irf_reverse: + irf = irf[::-1].copy() + + laser = torch.tensor(irf, dtype=torch.float32, device=device) + return torch_laser_kernel(laser, device=device) + + +@torch.no_grad() +def eval(): + args = load_eval_args() + print("version =", args.version) + + device = args.device + scale_int = float(args.scale_int) + if scale_int <= 0: + raise ValueError(f"scale_int must be > 0, got {scale_int}") + print(f"Using fixed intensity scale from config: {scale_int}") + + ckpt_dir = args.checkpoint_dir + outpath = os.path.join(args.checkpoint_dir, "results_revise") + os.makedirs(outpath, exist_ok=True) + + transforms_path = os.path.join(args.test_folder_path, f"transforms_{args.split}.json") + positions = read_json(transforms_path) + frames = positions.get("frames", []) + print(f"Using transforms: {transforms_path} (split={args.split}, frames={len(frames)})") + if args.split == "test": + train_tf_path = os.path.join(args.test_folder_path, "transforms_train.json") + if os.path.isfile(train_tf_path): + train_positions = read_json(train_tf_path) + train_frames = train_positions.get("frames", []) + test_ids = {_frame_token(f) for f in frames} + train_ids = {_frame_token(f) for f in train_frames} + overlap = sorted(test_ids.intersection(train_ids)) + if overlap: + print( + f"[WARN] test/train overlap detected: {len(overlap)} shared frame ids. " + f"Examples: {overlap[:10]}" + ) + else: + print("Train/test overlap check: no shared frame ids.") + else: + print(f"Train overlap check skipped: not found {train_tf_path}") + + used_views = [] + for idx, f in enumerate(frames): + raw = str(f.get("file_path", f.get("filepath", ""))) + used_views.append( + { + "index": idx, + "frame_file_path": raw, + "frame_name": os.path.basename(raw), + "frame_stem": _frame_token(f), + } + ) + used_views_json_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_used_views.json") + used_views_csv_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_used_views.csv") + used_views_txt_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_used_views.txt") + with open(used_views_json_path, "w", encoding="utf-8") as f: + json.dump( + { + "split": args.split, + "transforms_path": transforms_path, + "num_frames": len(used_views), + "views": used_views, + }, + f, + indent=2, + ) + _save_metrics_csv(used_views_csv_path, used_views) + with open(used_views_txt_path, "w", encoding="utf-8") as f: + for v in used_views: + f.write(f"{v['index']}\t{v['frame_file_path']}\n") + print(f"Saved used-view list: {used_views_json_path}") + + ckpt_path_rf = os.path.join(ckpt_dir, f"radiance_field_{args.step:04d}.pth") + ckpt_path_oc = os.path.join(ckpt_dir, f"occupancy_grid_{args.step:04d}.pth") + + aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device) + img_h = int(getattr(args, "img_height_test", None) or args.img_shape_test) + img_w = int(getattr(args, "img_width_test", None) or args.img_shape_test) + img_shape = (img_h, img_w) + + if args.version == "simulated": + from loaders.loader_synthetic import SubjectLoaderTransient as SubjectLoader + + test_dataset_kwargs = { + "img_shape": img_shape, + "have_images": True, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": args.rfilter_sigma, + } + else: + from loaders.loader_captured_ours import LearnRays, SubjectLoaderTransientRealOurs as SubjectLoader + + params = np.load(args.intrinsics, allow_pickle=True)[()] + shift = _to_numpy(params["shift"]) + rays = _to_numpy(params["rays"]) + source_img_shape = (int(rays.shape[0]), int(rays.shape[1])) + args.laser_kernel = build_irf_kernel(args, device=device) + measurement_root = getattr(args, "measurement_root", "").strip() or None + invalid_mask_path = getattr(args, "invalid_mask_path", "").strip() or None + data_exts = tuple( + e.strip() + for e in getattr(args, "data_exts", ".npz,.txt,.pt,.h5,.hdf5").split(",") + if e.strip() + ) + if getattr(args, "bin_width_s_loader", None) is not None: + bin_width_s_loader = float(args.bin_width_s_loader) + else: + bin_width_s_loader = float(args.exposure_time) / 299792458.0 + + test_dataset_kwargs = { + "img_shape": img_shape, + "have_images": True, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": args.rfilter_sigma, + "shift": shift, + "measurement_root": measurement_root, + "data_exts": data_exts, + "bin_width_s": bin_width_s_loader, + "source_img_shape": source_img_shape, + "invalid_mask_path": invalid_mask_path, + "invalid_mask_invalid_gt": float(getattr(args, "invalid_mask_invalid_gt", 10.0)), + } + + render_step_size = (((aabb[3:] - aabb[:3]).max() * math.sqrt(3)) / args.render_n_samples).item() + + occupancy_grid = OccGridEstimator( + roi_aabb=aabb, + resolution=args.grid_resolution, + levels=args.grid_nlvl, + ).to(device) + + radiance_field = NGPRadianceField( + use_viewdirs=True, + aabb=aabb, + unbounded=False, + radiance_activation=torch.exp, + args=args, + ).to(device) + + ckpt = torch.load(ckpt_path_rf, map_location=device) + radiance_field.load_state_dict(ckpt) + ckpt = torch.load(ckpt_path_oc, map_location=device) + occupancy_grid.load_state_dict(ckpt) + radiance_field.eval() + occupancy_grid.eval() + + test_dataset = SubjectLoader( + subject_id=f"{args.scene}", + root_fp=args.test_folder_path, + split=args.split, + num_rays=None, + **test_dataset_kwargs, + testing=True, + sample_as_per_distribution=args.sample_as_per_distribution, + ) + if args.version == "captured": + test_dataset.K = LearnRays(rays, device=device, img_shape=img_shape).to(device) + + test_dataset.rep = 1 + test_dataset.camtoworlds = test_dataset.camtoworlds.to(device) + test_dataset.K = test_dataset.K.to(device) + if args.version == "captured": + eval_dataset_scale = float(_to_numpy(test_dataset.max).reshape(-1)[0]) + if eval_dataset_scale <= 0: + eval_dataset_scale = 1.0 + else: + eval_dataset_scale = 1.0 + + lpips_model = lpips.LPIPS(net="vgg").eval().cpu() + + per_image_metrics = [] + + for i in range(len(test_dataset)): + frame_info = positions["frames"][i] + frame_key = frame_info.get("file_path", frame_info.get("filepath", str(i))) + frame_file_path = str(frame_key) + frame_name = os.path.basename(frame_file_path) + try: + ind = int(str(frame_key).split("_")[-1]) + except Exception: + ind = i + + print(f"test image {ind} | file={frame_file_path}") + + pred_hist = np.zeros((img_h, img_w, args.n_bins, 3), dtype=np.float32) + pred_depth = np.zeros((img_h, img_w), dtype=np.float32) + pred_depth_viz = np.zeros((img_h, img_w), dtype=np.float32) + weights_sum = np.zeros((img_h, img_w), dtype=np.float32) + + gt_hist = None + valid_mask = None + + for _ in tqdm.tqdm(range(args.rep_number)): + data = test_dataset[i] + pixels = data["pixels"].detach().cpu().numpy().reshape(img_h, img_w, args.n_bins, 3) + if gt_hist is None: + gt_hist = pixels.astype(np.float32) + + if "valid_mask" in data: + valid_mask = data["valid_mask"].detach().cpu().numpy().reshape(img_h, img_w).astype(bool) + + rays = data["rays"] + sample_weights = data["weights"].detach().cpu().numpy().reshape(img_h, img_w) + + out = render_transient( + radiance_field, + occupancy_grid, + rays, + near_plane=args.near_plane, + far_plane=args.far_plane, + render_step_size=render_step_size, + cone_angle=args.cone_angle, + alpha_thre=args.alpha_thre, + use_normals=False, + args=args, + ) + + pred_depth += ( + out["depths"] * data["weights"][:, None] + ).reshape(img_h, img_w).detach().cpu().numpy() + pred_depth_viz += ( + out["depths"] * data["weights"][:, None] * (out["opacities"] > 0) + ).reshape(img_h, img_w).detach().cpu().numpy() + pred_hist += ( + out["colors"] * data["weights"][:, None] + ).reshape(img_h, img_w, args.n_bins, 3).detach().cpu().numpy() + + weights_sum += sample_weights + del out + + weights_sum = np.clip(weights_sum, 1e-8, None) + pred_hist /= weights_sum[..., None, None] + pred_depth /= weights_sum + pred_depth_viz /= weights_sum + + if valid_mask is None: + valid_mask = np.ones((img_h, img_w), dtype=bool) + + gt_hist_1 = gt_hist[..., 0].astype(np.float32) + pred_hist_1 = pred_hist[..., 0].astype(np.float32) + + gt_intensity = _extract_intensity_from_hist(gt_hist) + pred_intensity = _extract_intensity_from_hist(pred_hist) + + if args.version == "simulated": + gt_depth = get_gt_depth(frame_info, test_dataset.camtoworlds[i].cpu().numpy(), args.test_folder_path) + else: + gt_depth = np.argmax(gt_hist_1, axis=-1).astype(np.float32) * float(args.exposure_time) / 2.0 + # Keep captured depth definition consistent with histogram peak depth. + pred_depth = np.argmax(pred_hist_1, axis=-1).astype(np.float32) * float(args.exposure_time) / 2.0 + + signal_mask = gt_intensity > 0 + if args.version == "captured" and float(getattr(args, "meas_peak_min", 100.0)) > 0: + peak_thre_norm = float(args.meas_peak_min) / float(eval_dataset_scale) + meas_peak_mask = np.max(gt_hist_1, axis=-1) >= peak_thre_norm + else: + meas_peak_mask = np.ones_like(signal_mask, dtype=bool) + metric_mask = valid_mask & signal_mask & meas_peak_mask + print( + f"mask ratio: valid={valid_mask.mean():.4f}, " + f"peak={meas_peak_mask.mean():.4f}, metric={metric_mask.mean():.4f}" + ) + + depth_mask = metric_mask & np.isfinite(gt_depth) & np.isfinite(pred_depth) + if np.any(depth_mask): + depth_l1 = float(np.mean(np.abs(gt_depth[depth_mask] - pred_depth[depth_mask]))) + else: + depth_l1 = float("nan") + + gt_intensity_01 = np.clip(gt_intensity / scale_int, 0.0, 1.0) + pred_intensity_01 = np.clip(pred_intensity / scale_int, 0.0, 1.0) + gt_hist_01 = np.clip(gt_hist_1 / scale_int, 0.0, 1.0) + pred_hist_01 = np.clip(pred_hist_1 / scale_int, 0.0, 1.0) + + gt_intensity_gamma = _to_gamma_domain(gt_intensity_01) + pred_intensity_gamma = _to_gamma_domain(pred_intensity_01) + + gt_intensity_eval = gt_intensity_gamma.copy() + pred_intensity_eval = pred_intensity_gamma.copy() + gt_intensity_eval[~metric_mask] = 0.0 + pred_intensity_eval[~metric_mask] = 0.0 + + intensity_ssim = float( + structural_similarity(gt_intensity_eval, pred_intensity_eval, data_range=1.0) + ) + + gt_lpips = _to_lpips_input(gt_intensity_eval) + pred_lpips = _to_lpips_input(pred_intensity_eval) + intensity_lpips = float(lpips_model(gt_lpips, pred_lpips).detach().cpu().item()) + + waveform_psnr = _safe_psnr(gt_hist_01, pred_hist_01, mask=metric_mask) + + prefix = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_test{ind}") + + np.save(prefix + "_hist_gt.npy", gt_hist_1.astype(np.float32)) + np.save(prefix + "_hist_pred.npy", pred_hist_1.astype(np.float32)) + np.save(prefix + "_depth_gt.npy", gt_depth.astype(np.float32)) + np.save(prefix + "_depth_pred.npy", pred_depth.astype(np.float32)) + np.save(prefix + "_intensity_gt.npy", gt_intensity.astype(np.float32)) + np.save(prefix + "_intensity_pred.npy", pred_intensity.astype(np.float32)) + np.save(prefix + "_valid_mask.npy", metric_mask.astype(np.uint8)) + np.save(prefix + "_meas_peak_mask.npy", meas_peak_mask.astype(np.uint8)) + torch.save(torch.from_numpy(pred_hist_1.astype(np.float32)), prefix + "_conv_pred.pt") + + gt_intensity_vis = _normalize_for_vis(gt_intensity, metric_mask) ** (1.0 / 2.2) + pred_intensity_vis = _normalize_for_vis(pred_intensity, metric_mask) ** (1.0 / 2.2) + imageio.imwrite(prefix + "_intensity_gt.png", (gt_intensity_vis * 255.0).astype(np.uint8)) + imageio.imwrite(prefix + "_intensity_pred.png", (pred_intensity_vis * 255.0).astype(np.uint8)) + + depth_for_viz = gt_depth[depth_mask] if np.any(depth_mask) else gt_depth[np.isfinite(gt_depth)] + if depth_for_viz.size > 0: + vmin = float(np.percentile(depth_for_viz, 1.0)) + vmax = float(np.percentile(depth_for_viz, 99.0)) + if vmax <= vmin: + vmax = vmin + 1e-6 + else: + vmin, vmax = 0.0, 1.0 + + plt.imsave(prefix + "_depth_gt.png", gt_depth, cmap="inferno", vmin=vmin, vmax=vmax) + plt.imsave(prefix + "_depth_pred.png", pred_depth, cmap="inferno", vmin=vmin, vmax=vmax) + plt.imsave(prefix + "_depth_pred_viz.png", pred_depth_viz, cmap="inferno", vmin=vmin, vmax=vmax) + + metrics_row = { + "index": i, + "frame_id": ind, + "frame_file_path": frame_file_path, + "frame_name": frame_name, + "intensity_ssim": intensity_ssim, + "intensity_lpips": intensity_lpips, + "depth_l1": depth_l1, + "waveform_psnr": waveform_psnr, + } + per_image_metrics.append(metrics_row) + + print( + f"SSIM={intensity_ssim:.6f} LPIPS={intensity_lpips:.6f} " + f"DepthL1={depth_l1:.6f} WavePSNR={waveform_psnr:.4f}" + ) + print("-----") + + def _nanmean(key): + values = np.array([row[key] for row in per_image_metrics], dtype=np.float64) + return float(np.nanmean(values)) + + summary = { + "scene": args.scene, + "num_views": int(args.num_views), + "step": int(args.step), + "num_images": len(per_image_metrics), + "avg_intensity_ssim": _nanmean("intensity_ssim"), + "avg_intensity_lpips": _nanmean("intensity_lpips"), + "avg_depth_l1": _nanmean("depth_l1"), + "avg_waveform_psnr": _nanmean("waveform_psnr"), + } + + print(json.dumps(summary, indent=2)) + + csv_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_metrics_per_image.csv") + json_rows_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_metrics_per_image.json") + json_summary_path = os.path.join(outpath, f"{args.scene}_{args.num_views}_{args.step}_metrics_summary.json") + + _save_metrics_csv(csv_path, per_image_metrics) + with open(json_rows_path, "w", encoding="utf-8") as f: + json.dump(per_image_metrics, f, indent=2) + with open(json_summary_path, "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + +if __name__ == "__main__": + eval() diff --git a/codes/reconstruction/transientnerf/loaders/README.md b/codes/reconstruction/transientnerf/loaders/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4d4adcf1bfa01e98e193523b0e203f1cd8abe495 --- /dev/null +++ b/codes/reconstruction/transientnerf/loaders/README.md @@ -0,0 +1,57 @@ +1. The captured scenes include: carving, boots, baskets, cinema, chef. +The simulated scenes include lego, chair, ficus, hotdog, bench. +These have different structures and loaders. + +In the root of the dataset folder you can also find the `intrinsics.npy` which is the set of captured rays and parameters used in training and `pulse_low_flux.mat` which is the calibrated laser pulse. + + +2. Training transform files. + +For the *captured* scenes the training transforms are all named `transforms_train.json` and can be found under `/final_cams//transforms_train.json`. + +For the *simulated* scenes the training transforms are all named `transforms_train_v{i}.json` where `i` is the number of views (2, 3, 5). and can be found under `/transforms_train_v{i}.json`. + + +3. Test transform files. + +For the *captured* scenes the test transforms can be found under `/final_cams/test_jsons/transforms_test.json`. + +For the *simulated* scenes the test transforms can be found under `/transforms_test_final.json`. + + +4. Downloading. + +- You can download the data yourself either through the Dropbox download button, or by right-clicking a folder and selecting copy link address, then +``` +wget "copied link" +``` +will start a download of the folder. + +5. (!!!) Using the dataset. + +To use the dataset alongside its transforms please look at the loader in `loaders/loader_captured.py` in the [GitHub repository](https://github.com/anaghmalik/TransientNeRF). Most importantly to use the temporal *captured* data, you will have to resample the transient using the shift given in the `intrinsics.npy` file: + +``` +img_shape = (512, 512) +exposure_time= 299792458*4e-12 +n_bins = 1500 + +x = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5) +y = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5) +z = torch.arange(n_bins*2, device="cpu").float() +X, Y, Z = torch.meshgrid(x, y, z, indexing="xy") +Z = Z*exposure_time/2 +Z = Z - shift[0] +Z = Z*2/exposure_time +Z = (Z-n_bins*2//2+0.5)/(n_bins*2//2-0.5) +grid = torch.stack((Z, X, Y), dim=-1)[None, ...] +del X +del Y +del Z + +rgb = torch.Tensor(rgb)[..., :3000].float().cpu() +rgb = torch.nn.functional.grid_sample(rgb[None, None, ...], grid, align_corners=True).squeeze().cpu() + +``` + +where `shift` is the value from the `intrinsics.npy` file and `rgb` is the original loaded transient. diff --git a/codes/reconstruction/transientnerf/loaders/__init__.py b/codes/reconstruction/transientnerf/loaders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/codes/reconstruction/transientnerf/loaders/loader_captured.py b/codes/reconstruction/transientnerf/loaders/loader_captured.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3a1b2ecce4ee945001e5db93a01166779d2d30 --- /dev/null +++ b/codes/reconstruction/transientnerf/loaders/loader_captured.py @@ -0,0 +1,532 @@ +import collections +import json +import os +import matplotlib.pyplot as plt +import numpy as np +import torch +import scipy +from mat73 import loadmat +from .utils import Rays +from tqdm import tqdm +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from misc.dataset_utils import read_h5 + + +def _load_renderings(root_fp: str, subject_id: str, split: str, have_images=True, img_shape=(256, 256)): + """Load images from disk.""" + if not root_fp.startswith("/"): + # allow relative path. e.g., "./data/nerf_synthetic/" + root_fp = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "..", + "..", + root_fp, + ) + + data_dir = root_fp + with open( + os.path.join(data_dir, "transforms_{}.json".format(split)), "r" + ) as fp: + meta = json.load(fp) + images = [] + camtoworlds = [] + + if have_images: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + number = int(frame["file_path"].split("_")[-1]) + fname = os.path.join(data_dir, f"{number:03d}" + ".png") + + # fname = os.path.join(data_dir, frame["file_path"] + ".png") + rgba = imageio.imread(fname) + camtoworlds.append(frame["transform_matrix"]) + images.append(rgba) + + images = np.stack(images, axis=0) + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = images.shape[1:3] + else: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + camtoworlds.append(frame["transform_matrix"]) + + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = img_shape + + camera_angle_x = float(meta["camera_angle_x"]) + focal = 0.5 * w / np.tan(0.5 * camera_angle_x) + + return images, camtoworlds, focal + + +def _parse_shift_for_grid(shift, img_shape): + h, w = int(img_shape[0]), int(img_shape[1]) + arr = np.asarray(shift, dtype=np.float32) + if arr.ndim == 0: + return float(arr.item()), None + + arr = arr.squeeze() + if arr.ndim == 0 or arr.size == 1: + return float(arr.reshape(-1)[0]), None + + if arr.ndim == 1 and arr.size == h * w: + return 0.0, torch.from_numpy(arr.reshape(h, w)) + if arr.ndim == 2 and arr.shape == (h, w): + return 0.0, torch.from_numpy(arr) + + # Fallback to legacy behavior: use first value only. + return float(arr.reshape(-1)[0]), None + + + +def _load_renderings_transient_real(root_fp: str, subject_id: str, split: str, have_images=True, img_shape=(256, 256), n_bins=4096, shift=0, bin_width_s=4e-12): + """Load images from disk.""" + + data_dir = root_fp + with open( + os.path.join(data_dir, "transforms_{}.json".format(split)), "r" + ) as fp: + meta = json.load(fp) + + images = [] + camtoworlds = [] + + exposure_time = 299792458 * float(bin_width_s) + shift_scalar, shift_map = _parse_shift_for_grid(shift, img_shape) + + x = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5) + y = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5) + z = torch.arange(n_bins*2, device="cpu").float() + X, Y, Z = torch.meshgrid(x, y, z, indexing="xy") + Z = Z*exposure_time/2 + if shift_map is not None: + Z = Z - shift_map[..., None] + else: + Z = Z - float(shift_scalar) + Z = Z*2/exposure_time + Z = (Z-n_bins*2//2+0.5)/(n_bins*2//2-0.5) + grid = torch.stack((Z, X, Y), dim=-1)[None, ...] + del X + del Y + del Z + + + if have_images: + tqdm.write('Loading data') + for i in tqdm(range(len(meta["frames"]))): + frame = meta["frames"][i] + number = int(frame["file_path"].split("_")[-1]) + + fname = os.path.join(os.path.join(data_dir, "../.."), f"transient{number:03d}.pt") + rgba = torch.load(fname).to_dense() + rgba = torch.Tensor(rgba)[..., :3000].float().cpu() + # if img_shape[0]==256: + # rgba = (rgba[::2, ::2] + rgba[::2, 1::2] + rgba[1::2, ::2]+ rgba[1::2, 1::2] )/4 + + rgba = torch.nn.functional.grid_sample(rgba[None, None, ...], grid, align_corners=True).squeeze().cpu() + rgba = (rgba[..., 1::2]+ rgba[..., ::2] )/2 + + camtoworlds.append(frame["transform_matrix"]) + rgba = torch.clip(rgba, 0, None) + rgba = rgba[..., None].repeat(1, 1, 1, 3) + images.append(rgba) + + + + images = torch.stack(images, axis=0) + max = torch.max(images) + images /= torch.max(images) + + if split == "test": + quotient = images.shape[1]//img_shape[0] + times_downsample = int(np.log2(quotient)) + + for i in range(times_downsample): + images = (images[:, 1::2, ::2] + images[:, ::2, ::2] + images[:, 1::2, 1::2] + images[:, ::2, 1::2])/4 + + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = images.shape[1:3] + else: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + camtoworlds.append(frame["transform_matrix"]) + + camtoworlds = np.stack(camtoworlds, axis=0) + max = 1 + h, w = img_shape + + + return images, camtoworlds, max + + +class SubjectLoaderTransientReal(torch.utils.data.Dataset): + """Single subject data loader for training and evaluation.""" + + SPLITS = ["train", "val", "trainval", "test"] + SUBJECT_IDS = [ + "chair", + "drums", + "ficus", + "hotdog", + "lego", + "materials", + "mic", + "ship", + ] + + # WIDTH, HEIGHT = 64, 64 + NEAR, FAR = 0, 6 + OPENGL_CAMERA = True + + def __init__( + self, + subject_id: str, + root_fp: str, + split: str, + color_bkgd_aug: str = "black", + num_rays: int = None, + near: float = None, + far: float = None, + batch_over_images: bool = True, + have_images=True, + img_shape=(256, 256), + n_bins=10000, + rfilter_sigma=0.15, + sample_as_per_distribution = True, + shift = 0.3, + testing =False + ): + super().__init__() + #assert split in self.SPLITS, "%s" % split + # assert subject_id in self.SUBJECT_IDS, "%s" % subject_id + assert color_bkgd_aug in ["white", "black", "random"] + self.sample_as_per_distribution = sample_as_per_distribution + self.rfilter_sigma = rfilter_sigma + self.HEIGHT, self.WIDTH = img_shape + self.split = split + self.testing = testing + self.num_rays = num_rays + self.near = self.NEAR if near is None else near + self.far = self.FAR if far is None else far + self.training = (num_rays is not None) and ( + split in ["train", "trainval"] + ) + self.shift = shift + self.testing = testing + self.rep = 1 + self.color_bkgd_aug = color_bkgd_aug + self.batch_over_images = batch_over_images + self.have_images = have_images + self.n_bins = n_bins + shift = shift + + if split == "trainval": + _images_train, _camtoworlds_train, _focal_train = _load_renderings_transient_real( + root_fp, subject_id, "train", n_bins=self.n_bins, shift=shift + ) + _images_val, _camtoworlds_val, _focal_val = _load_renderings_transient_real( + root_fp, subject_id, "val", n_bins=self.n_bins, shift=shift + ) + self.images = np.concatenate([_images_train, _images_val]) + self.camtoworlds = np.concatenate( + [_camtoworlds_train, _camtoworlds_val] + ) + self.focal = _focal_train + self.images = torch.from_numpy(self.images).to(torch.float32) + + # ste for transient + self.images = torch.reshape(self.images, (-1, self.HEIGHT, self.WIDTH, self.n_bins*3)) + + + elif have_images: + self.images, self.camtoworlds, self.focal = _load_renderings_transient_real( + root_fp, subject_id, split, n_bins=self.n_bins, shift=shift, img_shape=img_shape + ) + self.images =self.images.to(torch.float32) + assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH) + else: + _, self.camtoworlds, self.focal = _load_renderings( + root_fp, subject_id, split, have_images=have_images, img_shape=img_shape + ) + + self.max = self.focal + + self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32) + self.camtoworlds[:, :3, 3] = self.camtoworlds[:, :3, 3] + # self.K = LearnRays(params["rays"], img_shape=(self.WIDTH, self.HEIGHT)) + + + + def __len__(self): + return len(self.camtoworlds) + + # @torch.no_grad() + def __getitem__(self, index): + data = self.fetch_data(index) + data = self.preprocess(data) + return data + + def preprocess(self, data): + """Process the fetched / cached data with randomness.""" + rgba, rays = data["rgba"], data["rays"] + # pixels, alpha = torch.split(rgba, [3, 1], dim=-1) + + if rgba is not None: + pixels = rgba.to(self.camtoworlds.device) + else: + pixels = rgba + + + if self.color_bkgd_aug == "random": + color_bkgd = torch.rand(3, device=self.camtoworlds.device) + elif self.color_bkgd_aug == "white": + color_bkgd = torch.ones(3, device=self.camtoworlds.device) + elif self.color_bkgd_aug == "black": + color_bkgd = torch.zeros(3, device=self.camtoworlds.device) + + # pixels = pixels * alpha + color_bkgd * (1.0 - alpha) + return { + "pixels": pixels, # [n_rays, 3] or [h, w, 3] + "rays": rays, # [n_rays,] or [h, w] + "color_bkgd": color_bkgd, # [3,] + **{k: v for k, v in data.items() if k not in ["rgba", "rays"]}, + } + + + def update_num_rays(self, num_rays): + self.num_rays = num_rays + + def fetch_data(self, index, rep=None, num_rays=None): + """Fetch the data (it maybe cached for multiple batches).""" + if num_rays==None: + num_rays = self.num_rays + if rep==None: + rep = self.rep + + + + if self.training: + if self.batch_over_images: + image_id = torch.randint( + 0, + len(self.images), + size=(num_rays,), + device=self.images.device, + ) + else: + image_id = [index] + x = torch.randint( + 0, self.WIDTH, size=(num_rays,), device="cpu" + ) + y = torch.randint( + 0, self.HEIGHT, size=(num_rays,), device="cpu" + ) + x = x.repeat(rep) + y = y.repeat(rep) + image_id = image_id.repeat(rep) + + + rgba = self.images[image_id, y, x] # (num_rays, 4) + + elif self.testing: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device="cpu"), + torch.arange(self.HEIGHT, device="cpu"), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + x = x.repeat(rep) + y = y.repeat(rep) + # image_id = image_id.repeat(rep) + if self.have_images: + rgba = self.images[image_id, y, x] # (num_rays, 4) + else: + rgba = None + elif self.have_images: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device=self.camtoworlds.device), + torch.arange(self.HEIGHT, device=self.camtoworlds.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + rgba = self.images[image_id, y, x] # (num_rays, 4) + else: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device=self.camtoworlds.device), + torch.arange(self.HEIGHT, device=self.camtoworlds.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + + # generate rays + + scale = self.rfilter_sigma + c2w = self.camtoworlds[image_id] + + bounds_max = [4*scale]*x.shape[0] + loc = 0 + if self.training: + s_x, s_y, weights = spatial_filter(x, y, sigma=scale, rep = self.rep, prob_dithering=self.sample_as_per_distribution) + s_x = (torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH-1).to(self.camtoworlds.device)).to(torch.float32) + s_y = (torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT-1).to(self.camtoworlds.device)).to(torch.float32) + weights = torch.Tensor(weights).to(self.camtoworlds.device) + + elif self.testing: + s_x, s_y, weights = spatial_filter(x, y, sigma=scale, rep = self.rep, prob_dithering=self.sample_as_per_distribution) + s_x = (torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH-1).to(self.camtoworlds.device)).to(torch.float32) + s_y = (torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT-1).to(self.camtoworlds.device)).to(torch.float32) + weights = torch.Tensor(weights).to(self.camtoworlds.device) + else: + s_x = x + s_y = y + + + + camera_dirs = self.K(s_x, s_y) + + directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1) + origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape) + viewdirs = directions / torch.linalg.norm( + directions, dim=-1, keepdims=True + ) + + if self.training: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + # here + rgba = torch.reshape(rgba, (-1,self.n_bins*3)) + elif self.testing: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + # here + if self.have_images: + rgba = torch.reshape(rgba, (-1,self.n_bins*3)) + + elif self.have_images: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = torch.reshape(rgba, (self.HEIGHT, self.WIDTH, self.n_bins * 3)) + else: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = None + + rays = Rays(origins=origins, viewdirs=viewdirs) + if self.training or self.testing: + return { + "rgba": rgba, # [h, w, 4] or [num_rays, 4] + "rays": rays, # [h, w, 3] or [num_rays, 3] + "weights":weights + } + + return { + "rgba": rgba, # [h, w, 4] or [num_rays, 4] + "rays": rays, # [h, w, 3] or [num_rays, 3] + } + + + + + +class LearnRays(torch.nn.Module): + def __init__(self, rays, device ="cuda:0", img_shape = (256, 256)): + """ + :param num_cams: + :param learn_R: True/False + :param learn_t: True/False + :param init_c2w: (N, 4, 4) torch tensor + """ + super(LearnRays, self).__init__() + self.device = device + self.init_c2w = None + self.img_shape = img_shape + + x = np.arange(32, 480) + X, Y = np.meshgrid(x, x) + + tar_x = np.arange(0, 512) + tar_X, tar_Y = np.meshgrid(tar_x, tar_x) + # rays = rays.detach().cpu().numpy() + + ray_x = scipy.interpolate.interpn((x, x), rays[32:-32, 32:-32, 0].transpose(1, 0), np.stack([tar_X, tar_Y], axis=-1).squeeze().flatten(), bounds_error = False, fill_value=None).reshape(512, 512) + ray_y = scipy.interpolate.interpn((x, x), rays[32:-32, 32:-32, 1].transpose(1, 0), np.stack([tar_X, tar_Y], axis=-1).squeeze().flatten(), bounds_error = False, fill_value=None).reshape(512, 512) + ray_z = scipy.interpolate.interpn((x, x), rays[32:-32, 32:-32, 2].transpose(1, 0), np.stack([tar_X, tar_Y], axis=-1).squeeze().flatten(), bounds_error = False, fill_value=None).reshape(512, 512) + + rays = torch.from_numpy(np.stack([ray_x, ray_y, ray_z], axis=-1)).to(self.device) + + quotient = rays.shape[1]//img_shape[0] + times_downsample = int(np.log2(quotient)) + + for i in range(times_downsample): + rays = (rays[1::2, ::2] + rays[::2, ::2] + rays[1::2, 1::2] + rays[::2, 1::2])/4 + + rays = rays/torch.linalg.norm(rays, dim=-1, keepdims=True) + self.rays = rays + # self.rays = torch.nn.Parameter(rays, requires_grad=learn_rays) + + def forward(self, x0, y0): + """input coord = (n, 2) + rays = (512, 512, 3) + """ + rays = self.rays + x1, y1 = torch.floor(x0.float()), torch.floor(y0.float()) + x2, y2 = x1+1, y1+1 + """ + Perform bilinear interpolation to estimate the value of the function f(x, y) + at the continuous point (x0, y0), given that f is known at integer values of x, y. + """ + # if (y1>self.img_shape[0]-1).any() or (x1>self.img_shape[0]-1).any(): + # print("hello") + x1, y1 = torch.clip(x1, 0, self.img_shape[0]-1), torch.clip(y1, 0, self.img_shape[0]-1) + + # x2, y2 = torch.clip(x2, 0, self.img_shape[0]-1), torch.clip(y2, 0, self.img_shape[0]-1) + + # Compute the weights for the interpolation + wx1 = ((x2 - x0) / (x2 - x1 + 1e-8))[:, None] + wx2 = ((x0 - x1) / (x2 - x1 + 1e-8))[:, None] + wy1 = ((y2 - y0) / (y2 - y1 + 1e-8))[:, None] + wy2 = ((y0 - y1) / (y2 - y1 + 1e-8))[:, None] + + x1, y1, x2, y2 = x1.long(), y1.long(), x2.long(), y2.long() + x2, y2 = torch.clip(x2, 0, self.img_shape[0] - 1), torch.clip(y2, 0, self.img_shape[0] - 1) + + # Compute the interpolated value of f(x, y) at (x0, y0) + f_interp = wx1 * wy1 * rays[y1, x1] + \ + wx1 * wy2 * rays[y2, x1] + \ + wx2 * wy1 * rays[y1, x2] + \ + wx2 * wy2 * rays[y2, x2] + + f_interp = f_interp/torch.linalg.norm(f_interp, dim=-1, keepdims=True) + return f_interp.float() + + +def spatial_filter(x, y, sigma, rep, prob_dithering=True): + pdf_fn = lambda x: np.exp(-x/(2*sigma**2)) - np.exp(-16) + if prob_dithering: + bounds_max = [4*sigma]*x.shape[0] + loc = 0 + s_x = scipy.stats.truncnorm.rvs((-np.array(bounds_max)-loc)/sigma, (np.array(bounds_max)-loc)/sigma, loc=loc, scale=sigma) + s_y = scipy.stats.truncnorm.rvs((-np.array(bounds_max)-loc)/sigma, (np.array(bounds_max)-loc)/sigma, loc=loc, scale=sigma) + weights = np.ones_like(s_x)*1/rep + + else: + s_x = np.random.uniform(low=-4*sigma, high=4*sigma, size=(rep, x.shape[0]//rep)) + s_y = np.random.uniform(low=-4*sigma, high=4*sigma, size=(rep, x.shape[0]//rep)) + dists = (s_x**2 + s_y**2) + weights = pdf_fn(dists) + weights = weights/weights.sum(0)[None, :] + s_x = s_x.flatten() + s_y = s_y.flatten() + weights = weights.flatten() + + return s_x, s_y, weights diff --git a/codes/reconstruction/transientnerf/loaders/loader_captured_ours.py b/codes/reconstruction/transientnerf/loaders/loader_captured_ours.py new file mode 100644 index 0000000000000000000000000000000000000000..9d247e0468669c83db7e9e229ddebc2ac027c00e --- /dev/null +++ b/codes/reconstruction/transientnerf/loaders/loader_captured_ours.py @@ -0,0 +1,680 @@ +import json +import os +from typing import Sequence, Tuple + +# import h5py +import numpy as np +import scipy +import torch +import torch.nn.functional as F +from tqdm import tqdm + +from .utils import Rays + + +C = 299792458.0 + + +def _normalize_index(idx: torch.Tensor, size: int) -> torch.Tensor: + if size <= 1: + return torch.zeros_like(idx) + return 2.0 * idx / float(size - 1) - 1.0 + + +def _load_numpy_coo_npz(npz_path: str) -> np.ndarray: + pack = np.load(npz_path, allow_pickle=False) + try: + if "format" not in pack: + raise ValueError("missing 'format' key in npz") + fmt = str(np.asarray(pack["format"]).item()) + if fmt != "coo_numpy": + raise ValueError(f"unsupported npz format tag: {fmt}") + + shape = tuple(np.asarray(pack["shape"], dtype=np.int64).tolist()) + row = np.asarray(pack["row"], dtype=np.int64) + col = np.asarray(pack["col"], dtype=np.int64) + data = np.asarray(pack["data"]) + + dense = np.zeros(shape, dtype=data.dtype) + np.add.at(dense, (row, col), data) + return dense + finally: + pack.close() + + +def _load_histogram_2d(path: str, dtype=np.float32) -> np.ndarray: + ext = os.path.splitext(path)[1].lower() + if ext == ".txt": + arr = np.loadtxt(path, dtype=dtype) + elif ext == ".npz": + try: + from scipy import sparse # type: ignore + + arr = sparse.load_npz(path).toarray() + except Exception: + arr = _load_numpy_coo_npz(path) + if dtype is not None: + arr = np.asarray(arr, dtype=dtype) + else: + raise ValueError(f"unsupported histogram extension for 2D loader: {ext}") + + arr = np.asarray(arr, dtype=dtype) + if arr.ndim == 1: + arr = arr.reshape(1, -1) + return arr + + +def _reshape_histogram_to_hwt(arr: np.ndarray, hw: Tuple[int, int]) -> torch.Tensor: + h, w = int(hw[0]), int(hw[1]) + if arr.ndim == 3: + if arr.shape[0] == h and arr.shape[1] == w: + out = arr + elif arr.shape[0] == w and arr.shape[1] == h: + out = arr.transpose(1, 0, 2) + else: + raise ValueError(f"3D histogram shape {arr.shape} incompatible with target ({h}, {w}, T)") + return torch.from_numpy(np.asarray(out, dtype=np.float32)) + + if arr.ndim != 2: + raise ValueError(f"expected 2D/3D histogram array, got shape={arr.shape}") + + if arr.shape[0] == h * w: + out = arr.reshape(h, w, arr.shape[1]) + elif arr.shape[1] == h * w: + out = arr.T.reshape(h, w, arr.shape[0]) + else: + raise ValueError( + f"2D histogram shape {arr.shape} does not match H*W={h*w}; " + "expected [H*W, bins] or [bins, H*W]." + ) + return torch.from_numpy(np.asarray(out, dtype=np.float32)) + + +def _load_valid_mask_from_offset( + offset_path: str, + source_hw: Tuple[int, int], + target_hw: Tuple[int, int], + invalid_gt: float = 10.0, +) -> torch.Tensor: + ext = os.path.splitext(offset_path)[1].lower() + if ext == ".npy": + arr = np.load(offset_path).astype(np.float32) + else: + arr = np.loadtxt(offset_path, dtype=np.float32) + arr = np.asarray(arr, dtype=np.float32).squeeze() + + src_h, src_w = int(source_hw[0]), int(source_hw[1]) + if arr.ndim == 1: + if arr.size != src_h * src_w: + raise ValueError( + f"offset map length {arr.size} does not match source H*W={src_h*src_w}" + ) + arr = arr.reshape(src_h, src_w) + elif arr.ndim == 2: + if arr.shape == (src_h, src_w): + pass + elif arr.shape == (src_w, src_h): + arr = arr.T + elif arr.size == src_h * src_w: + arr = arr.reshape(src_h, src_w) + else: + raise ValueError( + f"offset map shape {arr.shape} incompatible with source shape ({src_h}, {src_w})" + ) + else: + raise ValueError(f"offset map must be 1D/2D, got shape={arr.shape}") + + valid = (arr <= float(invalid_gt)).astype(np.float32) + valid_t = torch.from_numpy(valid)[None, None, ...] + + dst_h, dst_w = int(target_hw[0]), int(target_hw[1]) + if (src_h, src_w) != (dst_h, dst_w): + valid_t = F.interpolate( + valid_t, + size=(dst_h, dst_w), + mode="nearest", + ) + return valid_t.squeeze(0).squeeze(0) > 0.5 + + +def _load_measurement_histogram(path: str, hw: Tuple[int, int]) -> torch.Tensor: + ext = os.path.splitext(path)[1].lower() + if ext in (".txt", ".npz"): + arr = _load_histogram_2d(path, dtype=np.float32) + hist = _reshape_histogram_to_hwt(arr, hw) + elif ext == ".pt": + raw = torch.load(path, map_location="cpu") + if not isinstance(raw, torch.Tensor): + raise ValueError(f".pt file must contain a tensor: {path}") + hist = raw.to_dense() if raw.is_sparse else raw + hist = hist.to(torch.float32).cpu() + if hist.ndim == 2: + hist = _reshape_histogram_to_hwt(hist.numpy(), hw) + elif hist.ndim == 3: + pass + else: + raise ValueError(f"unsupported tensor shape in {path}: {tuple(hist.shape)}") + # elif ext in (".h5", ".hdf5"): + # with h5py.File(path, "r") as f: + # if "data" not in f: + # raise ValueError(f"h5 file missing key 'data': {path}") + # arr = np.asarray(f["data"]) + # hist = _reshape_histogram_to_hwt(arr, hw) + else: + raise ValueError(f"unsupported measurement extension: {ext}") + + if hist.ndim == 4 and hist.shape[-1] == 1: + hist = hist[..., 0] + if hist.ndim == 4 and hist.shape[-1] == 3: + hist = hist[..., 0] + if hist.ndim != 3: + raise ValueError(f"expected histogram shape [H,W,T], got {tuple(hist.shape)} from {path}") + return hist + + +def _parse_shift_for_grid(shift, hw: Tuple[int, int]): + h, w = int(hw[0]), int(hw[1]) + arr = np.asarray(shift, dtype=np.float32) + if arr.ndim == 0: + return float(arr.item()), None + + arr = arr.squeeze() + if arr.ndim == 0 or arr.size == 1: + return float(arr.reshape(-1)[0]), None + + if arr.ndim == 1 and arr.size == h * w: + return 0.0, torch.from_numpy(arr.reshape(h, w)) + + if arr.ndim == 2: + shift_map = torch.from_numpy(arr.astype(np.float32)) + if tuple(shift_map.shape) != (h, w): + shift_map = F.interpolate( + shift_map[None, None, ...], + size=(h, w), + mode="bilinear", + align_corners=True, + ).squeeze(0).squeeze(0) + return 0.0, shift_map + + return float(arr.reshape(-1)[0]), None + + +def _build_shift_grid( + hw: Tuple[int, int], + n_bins: int, + shift, + bin_width_s: float, + device: str = "cpu", +) -> torch.Tensor: + h, w = int(hw[0]), int(hw[1]) + exposure_time = C * float(bin_width_s) + shift_scalar, shift_map = _parse_shift_for_grid(shift, hw) + + z = torch.arange(n_bins, device=device, dtype=torch.float32)[:, None, None] + z = z * exposure_time / 2.0 + if shift_map is not None: + z = z - shift_map.to(device)[None, ...] + else: + z = z - float(shift_scalar) + z = z * 2.0 / exposure_time + z = _normalize_index(z, n_bins) + + x = _normalize_index(torch.arange(w, device=device, dtype=torch.float32), w) + y = _normalize_index(torch.arange(h, device=device, dtype=torch.float32), h) + + x = x[None, None, :].expand(n_bins, h, w) + y = y[None, :, None].expand(n_bins, h, w) + z = z.expand(n_bins, h, w) + + # grid_sample 5D grid order is (x, y, z) for input shaped [N,C,D,H,W]. + grid = torch.stack((x, y, z), dim=-1)[None, ...] + return grid + + +def _apply_shift(hist: torch.Tensor, shift, bin_width_s: float) -> torch.Tensor: + h, w, t = hist.shape + grid = _build_shift_grid((h, w), t, shift, bin_width_s, device=hist.device) + # [H, W, T] -> [N=1, C=1, D=T, H, W] + hist_dhw = hist.permute(2, 0, 1)[None, None, ...] + shifted = F.grid_sample(hist_dhw, grid, align_corners=True) + # back to [H, W, T] + return shifted.squeeze(0).squeeze(0).permute(1, 2, 0) + + +def _resize_hist_spatial(hist: torch.Tensor, target_hw: Tuple[int, int]) -> torch.Tensor: + target_h, target_w = int(target_hw[0]), int(target_hw[1]) + h, w, _ = hist.shape + if (h, w) == (target_h, target_w): + return hist + # [H,W,T] -> [1,T,H,W] for spatial resize only. + hist_chw = hist.permute(2, 0, 1)[None, ...] + resized = F.interpolate(hist_chw, size=(target_h, target_w), mode="area") + return resized.squeeze(0).permute(1, 2, 0) + + +def _resolve_measurement_path( + data_dir: str, + split: str, + frame_file_path: str, + measurement_root: str = None, + data_exts: Sequence[str] = (".npz", ".txt", ".pt", ".h5", ".hdf5"), +) -> str: + raw = str(frame_file_path) + if os.path.isabs(raw) and os.path.isfile(raw): + return raw + + rel = raw.replace("\\", "/").lstrip("./") + base = os.path.basename(rel) + stem, ext = os.path.splitext(base) + rel_stem = os.path.splitext(rel)[0] + + roots = [] + if measurement_root: + roots.extend([measurement_root, os.path.join(measurement_root, split)]) + roots.extend([data_dir, os.path.join(data_dir, split)]) + + seen_roots = set() + unique_roots = [] + for r in roots: + rr = os.path.normpath(r) + if rr not in seen_roots: + unique_roots.append(rr) + seen_roots.add(rr) + + # Prefer exact path if extension already exists. + if ext: + for root in unique_roots: + for rel_name in (rel, base): + cand = os.path.normpath(os.path.join(root, rel_name)) + if os.path.isfile(cand): + return cand + + # Then resolve by stem and supported extensions. + for root in unique_roots: + for data_ext in data_exts: + for rel_name in (rel_stem + data_ext, stem + data_ext): + cand = os.path.normpath(os.path.join(root, rel_name)) + if os.path.isfile(cand): + return cand + + raise FileNotFoundError( + f"Cannot resolve measurement file for frame '{frame_file_path}'. " + f"Searched roots={unique_roots}, exts={tuple(data_exts)}." + ) + + +def _load_renderings_transient_real_ours( + root_fp: str, + split: str, + have_images=True, + img_shape=(256, 256), + source_img_shape=None, + n_bins=4096, + shift=0, + bin_width_s=4e-12, + measurement_root=None, + data_exts=(".npz", ".txt", ".pt", ".h5", ".hdf5"), +): + data_dir = root_fp + with open(os.path.join(data_dir, f"transforms_{split}.json"), "r", encoding="utf-8") as fp: + meta = json.load(fp) + + images = [] + camtoworlds = [] + + if have_images: + tqdm.write("Loading data") + reshape_hw = tuple(source_img_shape) if source_img_shape is not None else tuple(img_shape) + for i in tqdm(range(len(meta["frames"]))): + frame = meta["frames"][i] + file_key = frame.get("file_path", frame.get("filepath")) + if file_key is None: + raise KeyError("Each frame in transforms json must contain 'file_path' or 'filepath'.") + measurement_path = _resolve_measurement_path( + data_dir=data_dir, + split=split, + frame_file_path=file_key, + measurement_root=measurement_root, + data_exts=data_exts, + ) + + hist = _load_measurement_histogram(measurement_path, reshape_hw).to(torch.float32).cpu() + hist = torch.clip(hist, min=0.0) + hist = _apply_shift(hist, shift=shift, bin_width_s=bin_width_s) + + if hist.shape[-1] != int(n_bins): + raise ValueError( + f"Histogram bin count mismatch for '{measurement_path}': " + f"loaded {hist.shape[-1]} vs config n_bins={n_bins}. " + "This pipeline does not truncate or adjacent-bin average." + ) + + hist = _resize_hist_spatial(hist, img_shape) + hist_rgb = hist[..., None].repeat(1, 1, 1, 3) + + camtoworlds.append(frame["transform_matrix"]) + images.append(hist_rgb) + + images = torch.stack(images, axis=0) + max_value = torch.max(images).clamp_min(1e-8) + images = images / max_value + camtoworlds = np.stack(camtoworlds, axis=0) + else: + for frame in meta["frames"]: + camtoworlds.append(frame["transform_matrix"]) + camtoworlds = np.stack(camtoworlds, axis=0) + max_value = torch.tensor(1.0) + + return images, camtoworlds, max_value + + +class SubjectLoaderTransientRealOurs(torch.utils.data.Dataset): + """Captured-data loader for custom txt/npz/pt histograms without bin truncation/averaging.""" + + SPLITS = ["train", "val", "trainval", "test"] + NEAR, FAR = 0, 6 + OPENGL_CAMERA = True + + def __init__( + self, + subject_id: str, + root_fp: str, + split: str, + color_bkgd_aug: str = "black", + num_rays: int = None, + near: float = None, + far: float = None, + batch_over_images: bool = True, + have_images=True, + img_shape=(256, 256), + source_img_shape=None, + n_bins=10000, + rfilter_sigma=0.15, + sample_as_per_distribution=True, + shift=0.0, + testing=False, + bin_width_s=4e-12, + measurement_root=None, + data_exts=(".npz", ".txt", ".pt", ".h5", ".hdf5"), + invalid_mask_path=None, + invalid_mask_invalid_gt=10.0, + ): + super().__init__() + assert color_bkgd_aug in ["white", "black", "random"] + self.sample_as_per_distribution = sample_as_per_distribution + self.rfilter_sigma = rfilter_sigma + self.HEIGHT, self.WIDTH = int(img_shape[0]), int(img_shape[1]) + self.split = split + self.testing = testing + self.num_rays = num_rays + self.near = self.NEAR if near is None else near + self.far = self.FAR if far is None else far + self.training = (num_rays is not None) and (split in ["train", "trainval"]) + self.shift = shift + self.rep = 1 + self.color_bkgd_aug = color_bkgd_aug + self.batch_over_images = batch_over_images + self.have_images = have_images + self.n_bins = int(n_bins) + self.bin_width_s = float(bin_width_s) + self.measurement_root = measurement_root + self.data_exts = tuple(data_exts) + self.source_img_shape = tuple(source_img_shape) if source_img_shape is not None else None + self.invalid_mask_path = invalid_mask_path + self.invalid_mask_invalid_gt = float(invalid_mask_invalid_gt) + + if have_images: + self.images, self.camtoworlds, self.max = _load_renderings_transient_real_ours( + root_fp=root_fp, + split=split, + n_bins=self.n_bins, + shift=shift, + img_shape=(self.HEIGHT, self.WIDTH), + source_img_shape=self.source_img_shape, + bin_width_s=self.bin_width_s, + measurement_root=self.measurement_root, + data_exts=self.data_exts, + ) + self.images = self.images.to(torch.float32) + assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH) + else: + raise ValueError("have_images=False is not supported in SubjectLoaderTransientRealOurs.") + + self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32) + if self.invalid_mask_path: + source_hw = ( + tuple(self.source_img_shape) + if self.source_img_shape is not None + else (self.HEIGHT, self.WIDTH) + ) + self.valid_mask = _load_valid_mask_from_offset( + self.invalid_mask_path, + source_hw=source_hw, + target_hw=(self.HEIGHT, self.WIDTH), + invalid_gt=self.invalid_mask_invalid_gt, + ).to(torch.bool) + else: + self.valid_mask = torch.ones((self.HEIGHT, self.WIDTH), dtype=torch.bool) + + def __len__(self): + return len(self.camtoworlds) + + def __getitem__(self, index): + data = self.fetch_data(index) + data = self.preprocess(data) + return data + + def preprocess(self, data): + rgba, rays = data["rgba"], data["rays"] + + if rgba is not None: + pixels = rgba.to(self.camtoworlds.device) + else: + pixels = rgba + + if self.color_bkgd_aug == "random": + color_bkgd = torch.rand(3, device=self.camtoworlds.device) + elif self.color_bkgd_aug == "white": + color_bkgd = torch.ones(3, device=self.camtoworlds.device) + else: + color_bkgd = torch.zeros(3, device=self.camtoworlds.device) + + return { + "pixels": pixels, + "rays": rays, + "color_bkgd": color_bkgd, + **{k: v for k, v in data.items() if k not in ["rgba", "rays"]}, + } + + def update_num_rays(self, num_rays): + self.num_rays = num_rays + + def fetch_data(self, index, rep=None, num_rays=None): + if num_rays is None: + num_rays = self.num_rays + if rep is None: + rep = self.rep + + if self.training: + if self.batch_over_images: + image_id = torch.randint( + 0, + len(self.images), + size=(num_rays,), + device=self.images.device, + ) + else: + image_id = [index] + x = torch.randint(0, self.WIDTH, size=(num_rays,), device="cpu") + y = torch.randint(0, self.HEIGHT, size=(num_rays,), device="cpu") + x = x.repeat(rep) + y = y.repeat(rep) + image_id = image_id.repeat(rep) + rgba = self.images[image_id, y, x] + elif self.testing: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device="cpu"), + torch.arange(self.HEIGHT, device="cpu"), + indexing="xy", + ) + x = x.flatten().repeat(rep) + y = y.flatten().repeat(rep) + rgba = self.images[image_id, y, x] if self.have_images else None + else: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device=self.camtoworlds.device), + torch.arange(self.HEIGHT, device=self.camtoworlds.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + rgba = self.images[image_id, y, x] if self.have_images else None + + x_cpu_long = x.detach().cpu().long() + y_cpu_long = y.detach().cpu().long() + valid_mask = self.valid_mask[y_cpu_long, x_cpu_long] + + c2w = self.camtoworlds[image_id] + scale = self.rfilter_sigma + + if self.training or self.testing: + s_x, s_y, weights = spatial_filter( + x, + y, + sigma=scale, + rep=self.rep, + prob_dithering=self.sample_as_per_distribution, + ) + s_x = torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH - 1).to(self.camtoworlds.device).to(torch.float32) + s_y = torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT - 1).to(self.camtoworlds.device).to(torch.float32) + weights = torch.tensor(weights, device=self.camtoworlds.device, dtype=torch.float32) + else: + s_x = x + s_y = y + weights = None + + camera_dirs = self.K(s_x, s_y) + + directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1) + origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape) + viewdirs = directions / torch.linalg.norm(directions, dim=-1, keepdims=True) + + if self.training: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + rgba = torch.reshape(rgba, (-1, self.n_bins * 3)) + valid_mask = torch.reshape(valid_mask, (-1,)) + elif self.testing: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + if self.have_images: + rgba = torch.reshape(rgba, (-1, self.n_bins * 3)) + valid_mask = torch.reshape(valid_mask, (-1,)) + elif self.have_images: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = torch.reshape(rgba, (self.HEIGHT, self.WIDTH, self.n_bins * 3)) + valid_mask = torch.reshape(valid_mask, (self.HEIGHT, self.WIDTH)) + else: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = None + valid_mask = torch.reshape(valid_mask, (self.HEIGHT, self.WIDTH)) + + rays = Rays(origins=origins, viewdirs=viewdirs) + if self.training or self.testing: + return {"rgba": rgba, "rays": rays, "weights": weights, "valid_mask": valid_mask} + return {"rgba": rgba, "rays": rays, "valid_mask": valid_mask} + + +class LearnRays(torch.nn.Module): + """Interpolation-based ray lookup from per-pixel ray table (H,W,3).""" + + def __init__(self, rays, device="cuda:0", img_shape=(256, 256)): + super().__init__() + self.device = device + self.img_shape = (int(img_shape[0]), int(img_shape[1])) + self.height = self.img_shape[0] + self.width = self.img_shape[1] + + rays = np.asarray(rays, dtype=np.float32) + if rays.ndim != 3 or rays.shape[-1] != 3: + raise ValueError(f"rays must be [H,W,3], got shape={rays.shape}") + + rays_t = torch.from_numpy(rays).to(self.device) + if tuple(rays_t.shape[:2]) != self.img_shape: + rays_t = F.interpolate( + rays_t.permute(2, 0, 1)[None, ...], + size=self.img_shape, + mode="bilinear", + align_corners=True, + ).squeeze(0).permute(1, 2, 0) + + rays_t = rays_t / torch.linalg.norm(rays_t, dim=-1, keepdims=True).clamp_min(1e-8) + self.rays = rays_t + + def forward(self, x0, y0): + rays = self.rays + x0 = x0.to(rays.device).float() + y0 = y0.to(rays.device).float() + + x1 = torch.floor(x0) + y1 = torch.floor(y0) + x2 = x1 + 1 + y2 = y1 + 1 + + x1 = torch.clip(x1, 0, self.width - 1) + y1 = torch.clip(y1, 0, self.height - 1) + x2 = torch.clip(x2, 0, self.width - 1) + y2 = torch.clip(y2, 0, self.height - 1) + + wx2 = ((x0 - x1) / (x2 - x1 + 1e-8))[:, None] + wx1 = 1.0 - wx2 + wy2 = ((y0 - y1) / (y2 - y1 + 1e-8))[:, None] + wy1 = 1.0 - wy2 + + x1 = x1.long() + y1 = y1.long() + x2 = x2.long() + y2 = y2.long() + + out = ( + wx1 * wy1 * rays[y1, x1] + + wx1 * wy2 * rays[y2, x1] + + wx2 * wy1 * rays[y1, x2] + + wx2 * wy2 * rays[y2, x2] + ) + out = out / torch.linalg.norm(out, dim=-1, keepdims=True).clamp_min(1e-8) + return out.float() + + +def spatial_filter(x, y, sigma, rep, prob_dithering=True): + pdf_fn = lambda z: np.exp(-z / (2 * sigma**2)) - np.exp(-16) + if prob_dithering: + bounds_max = [4 * sigma] * x.shape[0] + loc = 0 + s_x = scipy.stats.truncnorm.rvs( + (-np.array(bounds_max) - loc) / sigma, + (np.array(bounds_max) - loc) / sigma, + loc=loc, + scale=sigma, + ) + s_y = scipy.stats.truncnorm.rvs( + (-np.array(bounds_max) - loc) / sigma, + (np.array(bounds_max) - loc) / sigma, + loc=loc, + scale=sigma, + ) + weights = np.ones_like(s_x) * 1 / rep + else: + s_x = np.random.uniform(low=-4 * sigma, high=4 * sigma, size=(rep, x.shape[0] // rep)) + s_y = np.random.uniform(low=-4 * sigma, high=4 * sigma, size=(rep, x.shape[0] // rep)) + dists = s_x**2 + s_y**2 + weights = pdf_fn(dists) + weights = weights / weights.sum(0)[None, :] + s_x = s_x.flatten() + s_y = s_y.flatten() + weights = weights.flatten() + return s_x, s_y, weights diff --git a/codes/reconstruction/transientnerf/loaders/loader_synthetic.py b/codes/reconstruction/transientnerf/loaders/loader_synthetic.py new file mode 100644 index 0000000000000000000000000000000000000000..e1e2ef447981753cfe20c00a595cea8f154536e6 --- /dev/null +++ b/codes/reconstruction/transientnerf/loaders/loader_synthetic.py @@ -0,0 +1,453 @@ +import collections +import json +import os + +import imageio.v2 as imageio +import numpy as np +import torch +import torch.nn.functional as F +import scipy +import zipfile +from .utils import Rays +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from misc.dataset_utils import read_h5 +from tqdm import tqdm + +def _load_renderings(root_fp: str, subject_id: str, split: str, have_images=True, img_shape=(256, 256)): + """Load images from disk.""" + # if not root_fp.startswith("/"): + # # allow relative path. e.g., "./data/nerf_synthetic/" + # root_fp = os.path.join( + # os.path.dirname(os.path.abspath(__file__)), + # "..", + # "..", + # root_fp, + # ) + + data_dir = root_fp + with open( + os.path.join(data_dir, "transforms_{}.json".format(split)), "r" + ) as fp: + meta = json.load(fp) + images = [] + camtoworlds = [] + + if have_images: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + number = int(frame["file_path"].split("_")[-1]) + fname = os.path.join(data_dir, f"{number:03d}" + ".png") + + # fname = os.path.join(data_dir, frame["file_path"] + ".png") + rgba = imageio.imread(fname) + camtoworlds.append(frame["transform_matrix"]) + images.append(rgba) + + images = np.stack(images, axis=0) + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = images.shape[1:3] + else: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + camtoworlds.append(frame["transform_matrix"]) + + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = img_shape + + camera_angle_x = float(meta["camera_angle_x"]) + focal = 0.5 * w / np.tan(0.5 * camera_angle_x) + + return images, camtoworlds, focal + + +def _load_renderings_transient(root_fp: str, subject_id: str, split: str, num_views= None, have_images=True, img_shape=(256, 256), gamma=False): + """Load images from disk.""" + # if not root_fp.startswith("/"): + # # allow relative path. e.g., "./data/nerf_synthetic/" + # root_fp = os.path.join( + # os.path.dirname(os.path.abspath(__file__)), + # "..", + # "..", + # root_fp, + # ) + + data_dir = root_fp + if split == "train": tname = f"train_v{num_views}" + else: tname = split + + with open( + os.path.join(data_dir, "transforms_{}.json".format(tname)), "r" + ) as fp: + meta = json.load(fp) + images = [] + camtoworlds = [] + + if have_images: + for i in tqdm(range(len(meta["frames"]))): + frame = meta["frames"][i] + number = int(frame["file_path"].split("_")[-1]) + + try: + files_dir = os.path.join(data_dir, split) + fname = os.path.join(files_dir, f"{split}_{number:03d}" + ".h5") + rgba = read_h5(fname) + except: + try: + files_dir = os.path.join(data_dir, "test") + fname = os.path.join(files_dir, f"test_{number:03d}" + ".h5") + rgba = read_h5(fname) + except: + try: + files_dir = os.path.join(data_dir, "test") + fname = os.path.join(files_dir, f"test_{number:03d}" + ".h5") + archive = zipfile.ZipFile(f"{fname}.zip") + file = archive.open(f"test_{number:03d}" + ".h5") + rgba = read_h5(file) + file.close() + except: + pass + + + + rgba = rgba[..., :3] + + if gamma: + print("using gamma") + rgba_sum = rgba.sum(-2) + rgba_sum_normalized = rgba_sum/rgba_sum.max() + rgba_sum_norm_gamma = rgba_sum_normalized**(1/2.2) + rgba = (rgba*rgba_sum_norm_gamma[..., None, :])/(rgba_sum[..., None, :]+1e-10) + + camtoworlds.append(frame["transform_matrix"]) + rgba = torch.clip(torch.Tensor(rgba), 0, None) + images.append(torch.Tensor(rgba)) + + + images = torch.stack(images, axis=0) + + if split == "test": + quotient = images.shape[1]//img_shape[0] + times_downsample = int(np.log2(quotient)) + + for i in range(times_downsample): + images = (images[:, 1::2, ::2] + images[:, ::2, ::2] + images[:, 1::2, 1::2] + images[:, ::2, 1::2])/4 + + + if not gamma: + #np.save(os.path.join(data_dir, "max.npy"), torch.max(images).numpy()) + max = torch.max(images) + images /= torch.max(images) + + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = images.shape[1:3] + else: + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + camtoworlds.append(frame["transform_matrix"]) + + camtoworlds = np.stack(camtoworlds, axis=0) + + h, w = img_shape + + camera_angle_x = float(meta["camera_angle_x"]) + focal = 0.5 * w / np.tan(0.5 * camera_angle_x) + + return images, camtoworlds, focal, max + + + +class SubjectLoaderTransient(torch.utils.data.Dataset): + """Single subject data loader for training and evaluation.""" + + SPLITS = ["train", "val", "trainval", "test"] + + # WIDTH, HEIGHT = 64, 64 + NEAR, FAR = 0, 6 + OPENGL_CAMERA = True + + def __init__( + self, + subject_id: str, + root_fp: str, + split: str, + color_bkgd_aug: str = "black", + num_rays: int = None, + near: float = None, + far: float = None, + batch_over_images: bool = True, + have_images=True, + img_shape=(256, 256), + n_bins=10000, + testing=False, + rfilter_sigma=0.3, + scene=None, + sample_as_per_distribution = True, + gamma=False, + num_views = None + ): + super().__init__() + self.testing = testing + # assert split in self.SPLITS, "%s" % split + assert color_bkgd_aug in ["white", "black", "random"] + self.sample_as_per_distribution = sample_as_per_distribution + + self.HEIGHT, self.WIDTH = img_shape + self.split = split + self.num_rays = num_rays + self.near = self.NEAR if near is None else near + self.far = self.FAR if far is None else far + self.training = (num_rays is not None) and ( + split in ["train", "trainval"] + ) + self.rep = 0 + self.color_bkgd_aug = color_bkgd_aug + self.batch_over_images = batch_over_images + self.have_images = have_images + self.rfilter_sigma = rfilter_sigma + self.n_bins = n_bins + if split == "trainval": + _images_train, _camtoworlds_train, _focal_train = _load_renderings_transient( + root_fp, subject_id, "train", gamma=gamma + ) + _images_val, _camtoworlds_val, _focal_val = _load_renderings_transient( + root_fp, subject_id, "val", gamma=gamma + ) + self.images = np.concatenate([_images_train, _images_val]) + self.camtoworlds = np.concatenate( + [_camtoworlds_train, _camtoworlds_val] + ) + self.focal = _focal_train + self.images = torch.from_numpy(self.images).to(torch.float32) + + # ste for transient + self.images = torch.reshape(self.images, (-1, self.HEIGHT, self.WIDTH, self.n_bins*3)) + # assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH) + + elif have_images: + self.images, self.camtoworlds, self.focal, self.max = _load_renderings_transient( + root_fp, subject_id, split, gamma=gamma, img_shape=img_shape, num_views=num_views + ) + self.images = self.images.to(torch.float32) + assert self.images.shape[1:3] == (self.HEIGHT, self.WIDTH) + else: + _, self.camtoworlds, self.focal = _load_renderings( + root_fp, subject_id, split, have_images=have_images, img_shape=img_shape, num_views=num_views + ) + + self.camtoworlds = torch.from_numpy(self.camtoworlds).to(torch.float32) + self.K = torch.tensor( + [ + [self.focal, 0, self.WIDTH / 2.0], + [0, self.focal, self.HEIGHT / 2.0], + [0, 0, 1], + ], + dtype=torch.float32, + ) # (3, 3) + + def __len__(self): + return len(self.camtoworlds) + + @torch.no_grad() + def __getitem__(self, index): + data = self.fetch_data(index) + data = self.preprocess(data) + return data + + def preprocess(self, data): + """Process the fetched / cached data with randomness.""" + rgba, rays = data["rgba"], data["rays"] + # pixels, alpha = torch.split(rgba, [3, 1], dim=-1) + if rgba is not None: + pixels = rgba.to(self.camtoworlds.device) + else: + pixels = rgba + + if self.color_bkgd_aug == "random": + color_bkgd = torch.rand(3, device=self.camtoworlds.device) + elif self.color_bkgd_aug == "white": + color_bkgd = torch.ones(3, device=self.camtoworlds.device) + elif self.color_bkgd_aug == "black": + color_bkgd = torch.zeros(3, device=self.camtoworlds.device) + + # pixels = pixels * alpha + color_bkgd * (1.0 - alpha) + return { + "pixels": pixels, # [n_rays, 3] or [h, w, 3] + "rays": rays, # [n_rays,] or [h, w] + "color_bkgd": color_bkgd, # [3,] + **{k: v for k, v in data.items() if k not in ["rgba", "rays"]}, + } + + def update_num_rays(self, num_rays): + self.num_rays = num_rays + + def fetch_data(self, index, rep=None, num_rays=None): + """Fetch the data (it maybe cached for multiple batches).""" + if num_rays==None: + num_rays = self.num_rays + if rep==None: + rep = self.rep + + if self.training: + if self.batch_over_images: + image_id = torch.randint( + 0, + len(self.images), + size=(num_rays,), + device=self.images.device, + ) + else: + image_id = [index] + x = torch.randint( + 0, self.WIDTH, size=(num_rays,), device=self.images.device + ) + y = torch.randint( + 0, self.HEIGHT, size=(num_rays,), device=self.images.device + ) + x = x.repeat(rep) + y = y.repeat(rep) + image_id = image_id.repeat(rep) + + + rgba = self.images[image_id, y, x] # (num_rays, 4) + + elif self.testing: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device="cpu"), + torch.arange(self.HEIGHT, device="cpu"), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + x = x.repeat(rep) + y = y.repeat(rep) + # image_id = image_id.repeat(rep) + try: + rgba = self.images[image_id, y, x] # (num_rays, 4) + except: rgba=None + + elif self.have_images: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device=self.camtoworlds.device), + torch.arange(self.HEIGHT, device=self.camtoworlds.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + rgba = self.images[image_id, y, x] # (num_rays, 4) + else: + image_id = [index] + x, y = torch.meshgrid( + torch.arange(self.WIDTH, device=self.camtoworlds.device), + torch.arange(self.HEIGHT, device=self.camtoworlds.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + + # generate rays + + scale = self.rfilter_sigma + c2w = self.camtoworlds[image_id] # (num_rays, 3, 4) + bounds_max = [4*scale]*x.shape[0] + loc = 0 + if self.training: + s_x, s_y, weights = spatial_filter(x, y, sigma=scale, rep = self.rep, prob_dithering=self.sample_as_per_distribution) + s_x = (torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH).to(self.camtoworlds.device)).to(torch.float32) + s_y = (torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT).to(self.camtoworlds.device)).to(torch.float32) + weights = torch.Tensor(weights).to(self.camtoworlds.device) + #s_x = x.to(self.camtoworlds.device).to(torch.float32) + #s_y = y.to(self.camtoworlds.device).to(torch.float32) + + elif self.testing: + s_x, s_y, weights = spatial_filter(x, y, sigma=scale, rep = self.rep, prob_dithering=self.sample_as_per_distribution, normalize=False) + s_x = (torch.clip(x + torch.from_numpy(s_x), 0, self.WIDTH).to(self.camtoworlds.device)).to(torch.float32) + s_y = (torch.clip(y + torch.from_numpy(s_y), 0, self.HEIGHT).to(self.camtoworlds.device)).to(torch.float32) + weights = torch.Tensor(weights).to(self.camtoworlds.device) + #s_x = x.to(self.camtoworlds.device).to(torch.float32) + #s_y = y.to(self.camtoworlds.device).to(torch.float32) + else: + s_x = x + s_y = y + + camera_dirs = F.pad( + torch.stack( + [ + (s_x - self.K[0, 2] + 0.5) / self.K[0, 0], + (s_y - self.K[1, 2] + 0.5) + / self.K[1, 1] + * (-1.0 if self.OPENGL_CAMERA else 1.0), + ], + dim=-1, + ), + (0, 1), + value=(-1.0 if self.OPENGL_CAMERA else 1.0), + ) + + directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1) + origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape) + viewdirs = directions / torch.linalg.norm( + directions, dim=-1, keepdims=True + ) + + if self.training: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + # here + rgba = torch.reshape(rgba, (-1,self.n_bins*3)) + elif self.testing: + origins = torch.reshape(origins, (-1, 3)) + viewdirs = torch.reshape(viewdirs, (-1, 3)) + # here + try: rgba = torch.reshape(rgba, (-1,self.n_bins*3)) + except: rgba = None + + elif self.have_images: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = torch.reshape(rgba, (self.HEIGHT, self.WIDTH, self.n_bins * 3)) + else: + origins = torch.reshape(origins, (self.HEIGHT, self.WIDTH, 3)) + viewdirs = torch.reshape(viewdirs, (self.HEIGHT, self.WIDTH, 3)) + rgba = None + + rays = Rays(origins=origins, viewdirs=viewdirs) + if self.training or self.testing: + return { + "rgba": rgba, # [h, w, 4] or [num_rays, 4] + "rays": rays, # [h, w, 3] or [num_rays, 3] + "weights":weights + } + + return { + "rgba": rgba, # [h, w, 4] or [num_rays, 4] + "rays": rays, # [h, w, 3] or [num_rays, 3] + } + + +def spatial_filter(x, y, sigma, rep, prob_dithering=True, normalize=True): + pdf_fn = lambda x: np.exp(-x/(2*sigma**2)) - np.exp(-16) + if prob_dithering: + bounds_max = [4*sigma]*x.shape[0] + loc = 0 + s_x = scipy.stats.truncnorm.rvs((-np.array(bounds_max)-loc)/sigma, (np.array(bounds_max)-loc)/sigma, loc=loc, scale=sigma) + s_y = scipy.stats.truncnorm.rvs((-np.array(bounds_max)-loc)/sigma, (np.array(bounds_max)-loc)/sigma, loc=loc, scale=sigma) + weights = np.ones_like(s_x)*1/rep + + else: + s_x = np.random.uniform(low=-4*sigma, high=4*sigma, size=(rep, x.shape[0]//rep)) + s_y = np.random.uniform(low=-4*sigma, high=4*sigma, size=(rep, x.shape[0]//rep)) + dists = (s_x**2 + s_y**2) + weights = pdf_fn(dists) + if normalize: + weights = weights/weights.sum(0)[None, :] + s_x = s_x.flatten() + s_y = s_y.flatten() + weights = weights.flatten() + + return s_x, s_y, weights \ No newline at end of file diff --git a/codes/reconstruction/transientnerf/loaders/utils.py b/codes/reconstruction/transientnerf/loaders/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..04d223f54022448c9827f4f26768a2b5973ba37a --- /dev/null +++ b/codes/reconstruction/transientnerf/loaders/utils.py @@ -0,0 +1,12 @@ +""" +Copyright (c) 2022 Ruilong Li, UC Berkeley. +""" + +import collections + +Rays = collections.namedtuple("Rays", ("origins", "viewdirs")) + + +def namedtuple_map(fn, tup): + """Apply `fn` to each element of `tup` and cast to `tup`'s namedtuple.""" + return type(tup)(*(None if x is None else fn(x) for x in tup)) diff --git a/codes/reconstruction/transientnerf/misc/__init__.py b/codes/reconstruction/transientnerf/misc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/codes/reconstruction/transientnerf/misc/dataset_utils.py b/codes/reconstruction/transientnerf/misc/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fc4538af85beee39b58ba38ff8655fc7bcc4ffe0 --- /dev/null +++ b/codes/reconstruction/transientnerf/misc/dataset_utils.py @@ -0,0 +1,169 @@ +import os +import mat73 +import torch +import numpy as np +import json +import h5py + +def read_h5(path): + with h5py.File(path, 'r') as f: + frames = np.array(f['data']) + return frames + +def process_captured_data(files_path, savepath): + for file in os.listdir(files_path): + if file[-3:] == "mat" and (file[:-3]+"pt") not in os.listdir(savepath): + filepath = os.path.join(files_path, file) + rgba = mat73.loadmat(filepath)["transient"].transpose(1, 2, 0)[..., :][..., None] + rgba = np.flip(np.flip(rgba, 0), 1) + for i in range(3): + rgba = (rgba[1::2, ::2] + rgba[::2, ::2] + rgba[1::2, 1::2] + rgba[::2, 1::2])/4 + torch.save(rgba, os.path.join(savepath, file[:-3]+"pt")) + +def bundle_rays(pathToH5s, outputPath, trainJsonPath): + with open(trainJsonPath, "r") as fp: + meta = json.load(fp) + train_fnames = [] + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + fname = frame["filepath"][:-2]+"h5" + train_fnames.append(fname) + + frames = read_h5(os.path.join(pathToH5s, train_fnames[0])) + w = frames.shape[0] + h = frames.shape[1] + bins = frames.shape[2] + + x = np.linspace(0, h-1, h) + y = np.linspace(0, w-1, w) + X, Y = np.meshgrid(x, y) + + if len(frames.shape) == 4: + channels = 3 + else: + channels = 1 + num_train_files = len(train_fnames) + + data_array = np.zeros((w*h*num_train_files, bins, channels), dtype=np.float32) + x_array = np.zeros(w*h*num_train_files) + y_array = np.zeros(w*h*num_train_files) + file_prefix_array = np.zeros(w*h*num_train_files) + + for ind, file in enumerate(train_fnames): + print("Opening: " + file) + frames = read_h5(os.path.join(pathToH5s, file)) + frames = frames.reshape(-1, frames.shape[2], frames.shape[3]) + + data_array[ind*w*h:(ind+1)*w*h] = frames[..., :3] + x_array[ind*w*h:(ind+1)*w*h] = X.flatten() + y_array[ind*w*h:(ind+1)*w*h] = Y.flatten() + file_prefix_array[ind*w*h:(ind+1)*w*h] = ind + + p = np.random.permutation(data_array.shape[0]) + data_array = data_array[p] + x_array = x_array[p] + y_array = y_array[p] + file_prefix_array = file_prefix_array[p] + + + print("Outputting to files") + file = h5py.File(os.path.join(outputPath, "samples.h5"), 'w') + dataset = file.create_dataset( + "dataset", data_array.shape, dtype='f', data=data_array + ) + file.close() + + file = h5py.File(os.path.join(outputPath, "x.h5"), 'w') + dataset = file.create_dataset( + "dataset", x_array.shape, dtype='f', data=x_array + ) + file.close() + + file = h5py.File(os.path.join(outputPath, "y.h5"), 'w') + dataset = file.create_dataset( + "dataset", y_array.shape, dtype='f', data=y_array + ) + file.close() + file = h5py.File(os.path.join(outputPath, "file_indices.h5"), 'w') + dataset = file.create_dataset( + "dataset", file_prefix_array.shape, dtype='f', data=file_prefix_array + ) + file.close() + + +def bundle_rays_cap(pathToH5s, outputPath, trainJsonPath): + with open(trainJsonPath, "r") as fp: + meta = json.load(fp) + train_fnames = [] + for i in range(len(meta["frames"])): + frame = meta["frames"][i] + fname = frame["filepath"][:-2].split("/")[-1]+"mat" + train_fnames.append(fname) + + # frames = read_h5_dataset(os.path.join(pathToH5s, train_fnames[0])) + frames = mat73.loadmat(os.path.join(pathToH5s, train_fnames[0]))["transient"].transpose(1, 2, 0) + w = frames.shape[0] + h = frames.shape[1] + # bins = frames.shape[2] + bins = 3000 + + x = np.linspace(0, h-1, h) + y = np.linspace(0, w-1, w) + X, Y = np.meshgrid(x, y) + + # if len(frames.shape) == 4: + # channels = 3 + # else: + channels = 1 + num_train_files = len(train_fnames) + + data_array = np.zeros((w*h*num_train_files, bins, channels), dtype=np.float32) + x_array = np.zeros(w*h*num_train_files) + y_array = np.zeros(w*h*num_train_files) + file_prefix_array = np.zeros(w*h*num_train_files) + + for ind, file in enumerate(train_fnames): + print("Opening: " + file) + # frames = read_h5_dataset(os.path.join(pathToH5s, file)) + frames = mat73.loadmat(os.path.join(pathToH5s, file))["transient"].transpose(1, 2, 0) + frames = frames.reshape(-1, frames.shape[2]) + + data_array[ind*w*h:(ind+1)*w*h] = frames[..., :bins, None] + # del frames + x_array[ind*w*h:(ind+1)*w*h] = X.flatten() + y_array[ind*w*h:(ind+1)*w*h] = Y.flatten() + file_prefix_array[ind*w*h:(ind+1)*w*h] = ind + + p = np.random.permutation(data_array.shape[0]) + data_array = data_array[p] + x_array = x_array[p] + y_array = y_array[p] + file_prefix_array = file_prefix_array[p] + + + print("Outputting to files") + file = h5py.File(os.path.join(outputPath, "samples.h5"), 'w') + dataset = file.create_dataset( + "dataset", data_array.shape, dtype='f', data=data_array + ) + file.close() + + file = h5py.File(os.path.join(outputPath, "x.h5"), 'w') + dataset = file.create_dataset( + "dataset", x_array.shape, dtype='f', data=x_array + ) + file.close() + + file = h5py.File(os.path.join(outputPath, "y.h5"), 'w') + dataset = file.create_dataset( + "dataset", y_array.shape, dtype='f', data=y_array + ) + file.close() + file = h5py.File(os.path.join(outputPath, "file_indices.h5"), 'w') + dataset = file.create_dataset( + "dataset", file_prefix_array.shape, dtype='f', data=file_prefix_array + ) + file.close() + +if __name__=="__main__": + pass \ No newline at end of file diff --git a/codes/reconstruction/transientnerf/misc/download_dataset.py b/codes/reconstruction/transientnerf/misc/download_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..40eee438f1e155f3e6d0c74b70b5f0db4ce28471 --- /dev/null +++ b/codes/reconstruction/transientnerf/misc/download_dataset.py @@ -0,0 +1,67 @@ +import subprocess +import configargparse +import os + +def download_dataset(scenes): + link_dict = { + "bench":"https://www.dropbox.com/scl/fo/a7c6ej2zj5julibmi0oe7/AOzgkS7_PkMNzg4ybZBTTZ8?rlkey=jjkqqz6ifi2ib3g01yf03j12h&st=v02sztb4&dl=0", + "lego":"https://www.dropbox.com/scl/fo/xrvnu5i7cwg6ng4iveerd/APXNbHRR2IJqP6q384ZrsTY?rlkey=qag95l1xn3hgebxqcqb4grdbw&st=1xjm5ob9&dl=0", + "chair":"https://www.dropbox.com/scl/fo/rw1kx28apf2nj9nmmankn/AAXBD63yuU-TcaKJRh2WlUQ?rlkey=58tb99g1d41ml4asqxdp5lrxg&st=og6clu8k&dl=0", + "ficus":"https://www.dropbox.com/scl/fo/ash43k5stxykgu82y0rty/AJM0R8PdduoE4BMl31BI7xY?rlkey=ad1uacytq2mr5e0hc4tpmm7xp&st=uwm356c4&dl=0", + "hotdog":"https://www.dropbox.com/scl/fo/lrbe9b8tsmpu6m3e25s7q/ANANvfFhEdoib9rHv5QmPwo?rlkey=bea0k5zi6ahgts88mlpvs4mta&st=8itg14zh&dl=0", + "boots":"https://www.dropbox.com/scl/fo/cx5tl37qjytcv8v652q00/ADOzXV5AbW0_y_wpptIk8PY?rlkey=ne75mf8kc3hg6wg7coak74ww5&st=hklnyi76&dl=0", + "carving":"https://www.dropbox.com/scl/fo/962tg8nqtx42m7lasqo7s/AIAl3BFPt7U1xD8eanwZnP8?rlkey=qm3r7d52dnvroac8lwsbq93pb&st=58hvm91y&dl=0", + "baskets":"https://www.dropbox.com/scl/fo/jxvcp63h0z7u0hptk2b0f/AOvcyi2RB0R-999nzQZtwvk?rlkey=ce229qfku8brdfznzl62fi792&st=yf4aii6e&dl=0", + "chef":"https://www.dropbox.com/scl/fo/15mcemsypabotbsr07gf0/AIdsXG0GHVp_FLf7i95Orr4?rlkey=abugupv7fo1gtjue73h0jm8uf&st=omb14j7c&dl=0", + "cinema":"https://www.dropbox.com/scl/fo/hdytz9zw73jq2f3hxp44b/ACQvEjYApQ6hDfhpf2vvoSw?rlkey=caeizu4zklzziyzt3wlcb4pgg&st=nherw66u&dl=0", + "food":"https://www.dropbox.com/scl/fo/brg2po03txm7nftnljhyb/AFfgHP1517nCEfY1oVGhfYo?rlkey=znxzp37wao2y9bydq8ceapimr&st=8kfsaiob&dl=0", + "intrinsics.npy":"https://www.dropbox.com/scl/fi/s4whgdajo5jy5wmdtlhwf/intrinsics.npy?rlkey=s247t4pvtubn726dzpnwmnlrv&st=0hnobsb9&dl=0", + "pulse_low_flux.mat":"https://www.dropbox.com/scl/fi/x63omsjecjijpg5q5dpiw/pulse_low_flux.mat?rlkey=1dm9d8jrdrogtw2xbgg2x8x17&st=4gll02on&dl=0", + } + os.makedirs("dataset", exist_ok = True) + for file in ["pulse_low_flux.mat", "intrinsics.npy"]: + command = f'wget "{link_dict[file]}" -O ./dataset/{file}' + subprocess.run(command, shell=True) + + + for folder in scenes: + os.makedirs(f"dataset/{folder}", exist_ok = True) + + command = f'wget "{link_dict[folder]}" -O dataset/{folder}.zip' + subprocess.run(command, shell=True) + + command = f"unzip dataset/{folder}.zip -d dataset/{folder}" + subprocess.run(command, shell=True) + + command = f"rm dataset/{folder}.zip" + subprocess.run(command, shell=True) + + + + +if __name__=="__main__": + parser = configargparse.ArgumentParser() + parser.add_argument('--scenes', nargs='+', help='list of files to download') + args = parser.parse_args() + + final_scenes = [] + all_scenes = ["cinema", "carving", "boots", "food", "chef", "baskets", "lego", "chair", "ficus", "hotdog", "bench"] + + if "all" in args.scenes: + final_scenes = all_scenes.copy() + + if "captured" in args.scenes: + scenes = ["cinema", "carving", "boots", "food", "chef", "baskets"] + if "simulated" in args.scenes: + scenes = ["lego", "chair", "ficus", "hotdog", "bench"] + + for scene in all_scenes: + if scene in args.scenes: + final_scenes += [scene] + # final_scenes += ["pulse", "intrinsics"] + + final_scenes = list(set(final_scenes)) + download_dataset(final_scenes) + + + diff --git a/codes/reconstruction/transientnerf/misc/eval_utils.py b/codes/reconstruction/transientnerf/misc/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e1519e0e812560b874c8ff0fdf6f859be874d33c --- /dev/null +++ b/codes/reconstruction/transientnerf/misc/eval_utils.py @@ -0,0 +1,224 @@ +import numpy as np +import imageio +import json +import torch +import sys +import os +import configargparse +import ast +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from loaders.utils import Rays +from utils import str2bool, load_args + +def calc_psnr(img1, img2): + # Calculate the mean squared error + mse = np.mean((img1 - img2) ** 2) + # Calculate the maximum possible pixel value (for data scaled between 0 and 1) + max_pixel = 1.0 + # Calculate the PSNR + psnr_value = 20 * np.log10(max_pixel / np.sqrt(mse)) + return psnr_value + +def get_rays(img_shape, c2w, K, device): + OPENGL_CAMERA = True + x, y = torch.meshgrid( + torch.arange(img_shape, device=device), + torch.arange(img_shape, device=device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + + c2w = c2w.repeat(img_shape**2, 1, 1) + camera_dirs = torch.nn.functional.pad( + torch.stack( + [ + (x - K[0, 2] + 0.5) / K[0, 0], + (y - K[1, 2] + 0.5) + / K[1, 1] + * (-1.0 if OPENGL_CAMERA else 1.0), + ], + dim=-1, + ), + (0, 1), + value=(-1.0 if OPENGL_CAMERA else 1.0), + ) # [num_rays, 3] + + # [n_cams, height, width, 3] + directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1) + origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape) + viewdirs = directions / torch.linalg.norm( + directions, dim=-1, keepdims=True + ) + origins = torch.reshape(origins, (img_shape, img_shape, 3)) + viewdirs = torch.reshape(viewdirs, (img_shape, img_shape, 3)) + + + rays = Rays(origins=origins, viewdirs=viewdirs) + + return rays + + +def read_json(json_path): + f = open(json_path) + positions = json.load(f) + f.close() + return positions + +def generate_video(images, output_path, fps): + # Determine the width and height of the images + writer = imageio.get_writer(output_path, fps=fps) + for image in images: + writer.append_data(image) + writer.close() + +def calc_iou(rgb, gt_tran): + intersection = np.minimum(rgb, gt_tran) + union = np.maximum(rgb, gt_tran) + iou = np.sum(intersection) / np.sum(union) + return iou + +def load_eval_args(): + parser = configargparse.ArgumentParser() + parser.add('-tc', '--test_config', + is_config_file=True, + default="./configs/test/captured/cinema_quantitative.ini", + help='Path to config file.' + ) + parser.add_argument( + "--scene", + type=str, + default="cinema", + # choices=[ + # # nerf transient + # "lego", + # "chair", + # "drums", + # "ficus", + # "hotdog", + # "bench", + # "boar", + # "benches" + # ], + help="scene to evaluate the models on", + ) + parser.add_argument( + "--rep_number", + type=int, + default=30, + ) + parser.add_argument( + "--step", + type=int, + default=290000, + ) + parser.add_argument( + "--split", + type=str, + default="test", + ) + parser.add_argument( + "--test_folder_path", + type=str, + default="test2", + ) + parser.add_argument( + "--checkpoint_dir", + type=str, + default="/scratch/ondemand28/anagh/tnerf_release/multiview_transient/results/cinema_two_views_04-18_02:10:32", + ) + parser.add_argument( + "--data_folder_path", + type=str, + default="./data", + ) + parser.add_argument( + "--irf_path", + type=str, + default="", + help="Path to IRF file (.csv/.npy/.mat/.pt). If empty, fallback to --pulse_path.", + ) + parser.add_argument( + "--irf_column", + type=str, + default="irf", + help="CSV column name for IRF values.", + ) + parser.add_argument( + "--irf_half_window", + type=int, + default=50, + help="Half window around IRF peak. Set <=0 to disable cropping.", + ) + parser.add_argument( + "--no_irf_reverse", + action="store_true", + help="Disable reverse before Conv1d kernel creation.", + ) + parser.add_argument( + "--measurement_root", + type=str, + default="", + help="Optional measurement root for captured-ours loader.", + ) + parser.add_argument( + "--data_exts", + type=str, + default=".npz,.txt,.pt,.h5,.hdf5", + help="Comma-separated measurement extension lookup order.", + ) + parser.add_argument( + "--bin_width_s_loader", + type=float, + default=None, + help="Optional bin width in seconds for shift resampling.", + ) + parser.add_argument( + "--img_height_test", + type=int, + default=None, + help="Test image height. If empty, use --img_shape_test.", + ) + parser.add_argument( + "--img_width_test", + type=int, + default=None, + help="Test image width. If empty, use --img_shape_test.", + ) + parser.add_argument( + "--invalid_mask_path", + type=str, + default="", + help="Optional offset map path for valid-pixel mask.", + ) + parser.add_argument( + "--invalid_mask_invalid_gt", + type=float, + default=10.0, + help="Offset threshold: pixels with offset > threshold are invalid.", + ) + parser.add_argument( + "--meas_peak_min", + type=float, + default=100.0, + help=( + "Minimum raw histogram peak per pixel to keep it in evaluation metrics. " + "<=0 disables this mask." + ), + ) + parser.add_argument( + "--scale_int", + type=float, + default=1.0, + help="Fixed scale for intensity normalisation (replaces per-image dynamic max).", + ) + args = load_args(eval=True, parser=parser) + return args + +num2words = {1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five', + 6: 'six', 7: 'seven', 8: 'eight', 9: 'nine', 10: 'ten', + 11: 'eleven', 12: 'twelve', 13: 'thirteen', 14: 'fourteen', + 15: 'fifteen', 16: 'sixteen', 17: 'seventeen', 18: 'eighteen', 19: 'nineteen'} + +if __name__=="__main__": + pass diff --git a/codes/reconstruction/transientnerf/misc/summary.py b/codes/reconstruction/transientnerf/misc/summary.py new file mode 100644 index 0000000000000000000000000000000000000000..75b842665bbda7c4f46d26b14c48dc768b6ba186 --- /dev/null +++ b/codes/reconstruction/transientnerf/misc/summary.py @@ -0,0 +1,147 @@ +import imgviz +import numpy as np +from utils import render_transient +import matplotlib.pyplot as plt +import torchvision +import torch +import os + + +def _atomic_torch_save(obj, path): + folder = os.path.dirname(path) or "." + os.makedirs(folder, exist_ok=True) + tmp_path = os.path.join(folder, "." + os.path.basename(path) + ".tmp") + torch.save(obj, tmp_path) + os.replace(tmp_path, path) + + +@torch.no_grad() +def write_summary_histogram(radiance_field, occupancy_grid, writer, test_dataset, step, render_step_size, args): + img_scale = args.img_scale + radiance_field.eval() + occupancy_grid.eval() + rgb_images = [] + depth_images = [] + gt_imgs = [] + accs = [] + + pixels_to_plot = args.pixels_to_plot + plotting_transients = [] + plotting_transients_depth = [] + + plotting_transients_gt = [] + mse_list = [] + + test_list = list(range(len(test_dataset))) + # if args.version == "simulated": + # color_channels = 3 + # else: + # color_channels = 1 + # n_output_dim = args.n_bins*(color_channels) + with torch.no_grad(): + + # sample transients from network + for ind, i in enumerate(test_list): + data = test_dataset[i] + render_bkgd = data["color_bkgd"] + rays = data["rays"] + pixels = data["pixels"] + pixels = pixels.reshape(rays.origins.shape[0], rays.origins.shape[1], -1, 3) + valid_mask = data.get("valid_mask", None) + if valid_mask is not None: + valid_mask = valid_mask.to(torch.bool).cpu() + if valid_mask.ndim == 1: + valid_mask = valid_mask.reshape(rays.origins.shape[0], rays.origins.shape[1]) + + + # # rendering + out = render_transient( + radiance_field, + occupancy_grid, + rays, + near_plane=args.near_plane, + far_plane=args.far_plane, + render_step_size=render_step_size, + cone_angle=args.cone_angle, + alpha_thre=args.alpha_thre, + use_normals = False, + args = args + ) + + rgb, acc, n_rendering_samples, comp_weights, depth = [out[key] for key in ['colors', 'opacities', 'n_rendering_samples', "comp_weights", "depths"]] + del out + + rgb = rgb.reshape(rays.origins.shape[0], rays.origins.shape[1], -1, 3) + + _atomic_torch_save(rgb, os.path.join(args.outpath, f"test_{ind}_conv.pt")) + _atomic_torch_save(depth, os.path.join(args.outpath, f"test_{ind}_depth.pt")) + + # if color_channels ==1: + # gt_imgs.append(torch.clip(pixels.sum(-2).cpu().repeat(1, 1, 3).permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2)) + # rgb_images.append(torch.clip(rgb.sum(-2).cpu().repeat(1, 1, 3).permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2)) + # else: + gt_img = torch.clip(pixels.sum(-2).cpu().permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2) + rgb_img = torch.clip(rgb.sum(-2).cpu().permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2) + if valid_mask is not None: + valid3 = valid_mask[None, ...].expand_as(gt_img) + gt_imgs.append(gt_img * valid3.to(gt_img.dtype)) + rgb_images.append(rgb_img * valid3.to(rgb_img.dtype)) + if valid3.any(): + mse_list.append(((gt_img - rgb_img) ** 2)[valid3].mean()) + else: + mse_list.append(torch.tensor(float("nan"))) + else: + gt_imgs.append(gt_img) + rgb_images.append(rgb_img) + mse_list.append(torch.mean((gt_img - rgb_img) ** 2)) + accs.append(acc.repeat(1, 1, 3).permute(2, 0, 1).cpu()) + dp = imgviz.depth2rgb(depth.cpu().squeeze().numpy(), colormap="inferno") + depth_images.append(torch.from_numpy(dp).permute(2, 0, 1)) + + if ind == 0: + for pixel in pixels_to_plot: + plotting_transients.append(rgb[pixel[0], pixel[1], :, 0]) + plotting_transients_gt.append(pixels[pixel[0], pixel[1], :, 0]) + plotting_transients_depth.append(depth[pixel[0], pixel[1]]) + + + images = torchvision.utils.make_grid(torch.stack(gt_imgs + rgb_images + depth_images + accs), nrow=len(test_list), normalize=False) + mse = torch.stack(mse_list) + valid_mse = torch.isfinite(mse) + if valid_mse.any(): + psnr = -10.0 * torch.log10(torch.clamp(mse[valid_mse], min=1e-12)) + print(f"image psnr (masked): {psnr.mean():.2f}\n") + writer.add_scalar("Metric/psnr_masked", psnr.mean().item(), step) + else: + print("image psnr (masked): nan (no valid pixels)\n") + writer.add_image('rgbdn', images, step) + + figure = plt.figure(figsize=((len(pixels_to_plot)+1), 4), dpi=250) + + # plot the predicted intensity + plt.subplot(2, (len(pixels_to_plot)+1)//2, 1) + plt.imshow(gt_imgs[0].permute(1, 2, 0)) + for i, pixel in enumerate(pixels_to_plot): + plt.plot(pixel[1], pixel[0], '.', markersize=10, color='red') + plt.text(pixel[1], pixel[0], str(i), color="yellow", fontsize=10) + plt.gca().set_aspect(1.0/plt.gca().get_data_ratio(), adjustable='box') + plt.title('gt intensity') + + for i, pixel in enumerate(pixels_to_plot): + # plot transients + plt.subplot(2, (len(pixels_to_plot)+1)//2, i+2) + plt.plot(np.arange(args.n_bins), plotting_transients[i].detach().cpu(), label='pred', linewidth=0.5) + plt.plot(np.arange(args.n_bins), plotting_transients_gt[i].detach().cpu(), label='gt', linewidth=0.5) + plt.axvline(x = (plotting_transients_depth[i]/args.exposure_time).detach().cpu().numpy(), color = 'y') + plt.title(f"pixel {i}") + plt.ylabel('intensity') + plt.legend(borderpad=0, labelspacing=0) + plt.gca().set_aspect(1.0 / plt.gca().get_data_ratio(), adjustable='box') + + plt.tight_layout() + writer.add_figure("transient_plots", figure, step) + + + radiance_field.train() + occupancy_grid.train() + diff --git a/codes/reconstruction/transientnerf/misc/transient_volrend.py b/codes/reconstruction/transientnerf/misc/transient_volrend.py new file mode 100644 index 0000000000000000000000000000000000000000..0c2057c0da2996a903a9a607724d4b6e9c224890 --- /dev/null +++ b/codes/reconstruction/transientnerf/misc/transient_volrend.py @@ -0,0 +1,620 @@ +from typing import Callable, Dict, Optional, Tuple +import torch +from torch import Tensor +from nerfacc.pack import pack_info +from nerfacc.scan import exclusive_prod, exclusive_sum +from torch_scatter import scatter_max +import math + + +def rendering_transient_single_path( + # ray marching results + t_starts: Tensor, + t_ends: Tensor, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + # radiance field + rgb_sigma_fn: Optional[Callable] = None, + # rendering options + render_bkgd: Optional[Tensor] = None, + args = None +): + + # Query sigma and color with gradients + data = rgb_sigma_fn(t_starts, t_ends, ray_indices.long()) + rgbs, sigmas = data + dists = (t_starts + t_ends)/2 + + if args.exp: + rgbs = torch.exp(rgbs)-1 + + + # Rendering: compute weights and ray indices. + weights_non_squared, transmittance, alphas = render_weight_from_density( + t_starts, + t_ends, + sigmas, + ray_indices=ray_indices, + n_rays=n_rays + ) + + # modelling squared transmittance + # alphas = 1 - torch.exp(-sigmas * (t_ends - t_starts)) + weights = (weights_non_squared ** 2 / (alphas.squeeze() + 1e-9)) + # Some iterations can have no alive samples after occupancy sampling. + # Return zero buffers directly to avoid downstream shape edge-cases. + if t_starts.numel() == 0: + device = weights.device + dtype = weights.dtype + colors = torch.zeros((n_rays, args.n_bins, 3), device=device, dtype=dtype) + opacities = torch.zeros((n_rays, 1), device=device, dtype=dtype) + depths = torch.zeros((n_rays, 1), device=device, dtype=dtype) + depths_variance = torch.zeros((n_rays, 1), device=device, dtype=dtype) + comp_weights = torch.zeros((n_rays, args.n_bins), device=device, dtype=dtype) + return colors, opacities, depths, depths_variance, comp_weights, rgbs + weights_non_squared = weights_non_squared.reshape(-1) + weights = weights.reshape(-1) + ray_indices = ray_indices.reshape(-1).long() + t_starts = t_starts.reshape(-1) + t_ends = t_ends.reshape(-1) + dists = dists.reshape(-1) + rgbs = rgbs.reshape(-1, rgbs.shape[-1]) + + # Safety guard for occasional invalid indices from upstream CUDA kernels. + valid = (ray_indices >= 0) & (ray_indices < int(n_rays)) + if not torch.all(valid): + weights_non_squared = weights_non_squared[valid] + weights = weights[valid] + ray_indices = ray_indices[valid] + t_starts = t_starts[valid] + t_ends = t_ends[valid] + dists = dists[valid] + rgbs = rgbs[valid] + if ray_indices.numel() == 0: + device = weights.device + dtype = weights.dtype + colors = torch.zeros((n_rays, args.n_bins, 3), device=device, dtype=dtype) + opacities = torch.zeros((n_rays, 1), device=device, dtype=dtype) + depths = torch.zeros((n_rays, 1), device=device, dtype=dtype) + depths_variance = torch.zeros((n_rays, 1), device=device, dtype=dtype) + comp_weights = torch.zeros((n_rays, args.n_bins), device=device, dtype=dtype) + return colors, opacities, depths, depths_variance, comp_weights, rgbs + + # r**2 fall off + src = weights[:, None] * rgbs + src = src/(dists[:, None].detach()**2 + 1e-10) + + if args.version == "simulated": + # this code bins the output samples into a tensor of size [n_rays, n_bins, 3] + tfilter_sigma = args.tfilter_sigma + bin_mapping, dist_weights = mapping_dist_to_bin_mitsuba(dists, args.n_bins, args.exposure_time, c=1, sigma=tfilter_sigma) + src = (dist_weights[..., None] * src[:, None, :]).flatten(0, 1) + colors = torch.zeros((n_rays * args.n_bins, 3), device=weights.device) + index = ((torch.repeat_interleave(ray_indices, 8*tfilter_sigma) * args.n_bins) + bin_mapping.flatten().long())[:, None].expand(-1, 3).long() + colors.scatter_add_(0, index, src) + colors = colors.view(n_rays, args.n_bins, 3) + bin_numbers_floor, bin_numbers_ceil, alpha = mapping_dist_to_bin(dists, args.n_bins, args.exposure_time) + index_f = ((ray_indices * args.n_bins) + bin_numbers_floor.long())[:, None].expand(-1, 3).long() + index = index_f + + + if args.version == "captured": + bin_numbers_floor, bin_numbers_ceil, _ = mapping_dist_to_bin(dists, args.n_bins, args.exposure_time) + colors = torch.zeros((n_rays * args.n_bins, 3), device=weights.device) + index = ((ray_indices * args.n_bins) + bin_numbers_floor.long())[:, None].expand(-1, 3).long() + colors.scatter_add_(0, index, src) + colors = colors.view(n_rays, args.n_bins, 3) + colors = convolve_colour(colors, args.laser_kernel, n_bins=args.n_bins) + + + + # do the same for the sigmas + comp_weights = torch.zeros((n_rays * args.n_bins, 1), device=weights.device) + comp_weights.scatter_add_(0, index[:, [0]], weights_non_squared[:, None]) + comp_weights = comp_weights.reshape(n_rays, args.n_bins) + + + opacities = accumulate_along_rays( + weights, values=None, ray_indices=ray_indices, n_rays=n_rays + ) + + + out, argmax = scatter_max(weights_non_squared, ray_indices, out=torch.zeros(n_rays, device=ray_indices.device)) + + if t_starts.shape[0]!=0: + argmax[argmax==weights.shape[0]] = weights.shape[0]-1 + depths = (t_starts+t_ends)[argmax]/2 + else: + depths = out[:, None] + + to_accum_var = ((t_ends + t_starts) / 2 - depths.reshape(-1)[ray_indices]) ** 2 + depths_variance = accumulate_along_rays( + weights_non_squared.reshape(-1), + ray_indices=ray_indices, + values=to_accum_var.reshape(-1, 1), + n_rays=n_rays, + ) + depths_variance = depths_variance/(opacities+1e-10) + + + return colors, opacities, depths, depths_variance, comp_weights, rgbs + + +def mapping_dist_to_bin_mitsuba(dists, n_bins, exposure_time, c=1, sigma=5): + times = 2 * dists / c + ratio = times / exposure_time + ranges = torch.arange(0, 8 * sigma, device=dists.device)[None, :].repeat(ratio.shape[0], 1) + bin_mapping = (torch.ceil(ratio-4*sigma))[:, None]+ranges + ranges = bin_mapping - ratio[:, None] + dist_weights = torch.exp(-ranges**2/(2*sigma**2))-math.exp(-8) + + dist_weights[(bin_mapping<0) ] = 0 + dist_weights[(bin_mapping>n_bins) ] = 0 + + bin_mapping = torch.clip(bin_mapping, 0, n_bins-1) + dist_weights = (dist_weights.T/(dist_weights.sum(-1)[: None]+1e-10)).T + return bin_mapping, dist_weights + + +def mapping_dist_to_bin(dists, n_bins, exposure_time, c=1): + times = 2 * dists / c + # (torch.randn(times.shape[0])*7).to("cuda") + ratio = times / exposure_time + alpha = (torch.ceil(ratio) - ratio) / (torch.ceil(ratio) - torch.floor(ratio) + 1e-10) + + bin_numbers_floor = torch.floor(ratio) + bin_numbers_ceil = torch.ceil(ratio) + # if torch.max(bin_numbers)>bin_length: + # print("hello") + bin_numbers_floor = torch.clip(bin_numbers_floor, 0, n_bins - 1) + bin_numbers_ceil = torch.clip(bin_numbers_ceil, 0, n_bins - 1) + + return bin_numbers_floor, bin_numbers_ceil, alpha + + +def render_transmittance_from_alpha( + alphas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + prefix_trans: Optional[Tensor] = None, +) -> Tensor: + """Compute transmittance :math:`T_i` from alpha :math:`\\alpha_i`. + + .. math:: + T_i = \\prod_{j=1}^{i-1}(1-\\alpha_j) + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + alphas: The opacity values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + The rendering transmittance with the same shape as `alphas`. + + Examples: + + .. code-block:: python + + >>> alphas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> transmittance = render_transmittance_from_alpha(alphas, ray_indices=ray_indices) + tensor([1.0, 0.6, 0.12, 1.0, 0.2, 1.0, 1.0]) + """ + if ray_indices is not None and packed_info is None: + packed_info = pack_info(ray_indices, n_rays) + + trans = exclusive_prod(1 - alphas, packed_info) + if prefix_trans is not None: + trans *= prefix_trans + return trans + +def convolve_colour(color, kernel, n_bins): + color = color.transpose(1, 2).reshape(-1, n_bins) + color = kernel(color[:, None, :]).squeeze() + color = color.reshape(-1, 3, n_bins).transpose(1, 2) + return color + +def torch_laser_kernel(laser, device='cuda'): + m = torch.nn.Conv1d(1, 1, laser.shape[0], padding=(laser.shape[0] - 1) // 2, padding_mode="zeros", device=device) + m.weight.requires_grad = False + m.bias.requires_grad = False + m.bias *= 0 + m.weight = torch.nn.Parameter(laser[None, None, ...]) + return m + +def render_transmittance_from_density( + t_starts: Tensor, + t_ends: Tensor, + sigmas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + prefix_trans: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor]: + """Compute transmittance :math:`T_i` from density :math:`\\sigma_i`. + + .. math:: + T_i = exp(-\\sum_{j=1}^{i-1}\\sigma_j\delta_j) + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + t_starts: Where the frustum-shape sample starts along a ray. Tensor with \ + shape (all_samples,) or (n_rays, n_samples). + t_ends: Where the frustum-shape sample ends along a ray. Tensor with \ + shape (all_samples,) or (n_rays, n_samples). + sigmas: The density values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + The rendering transmittance and opacities, both with the same shape as `sigmas`. + + Examples: + + .. code-block:: python + + >>> t_starts = torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], device="cuda") + >>> t_ends = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], device="cuda") + >>> sigmas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> transmittance, alphas = render_transmittance_from_density( + >>> t_starts, t_ends, sigmas, ray_indices=ray_indices) + transmittance: [1.00, 0.67, 0.30, 1.00, 0.45, 1.00, 1.00] + alphas: [0.33, 0.55, 0.095, 0.55, 0.095, 0.00, 0.59] + + """ + if ray_indices is not None and packed_info is None: + packed_info = pack_info(ray_indices, n_rays) + + sigmas_dt = sigmas * (t_ends - t_starts) + alphas = 1.0 - torch.exp(-sigmas_dt) + trans = torch.exp(-exclusive_sum(sigmas_dt, packed_info)) + if prefix_trans is not None: + trans *= prefix_trans + return trans, alphas + + +def render_weight_from_alpha( + alphas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + prefix_trans: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor]: + """Compute rendering weights :math:`w_i` from opacity :math:`\\alpha_i`. + + .. math:: + w_i = T_i\\alpha_i, \\quad\\textrm{where}\\quad T_i = \\prod_{j=1}^{i-1}(1-\\alpha_j) + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + alphas: The opacity values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + The rendering weights and transmittance, both with the same shape as `alphas`. + + Examples: + + .. code-block:: python + + >>> alphas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> weights, transmittance = render_weight_from_alpha(alphas, ray_indices=ray_indices) + weights: [0.4, 0.48, 0.012, 0.8, 0.02, 0.0, 0.9]) + transmittance: [1.00, 0.60, 0.12, 1.00, 0.20, 1.00, 1.00] + + """ + trans = render_transmittance_from_alpha( + alphas, packed_info, ray_indices, n_rays, prefix_trans + ) + weights = trans * alphas + return weights, trans + + +def render_weight_from_density( + t_starts: Tensor, + t_ends: Tensor, + sigmas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + prefix_trans: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor, Tensor]: + """Compute rendering weights :math:`w_i` from density :math:`\\sigma_i` and interval :math:`\\delta_i`. + + .. math:: + w_i = T_i(1 - exp(-\\sigma_i\delta_i)), \\quad\\textrm{where}\\quad T_i = exp(-\\sum_{j=1}^{i-1}\\sigma_j\delta_j) + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + t_starts: The start time of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + t_ends: The end time of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + sigmas: The density values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + The rendering weights, transmittance and opacities, both with the same shape as `sigmas`. + + Examples: + + .. code-block:: python + + >>> t_starts = torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], device="cuda") + >>> t_ends = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], device="cuda") + >>> sigmas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> weights, transmittance, alphas = render_weight_from_density( + >>> t_starts, t_ends, sigmas, ray_indices=ray_indices) + weights: [0.33, 0.37, 0.03, 0.55, 0.04, 0.00, 0.59] + transmittance: [1.00, 0.67, 0.30, 1.00, 0.45, 1.00, 1.00] + alphas: [0.33, 0.55, 0.095, 0.55, 0.095, 0.00, 0.59] + + """ + trans, alphas = render_transmittance_from_density( + t_starts, t_ends, sigmas, packed_info, ray_indices, n_rays, prefix_trans + ) + weights = trans * alphas + return weights, trans, alphas + + +@torch.no_grad() +def render_visibility_from_alpha( + alphas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + early_stop_eps: float = 1e-4, + alpha_thre: float = 0.0, + prefix_trans: Optional[Tensor] = None, +) -> Tensor: + """Compute visibility from opacity :math:`\\alpha_i`. + + In this function, we first compute the transmittance from the sample opacity. The + transmittance is then used to filter out occluded samples. And opacity is used to + filter out transparent samples. The function returns a boolean tensor indicating + which samples are visible (`transmittance > early_stop_eps` and `opacity > alpha_thre`). + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + alphas: The opacity values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + early_stop_eps: The early stopping threshold on transmittance. + alpha_thre: The threshold on opacity. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + A boolean tensor indicating which samples are visible. Same shape as `alphas`. + + Examples: + + .. code-block:: python + + >>> alphas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> transmittance = render_transmittance_from_alpha(alphas, ray_indices=ray_indices) + tensor([1.0, 0.6, 0.12, 1.0, 0.2, 1.0, 1.0]) + >>> visibility = render_visibility_from_alpha( + >>> alphas, ray_indices=ray_indices, early_stop_eps=0.3, alpha_thre=0.2) + tensor([True, True, False, True, False, False, True]) + + """ + trans = render_transmittance_from_alpha( + alphas, packed_info, ray_indices, n_rays, prefix_trans + ) + vis = trans >= early_stop_eps + if alpha_thre > 0: + vis = vis & (alphas >= alpha_thre) + return vis + + +@torch.no_grad() +def render_visibility_from_density( + t_starts: Tensor, + t_ends: Tensor, + sigmas: Tensor, + packed_info: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, + early_stop_eps: float = 1e-4, + alpha_thre: float = 0.0, + prefix_trans: Optional[Tensor] = None, +) -> Tensor: + """Compute visibility from density :math:`\\sigma_i` and interval :math:`\\delta_i`. + + In this function, we first compute the transmittance and opacity from the sample density. The + transmittance is then used to filter out occluded samples. And opacity is used to + filter out transparent samples. The function returns a boolean tensor indicating + which samples are visible (`transmittance > early_stop_eps` and `opacity > alpha_thre`). + + This function supports both batched and flattened input tensor. For flattened input tensor, either + (`packed_info`) or (`ray_indices` and `n_rays`) should be provided. + + Args: + alphas: The opacity values of the samples. Tensor with shape (all_samples,) or (n_rays, n_samples). + packed_info: A tensor of shape (n_rays, 2) that specifies the start and count + of each chunk in the flattened samples, with in total n_rays chunks. + Useful for flattened input. + ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). + n_rays: Number of rays. Only useful when `ray_indices` is provided. + early_stop_eps: The early stopping threshold on transmittance. + alpha_thre: The threshold on opacity. + prefix_trans: The pre-computed transmittance of the samples. Tensor with shape (all_samples,). + + Returns: + A boolean tensor indicating which samples are visible. Same shape as `alphas`. + + Examples: + + .. code-block:: python + + >>> t_starts = torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], device="cuda") + >>> t_ends = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], device="cuda") + >>> sigmas = torch.tensor([0.4, 0.8, 0.1, 0.8, 0.1, 0.0, 0.9], device="cuda") + >>> ray_indices = torch.tensor([0, 0, 0, 1, 1, 2, 2], device="cuda") + >>> transmittance, alphas = render_transmittance_from_density( + >>> t_starts, t_ends, sigmas, ray_indices=ray_indices) + transmittance: [1.00, 0.67, 0.30, 1.00, 0.45, 1.00, 1.00] + alphas: [0.33, 0.55, 0.095, 0.55, 0.095, 0.00, 0.59] + >>> visibility = render_visibility_from_density( + >>> t_starts, t_ends, sigmas, ray_indices=ray_indices, early_stop_eps=0.3, alpha_thre=0.2) + tensor([True, True, False, True, False, False, True]) + + """ + trans, alphas = render_transmittance_from_density( + t_starts, t_ends, sigmas, packed_info, ray_indices, n_rays, prefix_trans + ) + vis = trans >= early_stop_eps + if alpha_thre > 0: + vis = vis & (alphas >= alpha_thre) + return vis + + +def accumulate_along_rays( + weights: Tensor, + values: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + n_rays: Optional[int] = None, +) -> Tensor: + """Accumulate volumetric values along the ray. + + This function supports both batched inputs and flattened inputs with + `ray_indices` and `n_rays` provided. + + Note: + This function is differentiable to `weights` and `values`. + + Args: + weights: Weights to be accumulated. If `ray_indices` not provided, + `weights` must be batched with shape (n_rays, n_samples). Else it + must be flattened with shape (all_samples,). + values: Values to be accumulated. If `ray_indices` not provided, + `values` must be batched with shape (n_rays, n_samples, D). Else it + must be flattened with shape (all_samples, D). None means + we accumulate weights along rays. Default: None. + ray_indices: Ray indices of the samples with shape (all_samples,). + If provided, `weights` must be a flattened tensor with shape (all_samples,) + and values (if not None) must be a flattened tensor with shape (all_samples, D). + Default: None. + n_rays: Number of rays. Should be provided together with `ray_indices`. Default: None. + + Returns: + Accumulated values with shape (n_rays, D). If `values` is not given we return + the accumulated weights, in which case D == 1. + + Examples: + + .. code-block:: python + + # Rendering: accumulate rgbs, opacities, and depths along the rays. + colors = accumulate_along_rays(weights, rgbs, ray_indices, n_rays) + opacities = accumulate_along_rays(weights, None, ray_indices, n_rays) + depths = accumulate_along_rays( + weights, + (t_starts + t_ends)[:, None] / 2.0, + ray_indices, + n_rays, + ) + # (n_rays, 3), (n_rays, 1), (n_rays, 1) + print(colors.shape, opacities.shape, depths.shape) + + """ + if values is None: + src = weights[..., None] + else: + assert values.dim() == weights.dim() + 1 + assert weights.shape == values.shape[:-1] + src = weights[..., None] * values + if ray_indices is not None: + assert n_rays is not None, "n_rays must be provided" + assert weights.dim() == 1, "weights must be flattened" + outputs = torch.zeros( + (n_rays, src.shape[-1]), device=src.device, dtype=src.dtype + ) + outputs.index_add_(0, ray_indices, src) + else: + outputs = torch.sum(src, dim=-2) + return outputs + + +def accumulate_along_rays_( + weights: Tensor, + values: Optional[Tensor] = None, + ray_indices: Optional[Tensor] = None, + outputs: Optional[Tensor] = None, +) -> None: + """Accumulate volumetric values along the ray. + + Inplace version of :func:`accumulate_along_rays`. + """ + if weights.shape[0] == 0: + return 0 + if values is None: + src = weights[..., None] + else: + assert values.dim() == weights.dim() + 1 + assert weights.shape == values.shape[:-1] + src = weights[..., None] * values + if ray_indices is not None: + # assert weights.dim() == 1, "weights must be flattened" + # assert ( + # outputs.dim() == 2 and outputs.shape[-1] == src.shape[-1] + # ), "outputs must be of shape (n_rays, D)" + outputs.index_add_(0, ray_indices, src) + else: + outputs.add_(src.sum(dim=-2)) + + +def shift_transient_grid_sample_3d(transient, depth, exposure_time, n_bins): + x_dim = transient.shape[0] + bins_move = depth/exposure_time + if x_dim%2 == 0: + x = (torch.arange(x_dim, device=transient.device)-x_dim//2+0.5)/(x_dim//2-0.5) + else: + x = (torch.arange(x_dim, device=transient.device)-x_dim//2)/(x_dim//2) + + if x_dim == 1: + x = torch.zeros_like(x) + + z = torch.arange(n_bins, device=transient.device).float() + X, Z = torch.meshgrid(x, z, indexing="ij") + Z = Z - bins_move + Z[Z<0] = n_bins+1 + Z = (Z-n_bins//2+0.5)/(n_bins//2-0.5) + grid = torch.stack((Z, X), dim=-1)[None, ...] + shifted_transient = torch.nn.functional.grid_sample(transient.permute(2, 0, 1)[None], grid, align_corners=True).squeeze(0).permute(1, 2, 0) + return shifted_transient diff --git a/codes/reconstruction/transientnerf/radiance_fields/__init__.py b/codes/reconstruction/transientnerf/radiance_fields/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/codes/reconstruction/transientnerf/radiance_fields/mlp.py b/codes/reconstruction/transientnerf/radiance_fields/mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..e290a796c2da3743a5bdee5cda0e3331a00ed39f --- /dev/null +++ b/codes/reconstruction/transientnerf/radiance_fields/mlp.py @@ -0,0 +1,395 @@ +""" +Copyright (c) 2022 Ruilong Li, UC Berkeley. +""" + +import functools +import math +from typing import Callable, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class MLP(nn.Module): + def __init__( + self, + input_dim: int, # The number of input tensor channels. + output_dim: int = None, # The number of output tensor channels. + net_depth: int = 8, # The depth of the MLP. + net_width: int = 256, # The width of the MLP. + skip_layer: int = 4, # The layer to add skip layers to. + hidden_init: Callable = nn.init.xavier_uniform_, + hidden_activation: Callable = nn.ReLU(), + output_enabled: bool = True, + output_init: Optional[Callable] = nn.init.xavier_uniform_, + output_activation: Optional[Callable] = nn.Identity(), + bias_enabled: bool = True, + bias_init: Callable = nn.init.zeros_, + ): + super().__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.net_depth = net_depth + self.net_width = net_width + self.skip_layer = skip_layer + self.hidden_init = hidden_init + self.hidden_activation = hidden_activation + self.output_enabled = output_enabled + self.output_init = output_init + self.output_activation = output_activation + self.bias_enabled = bias_enabled + self.bias_init = bias_init + + self.hidden_layers = nn.ModuleList() + in_features = self.input_dim + for i in range(self.net_depth): + self.hidden_layers.append( + nn.Linear(in_features, self.net_width, bias=bias_enabled) + ) + if ( + (self.skip_layer is not None) + and (i % self.skip_layer == 0) + and (i > 0) + ): + in_features = self.net_width + self.input_dim + else: + in_features = self.net_width + if self.output_enabled: + self.output_layer = nn.Linear( + in_features, self.output_dim, bias=bias_enabled + ) + else: + self.output_dim = in_features + + self.initialize() + + def initialize(self): + def init_func_hidden(m): + if isinstance(m, nn.Linear): + if self.hidden_init is not None: + self.hidden_init(m.weight) + if self.bias_enabled and self.bias_init is not None: + self.bias_init(m.bias) + + self.hidden_layers.apply(init_func_hidden) + if self.output_enabled: + + def init_func_output(m): + if isinstance(m, nn.Linear): + if self.output_init is not None: + self.output_init(m.weight) + if self.bias_enabled and self.bias_init is not None: + self.bias_init(m.bias) + + self.output_layer.apply(init_func_output) + + def forward(self, x): + inputs = x + for i in range(self.net_depth): + x = self.hidden_layers[i](x) + x = self.hidden_activation(x) + if ( + (self.skip_layer is not None) + and (i % self.skip_layer == 0) + and (i > 0) + ): + x = torch.cat([x, inputs], dim=-1) + if self.output_enabled: + x = self.output_layer(x) + x = self.output_activation(x) + return x + + +class DenseLayer(MLP): + def __init__(self, input_dim, output_dim, **kwargs): + super().__init__( + input_dim=input_dim, + output_dim=output_dim, + net_depth=0, # no hidden layers + **kwargs, + ) + + +class NerfMLP(nn.Module): + def __init__( + self, + input_dim: int, # The number of input tensor channels. + condition_dim: int, # The number of condition tensor channels. + net_depth: int = 8, # The depth of the MLP. + net_width: int = 256, # The width of the MLP. + skip_layer: int = 4, # The layer to add skip layers to. + net_depth_condition: int = 1, # The depth of the second part of MLP. + net_width_condition: int = 128, # The width of the second part of MLP. + ): + super().__init__() + self.base = MLP( + input_dim=input_dim, + net_depth=net_depth, + net_width=net_width, + skip_layer=skip_layer, + output_enabled=False, + ) + hidden_features = self.base.output_dim + self.sigma_layer = DenseLayer(hidden_features, 1) + + if condition_dim > 0: + self.bottleneck_layer = DenseLayer(hidden_features, net_width) + self.rgb_layer = MLP( + input_dim=net_width + condition_dim, + output_dim=3, + net_depth=net_depth_condition, + net_width=net_width_condition, + skip_layer=None, + ) + else: + self.rgb_layer = DenseLayer(hidden_features, 3) + + def query_density(self, x): + x = self.base(x) + raw_sigma = self.sigma_layer(x) + return raw_sigma + + def forward(self, x, condition=None): + x = self.base(x) + raw_sigma = self.sigma_layer(x) + if condition is not None: + if condition.shape[:-1] != x.shape[:-1]: + num_rays, n_dim = condition.shape + condition = condition.view( + [num_rays] + [1] * (x.dim() - condition.dim()) + [n_dim] + ).expand(list(x.shape[:-1]) + [n_dim]) + bottleneck = self.bottleneck_layer(x) + x = torch.cat([bottleneck, condition], dim=-1) + raw_rgb = self.rgb_layer(x) + return raw_rgb, raw_sigma + + +class SinusoidalEncoder(nn.Module): + """Sinusoidal Positional Encoder used in Nerf.""" + + def __init__(self, x_dim, min_deg, max_deg, use_identity: bool = True): + super().__init__() + self.x_dim = x_dim + self.min_deg = min_deg + self.max_deg = max_deg + self.use_identity = use_identity + self.register_buffer( + "scales", torch.tensor([2**i for i in range(min_deg, max_deg)]) + ) + + @property + def latent_dim(self) -> int: + return ( + int(self.use_identity) + (self.max_deg - self.min_deg) * 2 + ) * self.x_dim + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: [..., x_dim] + Returns: + latent: [..., latent_dim] + """ + if self.max_deg == self.min_deg: + return x + xb = torch.reshape( + (x[Ellipsis, None, :] * self.scales[:, None]), + list(x.shape[:-1]) + [(self.max_deg - self.min_deg) * self.x_dim], + ) + latent = torch.sin(torch.cat([xb, xb + 0.5 * math.pi], dim=-1)) + if self.use_identity: + latent = torch.cat([x] + [latent], dim=-1) + return latent + + +class VanillaNeRFRadianceField(nn.Module): + def __init__( + self, + net_depth: int = 8, # The depth of the MLP. + net_width: int = 256, # The width of the MLP. + skip_layer: int = 4, # The layer to add skip layers to. + net_depth_condition: int = 1, # The depth of the second part of MLP. + net_width_condition: int = 128, # The width of the second part of MLP. + ) -> None: + super().__init__() + self.posi_encoder = SinusoidalEncoder(3, 0, 10, True) + self.view_encoder = SinusoidalEncoder(3, 0, 4, True) + self.mlp = NerfMLP( + input_dim=self.posi_encoder.latent_dim, + condition_dim=self.view_encoder.latent_dim, + net_depth=net_depth, + net_width=net_width, + skip_layer=skip_layer, + net_depth_condition=net_depth_condition, + net_width_condition=net_width_condition, + ) + + def query_opacity(self, x, step_size): + density = self.query_density(x) + # if the density is small enough those two are the same. + # opacity = 1.0 - torch.exp(-density * step_size) + opacity = density * step_size + return opacity + + def query_density(self, x): + x = self.posi_encoder(x) + sigma = self.mlp.query_density(x) + return F.relu(sigma) + + def forward(self, x, condition=None): + x = self.posi_encoder(x) + if condition is not None: + condition = self.view_encoder(condition) + rgb, sigma = self.mlp(x, condition=condition) + return torch.sigmoid(rgb), F.relu(sigma) + + +class TNeRFRadianceField(nn.Module): + def __init__(self) -> None: + super().__init__() + self.posi_encoder = SinusoidalEncoder(3, 0, 4, True) + self.time_encoder = SinusoidalEncoder(1, 0, 4, True) + self.warp = MLP( + input_dim=self.posi_encoder.latent_dim + + self.time_encoder.latent_dim, + output_dim=3, + net_depth=4, + net_width=64, + skip_layer=2, + output_init=functools.partial(torch.nn.init.uniform_, b=1e-4), + ) + self.nerf = VanillaNeRFRadianceField() + + def query_opacity(self, x, timestamps, step_size): + idxs = torch.randint(0, len(timestamps), (x.shape[0],), device=x.device) + t = timestamps[idxs] + density = self.query_density(x, t) + # if the density is small enough those two are the same. + # opacity = 1.0 - torch.exp(-density * step_size) + opacity = density * step_size + return opacity + + def query_density(self, x, t): + x = x + self.warp( + torch.cat([self.posi_encoder(x), self.time_encoder(t)], dim=-1) + ) + return self.nerf.query_density(x) + + def forward(self, x, t, condition=None): + x = x + self.warp( + torch.cat([self.posi_encoder(x), self.time_encoder(t)], dim=-1) + ) + return self.nerf(x, condition=condition) + + +class NDRTNeRFRadianceField(nn.Module): + + """Invertble NN from https://arxiv.org/pdf/2206.15258.pdf""" + + def __init__(self) -> None: + super().__init__() + self.time_encoder = SinusoidalEncoder(1, 0, 4, True) + self.warp_layers_1 = nn.ModuleList() + self.time_layers_1 = nn.ModuleList() + self.warp_layers_2 = nn.ModuleList() + self.time_layers_2 = nn.ModuleList() + self.posi_encoder_1 = SinusoidalEncoder(2, 0, 4, True) + self.posi_encoder_2 = SinusoidalEncoder(1, 0, 4, True) + for _ in range(3): + self.warp_layers_1.append( + MLP( + input_dim=self.posi_encoder_1.latent_dim + 64, + output_dim=1, + net_depth=2, + net_width=128, + skip_layer=None, + output_init=functools.partial( + torch.nn.init.uniform_, b=1e-4 + ), + ) + ) + self.warp_layers_2.append( + MLP( + input_dim=self.posi_encoder_2.latent_dim + 64, + output_dim=1 + 2, + net_depth=1, + net_width=128, + skip_layer=None, + output_init=functools.partial( + torch.nn.init.uniform_, b=1e-4 + ), + ) + ) + self.time_layers_1.append( + DenseLayer( + input_dim=self.time_encoder.latent_dim, + output_dim=64, + ) + ) + self.time_layers_2.append( + DenseLayer( + input_dim=self.time_encoder.latent_dim, + output_dim=64, + ) + ) + + self.nerf = VanillaNeRFRadianceField() + + def _warp(self, x, t_enc, i_layer): + uv, w = x[:, :2], x[:, 2:] + dw = self.warp_layers_1[i_layer]( + torch.cat( + [self.posi_encoder_1(uv), self.time_layers_1[i_layer](t_enc)], + dim=-1, + ) + ) + w = w + dw + rt = self.warp_layers_2[i_layer]( + torch.cat( + [self.posi_encoder_2(w), self.time_layers_2[i_layer](t_enc)], + dim=-1, + ) + ) + r = self._euler2rot_2dinv(rt[:, :1]) + t = rt[:, 1:] + uv = torch.bmm(r, (uv - t)[..., None]).squeeze(-1) + return torch.cat([uv, w], dim=-1) + + def warp(self, x, t): + t_enc = self.time_encoder(t) + x = self._warp(x, t_enc, 0) + x = x[..., [1, 2, 0]] + x = self._warp(x, t_enc, 1) + x = x[..., [2, 0, 1]] + x = self._warp(x, t_enc, 2) + return x + + def query_opacity(self, x, timestamps, step_size): + idxs = torch.randint(0, len(timestamps), (x.shape[0],), device=x.device) + t = timestamps[idxs] + density = self.query_density(x, t) + # if the density is small enough those two are the same. + # opacity = 1.0 - torch.exp(-density * step_size) + opacity = density * step_size + return opacity + + def query_density(self, x, t): + x = self.warp(x, t) + return self.nerf.query_density(x) + + def forward(self, x, t, condition=None): + x = self.warp(x, t) + return self.nerf(x, condition=condition) + + def _euler2rot_2dinv(self, euler_angle): + # (B, 1) -> (B, 2, 2) + theta = euler_angle.reshape(-1, 1, 1) + rot = torch.cat( + ( + torch.cat((theta.cos(), -theta.sin()), 1), + torch.cat((theta.sin(), theta.cos()), 1), + ), + 2, + ) + return rot diff --git a/codes/reconstruction/transientnerf/radiance_fields/ngp.py b/codes/reconstruction/transientnerf/radiance_fields/ngp.py new file mode 100644 index 0000000000000000000000000000000000000000..04d869751d567b22dcfc1a932bf22d1dac38d302 --- /dev/null +++ b/codes/reconstruction/transientnerf/radiance_fields/ngp.py @@ -0,0 +1,299 @@ +""" +Copyright (c) 2022 Ruilong Li, UC Berkeley. +""" + +from typing import Callable, List, Union + +import numpy as np +import torch +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd +import torch.nn.functional as F + +try: + import tinycudann as tcnn +except ImportError as e: + print( + f"Error: {e}! " + "Please install tinycudann by: " + "pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch" + ) + exit() + + +class _TruncExp(Function): # pylint: disable=abstract-method + # Implementation from torch-ngp: + # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, x): # pylint: disable=arguments-differ + ctx.save_for_backward(x) + return torch.exp(x) + + @staticmethod + @custom_bwd + def backward(ctx, g): # pylint: disable=arguments-differ + x = ctx.saved_tensors[0] + return g * torch.exp(torch.clamp(x, max=15)) + + +trunc_exp = _TruncExp.apply + + +def contract_to_unisphere( + x: torch.Tensor, + aabb: torch.Tensor, + ord: Union[str, int] = 2, + # ord: Union[float, int] = float("inf"), + eps: float = 1e-6, + derivative: bool = False, +): + aabb_min, aabb_max = torch.split(aabb, 3, dim=-1) + x = (x - aabb_min) / (aabb_max - aabb_min) + x = x * 2 - 1 # aabb is at [-1, 1] + mag = torch.linalg.norm(x, ord=ord, dim=-1, keepdim=True) + mask = mag.squeeze(-1) > 1 + + if derivative: + dev = (2 * mag - 1) / mag**2 + 2 * x**2 * ( + 1 / mag**3 - (2 * mag - 1) / mag**4 + ) + dev[~mask] = 1.0 + dev = torch.clamp(dev, min=eps) + return dev + else: + x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask]) + x = x / 4 + 0.5 # [-inf, inf] is at [0, 1] + return x + + +class NGPRadianceField(torch.nn.Module): + """Instance-NGP Radiance Field""" + + def __init__( + self, + aabb: Union[torch.Tensor, List[float]], + num_dim: int = 3, + use_viewdirs: bool = True, + density_activation: Callable = lambda x: trunc_exp(x - 1), + radiance_activation: Callable = lambda x: F.sigmoid(x), + unbounded: bool = False, + base_resolution: int = 16, + max_resolution: int = 4096, + geo_feat_dim: int = 15, + n_levels: int = 16, + log2_hashmap_size: int = 19, + args = None + ) -> None: + super().__init__() + if not isinstance(aabb, torch.Tensor): + aabb = torch.tensor(aabb, dtype=torch.float32) + self.register_buffer("aabb", aabb) + self.num_dim = num_dim + self.radiance_activation = radiance_activation + self.use_viewdirs = use_viewdirs + self.density_activation = density_activation + self.unbounded = unbounded + self.base_resolution = base_resolution + self.max_resolution = max_resolution + self.geo_feat_dim = geo_feat_dim + self.n_levels = n_levels + self.log2_hashmap_size = log2_hashmap_size + self.version = args.version + self.radiance_activation = radiance_activation + self.n_output_dim = 3 + + + + + per_level_scale = np.exp( + (np.log(max_resolution) - np.log(base_resolution)) / (n_levels - 1) + ).tolist() + + if self.use_viewdirs: + self.direction_encoding = tcnn.Encoding( + n_input_dims=num_dim, + encoding_config={ + "otype": "Composite", + "nested": [ + { + "n_dims_to_encode": 3, + "otype": "SphericalHarmonics", + "degree": 4, + }, + # {"otype": "Identity", "n_bins": 4, "degree": 4}, + ], + }, + ) + + self.mlp_base = tcnn.NetworkWithInputEncoding( + n_input_dims=num_dim, + n_output_dims=1 + self.geo_feat_dim, + encoding_config={ + "otype": "HashGrid", + "n_levels": n_levels, + "n_features_per_level": 2, + "log2_hashmap_size": log2_hashmap_size, + "base_resolution": base_resolution, + "per_level_scale": per_level_scale, + }, + network_config={ + "otype": "FullyFusedMLP", + "activation": "ReLU", + "output_activation": "None", + "n_neurons": 64, + "n_hidden_layers": 1, + }, + ) + if self.geo_feat_dim > 0: + self.mlp_head = tcnn.Network( + n_input_dims=( + ( + self.direction_encoding.n_output_dims + if self.use_viewdirs + else 0 + ) + + self.geo_feat_dim + ), + n_output_dims=self.n_output_dim, + network_config={ + "otype": "FullyFusedMLP", + "activation": "ReLU", + "output_activation": "None", + "n_neurons": 64, + "n_hidden_layers": 2, + }, + ) + + def query_density(self, x, return_feat: bool = False): + if self.unbounded: + x = contract_to_unisphere(x, self.aabb) + else: + aabb_min, aabb_max = torch.split(self.aabb, self.num_dim, dim=-1) + x = (x - aabb_min) / (aabb_max - aabb_min) + selector = ((x > 0.0) & (x < 1.0)).all(dim=-1) + x = ( + self.mlp_base(x.view(-1, self.num_dim)) + .view(list(x.shape[:-1]) + [1 + self.geo_feat_dim]) + .to(x) + ) + density_before_activation, base_mlp_out = torch.split( + x, [1, self.geo_feat_dim], dim=-1 + ) + density = ( + self.density_activation(density_before_activation) + * selector[..., None] + ) + if return_feat: + return density, base_mlp_out + else: + return density + + def _query_rgb(self, dir, embedding, apply_act: bool = True): + # tcnn requires directions in the range [0, 1] + if self.use_viewdirs: + dir = (dir + 1.0) / 2.0 + d = self.direction_encoding(dir.reshape(-1, dir.shape[-1])) + h = torch.cat([d, embedding.reshape(-1, self.geo_feat_dim)], dim=-1) + else: + h = embedding.reshape(-1, self.geo_feat_dim) + rgb = ( + self.mlp_head(h) + .reshape(list(embedding.shape[:-1]) + [self.n_output_dim]) + .to(embedding) + ) + if apply_act: + rgb = self.radiance_activation(rgb) + + return rgb + + def forward( + self, + positions: torch.Tensor, + directions: torch.Tensor = None, + ): + if positions.shape[0] == 0: + density = torch.zeros(0, device=positions.device) + color = torch.zeros(0, 1200, device=positions.device) + return color, density + + if self.use_viewdirs and (directions is not None): + assert ( + positions.shape == directions.shape + ), f"{positions.shape} v.s. {directions.shape}" + density, embedding = self.query_density(positions, return_feat=True) + rgb = self._query_rgb(directions, embedding=embedding) + else: + density, embedding = self.query_density(positions, return_feat=True) + rgb = self._query_rgb(directions, embedding=embedding) + return rgb, density # type: ignore + + +class NGPDensityField(torch.nn.Module): + """Instance-NGP Density Field used for resampling""" + + def __init__( + self, + aabb: Union[torch.Tensor, List[float]], + num_dim: int = 3, + density_activation: Callable = lambda x: trunc_exp(x - 1), + unbounded: bool = False, + base_resolution: int = 16, + max_resolution: int = 128, + n_levels: int = 5, + log2_hashmap_size: int = 17, + ) -> None: + super().__init__() + if not isinstance(aabb, torch.Tensor): + aabb = torch.tensor(aabb, dtype=torch.float32) + self.register_buffer("aabb", aabb) + self.num_dim = num_dim + self.density_activation = density_activation + self.unbounded = unbounded + self.base_resolution = base_resolution + self.max_resolution = max_resolution + self.n_levels = n_levels + self.log2_hashmap_size = log2_hashmap_size + + per_level_scale = np.exp( + (np.log(max_resolution) - np.log(base_resolution)) / (n_levels - 1) + ).tolist() + + self.mlp_base = tcnn.NetworkWithInputEncoding( + n_input_dims=num_dim, + n_output_dims=1, + encoding_config={ + "otype": "HashGrid", + "n_levels": n_levels, + "n_features_per_level": 2, + "log2_hashmap_size": log2_hashmap_size, + "base_resolution": base_resolution, + "per_level_scale": per_level_scale, + }, + network_config={ + "otype": "FullyFusedMLP", + "activation": "ReLU", + "output_activation": "None", + "n_neurons": 64, + "n_hidden_layers": 1, + }, + ) + + def forward(self, positions: torch.Tensor): + if self.unbounded: + positions = contract_to_unisphere(positions, self.aabb) + else: + aabb_min, aabb_max = torch.split(self.aabb, self.num_dim, dim=-1) + positions = (positions - aabb_min) / (aabb_max - aabb_min) + selector = ((positions > 0.0) & (positions < 1.0)).all(dim=-1) + density_before_activation = ( + self.mlp_base(positions.view(-1, self.num_dim)) + .view(list(positions.shape[:-1]) + [1]) + .to(positions) + ) + density = ( + self.density_activation(density_before_activation) + * selector[..., None] + ) + return density + \ No newline at end of file diff --git a/codes/reconstruction/transientnerf/train_ours.py b/codes/reconstruction/transientnerf/train_ours.py new file mode 100644 index 0000000000000000000000000000000000000000..3b2cf39e2b4c916f4231126ffbe2b6e532731386 --- /dev/null +++ b/codes/reconstruction/transientnerf/train_ours.py @@ -0,0 +1,521 @@ +import math +import os + +import configargparse +import numpy as np +import pandas as pd +import scipy.io as sio +import torch +import torch.multiprocessing as mp +import tqdm +from nerfacc.estimators.occ_grid import OccGridEstimator +from torch.utils.tensorboard import SummaryWriter + +from misc.summary import write_summary_histogram +from misc.transient_volrend import torch_laser_kernel +from radiance_fields.ngp import NGPRadianceField +from utils import ( + load_args, + make_save_folder, + make_save_folder_final, + render_transient, + set_random_seed, +) + +if mp.get_start_method(allow_none=True) is None: + mp.set_start_method("spawn") + + +def load_args_ours(): + parser = configargparse.ArgumentParser() + parser.add_argument( + "--irf_path", + type=str, + default="", + help="Path to IRF file. Supports .csv/.npy/.mat/.pt. If empty, fallback to --pulse_path.", + ) + parser.add_argument( + "--irf_column", + type=str, + default="irf", + help="CSV column name for IRF values.", + ) + parser.add_argument( + "--irf_half_window", + type=int, + default=50, + help="Half window size for cropping around IRF peak. Set <=0 to disable crop.", + ) + parser.add_argument( + "--no_irf_reverse", + action="store_true", + help="Disable reverse before Conv1d kernel creation.", + ) + parser.add_argument( + "--measurement_root", + type=str, + default="", + help="Optional root directory of measurement files (.npz/.txt/.pt/.h5).", + ) + parser.add_argument( + "--data_exts", + type=str, + default=".npz,.txt,.pt,.h5,.hdf5", + help="Comma-separated measurement extensions lookup order.", + ) + parser.add_argument( + "--bin_width_s_loader", + type=float, + default=None, + help="Bin width in seconds for shift resampling. If empty, derived from exposure_time / c.", + ) + parser.add_argument( + "--img_height", + type=int, + default=None, + help="Training image height. If empty, use --img_shape.", + ) + parser.add_argument( + "--img_width", + type=int, + default=None, + help="Training image width. If empty, use --img_shape.", + ) + parser.add_argument( + "--img_height_test", + type=int, + default=None, + help="Test image height. If empty, use --img_shape_test.", + ) + parser.add_argument( + "--img_width_test", + type=int, + default=None, + help="Test image width. If empty, use --img_shape_test.", + ) + parser.add_argument( + "--meas_peak_min", + type=float, + default=100.0, + help=( + "Minimum raw histogram peak per pixel to keep it in photometric loss. " + "<=0 disables this mask. Threshold is interpreted in pre-normalization measurement scale." + ), + ) + parser.add_argument( + "--invalid_mask_path", + type=str, + default="", + help=( + "Path to offset map used to build valid-pixel mask. " + "Pixels with offset > invalid_mask_invalid_gt are excluded from training/eval." + ), + ) + parser.add_argument( + "--invalid_mask_invalid_gt", + type=float, + default=10.0, + help="Offset threshold for invalid pixels in invalid_mask_path.", + ) + return load_args(eval=True, parser=parser) + + +def _to_numpy(x): + if isinstance(x, np.ndarray): + return x + if isinstance(x, torch.Tensor): + return x.detach().cpu().numpy() + return np.asarray(x) + + +def _load_irf_series(path: str, column: str) -> np.ndarray: + ext = os.path.splitext(path)[1].lower() + if ext == ".csv": + df = pd.read_csv(path, sep=",") + if column in df.columns: + arr = df[column].to_numpy(dtype=np.float64) + else: + numeric_cols = [c for c in df.columns if np.issubdtype(df[c].dtype, np.number)] + if not numeric_cols: + raise ValueError(f"No numeric columns found in IRF CSV: {path}") + arr = df[numeric_cols[0]].to_numpy(dtype=np.float64) + return arr.squeeze() + if ext == ".npy": + return np.load(path).astype(np.float64).squeeze() + if ext == ".mat": + mat = sio.loadmat(path) + if "out" in mat: + return _to_numpy(mat["out"]).astype(np.float64).squeeze() + for value in mat.values(): + if isinstance(value, np.ndarray) and value.ndim >= 1 and value.size > 1: + return _to_numpy(value).astype(np.float64).squeeze() + raise ValueError(f"Cannot find valid IRF series in mat file: {path}") + if ext == ".pt": + return _to_numpy(torch.load(path, map_location="cpu")).astype(np.float64).squeeze() + raise ValueError(f"Unsupported IRF extension: {ext}") + + +def build_irf_kernel(args, device): + irf_path = args.irf_path if args.irf_path else args.pulse_path + if not irf_path: + raise ValueError("IRF path is empty. Set --irf_path or --pulse_path.") + + irf = _load_irf_series(irf_path, args.irf_column) + if irf.ndim != 1: + irf = irf.reshape(-1) + if irf.size == 0: + raise ValueError(f"Loaded empty IRF from: {irf_path}") + + peak_idx = int(np.argmax(irf)) + if args.irf_half_window and args.irf_half_window > 0: + lo = max(0, peak_idx - int(args.irf_half_window)) + hi = min(len(irf), peak_idx + int(args.irf_half_window) + 1) + irf = irf[lo:hi] + + irf = irf / (irf.sum() + 1e-8) + if not args.no_irf_reverse: + irf = irf[::-1].copy() + + laser = torch.tensor(irf, dtype=torch.float32, device=device) + return torch_laser_kernel(laser, device=device) + + +def run(): + args = load_args_ours() + device = torch.device(args.device) + args.device = str(device) + if device.type == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError(f"CUDA device requested but CUDA is unavailable: {device}") + torch.cuda.set_device(device) + torch.cuda.empty_cache() + set_random_seed(args.seed) + + train_h = int(args.img_height) if args.img_height is not None else int(args.img_shape) + train_w = int(args.img_width) if args.img_width is not None else int(args.img_shape) + test_h = int(args.img_height_test) if args.img_height_test is not None else int(args.img_shape_test) + test_w = int(args.img_width_test) if args.img_width_test is not None else int(args.img_shape_test) + img_shape = (train_h, train_w) + img_shape_test = (test_h, test_w) + + aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device) + train_dataset_kwargs = {} + test_dataset_kwargs = {} + + rfilter_sigma = args.rfilter_sigma + max_steps = args.max_steps + sample_as_per_distribution = args.sample_as_per_distribution + target_sample_batch_size = 1 << 16 + + if args.version == "simulated": + from loaders.loader_synthetic import SubjectLoaderTransient as SubjectLoader + + test_dataset_kwargs = { + "img_shape": img_shape_test, + "have_images": True, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": rfilter_sigma, + "sample_as_per_distribution": sample_as_per_distribution, + } + train_dataset_kwargs = { + "img_shape": img_shape, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": rfilter_sigma, + "sample_as_per_distribution": sample_as_per_distribution, + } + + train_dataset = SubjectLoader( + root_fp=args.data_root_fp, + subject_id=args.exp_name, + split="train", + num_rays=target_sample_batch_size // args.render_n_samples, + **train_dataset_kwargs, + num_views=args.num_views, + ) + train_dataset.camtoworlds = train_dataset.camtoworlds.to(device) + train_dataset.K = train_dataset.K.to(device) + + test_dataset = SubjectLoader( + root_fp=args.data_root_fp, + subject_id=args.exp_name, + split="test", + num_rays=None, + **test_dataset_kwargs, + ) + if test_dataset_kwargs["have_images"]: + test_dataset.images = test_dataset.images.to(device) + test_dataset.camtoworlds = test_dataset.camtoworlds.to(device) + test_dataset.K = test_dataset.K.to(device) + else: + from loaders.loader_captured_ours import LearnRays, SubjectLoaderTransientRealOurs as SubjectLoader + + params = np.load(args.intrinsics, allow_pickle=True)[()] + shift = _to_numpy(params["shift"]) + rays = _to_numpy(params["rays"]) + source_img_shape = (int(rays.shape[0]), int(rays.shape[1])) + + measurement_root = args.measurement_root.strip() or None + invalid_mask_path = args.invalid_mask_path.strip() or None + data_exts = tuple(e.strip() for e in args.data_exts.split(",") if e.strip()) + if args.bin_width_s_loader is not None: + bin_width_s_loader = float(args.bin_width_s_loader) + else: + bin_width_s_loader = float(args.exposure_time) / 299792458.0 + + test_dataset_kwargs = { + "img_shape": img_shape_test, + "have_images": True, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": rfilter_sigma, + "sample_as_per_distribution": sample_as_per_distribution, + "shift": shift, + "measurement_root": measurement_root, + "data_exts": data_exts, + "bin_width_s": bin_width_s_loader, + "source_img_shape": source_img_shape, + "invalid_mask_path": invalid_mask_path, + "invalid_mask_invalid_gt": float(args.invalid_mask_invalid_gt), + } + train_dataset_kwargs = { + "img_shape": img_shape, + "n_bins": args.n_bins, + "color_bkgd_aug": "black", + "rfilter_sigma": rfilter_sigma, + "sample_as_per_distribution": sample_as_per_distribution, + "shift": shift, + "measurement_root": measurement_root, + "data_exts": data_exts, + "bin_width_s": bin_width_s_loader, + "source_img_shape": source_img_shape, + "invalid_mask_path": invalid_mask_path, + "invalid_mask_invalid_gt": float(args.invalid_mask_invalid_gt), + } + + train_dataset = SubjectLoader( + root_fp=args.data_root_fp, + subject_id=args.exp_name, + split="train", + num_rays=target_sample_batch_size // args.render_n_samples, + **train_dataset_kwargs, + ) + train_dataset.camtoworlds = train_dataset.camtoworlds.to(device) + train_dataset.K = LearnRays(rays, device=device, img_shape=img_shape).to(device) + + test_dataset = SubjectLoader( + root_fp=args.data_root_fp, + subject_id=args.exp_name, + split="test", + num_rays=None, + **test_dataset_kwargs, + ) + if test_dataset_kwargs["have_images"]: + test_dataset.images = test_dataset.images.to(device) + test_dataset.camtoworlds = test_dataset.camtoworlds.to(device) + test_dataset.K = LearnRays(rays, device=device, img_shape=img_shape_test).to(device) + + args.laser_kernel = build_irf_kernel(args, device=device) + train_dataset_scale = float(_to_numpy(train_dataset.max).reshape(-1)[0]) + if train_dataset_scale <= 0: + train_dataset_scale = 1.0 + + scene_aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device) + render_step_size = ((scene_aabb[3:] - scene_aabb[:3]).max() * math.sqrt(3) / args.render_n_samples).item() + + grad_scaler = torch.cuda.amp.GradScaler(2**10) + radiance_field = NGPRadianceField( + use_viewdirs=True, + aabb=args.aabb, + unbounded=False, + radiance_activation=torch.exp, + args=args, + ).to(device) + + optimizer = torch.optim.Adam(radiance_field.parameters(), lr=args.lr, eps=1e-15) + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[max_steps // 2, max_steps * 3 // 4, max_steps * 9 // 10], + gamma=0.33, + ) + occupancy_grid = OccGridEstimator( + roi_aabb=aabb, + resolution=args.grid_resolution, + levels=args.grid_nlvl, + ).to(device) + + if args.final: + writer, step, outpath = make_save_folder_final( + args, + optimizer, + scheduler, + radiance_field, + occupancy_grid, + ) + args.outpath = outpath + else: + outpath = make_save_folder(args) + args.outpath = outpath + writer = SummaryWriter(log_dir=outpath) + step = 0 + + # When resuming (final=True), show progress from resumed step. + pbar = tqdm.tqdm(total=args.max_steps, initial=min(step, args.max_steps)) + zero_sample_streak = 0 + while step < max_steps: + pbar.update(1) + + if args.version == "simulated" and step % 1000 == 0: + if train_dataset.rep < 30: + train_dataset.rep += 2 + + radiance_field.train() + + i = torch.randint(0, len(train_dataset), (1,)).item() + data = train_dataset[i] + rays = data["rays"] + num_base_rays = int(rays.origins.shape[0] / train_dataset.rep) + pixs = torch.reshape( + data["pixels"][:num_base_rays], + (-1, args.n_bins, 3), + ) + data_valid_mask = data.get("valid_mask") + if data_valid_mask is not None: + data_valid_mask = data_valid_mask.to(device=device, dtype=torch.bool).reshape(-1) + if data_valid_mask.numel() < num_base_rays: + raise ValueError( + f"valid_mask has too few elements: {data_valid_mask.numel()} < base rays {num_base_rays}" + ) + data_valid_mask = data_valid_mask[:num_base_rays] + else: + data_valid_mask = torch.ones(pixs.shape[0], dtype=torch.bool, device=device) + # Use measurement peak (pre-log) to exclude low-signal / out-of-range pixels. + if args.version == "captured" and float(args.meas_peak_min) > 0: + peak_thre_norm = float(args.meas_peak_min) / float(train_dataset_scale) + meas_peak = torch.amax(pixs[..., 0], dim=-1) + meas_valid_mask = meas_peak >= peak_thre_norm + else: + meas_valid_mask = torch.ones(pixs.shape[0], dtype=torch.bool, device=pixs.device) + + def occ_eval_fn(x): + density = radiance_field.query_density(x) + density = torch.nan_to_num(density, nan=0.0, posinf=0.0, neginf=0.0) + return density.squeeze(-1) * render_step_size + + base_occ_thre = float(args.occ_thre) + if args.version == "captured": + warmup_steps = int(args.thold_warmup) if int(args.thold_warmup) > 0 else 10000 + occ_thre = min(base_occ_thre, 1e-6) if step < warmup_steps else base_occ_thre + else: + occ_thre = base_occ_thre + + try: + occupancy_grid.update_every_n_steps(step=step, occ_eval_fn=occ_eval_fn, occ_thre=occ_thre) + except RuntimeError as ex: + if "invalid configuration argument" in str(ex).lower(): + raise RuntimeError( + "CUDA invalid configuration argument during occupancy update. " + "This is often an async CUDA error from an earlier kernel. " + "Rerun with CUDA_LAUNCH_BLOCKING=1 to get the first failing op." + ) from ex + raise + + out = render_transient( + radiance_field, + occupancy_grid, + rays, + near_plane=args.near_plane, + far_plane=args.far_plane, + render_step_size=render_step_size, + cone_angle=args.cone_angle, + alpha_thre=args.alpha_thre, + use_normals=False, + args=args, + ) + + rgb, acc, n_rendering_samples, comp_weights = [ + out[key] for key in ["colors", "opacities", "n_rendering_samples", "comp_weights"] + ] + del out + + if n_rendering_samples == 0: + # Avoid infinite loops where step never advances. + zero_sample_streak += 1 + if zero_sample_streak % 100 == 0: + print( + f"[WARN] n_rendering_samples==0 streak={zero_sample_streak} " + f"at step={step}. Try lowering occ_thre (current={occ_thre})." + ) + step += 1 + continue + zero_sample_streak = 0 + + train_dataset.update_num_rays(args.num_rays_per_batch) + + alive_ray_mask = acc.squeeze(-1) > 0 + alive_ray_mask = alive_ray_mask.reshape(train_dataset.rep, -1) + alive_ray_mask = alive_ray_mask.sum(0).bool() + supervised_mask = alive_ray_mask & meas_valid_mask & data_valid_mask + + rgba = torch.reshape(rgb, (-1, args.n_bins, 3)) * data["weights"][:, None, None] + carve_mask = pixs.sum(-1).repeat(train_dataset.rep, 1) < 1e-7 + valid_mask_flat = data_valid_mask.repeat(train_dataset.rep)[:, None] + carve_mask = carve_mask & valid_mask_flat.expand(-1, args.n_bins) + carve_vals = comp_weights[carve_mask] + if carve_vals.numel() > 0: + comp_weights = carve_vals.mean() + else: + comp_weights = torch.tensor(0.0, device=device, dtype=rgba.dtype) + rgb = torch.zeros((int(rgba.shape[0] / train_dataset.rep), args.n_bins, 3), device=device) + index = ( + torch.arange(int(rgba.shape[0] / train_dataset.rep), device=device) + .repeat(train_dataset.rep)[:, None, None] + .expand(-1, args.n_bins, 3) + ) + rgb.scatter_add_(0, index.type(torch.int64), rgba) + + pixs = torch.log(pixs + 1) + rgb = torch.log(rgb + 1) + if supervised_mask.any(): + photometric_loss = torch.nn.functional.l1_loss(rgb[supervised_mask], pixs[supervised_mask]) + else: + photometric_loss = torch.tensor(0.0, device=device) + loss = photometric_loss + comp_weights * args.space_carving + + optimizer.zero_grad() + grad_scaler.scale(loss).backward() + optimizer.step() + scheduler.step() + + writer.add_scalar("Loss/train", loss.detach().cpu().numpy(), step) + writer.add_scalar("Loss/photometric", photometric_loss.detach().cpu().numpy(), step) + writer.add_scalar("Mask/supervised_ratio", supervised_mask.float().mean().detach().cpu().numpy(), step) + + if not step % args.steps_til_checkpoint: + path = os.path.join(outpath, "radiance_field_{:04d}.pth".format(step)) + torch.save(radiance_field.state_dict(), path) + path = os.path.join(outpath, "occupancy_grid_{:04d}.pth".format(step)) + torch.save(occupancy_grid.state_dict(), path) + path = os.path.join(outpath, "optimizer_{:04d}.pth".format(step)) + torch.save(optimizer.state_dict(), path) + path = os.path.join(outpath, "scheduler_{:04d}.pth".format(step)) + torch.save(scheduler.state_dict(), path) + torch.save({"step": step, "rays_per_pixel": train_dataset.rep}, os.path.join(outpath, "variables.pth")) + + if test_dataset_kwargs["have_images"]: + write_summary_histogram( + radiance_field, + occupancy_grid, + writer, + test_dataset, + step, + render_step_size, + args, + ) + + step += 1 + + +if __name__ == "__main__": + run() diff --git a/codes/reconstruction/transientnerf/utils.py b/codes/reconstruction/transientnerf/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a8eadc5868b60f4c6914b240dca22e86a4779854 --- /dev/null +++ b/codes/reconstruction/transientnerf/utils.py @@ -0,0 +1,539 @@ +from datetime import datetime +import random +from typing import Optional +import ast +import configargparse +import os +import numpy as np +import torch +from loaders.utils import Rays, namedtuple_map +from nerfacc.estimators.occ_grid import OccGridEstimator +from nerfacc.grid import ray_aabb_intersect, traverse_grids +from misc.transient_volrend import ( +rendering_transient_single_path) + +from torch.utils.tensorboard import SummaryWriter +import shutil + + +def set_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + +def render_transient( + # scene + radiance_field: torch.nn.Module, + occupancy_grid: OccGridEstimator, + rays: Rays, + # rendering options + near_plane = 0, + far_plane = 2**15, + render_step_size: float = 1e-3, + cone_angle: float = 0.0, + alpha_thre: float = 0.0, + # test options + # only useful for dnerf + chunk = 8192*128, + use_normals = False, + args = None +): + """Render the pixels of an image.""" + rays_shape = rays.origins.shape + if len(rays_shape) == 3: + height, width, _ = rays_shape + n_rays = height * width + rays = namedtuple_map( + lambda r: r.reshape([n_rays] + list(r.shape[2:])), rays + ) + else: + n_rays, _ = rays_shape + + results = [] + + def rgb_sigma_fn(t_starts, t_ends, ray_indices): + t_origins = chunk_rays.origins[ray_indices] + t_dirs = chunk_rays.viewdirs[ray_indices] + positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + rgbs, sigmas = radiance_field(positions, t_dirs) + return rgbs, sigmas.squeeze(-1) + + + for i in range(0, n_rays, chunk): + chunk_rays = namedtuple_map(lambda r: r[i : i + chunk], rays) + + def sigma_fn(t_starts, t_ends, ray_indices): + t_origins = chunk_rays.origins[ray_indices] + t_dirs = chunk_rays.viewdirs[ray_indices] + positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + sigmas = radiance_field.query_density(positions) + return sigmas.squeeze(-1) + + ray_indices, t_starts, t_ends = occupancy_grid.sampling( + chunk_rays.origins, + chunk_rays.viewdirs, + sigma_fn=sigma_fn, + near_plane=near_plane, + far_plane=far_plane, + render_step_size=render_step_size, + stratified=radiance_field.training, + cone_angle=cone_angle, + alpha_thre=alpha_thre, + ) + rgb, opacity, depth, depth_variance, comp_weights, raw_rgbs = rendering_transient_single_path( + t_starts=t_starts, + t_ends=t_ends, + ray_indices=ray_indices, + n_rays=n_rays, + # radiance field + rgb_sigma_fn=rgb_sigma_fn, + # rendering options + render_bkgd=None, + args = args + ) + + chunk_results_single = [rgb, opacity, depth, depth_variance, comp_weights, raw_rgbs, len(t_starts)] + results.append(chunk_results_single) + + colors_single, opacities_single, depths_single, depths_variance, densities, raw_rgbs, n_rendering_samples = [ + torch.cat(r, dim=0) if isinstance(r[0], torch.Tensor) else r + for r in zip(*results) + ] + + normals_loss = 0 + + colors = torch.reshape(colors_single, (-1, args.n_bins, 3)) + + return {'colors': colors.view((*rays_shape[:-1], -1)), + 'opacities': opacities_single.view((*rays_shape[:-1], -1)), + 'depths': depths_single.view((*rays_shape[:-1], -1)), + 'depths_variance' : depths_variance.view((*rays_shape[:-1], -1)), + 'n_rendering_samples': sum(n_rendering_samples), + 'normals_loss': normals_loss, + 'comp_weights': comp_weights, + "raw_rgbs":raw_rgbs} + + +def parse_list(arg): + try: + return ast.literal_eval(arg) + except (SyntaxError, ValueError): + raise configargparse.ArgumentTypeError(f"Invalid list format: {arg}") + +def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise configargparse.ArgumentTypeError('Boolean value expected.') + + +def load_args(eval = False, parser= None): + # parser = configargparse.ArgumentParser() + if not eval: + parser = configargparse.ArgumentParser() + + has_test_config = ( + eval + and parser is not None + and hasattr(parser, "_option_string_actions") + and "--test_config" in parser._option_string_actions + ) + my_config_default = None if has_test_config else "./configs/train/simulated/bench_two_views.ini" + parser.add('-c', '--my-config', + is_config_file=True, + default=my_config_default, + help='Path to config file.' + ) + parser.add_argument( + '--exp_name', + type=str, + default='lego_two_views', + help='Experiment name.' + ) + parser.add_argument( + "--aabb", + nargs='+', + type = lambda s: ast.literal_eval(s), + default="[-1.5,-1.5,-1.5,1.5,1.5, 1.5]", + help="AABB size.", + ) + parser.add_argument( + "--test_chunk_size", + type=int, + default=512, + help="Test chunk size..", + ) + parser.add_argument( + "--num_rays_per_batch", + type=int, + default=512, + help="Number of rays per batch.", + ) + parser.add_argument( + "--starting_rays_per_pixel", + type=int, + default=1, + help="Starting rays per pixels.", + ) + parser.add_argument( + "--tfilter_sigma", + type=int, + default=3, + help="Temporal filter standard deviation.", + ) + parser.add_argument( + "--space_carving", + type=float, + default=7*1e-3, + help="Space carvig regaularization strength.", + ) + # parser.add_argument( + # "--dataset_scale", + # type=int, + # default=46, + # help="Scale for all transient images.", + # ) + parser.add_argument( + "--rfilter_sigma", + type=float, + default=0.15, + help="Spatial filter standard deviation.", + ) + parser.add_argument( + "--exposure_time", + type=float, + default=0.01, + help="Exposure length per bin in meters.", + ) + parser.add_argument( + "--lr", + type=float, + default=1e-3, + help="Learning rate.", + ) + parser.add_argument( + "--steps_til_checkpoint", + type=int, + default=20000, + help="Steps per checkpoint.", + ) + parser.add_argument( + "--n_bins", + type=int, + default=1200, + help="Number of bins.", + ) + parser.add_argument( + "--img_shape", + type=int, + default=512, + help="Shape of training image.", + ) + parser.add_argument( + "--sample_as_per_distribution", + action="store_true", + help="Sample as per distribution or uniformly.", + ) + parser.add_argument( + "--render_n_samples", + type=int, + default=4096, + help="Num samples per ray.", + ) + parser.add_argument( + "--exp", + type=str2bool, + default="true", + help="Use double exp.", + ) + parser.add_argument( + "--max_steps", + type=int, + default=300000, + help="Max number of steps.", + ) + parser.add_argument( + "--near_plane", + type=float, + default=0.0, + help="Near plane value.", + ) + parser.add_argument( + "--alpha_thre", + type=float, + default=0, + ) + parser.add_argument( + "--far_plane", + type=float, + default=float(2**15), + help="Far plane value.", + ) + parser.add_argument( + "--version", + type=str, + default="simulated", + choices=["captured", "simulated"], + help="Dataset being trained, captured or simulated.", + ) + parser.add_argument( + "--occ_thre", + type=float, + default=0.01, + help="Occupancy threshold", + ) + parser.add_argument( + "--thold_warmup", + type=int, + default=-1, + help="Warmup period for the occupancy threshold.", + ) + parser.add_argument( + "--final", + type=str2bool, + default="false", + help="If final version or debug mode (creates dated folder).", + ) + parser.add_argument( + "--grid_resolution", + type=int, + default=128, + help="Occgrid resolution.", + ) + parser.add_argument( + "--grid_nlvl", + type=int, + default=1, + help="Number of grid levels.", + ) + parser.add_argument( + "--outpath", + type=str, + default="./results", + help="Path to results folder.", + ) + parser.add_argument( + "--data_root_fp", + type=str, + default="./data/lego_data/lego_jsons/two_views", + help="Root of dataset directory (where the transforms directory is).", + ) + parser.add_argument( + "--pulse_path", + type=str, + default="./datasets/pulse_low_flux.mat", + help="Path to pulse for captured dataset.", + ) + parser.add_argument( + "--intrinsics", + type=str, + default="./data/lego_data/lego_jsons/two_views", + help="Path to intrinsics for captured dataset", + ) + parser.add_argument( + "--pixels_to_plot", + nargs='+', + type = lambda s: ast.literal_eval(s), + default=[(16, 16), (20, 16), (28, 25)], + help="Pixels used for plotting in the summary.", + ) + parser.add_argument( + "--img_scale", + type=int, + default=100, + help="Image scale used in summary.", + ) + parser.add_argument( + "--num_views", + type=int, + default=2, + help="Number of views trained on.", + ) + parser.add_argument( + "--img_shape_test", + type=int, + default=64, + help="Test image shape.", + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="Seed.", + ) + parser.add_argument( + "--device", + type=str, + default="cuda:7", + help="Device.", + ) + parser.add_argument("--cone_angle", type=float, default=0.0) + parser.add_argument( + "--resume", + type=str, + default=None, + help="Path to a checkpoint directory to resume training from.", + ) + args = parser.parse_args() + return args + +def make_save_folder(args): + now = datetime.now() + now = now.strftime("%m-%d_%H:%M:%S") + exp_name = args.exp_name + "_" + now + outpath = os.path.join(args.outpath, exp_name) + os.makedirs(args.outpath, exist_ok=True) + os.mkdir(outpath) + shutil.copy(args.my_config, os.path.join(outpath, "params.txt")) + + with open(os.path.join(outpath, "params_full.txt"), "w") as out_file: + param_list = [] + for key, value in vars(args).items(): + if type(value) == list: + value = [eval(f"{x}") for x in value] + elif type(value) != int and type(value) != float: + value = str(value) + value = f"'{value}'" + param_list.append("%s= %s" % (key, value)) + + out_file.write('\n'.join(param_list)) + return outpath + +def make_save_folder_final(args, optimizer, scheduler, radiance_field, occupancy_grid): + outpath = os.path.join(args.outpath, args.exp_name) + + if not os.path.isdir(outpath): + + os.makedirs(outpath, exist_ok=True) + with open(os.path.join(outpath, "params_full.txt"), "w") as out_file: + param_list = [] + for key, value in vars(args).items(): + if type(value) != int and type(value) != float: + value = str(value) + value = f"'{value}'" + param_list.append("%s= %s" % (key, value)) + + out_file.write('\n'.join(param_list)) + step = 0 + writer = SummaryWriter(log_dir=outpath) + + else: + ckpt_path_var = os.path.join(outpath, 'variables.pth') + if not os.path.isfile(ckpt_path_var): + print(f"warning: '{ckpt_path_var}' not found; starting fresh in existing directory.") + step = 0 + writer = SummaryWriter(log_dir=outpath) + return writer, step, outpath + + ckpt = torch.load(ckpt_path_var, map_location="cpu") + step = int(ckpt.get('step', 0)) + + ckpt_path_rf = os.path.join(outpath, 'radiance_field_%04d.pth' % (step)) + ckpt_path_oc = os.path.join(outpath, 'occupancy_grid_%04d.pth' % (step)) + ckpt_path_opt = os.path.join(outpath, 'optimizer_%04d.pth' % (step)) + ckpt_path_sch = os.path.join(outpath, 'scheduler_%04d.pth' % (step)) + + if not (os.path.isfile(ckpt_path_rf) and os.path.isfile(ckpt_path_oc)): + print( + "warning: model checkpoint files missing for saved step; " + "starting fresh optimizer/model state." + ) + step = 0 + writer = SummaryWriter(log_dir=outpath) + return writer, step, outpath + + ckpt = torch.load(ckpt_path_rf, map_location=args.device) + radiance_field.load_state_dict(ckpt) + radiance_field = radiance_field.to(args.device) + + ckpt = torch.load(ckpt_path_oc, map_location=args.device) + occupancy_grid.load_state_dict(ckpt) + occupancy_grid = occupancy_grid.to(args.device) + + if os.path.isfile(ckpt_path_opt): + ckpt = torch.load(ckpt_path_opt, map_location=args.device) + optimizer.load_state_dict(ckpt) + else: + print(f"warning: optimizer checkpoint missing at '{ckpt_path_opt}', using fresh optimizer state.") + + if os.path.isfile(ckpt_path_sch): + ckpt = torch.load(ckpt_path_sch, map_location=args.device) + scheduler.load_state_dict(ckpt) + else: + print(f"warning: scheduler checkpoint missing at '{ckpt_path_sch}', using fresh scheduler state.") + + print(f"previous checkpoint loaded; current step: {step}") + writer = SummaryWriter(log_dir=outpath) + + return writer, step, outpath + + +def resume_training(args, optimizer, scheduler, radiance_field, occupancy_grid): + """Load a previous checkpoint and prepare writer/step/outpath.""" + ckpt_dir = args.resume + if ckpt_dir is None: + raise ValueError("args.resume is None, cannot resume.") + if not os.path.isdir(ckpt_dir): + raise FileNotFoundError(f"Checkpoint directory not found: {ckpt_dir}") + + variables_path = os.path.join(ckpt_dir, "variables.pth") + step = 0 + rays_per_pixel = None + if os.path.isfile(variables_path): + ckpt_vars = torch.load(variables_path, map_location="cpu") + step = ckpt_vars.get("step", 0) + rays_per_pixel = ckpt_vars.get("rays_per_pixel") + else: + ckpt_steps = [] + for name in os.listdir(ckpt_dir): + if name.startswith("radiance_field_") and name.endswith(".pth"): + try: + ckpt_steps.append(int(name.split("_")[-1].split(".")[0])) + except ValueError: + continue + if not ckpt_steps: + raise FileNotFoundError( + "No checkpoint files found to resume from in " + f"{ckpt_dir}. Expected radiance_field_XXXX.pth." + ) + step = max(ckpt_steps) + + rf_path = os.path.join(ckpt_dir, f"radiance_field_{step:04d}.pth") + oc_path = os.path.join(ckpt_dir, f"occupancy_grid_{step:04d}.pth") + opt_path = os.path.join(ckpt_dir, f"optimizer_{step:04d}.pth") + sch_path = os.path.join(ckpt_dir, f"scheduler_{step:04d}.pth") + + for required_path in [rf_path, oc_path]: + if not os.path.isfile(required_path): + raise FileNotFoundError(f"Missing checkpoint file: {required_path}") + + radiance_field.load_state_dict( + torch.load(rf_path, map_location=args.device) + ) + radiance_field = radiance_field.to(args.device) + + occupancy_grid.load_state_dict( + torch.load(oc_path, map_location=args.device) + ) + occupancy_grid = occupancy_grid.to(args.device) + + if os.path.isfile(opt_path): + optimizer.load_state_dict(torch.load(opt_path, map_location=args.device)) + else: + print(f"warning: missing optimizer checkpoint '{opt_path}', using fresh optimizer state.") + + if os.path.isfile(sch_path): + scheduler.load_state_dict(torch.load(sch_path, map_location=args.device)) + else: + print(f"warning: missing scheduler checkpoint '{sch_path}', using fresh scheduler state.") + + writer = SummaryWriter(log_dir=ckpt_dir) + args.outpath = ckpt_dir + return writer, step, ckpt_dir, rays_per_pixel + +if __name__=="__main__": + pass diff --git a/codes/simulator/generate_data_sim.py b/codes/simulator/generate_data_sim.py new file mode 100644 index 0000000000000000000000000000000000000000..5a0f05b33afa85960d188ddd52eafd32eb70ba5f --- /dev/null +++ b/codes/simulator/generate_data_sim.py @@ -0,0 +1,402 @@ +# 处理data文件夹数据生成仿真数据的脚本 +import include.simsp as simsp +import numpy as np +import cv2 +import argparse +import os +import torch +from tqdm import tqdm +import re +from pathlib import Path +from concurrent.futures import ThreadPoolExecutor, as_completed +import threading +import time + + +def get_matching_files(images_dir, depth_dir): + """ + 获取匹配的图像和深度文件对 + + Args: + images_dir (str): 图像文件夹路径 + depth_dir (str): 深度图文件夹路径 + + Returns: + list: 匹配的文件对列表 [(image_path, depth_path, base_name), ...] + """ + # 检查文件夹是否存在 + if not os.path.exists(images_dir): + raise FileNotFoundError(f"图像文件夹不存在: {images_dir}") + if not os.path.exists(depth_dir): + raise FileNotFoundError(f"深度文件夹不存在: {depth_dir}") + + # 获取所有图像文件 + image_files = [] + for ext in ['*.jpg', '*.jpeg', '*.JPG', '*.JPEG']: + image_files.extend(Path(images_dir).glob(ext)) + + # 获取所有深度文件 + depth_files = [] + for ext in ['*.png', '*.PNG']: + depth_files.extend(Path(depth_dir).glob(ext)) + + # 创建文件名到路径的映射 + image_dict = {} + depth_dict = {} + + for img_path in image_files: + # 提取基础文件名（不含扩展名） + base_name = img_path.stem + image_dict[base_name] = str(img_path) + + for depth_path in depth_files: + # 提取基础文件名（不含扩展名） + base_name = depth_path.stem + depth_dict[base_name] = str(depth_path) + + # 找到匹配的文件对 + matched_pairs = [] + for base_name in image_dict.keys(): + if base_name in depth_dict: + matched_pairs.append(( + image_dict[base_name], + depth_dict[base_name], + base_name + )) + + print(f"找到 {len(matched_pairs)} 对匹配的文件") + return matched_pairs + + +def png_to_npy_depth(png_path, output_path=None): + """ + 将PNG深度图转换为numpy数组格式 + + Args: + png_path (str): PNG深度图路径 + output_path (str): 输出npy文件路径（可选） + + Returns: + np.ndarray: 深度数组 + """ + # 读取PNG深度图 + depth_img = cv2.imread(png_path, cv2.IMREAD_UNCHANGED) + + if depth_img is None: + raise ValueError(f"无法读取深度图: {png_path}") + + # 如果是3通道图像，转换为单通道 + if len(depth_img.shape) == 3: + depth_img = cv2.cvtColor(depth_img, cv2.COLOR_BGR2GRAY) + + # 转换为浮点数并归一化 + depth_array = depth_img.astype(np.float64) + + # 根据深度图的编码方式进行处理 + # 假设PNG深度图使用16位编码，需要转换为实际深度值（米） + # 这里可能需要根据具体的深度图编码方式调整 + if depth_array.max() > 1.0: + # 如果最大值大于1，假设是16位编码，转换为0-1范围 + depth_array = depth_array / 65535.0 + + # 转换为实际深度值（假设最大深度为15米） + max_depth = 15.0 + depth_array = depth_array * max_depth + + # 保存为npy文件（如果指定了输出路径） + if output_path: + os.makedirs(os.path.dirname(output_path), exist_ok=True) + np.save(output_path, depth_array) + + return depth_array + + +def generate_sim_data_from_png(image_path, depth_png_path, SBR=0.2, meanSigDetect=4, save_path=None): + """ + 从PNG深度图和JPG图像生成仿真数据 + + Args: + image_path (str): RGB图像文件路径 + depth_png_path (str): PNG深度图文件路径 + SBR (float): 信号背景比 (默认0.2) + meanSigDetect (int): 每像素平均信号光子数 (推荐值2/3/4) + save_path (str): 数据保存路径 (.npz文件) + + Returns: + tuple: (Z_true, depth_ssp, tBinMax, binDuration) + """ + # 转换PNG深度图为numpy数组 + Z_true = png_to_npy_depth(depth_png_path) + + # 读取并处理RGB图像 + rgb_img = cv2.imread(image_path) + if rgb_img is None: + raise ValueError(f"无法读取图像: {image_path}") + + Alpha_true = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY).astype(np.float64) + + # 确保两个图像尺寸一致 + if Z_true.shape != Alpha_true.shape: + # 将Alpha_true调整为与Z_true相同的尺寸 + Alpha_true = cv2.resize(Alpha_true, (Z_true.shape[1], Z_true.shape[0]), + interpolation=cv2.INTER_AREA) + + # 动态计算参数 + zMax = int(np.max(Z_true) * 1.5) if np.max(Z_true) > 0 else 15 + binDuration = zMax / 2e11 + + # 计算目标尺寸（保持长宽比，缩放到64像素） + target_size = 64 + height, width = Z_true.shape + scale = target_size / min(height, width) + new_width = int(width * scale) + new_height = int(height * scale) + + # 缩放图像 + Z_true = cv2.resize(Z_true, (new_width, new_height), interpolation=cv2.INTER_AREA) + Alpha_true = cv2.resize(Alpha_true, (new_width, new_height), interpolation=cv2.INTER_AREA) + + # 生成仿真数据 + tBinMax, sigDetect, spad = simsp.generate_simdata( + Z_true, Alpha_true, SBR, meanSigDetect, save_path, zMax, binDuration + ) + + # 使用SSP算法进行深度估计 + from include.singlephoton import SinglePhotonImaging + lr, lc = Z_true.shape + sp = SinglePhotonImaging(lr, lc, binDuration) + # depth_ssp = sp.ssp(spad) + + # 保存结果 + if save_path: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + dic_data = { + # "depth_ssp": depth_ssp, + "Alpha_true": Alpha_true, + "Z_true": Z_true, + "tBinMax": tBinMax, + "binDuration": binDuration, + "spad_data": spad.data, + "spad_indices": spad.indices, + "spad_indptr": spad.indptr, + "spad_shape": spad.shape, + "sigDetect_data": sigDetect, + } + np.savez_compressed(save_path, **dic_data) + + # return Z_true, depth_ssp, tBinMax, binDuration + return Z_true, Alpha_true, tBinMax, binDuration + + +def process_single_file(args_tuple): + """ + 处理单个文件的线程安全函数 + + Args: + args_tuple: (image_path, depth_path, base_name, output_dir, sbr, meanSigDetect, thread_id) + + Returns: + tuple: (success, base_name, error_msg) + """ + image_path, depth_path, base_name, output_dir, sbr, meanSigDetect, thread_id = args_tuple + + try: + output_path = os.path.join(output_dir, f"{base_name}.npz") + + # 跳过已存在的文件 + if os.path.exists(output_path): + return True, base_name, f"文件已存在，跳过: {base_name}" + + # 生成仿真数据 + generate_sim_data_from_png( + image_path, depth_path, + sbr, meanSigDetect, + output_path + ) + + return True, base_name, None + + except Exception as e: + # 清理GPU内存（如果使用了GPU） + if torch.cuda.is_available(): + torch.cuda.empty_cache() + return False, base_name, str(e) + + +def process_files_multithread(matched_files, output_dir, sbr, meanSigDetect, max_workers=4): + """ + 多线程处理文件 + + Args: + matched_files: 匹配的文件对列表 + output_dir: 输出目录 + sbr: 信噪比 + meanSigDetect: 平均检测信号强度 + max_workers: 最大线程数 + + Returns: + tuple: (success_count, error_count, error_files) + """ + success_count = 0 + error_count = 0 + error_files = [] + + # 准备任务参数 + tasks = [] + for i, (image_path, depth_path, base_name) in enumerate(matched_files): + task_args = (image_path, depth_path, base_name, output_dir, sbr, meanSigDetect, i) + tasks.append(task_args) + + # 使用线程池执行任务 + with ThreadPoolExecutor(max_workers=max_workers) as executor: + # 提交所有任务 + future_to_task = {executor.submit(process_single_file, task): task for task in tasks} + + # 使用tqdm显示进度 + with tqdm(total=len(tasks), desc="多线程处理进度") as pbar: + for future in as_completed(future_to_task): + task = future_to_task[future] + try: + success, base_name, error_msg = future.result() + if success: + success_count += 1 + if error_msg: # 跳过的文件 + pbar.set_postfix_str(f"跳过: {base_name}") + else: + error_count += 1 + error_files.append((base_name, error_msg)) + pbar.set_postfix_str(f"错误: {base_name}") + print(f"\n处理 {base_name} 时发生错误: {error_msg}") + + except Exception as e: + error_count += 1 + base_name = task[2] + error_files.append((base_name, str(e))) + pbar.set_postfix_str(f"异常: {base_name}") + print(f"\n处理 {base_name} 时发生异常: {str(e)}") + + pbar.update(1) + + return success_count, error_count, error_files + + + +def main(): + parser = argparse.ArgumentParser(description='处理data文件夹生成仿真数据') + parser.add_argument('--sbr', type=float, default=0.5, + help='信噪比 (默认: 0.5)') + parser.add_argument('--meanSigDetect', type=float, default=2, + help='平均检测信号强度 (默认: 2)') + parser.add_argument('--data_dir', type=str, default='./data', + help='数据文件夹路径 (默认: ./data)') + parser.add_argument('--output_dir', type=str, default='./output_data', + help='输出文件夹路径 (默认: ./output_data)') + parser.add_argument('--max_files', type=int, default=None, + help='最大处理文件数量 (默认: 处理所有文件)') + parser.add_argument('--threads', type=int, default=16, + help='线程数量 (默认: 4)') + parser.add_argument('--single_thread', action='store_true', + help='使用单线程模式 (默认: 使用多线程)') + + args = parser.parse_args() + + # 设置路径 + images_dir = os.path.join(args.data_dir, 'images') + depth_dir = os.path.join(args.data_dir, 'depthpng') + output_dir = os.path.join(args.output_dir, f"{args.sbr}_{args.meanSigDetect}") + + print(f"图像目录: {images_dir}") + print(f"深度目录: {depth_dir}") + print(f"输出目录: {output_dir}") + + # 验证线程数设置 + if args.threads < 1: + print("错误: 线程数必须大于0") + return + + # 显示处理模式 + if args.single_thread: + print("使用单线程模式") + max_workers = 1 + else: + max_workers = args.threads + print(f"使用多线程模式，线程数: {max_workers}") + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + # 获取匹配的文件对 + try: + matched_files = get_matching_files(images_dir, depth_dir) + except FileNotFoundError as e: + print(f"错误: {e}") + return + + if not matched_files: + print("未找到匹配的文件对") + return + + # 限制处理文件数量 + if args.max_files: + matched_files = matched_files[:args.max_files] + print(f"限制处理文件数量为: {args.max_files}") + + # 记录开始时间 + start_time = time.time() + + # 选择处理模式 + if args.single_thread or max_workers == 1: + # 单线程模式（原始逻辑） + success_count = 0 + error_count = 0 + error_files = [] + + for image_path, depth_path, base_name in tqdm(matched_files, desc="单线程处理进度"): + output_path = os.path.join(output_dir, f"{base_name}.npz") + + # 跳过已存在的文件 + if os.path.exists(output_path): + print(f"跳过已存在的文件: {base_name}") + continue + + try: + generate_sim_data_from_png( + image_path, depth_path, + args.sbr, args.meanSigDetect, + output_path + ) + success_count += 1 + + except Exception as e: + print(f"处理 {base_name} 时发生错误: {str(e)}") + error_count += 1 + error_files.append((base_name, str(e))) + torch.cuda.empty_cache() # 清理GPU内存 + else: + # 多线程模式 + success_count, error_count, error_files = process_files_multithread( + matched_files, output_dir, args.sbr, args.meanSigDetect, max_workers + ) + + # 计算处理时间 + end_time = time.time() + total_time = end_time - start_time + + print(f"\n处理完成!") + print(f"成功处理: {success_count} 个文件") + print(f"处理失败: {error_count} 个文件") + print(f"总处理时间: {total_time:.2f} 秒") + print(f"平均每文件: {total_time/len(matched_files):.2f} 秒") + print(f"输出目录: {output_dir}") + + # 显示错误文件详情 + if error_files: + print(f"\n错误文件详情:") + for base_name, error_msg in error_files[:10]: # 只显示前10个错误 + print(f" {base_name}: {error_msg}") + if len(error_files) > 10: + print(f" ... 还有 {len(error_files) - 10} 个错误文件") + + +if __name__ == "__main__": + main() diff --git a/codes/simulator/include/simsp.py b/codes/simulator/include/simsp.py new file mode 100644 index 0000000000000000000000000000000000000000..3ab7c227c5c9c830c1b93439919e9c5e90bf344c --- /dev/null +++ b/codes/simulator/include/simsp.py @@ -0,0 +1,324 @@ +import numpy as np +import cv2 +import os +from scipy.sparse import csc_matrix +from include.singlephoton import SinglePhotonImaging + + +# import os +def fcn_PoissonRV(lambda_, ni=None, nj=None): + """ + 生成泊松分布随机数矩阵 (向量化实现) + + 参数: + lambda_ (float/array): 泊松分布参数λ（标量或二维数组） + ni (int): 输出矩阵的行数（当lambda为标量时必填） + nj (int): 输出矩阵的列数（当lambda为标量时必填） + + 返回: + np.ndarray: 生成的泊松随机数矩阵[ni, nj] + + 异常: + ValueError: 输入参数不符合要求时抛出 + """ + # ========================= 参数验证 ========================= + # 情况1：未指定ni/nj时，lambda必须是二维数组 + if ni is None and nj is None: + if not isinstance(lambda_, np.ndarray) or lambda_.ndim != 2: + raise ValueError("当未指定ni/nj时，lambda必须是二维数组!") + ni, nj = lambda_.shape + + # 情况2：指定了ni/nj + elif ni is not None and nj is not None: + # 如果lambda是标量，扩展为指定大小的矩阵 + if np.isscalar(lambda_): + lambda_ = np.full((ni, nj), lambda_) + # 如果lambda是数组，验证形状是否匹配 + elif isinstance(lambda_, np.ndarray) and lambda_.shape != (ni, nj): + raise ValueError(f"lambda形状{lambda_.shape}与({ni},{nj})不匹配!") + + # 情况3：参数不完整 + else: + raise ValueError("必须同时指定ni和nj，或都不指定!") + + # ========================= 初始化 ========================= + ks1 = np.zeros((ni, nj), dtype=int) # 结果矩阵 + ks2 = np.ones((ni, nj), dtype=int) # 比较矩阵 + produ = np.ones((ni, nj)) # 乘积累加器 + + # ========================= 主循环 ========================= + while np.any(ks1 != ks2): + # 更新乘积（向量化操作） + produ *= np.random.rand(ni, nj) + + # 保存当前状态 + ks2 = ks1.copy() + + # 找出需要增加计数的位置（向量化掩码操作） + mask = produ >= np.exp(-lambda_) + ks1[mask] += 1 + + return ks1 + + +def tof2spad(tof, binnum): + h, w = tof.shape + + # 将 data_processed 重塑为列向量 + tof = tof.T + data = tof.reshape(h * w) + + # 创建一个大小为 h * w 行，10000 列的零矩阵 + spad = np.zeros((h * w, int(binnum)), dtype=float) + + # 遍历 data + for i in range(h * w): + d = data[i] + for j in range(len(d)): + if int(d[j] - 1) < binnum: + spad[i, int(d[j] - 1)] += 1 + + spad = csc_matrix(spad) + return spad + + +def generate_simdata( + Z_true, + Alpha_true, + SBR=0.2, + meanSigDetect=2, + save_path=None, + zMax=15, + binDuration=8e-12, +): + """ + 生成单光子数据集仿真数据（Python版本） + + 参数: + Z_true (array): 深度真值 + Alpha_true (array): 反射率真值 + SBR (float): 信号背景比 (默认0.2) + meanSigDetect (int): 每像素平均信号光子数 (推荐值2/3/4) + save_path (str): 数据保存路径 (.mat文件) + + 返回: + tuple: (tBinMax, binDuration, sigDetect, totDetect) + tBinMax (int): 最大时间bin数 + binDuration (float): 单个时间bin持续时间 (秒) + sigDetect (np.ndarray): 信号光子 + totDetect (np.ndarray): 检测事件对象数组 [H,W] (每个元素包含时间bin序列) + """ + # 第一阶段: 物理参数配置 + # 场景参数 + # zMax = 15.0 # 最大探测距离(米) + # binDuration = 8e-12 # 单个时间bin持续时间(秒) + pulseRMS = 270 / 8 # 脉冲RMS宽度(时间bin数) + + # 计算衍生参数 + ttd = 3e8 * binDuration / 2 # 时间到距离转换因子 (米/bin) + tBinMax = int(round(zMax / ttd)) # 最大时间bin数 + pulseSTD = pulseRMS / 2 # 高斯脉冲标准差 + + # print("tBinMax:",tBinMax) + + # 第二阶段: 信号光子生成 + # 计算时间维度真实值 + T_true = np.floor(Z_true / ttd).astype(int) + + # 计算总帧数 + numFrames = int(meanSigDetect * 500) + + # 标准化反射率参数 + Lr, Lc = Alpha_true.shape + Alpha_true = meanSigDetect * Alpha_true / np.mean(Alpha_true) / numFrames + + # 生成信号光子数（泊松分布） + numSigDetect = fcn_PoissonRV(numFrames * Alpha_true) + actual_mean = np.mean(numSigDetect) + sigDetect = np.empty((Lr, Lc), dtype=object) + sampMean = np.zeros((Lr, Lc)) + + # 遍历每个像素生成时间分布 + for i in range(Lr): + for j in range(Lc): + mu = T_true[i, j] + n = numSigDetect[i, j] + # 生成高斯分布时间偏移 + tempVect = np.round(mu + pulseSTD * np.random.randn(n)).astype(int) + sampMean[i, j] = np.mean(tempVect) if n > 0 else 0 + sigDetect[i, j] = tempVect + + # 第三阶段: 背景噪声生成 + # 计算背景光子率 + bgndRate = actual_mean / numFrames / SBR + + # 生成背景光子数 + numBgndDetect = fcn_PoissonRV(numFrames * bgndRate, Lr, Lc) + bgndDetect = np.empty((Lr, Lc), dtype=object) + + # 生成均匀分布时间 + for i in range(Lr): + for j in range(Lc): + n = numBgndDetect[i, j] + bgndDetect[i, j] = np.random.randint(0, tBinMax, n) + + # 第四阶段: 数据合并与后处理 + totDetect = np.empty((Lr, Lc), dtype=object) + for i in range(Lr): + for j in range(Lc): + combined = np.concatenate((sigDetect[i, j], bgndDetect[i, j])) + combined = np.sort(combined) + combined = combined[combined > 0] # 移除非法时间bin + totDetect[i, j] = combined + + spad = tof2spad(totDetect, tBinMax) + sigDetect = tof2spad(sigDetect, tBinMax) + + return tBinMax, sigDetect, spad + +# 补充泊松生成函数（原型实现） +def fcn_PoissonRV(lamda, *shape): + if not shape: + return np.random.poisson(lamda) + else: + return np.random.poisson(lamda, shape) + + +import cv2 +import numpy as np +import os +from scipy.io import savemat +from pathlib import Path + + + +def generate_sim_imageNet( + depth_path, rgb_path, SBR=0.2, meanSigDetect=4, save_path=None +): + """ + 生成cityscapes数据集仿真数据（Python版本） + + 参数: + depth_path (str): 深度图文件路径 (npz) + rgb_path (str): RGB图像文件路径 + SBR (float): 信号背景比 (默认0.2) + meanSigDetect (int): 每像素平均信号光子数 (推荐值2/3/4) + save_path (str): 数据保存路径 (.npz文件) + + 返回: + tuple: (Z_true, totDetect, tBinMax, binDuration) + Z_true (np.ndarray): 真实深度图矩阵 [H,W] (单位:米) + totDetect (np.ndarray): 检测事件对象数组 [H,W] (每个元素包含时间bin序列) + tBinMax (int): 最大时间bin数 + binDuration (float): 单个时间bin持续时间 (秒) + + PS: + 由于使用DepthAnything数据生成的深度图,是三通道的,所以在处理前需要先将其转换为单通道的 + 为了保证数据兼容性，所以统一提取第一个通道作为深度数据 + """ + # 第一阶段: 基础数据加载与参数初始化 + # 读取深度图并转换为米单位 + # 读取深度图的最大深度 + + # binDuration = 4e-10 + # zMax = 15 + # binDuration = zMax/1.5e11 + + # 读取深度数据 + Z_true = np.load(depth_path, allow_pickle=True).astype(np.float64) + if Z_true.ndim == 3: + Z_true = Z_true[:, :, 0] + + # 1. 避免除零和过小值 + threshold = 1e-10 + Z_affine_safe = np.clip(Z_true, threshold, None) + + # 2. 取倒数得到缩放深度 + Z_scaled = 1 / Z_affine_safe + max_value = np.max(Z_scaled) + min_value = np.min(Z_scaled) + + # # 3. 截断离群值（可选） + # lower, upper = np.percentile(Z_scaled, [1, 99]) + # Z_clipped = np.clip(Z_scaled, lower, upper) + + # 4. 归一化到[0,1] + Z_normalized = (Z_scaled - min_value) / (max_value - min_value) + + # 5. 反转方向：近处亮，远处暗 + Z_depth = (1 - Z_normalized)*max_value + + # zMax = np.max(Z_true) + zMax = int(np.max(Z_true)*1.5) + binDuration = zMax / 2e11 + + # 验证时间箱数量 + ttd = 3e8 * binDuration / 2 # 时间到距离转换因子 (米/bin) + tBinMax = int(round(zMax / ttd)) # 最大时间bin数 + print(f"tBinMax: {tBinMax}") + + # 计算深度范围 (必须在缩放前获取原始深度最大值) + # zMax = int(np.max(Z_true) + 1) + # zMax = 15 + # binDuration = 10e-11 + + + # 计算目标尺寸（保持长宽比） + target_size = 64 + height, width = Z_true.shape + scale = target_size / min(height, width) + new_width = int(width * scale) + new_height = int(height * scale) + + # 缩放深度图（使用INTER_AREA保持精度） + Z_true = cv2.resize( + Z_true, + (new_width, new_height), + interpolation=cv2.INTER_AREA + ) + + # 读取并处理RGB图像 + rgb_img = cv2.imread(rgb_path) + Alpha_true = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY).astype(np.float64) + + # 以相同比例缩放灰度图 + Alpha_true = cv2.resize( + Alpha_true, + (new_width, new_height), + interpolation=cv2.INTER_AREA + ) + + # # 输出最终尺寸信息 + # print(f"深度图最终尺寸: {Z_true.shape}") + # print(f"反射率图最终尺寸: {Alpha_true.shape}") + # # 动态计算zMax（忽略深度为0的无效点） + # valid_depths = Z_true[Z_true > 0] # 筛选有效深度点（>0） + # if valid_depths.size > 0: + # zMax = np.max(valid_depths) * 2 # 取最大值并增加10%余量 + # else: + # zMax = 15 # 默认值（无有效点时使用） + + tBinMax, sigDetect, spad = generate_simdata( + Z_true, Alpha_true, SBR, meanSigDetect, save_path, zMax, binDuration + ) + + lr, lc = Z_true.shape + + sp = SinglePhotonImaging(lr, lc, binDuration) + depth = sp.ssp(spad) + + if save_path: + # 创建保存目录（如果不存在） + os.makedirs(os.path.dirname(save_path), exist_ok=True) + dic_data = {} + dic_data["depth_ssp"] = depth + dic_data["Z_true"] = Z_true + dic_data["tBinMax"] = tBinMax + dic_data["binDuration"] = binDuration + dic_data["spad_data"] = spad.data + dic_data["spad_indices"] = spad.indices + dic_data["spad_indptr"] = spad.indptr + dic_data["spad_shape"] = spad.shape + dic_data["sigDetect_data"] = sigDetect + # scipy.io.savemat(save_path, dic_data) + np.savez_compressed(save_path, **dic_data) \ No newline at end of file diff --git a/codes/simulator/include/singlephoton.py b/codes/simulator/include/singlephoton.py new file mode 100644 index 0000000000000000000000000000000000000000..2478615a580bd14dace5bf9279e8e2448abf4c7f --- /dev/null +++ b/codes/simulator/include/singlephoton.py @@ -0,0 +1,195 @@ +import numpy as np +# import matlab.engine +from .SSP.ssp import get_ssp_depth + +c = 3e8 + + +class SinglePhotonImaging: + def __init__(self, lr: int, lc: int, tp: float): + """ + 初始化深度估计器 + + 参数: + lr: 图像行数 + lc: 图像列数 + tp: 时间bin持续时间 (秒) + """ + self.lr = lr + self.lc = lc + self.tp = tp + + def _spad2tof(self, spad: np.ndarray) -> np.ndarray: + """ + 将稀疏SPAD数据转换为时间飞行(TOF)对象数组 [内部方法] + + 参数: + spad: 稀疏SPAD数据矩阵 [N, M] + + 返回: + np.ndarray: TOF对象数组 [lr, lc]，每个元素为时间bin数组 + """ + rows, cols = spad.nonzero() + values = spad.data + tof = np.empty((self.lr, self.lc), dtype=np.ndarray) + + for i in range(self.lr * self.lc): + indices = np.where(rows == i)[0] + ph = [] + for j in indices: + ph += [int(cols[j])] * int(values[j]) + + ph = np.array([ph]).T + + x = i % self.lc + y = i // self.lc + + tof[x, y] = ph + + return tof + + def _time_to_depth(self, time_data: np.ndarray, time_bin: float) -> np.ndarray: + """统一时间到深度转换""" + return time_data * c / 2 * time_bin + + def max_hist(self, spads: np.ndarray) -> np.ndarray: + """ + 通过滑动窗口均值法获取深度图 + + 参数: + spads: 稀疏SPAD数据矩阵 [N, T] + + 返回: + np.ndarray: 深度图矩阵 [lc, lr] (单位:米) + """ + print("Max Hist Processing...") + + spads_dense = spads.todense() + max_histogram_mean = np.empty(self.lr * self.lc, dtype=float) + + for i in range(self.lr * self.lc): + data = np.ascontiguousarray(spads_dense[i, :]).ravel() + max_index = np.argmax(data) + max_histogram_mean[i] = max_index + + depth = self._time_to_depth( + max_histogram_mean.reshape(self.lr, self.lc).T, self.tp + ) + + return depth + + # def shin(self, spads: np.ndarray) -> np.ndarray: + # """ + # 使用Shin算法估计深度图 + + # 参数: + # spads: 稀疏SPAD数据矩阵 [N, T] + + # 返回: + # np.ndarray: 深度图矩阵 [lc, lr] (单位:米) + # """ + # print("Shin Processing...") + + # tofs = self._spad2tof(spads) + # eng = matlab.engine.start_matlab() + # eng.addpath(r"matlab\fcns_Shin") + # matlab_tofs = eng.eval("cell(1, 3136)", nargout=1) + # matlab_tofs = [tof for row in tofs for tof in row] + + # tof_shin = np.array(eng.cal_Shin(matlab_tofs)) + # eng.quit() + + # depth = self._time_to_depth(tof_shin, self.tp) + # return depth + + # def rapp( + # self, spads: np.ndarray, signal_per_pixel: float, frame_num: int + # ) -> np.ndarray: + # """ + # 使用Rapp算法估计深度图 + + # 参数: + # spads: 稀疏SPAD数据矩阵 [N, T] + # signal_per_pixel: 每像素平均信号光子数 + # frame_num: 总帧数 + + # 返回: + # np.ndarray: 深度图矩阵 [lc, lr] (单位:米) + # """ + # print("Rapp Processing...") + + # binnum = spads.shape[1] + # tofs = self._spad2tof(spads) + + # sbr = 0.2 + # perframenum = 50.0 + # numFrames = perframenum * frame_num + # eng = matlab.engine.start_matlab() + # eng.addpath(r"matlab\fcns_Rapp") + + # matlab_tofs = eng.eval("cell(1, 3136)", nargout=1) + # matlab_tofs = [tof for row in tofs for tof in row] + + # tof_rapp = np.array( + # eng.cal_Rapp_py( + # matlab_tofs, + # signal_per_pixel, + # numFrames, + # sbr, + # float(self.tp), + # float(binnum), + # ) + # ) + + # eng.quit() + + # depth = self._time_to_depth(tof_rapp, self.tp) + + # return depth + + # def li(self, spads: np.ndarray) -> np.ndarray: + # """ + # 使用Li算法估计深度图 + + # 参数: + # spads: 稀疏SPAD数据矩阵 [N, T] + + # 返回: + # np.ndarray: 深度图矩阵 [lc, lr] (单位:米) + # """ + # print("Li Processing...") + # eng = matlab.engine.start_matlab() + # eng.addpath(r"matlab\fcns_Li", nargout=0) + + # # 直接使用原始SPAD数据 + # spads = spads.todense() + # tof_li = np.array( + # eng.cal_Li_py( + # spads, + # float(self.tp), + # float(spads.shape[1]), + # nargout=1, + # ) + # ) + # eng.quit() + + # depth = self._time_to_depth(tof_li, self.tp) + + # return depth + + def ssp(self, spads: np.ndarray) -> np.ndarray: + """ + 使用SSP算法估计深度图 + + 参数: + spads: 稀疏SPAD数据矩阵 [N, T] + + 返回: + np.ndarray: 深度图矩阵 [lc, lr] (单位:米) + """ + # print("SSP Processing...") + tp = self.tp + tr = spads.shape[1] + depth = get_ssp_depth(spads, tr, tp, self.lr, self.lc) + + return depth diff --git a/config/IRF_global.csv b/config/IRF_global.csv new file mode 100644 index 0000000000000000000000000000000000000000..98067432534ed736950b862e0b2971363d709f61 --- /dev/null +++ b/config/IRF_global.csv @@ -0,0 +1,502 @@ +t_ps,irf,irf_std +-1.875000000000000000e+05,1.819323145871034091e-07,4.214091311919881012e-07 +-1.867500000000000000e+05,-1.826940935423844464e-07,4.235333670144294901e-07 +-1.860000000000000000e+05,1.834606152288399372e-07,4.256733987032263046e-07 +-1.852500000000000000e+05,-1.842319296036020694e-07,4.278294408733125881e-07 +-1.845000000000000000e+05,1.850080874263158474e-07,4.300017120311662354e-07 +-1.837500000000000000e+05,-1.857891399929540641e-07,4.321904347467294168e-07 +-1.830000000000000000e+05,1.865751394435072612e-07,4.343958356823572531e-07 +-1.822500000000000000e+05,-1.873661387451924912e-07,4.366181456724565939e-07 +-1.815000000000000000e+05,1.881621914381565835e-07,4.388575999005052100e-07 +-1.807500000000000000e+05,-1.889633520061575614e-07,4.411144379508588699e-07 +-1.800000000000000000e+05,1.897696755677601993e-07,4.433889039237238856e-07 +-1.792500000000000000e+05,-1.905812180631071055e-07,4.456812465165836608e-07 +-1.785000000000000000e+05,1.913980363311275486e-07,4.479917191808730519e-07 +-1.777500000000000000e+05,-1.922201879837601851e-07,4.503205802001291739e-07 +-1.770000000000000000e+05,1.930477314587019306e-07,4.526680928256253047e-07 +-1.762500000000000000e+05,-1.938807261125642460e-07,4.550345253902725170e-07 +-1.755000000000000000e+05,1.947192320586532523e-07,4.574201514259518994e-07 +-1.747500000000000000e+05,-1.955633104369168543e-07,4.598252498114476437e-07 +-1.740000000000000000e+05,1.964130231878297188e-07,4.622501048885871198e-07 +-1.732500000000000000e+05,-1.972684331913950873e-07,4.646950065883567763e-07 +-1.725000000000000000e+05,1.981296043660677420e-07,4.671602505844300913e-07 +-1.717500000000000000e+05,-1.989966015028602736e-07,4.696461384384753334e-07 +-1.710000000000000000e+05,1.998694902891130230e-07,4.721529777625374952e-07 +-1.702500000000000000e+05,-2.007483375482807709e-07,4.746810823034804372e-07 +-1.695000000000000000e+05,2.016332111786589561e-07,4.772307721900880507e-07 +-1.687500000000000000e+05,-2.025241798675633136e-07,4.798023740536956195e-07 +-1.680000000000000000e+05,2.034213135179677174e-07,4.823962211532658289e-07 +-1.672500000000000000e+05,-2.043246830825908605e-07,4.850126536359286880e-07 +-1.665000000000000000e+05,2.052343606413350250e-07,4.876520186391697026e-07 +-1.657500000000000000e+05,-2.061504194054944325e-07,4.903146704827894578e-07 +-1.650000000000000000e+05,2.070729335961655706e-07,4.930009709225801390e-07 +-1.642500000000000000e+05,-2.080019787249363887e-07,4.957112892586791509e-07 +-1.635000000000000000e+05,2.089376313694461778e-07,4.984460025831597073e-07 +-1.627500000000000000e+05,-2.098799694782862501e-07,5.012054959798833841e-07 +-1.620000000000000000e+05,2.108290721527773851e-07,5.039901627032710815e-07 +-1.612500000000000000e+05,-2.117850197047957896e-07,5.068004044805944720e-07 +-1.605000000000000000e+05,2.127478937661689907e-07,5.096366316486711297e-07 +-1.597500000000000000e+05,-2.137177773008665728e-07,5.124992634213293716e-07 +-1.590000000000000000e+05,2.146947546193256479e-07,5.153887281734521805e-07 +-1.582500000000000000e+05,-2.156789112978616008e-07,5.183054636130173250e-07 +-1.575000000000000000e+05,2.166703344205304057e-07,5.212499171001171790e-07 +-1.567500000000000000e+05,-2.176691124527160529e-07,5.242225458733002188e-07 +-1.560000000000000000e+05,2.186753353102281350e-07,5.272238173492092456e-07 +-1.552500000000000000e+05,-2.196890943557977772e-07,5.302542094189757898e-07 +-1.545000000000000000e+05,2.207104824791837937e-07,5.333142107089929683e-07 +-1.537500000000000000e+05,-2.217395941734620859e-07,5.364043208692602163e-07 +-1.530000000000000000e+05,2.227765254084057312e-07,5.395250509878748377e-07 +-1.522500000000000000e+05,-2.238213738534048909e-07,5.426769237810869272e-07 +-1.515000000000000000e+05,2.248742387797954940e-07,5.458604739685904478e-07 +-1.507500000000000000e+05,-2.259352211449498619e-07,5.490762487173995168e-07 +-1.500000000000000000e+05,2.270044235848069636e-07,5.523248079320495174e-07 +-1.492500000000000000e+05,-2.280819505356244815e-07,5.556067245328011523e-07 +-1.485000000000000000e+05,2.291679082657122651e-07,5.589225849722458387e-07 +-1.477500000000000000e+05,-2.302624047922869949e-07,5.622729896116927390e-07 +-1.470000000000000000e+05,2.313655499731301205e-07,5.656585530961704693e-07 +-1.462500000000000000e+05,-2.324774556637726647e-07,5.690799047899999229e-07 +-1.455000000000000000e+05,2.335982357042129524e-07,5.725376892036409594e-07 +-1.447500000000000000e+05,-2.347280058525172482e-07,5.760325665018159291e-07 +-1.440000000000000000e+05,2.358668839304280818e-07,5.795652129194353549e-07 +-1.432500000000000000e+05,-2.370149897898657092e-07,5.831363213072272287e-07 +-1.425000000000000000e+05,2.381724455793768586e-07,5.867466015940327704e-07 +-1.417500000000000000e+05,-2.393393754855221776e-07,5.903967813222645529e-07 +-1.410000000000000000e+05,2.405159059567259447e-07,5.940876062227003069e-07 +-1.402500000000000000e+05,-2.417021658269505032e-07,5.978198407659262513e-07 +-1.395000000000000000e+05,2.428982861321995695e-07,6.015942687330595287e-07 +-1.387500000000000000e+05,-2.441044003546085915e-07,6.054116938566760135e-07 +-1.380000000000000000e+05,2.453206445282694973e-07,6.092729404078373906e-07 +-1.372500000000000000e+05,-2.465471570154865725e-07,6.131788539314888659e-07 +-1.365000000000000000e+05,2.477840788462146958e-07,6.171303018522960935e-07 +-1.357500000000000000e+05,-2.490315537468804822e-07,6.211281742045369608e-07 +-1.350000000000000000e+05,2.502897279646912389e-07,6.251733844085607032e-07 +-1.342500000000000000e+05,-2.515587506306188641e-07,6.292668699547054930e-07 +-1.335000000000000000e+05,2.528387736968564696e-07,6.334095932773469738e-07 +-1.327500000000000000e+05,-2.541299519050729095e-07,6.376025425616161445e-07 +-1.320000000000000000e+05,2.554324430901320607e-07,6.418467325348520168e-07 +-1.312500000000000000e+05,-2.567464080688730556e-07,6.461432054501991432e-07 +-1.305000000000000000e+05,2.580720106775316684e-07,6.504930319653187493e-07 +-1.297500000000000000e+05,-2.594094180909101119e-07,6.548973121153349290e-07 +-1.290000000000000000e+05,2.607588006873418273e-07,6.593571763010312437e-07 +-1.282500000000000000e+05,-2.621203321326691895e-07,6.638737863590198884e-07 +-1.275000000000000000e+05,2.634941896168894774e-07,6.684483366669654216e-07 +-1.267500000000000000e+05,-2.648805538136539408e-07,6.730820551719655612e-07 +-1.260000000000000000e+05,2.662796090284094161e-07,6.777762047233273514e-07 +-1.252500000000000000e+05,-2.676915432609934255e-07,6.825320841930425678e-07 +-1.245000000000000000e+05,2.691165482616375604e-07,6.873510297403788974e-07 +-1.237500000000000000e+05,-2.705548197369096196e-07,6.922344162578747638e-07 +-1.230000000000000000e+05,2.720065573864036352e-07,6.971836586480419137e-07 +-1.222500000000000000e+05,-2.734719650819437216e-07,7.022002133001670423e-07 +-1.215000000000000000e+05,2.749512507764569363e-07,7.072855796783666442e-07 +-1.207500000000000000e+05,-2.764446268157350845e-07,7.124413017609780528e-07 +-1.200000000000000000e+05,2.779523100290861445e-07,7.176689698242487262e-07 +-1.192500000000000000e+05,-2.794745217806036363e-07,7.229702221035281579e-07 +-1.185000000000000000e+05,2.810114881362872282e-07,7.283467464565705932e-07 +-1.177500000000000000e+05,-2.825634399733987666e-07,7.338002825613886984e-07 +-1.170000000000000000e+05,2.841306131234465725e-07,7.393326235294495135e-07 +-1.162500000000000000e+05,-2.857132484960251526e-07,7.449456181140191543e-07 +-1.155000000000000000e+05,2.873115922784204720e-07,7.506411729114106474e-07 +-1.147500000000000000e+05,-2.889258960001119895e-07,7.564212544181693556e-07 +-1.140000000000000000e+05,2.905564167612327431e-07,7.622878914990189835e-07 +-1.132500000000000000e+05,-2.922034173265537964e-07,7.682431778461508014e-07 +-1.125000000000000000e+05,2.938671662851624961e-07,7.742892744371874733e-07 +-1.117500000000000000e+05,-2.955479383719064987e-07,7.804284122972117026e-07 +-1.110000000000000000e+05,2.972460143789228738e-07,7.866628952828277927e-07 +-1.102500000000000000e+05,-2.989616815924620999e-07,7.929951030237729237e-07 +-1.095000000000000000e+05,3.006952338817965079e-07,7.994274939856972342e-07 +-1.087500000000000000e+05,-3.024469717626204320e-07,8.059626087500858801e-07 +-1.080000000000000000e+05,3.042172028591984267e-07,8.126030733567917583e-07 +-1.072500000000000000e+05,-3.060062419264440807e-07,8.193516028386289271e-07 +-1.065000000000000000e+05,3.078144111645406644e-07,8.262110050378851672e-07 +-1.057500000000000000e+05,-3.096420403524672445e-07,8.331841844280713243e-07 +-1.050000000000000000e+05,3.114894672173955231e-07,8.402741462597292572e-07 +-1.042500000000000000e+05,-3.133570375338475906e-07,8.474840008776718433e-07 +-1.035000000000000000e+05,3.152451055253083919e-07,8.548169682617392029e-07 +-1.027500000000000000e+05,-3.171540340149592105e-07,8.622763827797197360e-07 +-1.020000000000000000e+05,3.190841947501181267e-07,8.698656982073526116e-07 +-1.012500000000000000e+05,-3.210359687047599531e-07,8.775884930060029736e-07 +-1.005000000000000000e+05,3.230097464141073772e-07,8.854484758469175436e-07 +-9.975000000000000000e+04,-3.250059281197830804e-07,8.934494914997589181e-07 +-9.900000000000000000e+04,3.270249242883929015e-07,9.015955269478461171e-07 +-9.825000000000000000e+04,-3.290671560012152553e-07,9.098907178711175454e-07 +-9.750000000000000000e+04,3.311330549328430910e-07,9.183393554850060819e-07 +-9.675000000000000000e+04,-3.332230641830515087e-07,9.269458937585300776e-07 +-9.600000000000000000e+04,3.353376384145095408e-07,9.357149570024522011e-07 +-9.525000000000000000e+04,-3.374772442050058854e-07,9.446513478516252896e-07 +-9.450000000000000000e+04,3.396423607349809444e-07,9.537600558074012546e-07 +-9.375000000000000000e+04,-3.418334799373082414e-07,9.630462660875368766e-07 +-9.300000000000000000e+04,3.440511071434329652e-07,9.725153691474791947e-07 +-9.225000000000000000e+04,-3.462957614391610656e-07,9.821729706454701152e-07 +-9.150000000000000000e+04,3.485679762912413408e-07,9.920249019716602952e-07 +-9.075000000000000000e+04,-3.508683001158293653e-07,1.002077231455659678e-06 +-9.000000000000000000e+04,3.531972965406621869e-07,1.012336276223440213e-06 +-8.925000000000000000e+04,-3.555555453009652866e-07,1.022808614676459239e-06 +-8.850000000000000000e+04,3.579436427671752275e-07,1.033501099757409026e-06 +-8.775000000000000000e+04,-3.603622025288278940e-07,1.044420873106977847e-06 +-8.700000000000000000e+04,3.628118561066627433e-07,1.055575379909468645e-06 +-8.625000000000000000e+04,-3.652932535563438447e-07,1.066972384784550182e-06 +-8.550000000000000000e+04,3.678070644394717684e-07,1.078619988591576932e-06 +-8.475000000000000000e+04,-3.703539784370975436e-07,1.090526646267772388e-06 +-8.400000000000000000e+04,3.729347061826107374e-07,1.102701185886809831e-06 +-8.325000000000000000e+04,-3.755499801787335122e-07,1.115152828799861171e-06 +-8.250000000000000000e+04,3.782005556339518435e-07,1.127891211172305794e-06 +-8.175000000000000000e+04,-3.808872116589476525e-07,1.140926406789228268e-06 +-8.100000000000000000e+04,3.836107519048530075e-07,1.154268951514709555e-06 +-8.025000000000000000e+04,-3.863720059996371612e-07,1.167929869247693508e-06 +-7.950000000000000000e+04,3.891718305490498313e-07,1.181920699569273142e-06 +-7.875000000000000000e+04,-3.920111101902231429e-07,1.196253527422228948e-06 +-7.800000000000000000e+04,3.948907592613517096e-07,1.210941014546353868e-06 +-7.725000000000000000e+04,-3.978117226581453806e-07,1.225996433349851154e-06 +-7.650000000000000000e+04,4.007749777175142077e-07,1.241433702916324638e-06 +-7.575000000000000000e+04,-4.037815355266960347e-07,1.257267427580350465e-06 +-7.500000000000000000e+04,4.068324425037523726e-07,1.273512938282731435e-06 +-7.425000000000000000e+04,-4.099287822756708374e-07,1.290186336835064091e-06 +-7.350000000000000000e+04,4.130716773804820921e-07,1.307304543352094261e-06 +-7.275000000000000000e+04,-4.162622913832415348e-07,1.324885347184479444e-06 +-7.200000000000000000e+04,4.195018307828450936e-07,1.342947461590510071e-06 +-7.125000000000000000e+04,-4.227915473747941752e-07,1.361510582429721480e-06 +-7.050000000000000000e+04,4.261327406234245125e-07,1.380595451361678705e-06 +-6.975000000000000000e+04,-4.295267602447975593e-07,1.400223923778503976e-06 +-6.900000000000000000e+04,4.329750088173880118e-07,1.420419041958656139e-06 +-6.825000000000000000e+04,-4.364789448856241744e-07,1.441205114001312885e-06 +-6.750000000000000000e+04,4.400400860228012362e-07,1.462607798873460268e-06 +-6.675000000000000000e+04,-4.436600122612864047e-07,1.484654198227394708e-06 +-6.600000000000000000e+04,4.473403698561315065e-07,1.507372955670907272e-06 +-6.525000000000000000e+04,-4.510828750253286540e-07,1.530794364171004636e-06 +-6.450000000000000000e+04,4.548893183543722542e-07,1.554950482094992507e-06 +-6.375000000000000000e+04,-4.587615694112186541e-07,1.579875259167166654e-06 +-6.300000000000000000e+04,4.627015817158468122e-07,1.605604672993611507e-06 +-6.225000000000000000e+04,-4.667113981197715346e-07,1.632176877193254268e-06 +-6.150000000000000000e+04,4.707931567611778028e-07,1.659632362385534706e-06 +-6.075000000000000000e+04,-4.749490974840540850e-07,1.688014131336791618e-06 +-6.000000000000000000e+04,4.791815685994972599e-07,1.717367889765238732e-06 +-5.925000000000000000e+04,-4.834930347550095322e-07,1.747742254011921118e-06 +-5.850000000000000000e+04,4.878860852369404694e-07,1.779188977812111242e-06 +-5.775000000000000000e+04,-4.923634427862137466e-07,1.811763200242889536e-06 +-5.700000000000000000e+04,4.969279738926045863e-07,1.845523716583782947e-06 +-5.625000000000000000e+04,-5.015826996965517396e-07,1.880533274925122221e-06 +-5.550000000000000000e+04,5.063308078775753382e-07,1.916858901485622740e-06 +-5.475000000000000000e+04,-5.111756662701853025e-07,1.954572257698563258e-06 +-5.400000000000000000e+04,5.161208373874732207e-07,1.993750032492607131e-06 +-5.325000000000000000e+04,-5.211700946465938477e-07,2.034474374151011327e-06 +-5.250000000000000000e+04,5.263274406587236065e-07,2.076833366291224622e-06 +-5.175000000000000000e+04,-5.315971270733886051e-07,2.120921552884184376e-06 +-5.100000000000000000e+04,5.369836772705010096e-07,2.166840518742968139e-06 +-5.025000000000000000e+04,-5.424919108155506145e-07,2.214699531847980610e-06 +-4.950000000000000000e+04,5.481269719181246303e-07,2.264616255328924578e-06 +-4.875000000000000000e+04,-5.538943605833301822e-07,2.316717538051951996e-06 +-4.800000000000000000e+04,5.597999675213368240e-07,2.371140293629977783e-06 +-4.725000000000000000e+04,-5.658501140205040649e-07,2.428032479586592402e-06 +-4.650000000000000000e+04,5.720515969490340938e-07,2.487554189882780979e-06 +-4.575000000000000000e+04,-5.784117389647054715e-07,2.549878876288138969e-06 +-4.500000000000000000e+04,5.849384458505825640e-07,2.615194715958425785e-06 +-4.425000000000000000e+04,-5.916402725971553194e-07,2.683706145992111036e-06 +-4.350000000000000000e+04,5.985264970221509159e-07,2.755635588701935205e-06 +-4.275000000000000000e+04,-6.056072053471156440e-07,2.831225395106645259e-06 +-4.200000000000000000e+04,6.128933897638646244e-07,2.910740039131015257e-06 +-4.125000000000000000e+04,-6.203970603594520358e-07,2.994468600170571580e-06 +-4.050000000000000000e+04,6.281313741073043125e-07,3.082727578632021282e-06 +-3.975000000000000000e+04,-6.361107841306559784e-07,3.175864096358708864e-06 +-3.900000000000000000e+04,6.443512121801690681e-07,3.274259544036825531e-06 +-3.825000000000000000e+04,-6.528702483320816975e-07,3.378333748717928967e-06 +-3.750000000000000000e+04,6.616873842668378556e-07,3.488549748461328633e-06 +-3.675000000000000000e+04,-6.708242839548555758e-07,3.605419278477253596e-06 +-3.600000000000000000e+04,6.803050999272418235e-07,3.729509093587418501e-06 +-3.525000000000000000e+04,-6.901568429308409412e-07,3.861448277717165533e-06 +-3.450000000000000000e+04,7.004098131488468959e-07,4.001936723132557352e-06 +-3.375000000000000000e+04,-7.110981044837880155e-07,4.151755001764650026e-06 +-3.300000000000000000e+04,7.222601903937335888e-07,4.311775902025070473e-06 +-3.225000000000000000e+04,-7.339396032451521489e-07,4.482977968576925806e-06 +-3.150000000000000000e+04,7.461857115198169127e-07,4.666461466735937696e-06 +-3.075000000000000000e+04,-7.590545944992988168e-07,4.863467303636960918e-06 +-3.000000000000000000e+04,7.726099923199343549e-07,5.075399588598352888e-06 +-2.925000000000000000e+04,-7.869242699958570422e-07,5.303852724688838723e-06 +-2.850000000000000000e+04,8.020792494805911634e-07,5.550644230954116960e-06 +-2.775000000000000000e+04,-8.181665821029934516e-07,5.817854971086903092e-06 +-2.700000000000000000e+04,8.352869477117735912e-07,6.107879261620677417e-06 +-2.625000000000000000e+04,-8.535464823401833740e-07,6.423488803278266133e-06 +-2.550000000000000000e+04,8.730467281458153869e-07,6.767917410224010864e-06 +-2.475000000000000000e+04,-8.938589198253842487e-07,7.144980635953761157e-06 +-2.400000000000000000e+04,9.159576348303270731e-07,7.559263787739828878e-06 +-2.325000000000000000e+04,-9.390362789902473932e-07,8.016474590951471022e-06 +-2.250000000000000000e+04,9.619125334351668747e-07,8.524309992597595820e-06 +-2.175000000000000000e+04,-9.800386775054893029e-07,9.095590095297539766e-06 +-2.100000000000000000e+04,9.679344245818805260e-07,9.769118188468480055e-06 +-2.025000000000000000e+04,4.233304545598609571e-07,1.217521344715648266e-05 +-1.950000000000000000e+04,2.568676542469717355e-06,9.590286142081847336e-06 +-1.875000000000000000e+04,2.423223057518267314e-06,1.365441017523624791e-05 +-1.800000000000000000e+04,1.076164711111216825e-05,1.008734912248119948e-05 +-1.725000000000000000e+04,4.269024245790321425e-07,1.575979899727190174e-05 +-1.650000000000000000e+04,3.663766494005607971e-06,1.560728555692962900e-05 +-1.575000000000000000e+04,7.745186032432398591e-06,1.746064788901268149e-05 +-1.500000000000000000e+04,4.797427769580023862e-06,1.670461427419060361e-05 +-1.425000000000000000e+04,7.561019747322958271e-06,1.673342124646921554e-05 +-1.350000000000000000e+04,1.106485988413895607e-05,2.583793612241848758e-05 +-1.275000000000000000e+04,3.841469812444396425e-06,2.668506228200225467e-05 +-1.200000000000000000e+04,1.394562623648724366e-05,2.960518750781087184e-05 +-1.125000000000000000e+04,5.880472168641655949e-06,3.558093602970944543e-05 +-1.050000000000000000e+04,6.518682631768687632e-06,3.827420668048210904e-05 +-9.750000000000000000e+03,6.795181327688135807e-06,4.678598047897985314e-05 +-9.000000000000000000e+03,1.071858201609052087e-05,5.286770222864300598e-05 +-8.250000000000000000e+03,3.958846110568549565e-06,6.285667035295102823e-05 +-7.500000000000000000e+03,1.012893922161310319e-05,7.898886399772981628e-05 +-6.750000000000000000e+03,-2.736525725846347285e-06,1.044267835165250496e-04 +-6.000000000000000000e+03,2.026844767583023716e-05,1.286925758450507547e-04 +-5.250000000000000000e+03,-4.090164022135912753e-06,1.934764729439597813e-04 +-4.500000000000000000e+03,7.531118833023253733e-05,2.377795247749584613e-04 +-3.750000000000000000e+03,-6.718132182844633680e-07,4.973753853916034915e-04 +-3.000000000000000000e+03,3.339936134807379290e-04,5.920561768123449144e-04 +-2.250000000000000000e+03,-1.650726528613831944e-04,2.039855169014565938e-03 +-1.500000000000000000e+03,3.785271452626492051e-03,6.641094094924387091e-03 +-7.500000000000000000e+02,2.606670108416847542e-01,9.497090454109749502e-03 +0.000000000000000000e+00,4.808879930150953830e-01,1.834193617780075641e-02 +7.500000000000000000e+02,2.396120759109845455e-01,6.390091513564097152e-03 +1.500000000000000000e+03,1.496414346997052870e-02,1.103063328521809716e-02 +2.250000000000000000e+03,-2.872858295719874096e-04,4.454061992235510380e-04 +3.000000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +3.750000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +4.500000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +5.250000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +6.000000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +6.750000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +7.500000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +8.250000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +9.000000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +9.750000000000000000e+03,0.000000000000000000e+00,0.000000000000000000e+00 +1.050000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.125000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.200000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.275000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.350000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.425000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.500000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.575000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.650000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.725000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.800000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.875000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.950000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.025000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.100000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.175000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.250000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.325000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.400000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.475000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.550000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.625000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.700000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.775000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.850000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +2.925000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.000000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.075000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.150000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.225000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.300000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.375000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.450000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.525000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.600000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.675000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.750000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.825000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.900000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +3.975000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.050000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.125000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.200000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.275000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.350000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.425000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.500000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.575000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.650000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.725000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.800000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.875000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +4.950000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.025000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.100000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.175000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.250000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.325000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.400000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.475000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.550000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.625000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.700000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.775000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.850000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +5.925000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.000000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.075000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.150000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.225000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.300000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.375000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.450000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.525000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.600000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.675000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.750000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.825000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.900000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +6.975000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.050000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.125000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.200000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.275000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.350000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.425000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.500000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.575000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.650000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.725000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.800000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.875000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +7.950000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.025000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.100000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.175000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.250000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.325000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.400000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.475000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.550000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.625000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.700000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.775000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.850000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +8.925000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.000000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.075000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.150000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.225000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.300000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.375000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.450000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.525000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.600000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.675000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.750000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.825000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.900000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +9.975000000000000000e+04,0.000000000000000000e+00,0.000000000000000000e+00 +1.005000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.012500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.020000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.027500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.035000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.042500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.050000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.057500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.065000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.072500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.080000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.087500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.095000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.102500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.110000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.117500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.125000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.132500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.140000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.147500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.155000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.162500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.170000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.177500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.185000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.192500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.200000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.207500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.215000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.222500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.230000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.237500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.245000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.252500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.260000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.267500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.275000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.282500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.290000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.297500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.305000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.312500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.320000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.327500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.335000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.342500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.350000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.357500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.365000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.372500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.380000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.387500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.395000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.402500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.410000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.417500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.425000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.432500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.440000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.447500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.455000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.462500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.470000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.477500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.485000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.492500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.500000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.507500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.515000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.522500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.530000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.537500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.545000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.552500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.560000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.567500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.575000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.582500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.590000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.597500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.605000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.612500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.620000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.627500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.635000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.642500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.650000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.657500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.665000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.672500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.680000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.687500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.695000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.702500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.710000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.717500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.725000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.732500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.740000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.747500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.755000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.762500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.770000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.777500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.785000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.792500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.800000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.807500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.815000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.822500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.830000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.837500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.845000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.852500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.860000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.867500000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 +1.875000000000000000e+05,0.000000000000000000e+00,0.000000000000000000e+00 diff --git a/config/config.yaml b/config/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b43e92cfd2dbc08c1a4735e1b4673ab5e337c50 --- /dev/null +++ b/config/config.yaml @@ -0,0 +1,25 @@ +common: + depth_is_range: true + dt_ps: 750 + image_sizes: + - [256, 192] + +datasets: + p1: + cx: 130.412638624449 + cy: 97.1258022886799 + fx: 120.940059132687 + fy: 121.125671532013 + k1: -0.276944155790098 + k2: 0.062316608072363 + p1: 0.0 + p2: 0.0 + p2: + cx: 130.6802784645003 + cy: 100.360570246814 + fx: 118.6514575329715 + fy: 118.7964934010577 + k1: -0.276944155790098 + k2: 0.062316608072363 + p1: 0.0 + p2: 0.0 \ No newline at end of file diff --git a/config/final_extrinsic_sp_to_livox.txt b/config/final_extrinsic_sp_to_livox.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b8ef5b5f54890ec00fede3250c653e98b91991a --- /dev/null +++ b/config/final_extrinsic_sp_to_livox.txt @@ -0,0 +1,21 @@ +# final direct transform: SP(raw) -> Livox(raw) +# source_script=project_sp_to_livox.py +# calib_matrix=C:\document\code\singlephotongs\calib2lidar\extrinsic_livox_to_sp.txt +# calib_direction_input=livox_to_sp +# apply_axis_remap=True +# sp_axis_order=xzy +# sp_flip_x=False +# sp_flip_y=False +# sp_flip_z=True +# sp_scale=1.0 +# livox_axis_order=yxz +# livox_flip_x=False +# livox_flip_y=True +# livox_flip_z=True +# matrix_direction=sp_to_livox +# matrix_semantic=raw_to_raw +# pipeline_ready=True +0.000000000000 0.052256455795 0.997872812763 0.049398541753 +1.000000000000 0.000000000000 0.000000000000 -0.081712147000 +-0.000000000000 -0.997872812763 0.052256455795 0.013621837804 +0.000000000000 0.000000000000 0.000000000000 1.000000000000 diff --git a/config/offset.txt b/config/offset.txt new file mode 100644 index 0000000000000000000000000000000000000000..a31d754092f8c15e89a916250f54cc7b171867c1 --- /dev/null +++ b/config/offset.txt @@ -0,0 +1,49152 @@ +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.036514 +0.394709 +1.042979 +0.391065 +1.042723 +0.395255 +1.000333 +0.386370 +1.066487 +0.402503 +1.040586 +0.372428 +1.074029 +0.400161 +1.008905 +0.366808 +1.025067 +0.341993 +1.087366 +0.370216 +1.041536 +0.402022 +1.041446 +0.353774 +1.041489 +0.407848 +1.037274 +0.416016 +1.059122 +0.402907 +1.043735 +0.420597 +1.046738 +0.390210 +1.040475 +0.403420 +1.044080 +0.429286 +1.051117 +0.407143 +1.018814 +0.432394 +1.019527 +0.421490 +1.043768 +0.396560 +1.075391 +0.406266 +1.078399 +0.425667 +1.070475 +0.413035 +1.064198 +0.432870 +1.097424 +0.432770 +1.071180 +0.438465 +1.037997 +0.442730 +1.082831 +0.448298 +1.069549 +0.434228 +1.088560 +0.435927 +1.063885 +0.449700 +1.067670 +0.417120 +1.082253 +0.457329 +1.064957 +0.440277 +1.102747 +0.464032 +1.059391 +0.444722 +1.078752 +0.440784 +1.091723 +0.447101 +1.073258 +0.449092 +1.059518 +0.409163 +1.083227 +0.434646 +1.093302 +0.431779 +1.053821 +0.426145 +1.089963 +0.432532 +1.089591 +0.420692 +1.062096 +0.433510 +1.099650 +0.441752 +1.077361 +0.417540 +1.064625 +0.435685 +1.061258 +0.437342 +1.081443 +0.467919 +1.066789 +0.426665 +1.035233 +0.394737 +1.000774 +0.359576 +0.950383 +0.332360 +0.951366 +0.292342 +0.943513 +0.283539 +0.929063 +0.279203 +0.943737 +0.264964 +0.944509 +0.294359 +0.928207 +0.272253 +0.934957 +0.297760 +0.919777 +0.264108 +0.938030 +0.282414 +0.935733 +0.296139 +0.922807 +0.262963 +0.920933 +0.256494 +0.922352 +0.277163 +0.913433 +0.260330 +0.933107 +0.293238 +0.928048 +0.271608 +0.930952 +0.289865 +0.922451 +0.300889 +0.913102 +0.300588 +0.922418 +0.280406 +0.914097 +0.270496 +0.890610 +0.273444 +0.896572 +0.262592 +0.873656 +0.273558 +0.914754 +0.232263 +0.929562 +0.263873 +0.930633 +0.235710 +0.890666 +0.233320 +0.922755 +0.266942 +0.920642 +0.261634 +0.897439 +0.254043 +0.897932 +0.242380 +0.890950 +0.225235 +0.896165 +0.229137 +0.886952 +0.239518 +0.895138 +0.252630 +0.898367 +0.234711 +0.898826 +0.245680 +0.898608 +0.254298 +0.893378 +0.232238 +0.892758 +0.231039 +0.914271 +0.228681 +0.899287 +0.250072 +0.890002 +0.244940 +0.901011 +0.228582 +0.905087 +0.222152 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +0.241198 +0.896767 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.008914 +0.385089 +1.002867 +0.413826 +1.007214 +0.363322 +0.990386 +0.428297 +1.029141 +0.363648 +1.037880 +0.400235 +1.036002 +0.387679 +1.016591 +0.393503 +0.995163 +0.357378 +1.040789 +0.399045 +1.056277 +0.414114 +1.022308 +0.403886 +1.008082 +0.388326 +1.062240 +0.397432 +1.023084 +0.404727 +1.042377 +0.393989 +1.012314 +0.412869 +1.012989 +0.384179 +1.015860 +0.405304 +1.037316 +0.408364 +1.036733 +0.405920 +1.030131 +0.383374 +1.033376 +0.426077 +1.020903 +0.413668 +1.036632 +0.390808 +1.037369 +0.406520 +1.074190 +0.410296 +1.074159 +0.438650 +1.048733 +0.446711 +1.048907 +0.442822 +1.045711 +0.425564 +1.066666 +0.435312 +1.059362 +0.440235 +1.044582 +0.427669 +1.045053 +0.420363 +1.063175 +0.431442 +1.077448 +0.457821 +1.055211 +0.455500 +1.040361 +0.418333 +1.043498 +0.435210 +1.061648 +0.447655 +1.065194 +0.435562 +1.064185 +0.424887 +1.051595 +0.427526 +1.055217 +0.430613 +1.036061 +0.427834 +1.079361 +0.440941 +1.061788 +0.432035 +1.069916 +0.418951 +1.060125 +0.430460 +1.064975 +0.430255 +1.031687 +0.431365 +1.034599 +0.447740 +1.057257 +0.449511 +1.042066 +0.418659 +0.997019 +0.389654 +0.981944 +0.359023 +0.937514 +0.325212 +0.921835 +0.314373 +0.915313 +0.292246 +0.913125 +0.307932 +0.916638 +0.313626 +0.931635 +0.308136 +0.918319 +0.299200 +0.927841 +0.301414 +0.922341 +0.292188 +0.907626 +0.296329 +0.927053 +0.290717 +0.905897 +0.303941 +0.894834 +0.292595 +0.906230 +0.301388 +0.891475 +0.294928 +0.913867 +0.298991 +0.911370 +0.291820 +0.895264 +0.300633 +0.898801 +0.282134 +0.879895 +0.290044 +0.908012 +0.275930 +0.890328 +0.285750 +0.889168 +0.277848 +0.875837 +0.270509 +0.866225 +0.253927 +0.907438 +0.264570 +0.894009 +0.266839 +0.905656 +0.252248 +0.901663 +0.256450 +0.905025 +0.245739 +0.889382 +0.248862 +0.895322 +0.257656 +0.861173 +0.252767 +0.875344 +0.237146 +0.868681 +0.253657 +0.860194 +0.256034 +0.874005 +0.239039 +0.870065 +0.240157 +0.881081 +0.249124 +0.873969 +0.226899 +0.871231 +0.237623 +0.865317 +0.225662 +0.881088 +0.225553 +0.885638 +0.235596 +0.875623 +0.231414 +0.871059 +0.239177 +0.874567 +0.233611 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +0.235395 +0.877440 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +1.018919 +0.393283 +0.986509 +0.429355 +0.992790 +0.392043 +1.034543 +0.381278 +1.029423 +0.406529 +1.027242 +0.386970 +1.042455 +0.386218 +1.002514 +0.418161 +1.039412 +0.396745 +1.010213 +0.398095 +1.026606 +0.378808 +1.025245 +0.434031 +1.035424 +0.400330 +1.039139 +0.421292 +1.039351 +0.407880 +1.049686 +0.419339 +1.019144 +0.419734 +1.021260 +0.437831 +1.066638 +0.431217 +1.081816 +0.425520 +1.060370 +0.447617 +1.070772 +0.449778 +1.061150 +0.421744 +1.042027 +0.433756 +1.070360 +0.429489 +1.044929 +0.441625 +1.048963 +0.432732 +1.060394 +0.447581 +1.028697 +0.452398 +1.046568 +0.439375 +1.058574 +0.467121 +1.057884 +0.440988 +1.064368 +0.438606 +1.048786 +0.451641 +1.037966 +0.431885 +1.063542 +0.428246 +1.061514 +0.440285 +1.081061 +0.420224 +1.053185 +0.425599 +1.062789 +0.438233 +1.065501 +0.439836 +1.052899 +0.414690 +1.058289 +0.440725 +1.062473 +0.456670 +1.065120 +0.435716 +1.034000 +0.418147 +1.003767 +0.398512 +0.999331 +0.354705 +0.942976 +0.317684 +0.921002 +0.298162 +0.919077 +0.309823 +0.893427 +0.292326 +0.904920 +0.304812 +0.924342 +0.298546 +0.919625 +0.298654 +0.926338 +0.303851 +0.916774 +0.293225 +0.914429 +0.291761 +0.891499 +0.296913 +0.894128 +0.303466 +0.903830 +0.285889 +0.897206 +0.284311 +0.895003 +0.291052 +0.916656 +0.289240 +0.908322 +0.296954 +0.893961 +0.298531 +0.887566 +0.283213 +0.900475 +0.275217 +0.891082 +0.283432 +0.883443 +0.283985 +0.878938 +0.271576 +0.896121 +0.272594 +0.879220 +0.273737 +0.883761 +0.245329 +0.893195 +0.261904 +0.908094 +0.240799 +0.877212 +0.248324 +0.906686 +0.235325 +0.875374 +0.250376 +0.881386 +0.251106 +0.876817 +0.241807 +0.858495 +0.241061 +0.860413 +0.234488 +0.886043 +0.229900 +0.857432 +0.228456 +0.870056 +0.229747 +0.863339 +0.242066 +0.881319 +0.220497 +0.866319 +0.241410 +0.868299 +0.251444 +0.851536 +0.237066 +0.876543 +0.206834 +0.881295 +0.233070 +0.882572 +0.206256 +0.857737 +0.224583 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +0.215387 +0.880136 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.998182 +0.349328 +0.981691 +0.372199 +1.007245 +0.352295 +0.973307 +0.298480 +1.013994 +0.397209 +1.039525 +0.403487 +1.085574 +0.423578 +1.009152 +0.400700 +1.006797 +0.347274 +0.997332 +0.381527 +0.981128 +0.381080 +0.997058 +0.347083 +1.035962 +0.382330 +1.046578 +0.391254 +1.005849 +0.385255 +1.011198 +0.403690 +1.021034 +0.390267 +1.013719 +0.412758 +1.064691 +0.392582 +1.003195 +0.412483 +1.050089 +0.391167 +1.050824 +0.417400 +1.038508 +0.390649 +1.014549 +0.408266 +1.031326 +0.419743 +1.049014 +0.412860 +1.050507 +0.381831 +1.054383 +0.432852 +1.058951 +0.432328 +1.067190 +0.428078 +1.041005 +0.418424 +1.042568 +0.436669 +1.062092 +0.424889 +1.060387 +0.427325 +1.067613 +0.443584 +1.034970 +0.402980 +1.035980 +0.457581 +1.048754 +0.433982 +1.053256 +0.445151 +1.057939 +0.441457 +1.034337 +0.449580 +1.063029 +0.452206 +1.052893 +0.423102 +1.049467 +0.458664 +1.073118 +0.443608 +1.055503 +0.440572 +1.064268 +0.445084 +1.065176 +0.435252 +1.054462 +0.439250 +1.061517 +0.440832 +1.065423 +0.419470 +1.043713 +0.429235 +1.046456 +0.437351 +1.064743 +0.458639 +1.057237 +0.453546 +1.050783 +0.427283 +1.037919 +0.393682 +0.984207 +0.371879 +0.953763 +0.303146 +0.931799 +0.314564 +0.934486 +0.291200 +0.935434 +0.286739 +0.926842 +0.305999 +0.921919 +0.311556 +0.915748 +0.297752 +0.911044 +0.292632 +0.905023 +0.303068 +0.910149 +0.292302 +0.889512 +0.278024 +0.909529 +0.290735 +0.917568 +0.295256 +0.907472 +0.308896 +0.888594 +0.287024 +0.917683 +0.282239 +0.896321 +0.303706 +0.887001 +0.273976 +0.901352 +0.288400 +0.899104 +0.277431 +0.896252 +0.263739 +0.871955 +0.281436 +0.885468 +0.266913 +0.877515 +0.278567 +0.876555 +0.266159 +0.900762 +0.239436 +0.901703 +0.246238 +0.892886 +0.258673 +0.896166 +0.256411 +0.893319 +0.257811 +0.877269 +0.265018 +0.863460 +0.237513 +0.877369 +0.227953 +0.877879 +0.246674 +0.883555 +0.228314 +0.847434 +0.245243 +0.870563 +0.246888 +0.862426 +0.227042 +0.876964 +0.241288 +0.863349 +0.224062 +0.864967 +0.237187 +0.876064 +0.234842 +0.883595 +0.221157 +0.876872 +0.217695 +0.874632 +0.225513 +0.885783 +0.228461 +0.872880 +0.221983 +0.861183 +0.220152 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +0.225319 +0.877765 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.996680 +0.350229 +0.938422 +0.408661 +0.989831 +0.339677 +1.004104 +0.338166 +0.996107 +0.372845 +1.013301 +0.424290 +1.040448 +0.386975 +1.029737 +0.377118 +1.067575 +0.331146 +1.020120 +0.399297 +0.984372 +0.359915 +1.026263 +0.405491 +1.029993 +0.398992 +1.014251 +0.409430 +1.027225 +0.409963 +1.034794 +0.402925 +1.016722 +0.384487 +1.052195 +0.401950 +1.015904 +0.393122 +1.033845 +0.402014 +1.035119 +0.403681 +1.052852 +0.399686 +1.010759 +0.426532 +1.014829 +0.378672 +1.011333 +0.425877 +1.012877 +0.405391 +1.031648 +0.408590 +1.056939 +0.411657 +1.071351 +0.433113 +1.038490 +0.417533 +1.069479 +0.448119 +1.055000 +0.447505 +1.062567 +0.400945 +1.060825 +0.454753 +1.062943 +0.436222 +1.037600 +0.444474 +1.048208 +0.458901 +1.048984 +0.445508 +1.049859 +0.426176 +1.050208 +0.439678 +1.061599 +0.444395 +1.061566 +0.444111 +1.052831 +0.430274 +1.056343 +0.410388 +1.046312 +0.418614 +1.051215 +0.451825 +1.071593 +0.451378 +1.066545 +0.432722 +1.055543 +0.421754 +1.056932 +0.433761 +1.061745 +0.437362 +1.042949 +0.439723 +1.040362 +0.441432 +1.056328 +0.449214 +1.060058 +0.448789 +1.037834 +0.419631 +1.027397 +0.406824 +0.982740 +0.352744 +0.929517 +0.302328 +0.925933 +0.297808 +0.912062 +0.304563 +0.911420 +0.291935 +0.924239 +0.306170 +0.925273 +0.295684 +0.923171 +0.300707 +0.908809 +0.300173 +0.916610 +0.297799 +0.916530 +0.299009 +0.913966 +0.316946 +0.915630 +0.289310 +0.919718 +0.286296 +0.905063 +0.292514 +0.899793 +0.283623 +0.902870 +0.299462 +0.889119 +0.298685 +0.890444 +0.296185 +0.899374 +0.301509 +0.902431 +0.284690 +0.893743 +0.296519 +0.881509 +0.279896 +0.884992 +0.281135 +0.886467 +0.289504 +0.867144 +0.270098 +0.890037 +0.258480 +0.905898 +0.254003 +0.888692 +0.257520 +0.900127 +0.252493 +0.890236 +0.256589 +0.897299 +0.263073 +0.878787 +0.245094 +0.886700 +0.241891 +0.848412 +0.243418 +0.869691 +0.218172 +0.847297 +0.238980 +0.878990 +0.236436 +0.873453 +0.236994 +0.844616 +0.231439 +0.858895 +0.229538 +0.850931 +0.230587 +0.865084 +0.235092 +0.880747 +0.208527 +0.865123 +0.217752 +0.882363 +0.241501 +0.861482 +0.230525 +0.872532 +0.222122 +0.871959 +0.250441 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +0.231383 +0.872126 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.993024 +0.369932 +0.970378 +0.334753 +1.005424 +0.380801 +0.982016 +0.349399 +0.991633 +0.379596 +1.006634 +0.387275 +0.971220 +0.378037 +0.979437 +0.339782 +0.976395 +0.381906 +0.996556 +0.355915 +1.019775 +0.381498 +1.022959 +0.344882 +0.989601 +0.365083 +0.963984 +0.398445 +0.995263 +0.372217 +0.994021 +0.393019 +1.014338 +0.377376 +1.010732 +0.403396 +1.008525 +0.374534 +0.996388 +0.353339 +1.019197 +0.367662 +1.005877 +0.370068 +1.003656 +0.375115 +0.992978 +0.384176 +1.006292 +0.411760 +1.015582 +0.401971 +1.020199 +0.374286 +1.018173 +0.398423 +1.031276 +0.400681 +1.007089 +0.387183 +1.044699 +0.407149 +1.017884 +0.408306 +1.035790 +0.404527 +1.026060 +0.402627 +1.015554 +0.406379 +1.045635 +0.421485 +1.033046 +0.418795 +1.011943 +0.409362 +1.021231 +0.425371 +1.004912 +0.418551 +1.037549 +0.436088 +1.032441 +0.428550 +1.049918 +0.417200 +1.011557 +0.430247 +1.015927 +0.406558 +1.039491 +0.411871 +1.029708 +0.419020 +1.044497 +0.429718 +1.034229 +0.432069 +1.028397 +0.401980 +1.030317 +0.401053 +1.030218 +0.407203 +1.029691 +0.419723 +1.030183 +0.426520 +1.036912 +0.418120 +1.031613 +0.441477 +1.033175 +0.422663 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +0.428706 +1.032902 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.980845 +0.392805 +0.998764 +0.404357 +0.977996 +0.405682 +1.000075 +0.354241 +0.964463 +0.418492 +1.002231 +0.403214 +0.975819 +0.359200 +0.997601 +0.391421 +0.951076 +0.370197 +0.999686 +0.391411 +0.982567 +0.377595 +0.967593 +0.394147 +0.985886 +0.398114 +0.999711 +0.399349 +1.017562 +0.406321 +0.972852 +0.400000 +1.021364 +0.389730 +1.011385 +0.404312 +1.000991 +0.420544 +1.010623 +0.374682 +1.003912 +0.402768 +1.027636 +0.406890 +1.018406 +0.381048 +1.007627 +0.411344 +1.004918 +0.403285 +1.009889 +0.415501 +1.003596 +0.411487 +1.004202 +0.405576 +1.032490 +0.434640 +1.054826 +0.429139 +1.036099 +0.415374 +1.022741 +0.424141 +1.039994 +0.449503 +1.032897 +0.414008 +1.040423 +0.429671 +1.021819 +0.439206 +1.030799 +0.428543 +1.019041 +0.422509 +1.012839 +0.430038 +1.014672 +0.429858 +1.022876 +0.449026 +1.026012 +0.443674 +1.039361 +0.466354 +1.057103 +0.428144 +1.009749 +0.415229 +1.043159 +0.432875 +1.017467 +0.439678 +1.028096 +0.442714 +1.039308 +0.414398 +1.027297 +0.436415 +1.026715 +0.433837 +1.039852 +0.441458 +1.020014 +0.455736 +1.021053 +0.431465 +1.030930 +0.446997 +1.008878 +0.460208 +1.039366 +0.442872 +1.048343 +0.457802 +1.032496 +0.435315 +1.024217 +0.428997 +1.009501 +0.427195 +1.040974 +0.426849 +1.034322 +0.448666 +1.030405 +0.415359 +1.055796 +0.432941 +1.040723 +0.412966 +1.033957 +0.415914 +1.033928 +0.433384 +1.017347 +0.427201 +1.017677 +0.438809 +1.040756 +0.438903 +1.044958 +0.420338 +1.005288 +0.425341 +1.025243 +0.430613 +1.035685 +0.428914 +1.018775 +0.426997 +1.008645 +0.402023 +1.007259 +0.418850 +1.015606 +0.412222 +1.006822 +0.402555 +1.012948 +0.399618 +0.982020 +0.360177 +0.973013 +0.386334 +0.979738 +0.414795 +1.007338 +0.392065 +1.026752 +0.418924 +1.031783 +0.391897 +1.031064 +0.369580 +1.028028 +0.403932 +1.001962 +0.380750 +0.983735 +0.392702 +0.986339 +0.369960 +0.975030 +0.375288 +0.962634 +0.336758 +0.967915 +0.376461 +0.996775 +0.371388 +0.986749 +0.368712 +0.965016 +0.368962 +0.964842 +0.371658 +0.963201 +0.353876 +0.957685 +0.345316 +1.003672 +0.361259 +0.979002 +0.357008 +0.975651 +0.337887 +1.003693 +0.375677 +0.997593 +0.358245 +0.977482 +0.361494 +0.985635 +0.378547 +0.992923 +0.365139 +0.992923 +0.365139 +0.992923 +0.365139 +0.992923 +0.365139 +0.992923 +0.365139 +0.992923 +0.365139 +0.992923 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.972827 +0.383509 +0.952207 +0.407463 +0.962182 +0.392548 +0.999020 +0.376012 +0.957279 +0.381965 +0.944010 +0.367773 +0.931322 +0.370996 +0.969657 +0.367367 +0.994955 +0.415335 +0.936675 +0.384030 +0.990997 +0.370445 +0.925957 +0.380518 +0.980510 +0.408612 +0.989451 +0.398203 +0.973254 +0.405956 +0.957077 +0.368658 +0.981165 +0.391556 +0.994370 +0.386278 +0.975039 +0.412544 +0.986215 +0.392296 +0.986709 +0.425055 +0.991723 +0.405869 +0.991721 +0.414037 +0.999791 +0.409064 +0.986352 +0.404998 +1.012435 +0.388895 +0.993159 +0.399361 +0.984790 +0.393674 +0.995502 +0.436193 +1.003214 +0.428016 +1.019044 +0.419919 +1.011732 +0.434519 +0.999210 +0.422221 +1.011645 +0.421006 +1.009538 +0.424402 +0.991058 +0.428752 +0.979295 +0.424516 +1.011602 +0.437799 +0.991571 +0.442125 +1.002068 +0.429663 +0.996162 +0.445307 +0.996743 +0.431267 +0.999298 +0.441648 +1.021057 +0.443772 +0.981286 +0.433971 +1.031806 +0.449290 +1.003671 +0.439995 +1.004616 +0.436570 +1.017501 +0.444052 +1.000085 +0.428718 +1.010068 +0.422142 +0.998424 +0.423854 +1.009498 +0.453160 +1.019112 +0.433351 +1.007600 +0.428629 +1.007002 +0.430154 +1.001036 +0.464178 +1.017400 +0.478698 +0.999258 +0.446182 +0.998886 +0.448820 +1.014812 +0.436142 +1.017834 +0.444174 +1.015430 +0.432282 +1.016755 +0.447101 +1.020014 +0.451147 +1.013682 +0.446724 +1.006658 +0.452242 +1.028305 +0.453380 +1.010518 +0.444427 +1.013088 +0.451663 +1.004027 +0.452594 +1.019668 +0.440500 +0.991446 +0.446360 +1.018442 +0.442928 +1.012788 +0.452395 +1.005697 +0.434401 +1.004115 +0.411023 +0.983840 +0.443961 +0.990317 +0.441167 +0.988384 +0.417044 +0.994582 +0.427296 +0.978527 +0.422058 +0.973611 +0.400699 +0.972076 +0.424607 +0.997751 +0.385945 +1.019329 +0.405563 +0.998167 +0.397522 +0.986007 +0.388892 +0.968732 +0.390619 +0.961390 +0.394596 +0.982610 +0.391238 +0.964868 +0.367409 +0.965982 +0.362299 +0.973392 +0.382286 +0.970584 +0.383324 +0.942963 +0.375788 +0.985677 +0.362193 +0.966905 +0.379327 +0.967410 +0.341053 +0.965179 +0.375010 +0.952956 +0.359601 +0.956476 +0.359893 +0.985673 +0.353567 +0.950996 +0.337496 +0.971051 +0.365673 +0.959084 +0.348582 +0.949253 +0.333602 +0.956781 +0.377068 +0.959796 +0.349286 +0.959796 +0.349286 +0.959796 +0.349286 +0.959796 +0.349286 +0.959796 +0.349286 +0.959796 +0.349286 +0.959796 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.955909 +0.369411 +0.960909 +0.389890 +0.988853 +0.404418 +0.923042 +0.353217 +0.955833 +0.350598 +0.970406 +0.362918 +0.930361 +0.361431 +0.971767 +0.382003 +0.959382 +0.353870 +0.990470 +0.392892 +0.997301 +0.352288 +0.968560 +0.356314 +0.952323 +0.369375 +0.989760 +0.376328 +0.987817 +0.396897 +0.946325 +0.401572 +0.979564 +0.371425 +0.998436 +0.398210 +0.994987 +0.407269 +0.964020 +0.393835 +0.984731 +0.394386 +0.959756 +0.406772 +1.010565 +0.413035 +0.965715 +0.396486 +0.983267 +0.396274 +0.971996 +0.407079 +0.990587 +0.414707 +0.996737 +0.408038 +0.964980 +0.402423 +1.032029 +0.418382 +0.994869 +0.411276 +1.018012 +0.433909 +1.008102 +0.420640 +1.022624 +0.419761 +1.012913 +0.426904 +0.992872 +0.420396 +0.985528 +0.424460 +1.012708 +0.425908 +1.022971 +0.450547 +0.993749 +0.427460 +1.002500 +0.434032 +1.005137 +0.437431 +1.002313 +0.432493 +1.019749 +0.436067 +1.010776 +0.418921 +1.012128 +0.432616 +0.995033 +0.435321 +1.027785 +0.420457 +1.019820 +0.433991 +1.016366 +0.440015 +0.998347 +0.433105 +1.013466 +0.424270 +1.005003 +0.433207 +0.997840 +0.439656 +1.007929 +0.450013 +1.007491 +0.456071 +1.003471 +0.453874 +1.006967 +0.444020 +1.013088 +0.439867 +1.008023 +0.440589 +0.997390 +0.438228 +1.026072 +0.435425 +1.024149 +0.450705 +1.004730 +0.445480 +1.029625 +0.446743 +1.003149 +0.443539 +1.014360 +0.429221 +1.010755 +0.424696 +1.021296 +0.450926 +0.991200 +0.440808 +0.997489 +0.439213 +1.013415 +0.455366 +0.984933 +0.442211 +1.003764 +0.422546 +0.996527 +0.433382 +0.997629 +0.437109 +0.979984 +0.433499 +0.981146 +0.439726 +0.979938 +0.447370 +0.981862 +0.416478 +0.995533 +0.441023 +0.991883 +0.430190 +0.976605 +0.404942 +0.949612 +0.409119 +0.953771 +0.419486 +0.977492 +0.399266 +0.984671 +0.401428 +0.975873 +0.394990 +0.980396 +0.394900 +0.975612 +0.377768 +0.984262 +0.381986 +0.980991 +0.381190 +0.948512 +0.370470 +0.954075 +0.369377 +0.960239 +0.352792 +0.941337 +0.381173 +0.938971 +0.368103 +0.944469 +0.372724 +0.956292 +0.357207 +0.951744 +0.352061 +0.945048 +0.355512 +0.964898 +0.337121 +0.948276 +0.345620 +0.956465 +0.360651 +0.941269 +0.349580 +0.931165 +0.364620 +0.961609 +0.352209 +0.953952 +0.348031 +0.948329 +0.365906 +0.948909 +0.354953 +0.948909 +0.354953 +0.948909 +0.354953 +0.948909 +0.354953 +0.948909 +0.354953 +0.948909 +0.354953 +0.948909 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.956645 +0.389209 +0.944652 +0.341491 +0.925440 +0.357845 +0.950723 +0.378031 +0.993771 +0.431750 +0.960112 +0.343071 +0.940563 +0.345389 +0.993510 +0.354703 +0.993680 +0.435570 +0.958819 +0.397894 +0.946753 +0.404206 +0.952289 +0.403897 +0.966200 +0.398748 +0.982043 +0.393303 +0.971640 +0.391797 +0.984894 +0.409668 +0.956026 +0.404856 +1.002192 +0.421069 +0.962893 +0.371340 +1.006071 +0.390203 +1.006409 +0.399552 +0.958871 +0.418038 +0.972920 +0.375246 +0.980163 +0.398528 +1.002373 +0.391357 +0.996401 +0.428813 +1.003092 +0.393819 +0.982976 +0.418234 +0.969075 +0.417974 +1.022409 +0.433919 +1.011050 +0.419917 +1.026833 +0.395986 +0.998221 +0.414714 +1.016046 +0.416474 +1.018937 +0.436749 +1.006927 +0.428001 +1.006688 +0.426306 +1.010168 +0.419584 +0.999149 +0.414828 +0.996335 +0.432745 +1.003885 +0.435986 +1.004301 +0.434111 +1.020803 +0.424703 +1.007656 +0.437347 +1.008577 +0.437123 +1.004414 +0.412868 +1.005417 +0.403831 +1.012444 +0.432557 +1.010945 +0.459218 +1.016356 +0.411130 +0.992496 +0.432711 +1.019557 +0.428226 +1.010087 +0.409603 +1.015754 +0.420382 +0.995981 +0.446105 +1.004744 +0.427227 +1.004369 +0.460763 +1.001994 +0.444526 +1.018019 +0.447952 +1.009677 +0.433480 +1.016651 +0.436453 +1.023959 +0.466482 +1.012712 +0.443911 +1.015049 +0.458363 +1.009033 +0.462809 +1.023268 +0.448082 +1.013613 +0.446595 +0.998327 +0.445242 +1.015531 +0.444662 +1.013473 +0.450511 +1.011880 +0.432030 +1.017971 +0.442458 +1.003218 +0.441563 +1.000282 +0.417451 +0.992675 +0.444140 +0.994602 +0.426272 +0.996363 +0.430647 +0.980290 +0.423633 +0.989771 +0.437621 +0.992537 +0.408008 +0.978239 +0.456275 +1.003309 +0.415261 +0.976851 +0.425808 +0.967902 +0.424786 +0.980911 +0.412070 +0.994775 +0.403497 +0.969746 +0.400800 +0.999745 +0.388033 +0.976663 +0.393248 +0.980186 +0.386721 +0.980412 +0.393629 +0.972176 +0.383225 +0.951453 +0.373905 +0.950368 +0.362967 +0.944437 +0.358123 +0.957616 +0.375276 +0.951142 +0.370630 +0.953684 +0.368986 +0.947820 +0.353424 +0.945826 +0.371149 +0.944539 +0.357899 +0.959630 +0.346590 +0.953191 +0.360586 +0.947919 +0.347542 +0.962965 +0.349221 +0.940642 +0.339622 +0.960300 +0.352985 +0.961174 +0.360147 +0.942897 +0.352475 +0.954039 +0.350918 +0.954039 +0.350918 +0.954039 +0.350918 +0.954039 +0.350918 +0.954039 +0.350918 +0.954039 +0.350918 +0.954039 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.961530 +0.386620 +0.986952 +0.411087 +0.931772 +0.367380 +1.000501 +0.411705 +0.952317 +0.380774 +0.963386 +0.377846 +0.971376 +0.381604 +0.977917 +0.360852 +0.951952 +0.384705 +0.977877 +0.386100 +0.949765 +0.405903 +0.960786 +0.390381 +1.010137 +0.387703 +1.000821 +0.384425 +0.969149 +0.386041 +0.978304 +0.411429 +0.982999 +0.399810 +0.988613 +0.397089 +0.978099 +0.388287 +1.005172 +0.435296 +0.969478 +0.391683 +0.979895 +0.423291 +1.001251 +0.428250 +0.973531 +0.409707 +0.999226 +0.397422 +0.990739 +0.394141 +1.012582 +0.451099 +1.016524 +0.446064 +0.997281 +0.419145 +1.006555 +0.424444 +1.026141 +0.433705 +1.038981 +0.435097 +1.008917 +0.426831 +0.987590 +0.431989 +0.999371 +0.434320 +1.008944 +0.444184 +0.995133 +0.436209 +1.006837 +0.439523 +0.992751 +0.438771 +1.012270 +0.439418 +1.003792 +0.439219 +1.028155 +0.452358 +1.016033 +0.424282 +1.002897 +0.427512 +1.016919 +0.426151 +1.007982 +0.434996 +0.997401 +0.438826 +1.005105 +0.436067 +1.010049 +0.420377 +1.019011 +0.450148 +1.005101 +0.440281 +0.992286 +0.443723 +1.022464 +0.444669 +0.999362 +0.447745 +1.003583 +0.446455 +1.028120 +0.441421 +0.994790 +0.444661 +1.006814 +0.450734 +1.019668 +0.437408 +0.997023 +0.444389 +1.018770 +0.454913 +1.022007 +0.453967 +1.011654 +0.452497 +1.024732 +0.429906 +1.026254 +0.449784 +1.000180 +0.441840 +1.003425 +0.432316 +0.989840 +0.446749 +1.005728 +0.430781 +0.995785 +0.447666 +1.017750 +0.439368 +1.000752 +0.437786 +1.019410 +0.420044 +0.979457 +0.429272 +1.008414 +0.417142 +0.993605 +0.455266 +0.983909 +0.430262 +0.997897 +0.426127 +0.998573 +0.415756 +0.991948 +0.403629 +0.958560 +0.402459 +0.977359 +0.404481 +1.010441 +0.392123 +0.997100 +0.377388 +1.000968 +0.408731 +0.969975 +0.395401 +0.991587 +0.390642 +0.970808 +0.384517 +0.966939 +0.384650 +0.956901 +0.378397 +0.968900 +0.367805 +0.958151 +0.375620 +0.952939 +0.376188 +0.940521 +0.373797 +0.957695 +0.371413 +0.961442 +0.377717 +0.937300 +0.339637 +0.955113 +0.374394 +0.949538 +0.344461 +0.952342 +0.357507 +0.947338 +0.333614 +0.950021 +0.340063 +0.930583 +0.352326 +0.956447 +0.342479 +0.947980 +0.332267 +0.942209 +0.354561 +0.981001 +0.351002 +0.948879 +0.343102 +0.948879 +0.343102 +0.948879 +0.343102 +0.948879 +0.343102 +0.948879 +0.343102 +0.948879 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.927445 +0.346583 +0.910037 +0.323092 +0.904555 +0.375356 +0.942213 +0.309227 +0.935567 +0.355166 +0.964376 +0.347792 +0.900664 +0.343655 +0.916574 +0.373844 +0.909862 +0.357857 +0.925243 +0.361597 +0.947804 +0.364182 +0.911125 +0.376835 +0.929343 +0.318932 +0.943293 +0.336792 +0.959743 +0.339536 +0.907541 +0.360194 +0.927863 +0.368111 +0.951943 +0.372705 +0.939753 +0.392248 +0.962747 +0.382919 +0.966998 +0.362795 +0.945853 +0.395033 +0.963333 +0.373898 +0.916549 +0.373668 +0.976655 +0.383247 +0.973319 +0.354718 +0.979100 +0.389309 +0.977216 +0.388744 +0.968397 +0.388193 +0.961081 +0.405573 +0.974071 +0.364698 +0.983465 +0.390354 +0.988053 +0.407416 +0.967204 +0.383859 +0.989932 +0.385711 +0.987967 +0.409978 +0.999453 +0.418031 +0.966029 +0.408237 +0.960734 +0.407914 +0.971532 +0.404096 +0.991435 +0.432244 +0.989762 +0.415591 +0.980026 +0.423952 +0.983096 +0.410004 +0.993146 +0.425002 +0.977486 +0.394093 +0.975705 +0.395005 +0.988980 +0.404420 +0.991680 +0.407699 +0.978876 +0.417096 +0.992298 +0.414053 +0.980014 +0.393350 +0.988196 +0.396630 +0.979004 +0.426666 +0.990829 +0.437363 +0.998631 +0.437278 +0.980839 +0.435968 +0.980747 +0.415013 +0.988587 +0.446751 +0.981079 +0.427817 +0.996346 +0.430967 +1.002673 +0.415423 +1.003902 +0.445134 +0.997476 +0.450158 +1.001320 +0.431810 +0.974715 +0.437772 +1.006200 +0.450580 +1.018636 +0.451217 +1.002110 +0.418694 +1.004522 +0.434662 +1.005520 +0.436369 +1.020262 +0.440819 +1.010338 +0.451334 +1.005955 +0.422029 +0.994956 +0.444375 +1.002361 +0.432518 +0.991704 +0.446854 +0.987886 +0.436561 +0.994192 +0.415723 +0.986548 +0.425825 +0.999484 +0.423325 +0.992175 +0.423521 +0.965818 +0.397779 +0.976596 +0.404845 +0.967557 +0.426539 +0.967404 +0.385394 +0.991321 +0.388007 +0.992244 +0.400577 +0.975010 +0.382968 +1.001773 +0.394966 +0.993187 +0.391339 +0.978422 +0.392732 +0.974569 +0.387618 +0.960144 +0.363005 +0.941545 +0.364125 +0.953169 +0.353595 +0.954539 +0.361508 +0.946682 +0.372811 +0.939216 +0.362683 +0.941887 +0.355835 +0.937939 +0.358787 +0.941795 +0.342459 +0.951124 +0.352346 +0.938789 +0.342459 +0.946868 +0.318126 +0.940424 +0.356398 +0.940238 +0.348179 +0.942392 +0.362201 +0.949319 +0.332632 +0.940908 +0.356824 +0.943983 +0.347671 +0.943983 +0.347671 +0.943983 +0.347671 +0.943983 +0.347671 +0.943983 +0.347671 +0.943983 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.929926 +0.396659 +0.958764 +0.352298 +0.912615 +0.442383 +0.938535 +0.370819 +0.938628 +0.376774 +0.958350 +0.352271 +0.958274 +0.357410 +0.929352 +0.389493 +0.974199 +0.363149 +0.929213 +0.370150 +0.897705 +0.365526 +0.914333 +0.375344 +0.943750 +0.384059 +0.935454 +0.379522 +0.925081 +0.390387 +0.951424 +0.372248 +0.945021 +0.381952 +0.948933 +0.402152 +0.942748 +0.402556 +0.925129 +0.383074 +0.942795 +0.401938 +0.941588 +0.391683 +0.950730 +0.383794 +0.947399 +0.406149 +0.957192 +0.401930 +0.959540 +0.392608 +0.959953 +0.400674 +0.964160 +0.400573 +0.972955 +0.399035 +0.971342 +0.393041 +0.980009 +0.422500 +0.977975 +0.418837 +0.990865 +0.428637 +0.999292 +0.409471 +0.987300 +0.428265 +0.975461 +0.399435 +1.004153 +0.414281 +0.992070 +0.436222 +0.979022 +0.420694 +0.964438 +0.423255 +0.994996 +0.415454 +0.976728 +0.436086 +0.976640 +0.438069 +0.992002 +0.447100 +0.966138 +0.434348 +0.971846 +0.431621 +0.977291 +0.411931 +0.984002 +0.409960 +0.977544 +0.434535 +0.997221 +0.420282 +0.990573 +0.432380 +0.990414 +0.423720 +0.979496 +0.431132 +0.974217 +0.420592 +0.976453 +0.432926 +0.980587 +0.438234 +0.973949 +0.432768 +0.985382 +0.444298 +0.992616 +0.440965 +0.990016 +0.437177 +0.980856 +0.442315 +0.989486 +0.444391 +0.990461 +0.438050 +0.994384 +0.462245 +0.997662 +0.464121 +1.000215 +0.439987 +0.997642 +0.454711 +0.973136 +0.447901 +0.994754 +0.450477 +1.003696 +0.440014 +0.996018 +0.427032 +0.992647 +0.424465 +0.977403 +0.417854 +0.994600 +0.415580 +0.960108 +0.423009 +0.999206 +0.432662 +0.967782 +0.412804 +0.970175 +0.426679 +0.990981 +0.429162 +0.961356 +0.430734 +0.968836 +0.428428 +0.978540 +0.427274 +0.967171 +0.399966 +0.973579 +0.397578 +0.946377 +0.393613 +0.964850 +0.397600 +0.962637 +0.380383 +0.990188 +0.397263 +0.982786 +0.381740 +0.944160 +0.402219 +0.969683 +0.381530 +0.965531 +0.376454 +0.934505 +0.378329 +0.976633 +0.351021 +0.935982 +0.345642 +0.971015 +0.379952 +0.945049 +0.375519 +0.936240 +0.352030 +0.925747 +0.367421 +0.947884 +0.331007 +0.949319 +0.350916 +0.926910 +0.332111 +0.931459 +0.354835 +0.946650 +0.325776 +0.948317 +0.334962 +0.933126 +0.344539 +0.933080 +0.330480 +0.947378 +0.332001 +0.950407 +0.334963 +0.934837 +0.321024 +0.923245 +0.340316 +0.944207 +0.329329 +0.944207 +0.329329 +0.944207 +0.329329 +0.944207 +0.329329 +0.944207 +0.329329 +0.944207 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.911020 +0.372898 +0.881548 +0.340137 +0.939177 +0.374507 +0.875932 +0.385858 +0.917950 +0.358328 +0.907587 +0.335816 +0.917534 +0.331059 +0.916899 +0.366394 +0.918892 +0.368173 +0.916631 +0.383574 +0.915558 +0.387200 +0.914270 +0.372664 +0.935083 +0.379277 +0.907556 +0.351483 +0.906428 +0.375059 +0.951368 +0.399762 +0.925044 +0.390643 +0.934315 +0.368241 +0.936581 +0.371272 +0.925892 +0.383154 +0.935871 +0.398882 +0.947394 +0.391854 +0.938956 +0.405970 +0.952636 +0.401527 +0.936370 +0.392698 +0.942511 +0.387606 +0.932536 +0.397855 +0.944786 +0.383050 +0.948768 +0.385605 +0.919555 +0.380733 +0.959474 +0.418526 +0.962740 +0.411835 +0.969065 +0.401126 +0.942983 +0.423865 +0.957007 +0.417354 +0.948261 +0.413435 +0.978410 +0.418017 +0.974203 +0.439727 +0.948791 +0.418457 +0.970137 +0.431414 +0.954562 +0.417927 +0.968104 +0.414604 +0.958048 +0.442921 +0.972321 +0.444978 +0.961254 +0.440716 +0.968773 +0.441800 +0.953550 +0.417201 +0.958238 +0.421502 +0.961739 +0.405375 +0.950769 +0.438289 +0.954666 +0.422456 +0.970724 +0.425105 +0.960374 +0.418321 +0.950614 +0.411452 +0.953776 +0.421169 +0.954723 +0.443052 +0.962840 +0.435288 +0.967696 +0.437023 +0.954229 +0.437293 +0.953785 +0.429872 +0.949169 +0.423734 +0.967455 +0.431443 +0.966835 +0.433648 +0.971874 +0.432527 +0.984771 +0.446480 +0.969111 +0.430913 +0.960817 +0.449510 +0.969434 +0.446593 +0.976067 +0.446219 +0.966342 +0.431197 +0.956815 +0.436730 +0.971568 +0.451994 +0.969951 +0.456236 +0.958390 +0.443192 +0.970404 +0.430725 +0.977564 +0.451737 +0.957569 +0.437559 +0.960257 +0.441656 +0.968196 +0.441952 +0.923269 +0.439826 +0.958768 +0.443398 +0.973626 +0.438079 +0.963980 +0.431355 +0.929460 +0.423383 +0.946336 +0.424571 +0.926177 +0.404984 +0.969861 +0.419758 +0.962813 +0.418211 +0.958053 +0.382873 +0.959625 +0.402453 +0.964407 +0.395992 +0.960526 +0.376211 +0.947533 +0.401796 +0.924264 +0.387646 +0.907437 +0.387553 +0.916096 +0.365254 +0.917797 +0.370289 +0.935870 +0.387728 +0.943748 +0.386421 +0.928338 +0.377077 +0.907549 +0.373132 +0.913134 +0.382607 +0.939181 +0.356323 +0.915504 +0.357769 +0.936743 +0.361708 +0.924098 +0.358344 +0.933005 +0.338834 +0.903761 +0.373428 +0.915122 +0.367734 +0.914474 +0.322932 +0.909173 +0.376665 +0.911119 +0.354698 +0.911119 +0.354698 +0.911119 +0.354698 +0.911119 +0.354698 +0.911119 +0.354698 +0.911119 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.888881 +0.358831 +0.885739 +0.397873 +0.876835 +0.348406 +0.894679 +0.371467 +0.895130 +0.356619 +0.918332 +0.357084 +0.902456 +0.328825 +0.897769 +0.381673 +0.881346 +0.360550 +0.912414 +0.341360 +0.908850 +0.341975 +0.902823 +0.365718 +0.929578 +0.373146 +0.921095 +0.342795 +0.916059 +0.381324 +0.910854 +0.383904 +0.927551 +0.376076 +0.926449 +0.400877 +0.928704 +0.391860 +0.950883 +0.380048 +0.920778 +0.385186 +0.944574 +0.370975 +0.937678 +0.399840 +0.927857 +0.394203 +0.915596 +0.405850 +0.921495 +0.385532 +0.915836 +0.385399 +0.957379 +0.396474 +0.940084 +0.407275 +0.928525 +0.409322 +0.971033 +0.418826 +0.948012 +0.425329 +0.958380 +0.415993 +0.948768 +0.413796 +0.956934 +0.401635 +0.977670 +0.421059 +0.957609 +0.420024 +0.980095 +0.431171 +0.968999 +0.428565 +0.946680 +0.431124 +0.955777 +0.428322 +0.952106 +0.417660 +0.969363 +0.435617 +0.967114 +0.422080 +0.967734 +0.435519 +0.954280 +0.422390 +0.960662 +0.419332 +0.943503 +0.424734 +0.972980 +0.416285 +0.942486 +0.425984 +0.965859 +0.429454 +0.978777 +0.434847 +0.961168 +0.432812 +0.956548 +0.415484 +0.947487 +0.431894 +0.957294 +0.417749 +0.947862 +0.431037 +0.970307 +0.430923 +0.959107 +0.436432 +0.940856 +0.449536 +0.966547 +0.462473 +0.978441 +0.440265 +0.973359 +0.445740 +0.986063 +0.442341 +0.973605 +0.445239 +0.979642 +0.455757 +0.964316 +0.442490 +0.972927 +0.447002 +0.973346 +0.438368 +0.973426 +0.431242 +0.959923 +0.439731 +0.977657 +0.445324 +0.970130 +0.447476 +0.950351 +0.421711 +0.967914 +0.444422 +0.974396 +0.448350 +0.961023 +0.441978 +0.931889 +0.439680 +0.938893 +0.446428 +0.944673 +0.416431 +0.947017 +0.441816 +0.959045 +0.441931 +0.941965 +0.410566 +0.922157 +0.437350 +0.935116 +0.440819 +0.938133 +0.408953 +0.957643 +0.406205 +0.942012 +0.397126 +0.942033 +0.385143 +0.941911 +0.404799 +0.946541 +0.393779 +0.937149 +0.389431 +0.944095 +0.391201 +0.929269 +0.387247 +0.912613 +0.367931 +0.925112 +0.362972 +0.898136 +0.386830 +0.901679 +0.359313 +0.907542 +0.354091 +0.916044 +0.364016 +0.907033 +0.359976 +0.897855 +0.369729 +0.918193 +0.344818 +0.917019 +0.346841 +0.919815 +0.374785 +0.897292 +0.349405 +0.922483 +0.367954 +0.929637 +0.353305 +0.897378 +0.351690 +0.920469 +0.342036 +0.912483 +0.359828 +0.911835 +0.339827 +0.910110 +0.351185 +0.910110 +0.351185 +0.910110 +0.351185 +0.910110 +0.351185 +0.910110 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.925440 +0.342773 +0.910576 +0.325774 +0.930560 +0.344220 +0.913829 +0.344791 +0.931932 +0.339308 +0.932458 +0.346909 +0.871747 +0.364749 +0.884974 +0.367485 +0.932106 +0.365378 +0.927264 +0.357555 +0.904949 +0.338997 +0.894350 +0.386763 +0.925091 +0.383326 +0.932943 +0.350767 +0.935897 +0.386189 +0.905861 +0.372752 +0.934528 +0.397756 +0.924771 +0.407502 +0.945924 +0.385506 +0.925668 +0.384052 +0.923550 +0.390599 +0.919636 +0.391408 +0.947560 +0.406889 +0.938185 +0.387832 +0.961246 +0.388112 +0.925015 +0.377454 +0.935863 +0.391340 +0.941256 +0.396760 +0.944780 +0.412853 +0.927183 +0.426270 +0.952584 +0.422806 +0.963361 +0.414222 +0.961214 +0.420523 +0.957954 +0.427322 +0.966698 +0.413012 +0.944283 +0.414016 +0.985663 +0.428213 +0.967745 +0.411330 +0.953719 +0.423137 +0.957671 +0.432227 +0.917448 +0.417599 +0.966366 +0.426510 +0.946636 +0.439764 +0.965108 +0.432736 +0.955818 +0.432630 +0.953941 +0.419924 +0.955886 +0.416266 +0.955369 +0.416051 +0.967014 +0.423204 +0.950864 +0.433989 +0.957800 +0.436118 +0.966718 +0.444219 +0.948496 +0.415272 +0.959501 +0.435494 +0.950058 +0.438892 +0.968598 +0.430401 +0.954821 +0.436755 +0.963959 +0.459505 +0.961498 +0.442575 +0.955369 +0.443980 +0.970604 +0.447076 +0.964000 +0.445848 +0.981961 +0.440653 +0.965242 +0.450146 +0.975896 +0.468627 +0.962843 +0.456653 +0.975506 +0.448917 +0.972590 +0.454109 +0.975495 +0.435461 +0.972348 +0.460900 +0.962519 +0.444698 +0.950715 +0.447504 +0.975255 +0.448467 +0.964719 +0.444612 +0.956264 +0.448472 +0.966086 +0.440759 +0.943253 +0.443259 +0.968605 +0.460477 +0.965140 +0.440632 +0.954445 +0.420842 +0.954568 +0.447224 +0.949108 +0.445268 +0.948998 +0.441445 +0.933115 +0.424933 +0.954477 +0.414954 +0.921092 +0.406360 +0.953814 +0.392015 +0.944235 +0.408666 +0.933970 +0.397650 +0.943172 +0.403908 +0.949506 +0.392277 +0.933860 +0.394273 +0.947635 +0.385920 +0.937180 +0.383213 +0.933704 +0.394803 +0.921489 +0.380290 +0.899552 +0.380284 +0.906627 +0.370374 +0.919291 +0.381691 +0.925027 +0.379088 +0.907347 +0.363832 +0.903517 +0.371428 +0.910353 +0.346194 +0.929876 +0.366141 +0.917729 +0.368890 +0.906529 +0.341986 +0.926674 +0.346263 +0.911815 +0.347758 +0.881079 +0.337576 +0.904789 +0.349084 +0.903490 +0.351526 +0.902004 +0.350031 +0.896453 +0.346062 +0.896453 +0.346062 +0.896453 +0.346062 +0.896453 +0.346062 +0.896453 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.880531 +0.359869 +0.886353 +0.380087 +0.906737 +0.354438 +0.868921 +0.347407 +0.865935 +0.377762 +0.907823 +0.390816 +0.932684 +0.415452 +0.936410 +0.365753 +0.916264 +0.382264 +0.903603 +0.356916 +0.920650 +0.366829 +0.921185 +0.355765 +0.896242 +0.383929 +0.924693 +0.346821 +0.907482 +0.372208 +0.959152 +0.395579 +0.937042 +0.390818 +0.900268 +0.393040 +0.930308 +0.424720 +0.908405 +0.392301 +0.970355 +0.406545 +0.921732 +0.415503 +0.956548 +0.389603 +0.939726 +0.404812 +0.982111 +0.399897 +0.937982 +0.382669 +0.929696 +0.399015 +0.948511 +0.408517 +0.956267 +0.395487 +0.950738 +0.399135 +0.958272 +0.413396 +0.950814 +0.436952 +0.954470 +0.421389 +0.974215 +0.413845 +0.978262 +0.415994 +0.951190 +0.417875 +0.977668 +0.405474 +0.974181 +0.435769 +0.955754 +0.414909 +0.959422 +0.422450 +0.945008 +0.444209 +0.974968 +0.444932 +0.964862 +0.427851 +0.973597 +0.427543 +0.965763 +0.435084 +0.968850 +0.418333 +0.948648 +0.428846 +0.960513 +0.407669 +0.960007 +0.410769 +0.962589 +0.428586 +0.951801 +0.439374 +0.961189 +0.421043 +0.969976 +0.422489 +0.962474 +0.427757 +0.961218 +0.428087 +0.962544 +0.431512 +0.975199 +0.449703 +0.964239 +0.436934 +0.958557 +0.445373 +0.975097 +0.454068 +0.965345 +0.439175 +0.960846 +0.438522 +0.974028 +0.439341 +0.979871 +0.440367 +0.974011 +0.445753 +0.974823 +0.457123 +0.971999 +0.443131 +0.969325 +0.458675 +0.973399 +0.445728 +0.981102 +0.440382 +0.962608 +0.427143 +0.975484 +0.449435 +0.977634 +0.449304 +0.950952 +0.440091 +0.957867 +0.456319 +0.963781 +0.428897 +0.987241 +0.446592 +0.946482 +0.450530 +0.954906 +0.441026 +0.963165 +0.413550 +0.954514 +0.432507 +0.955344 +0.433699 +0.942174 +0.435980 +0.934878 +0.399715 +0.942564 +0.430047 +0.930694 +0.416266 +0.949574 +0.403192 +0.966025 +0.413634 +0.979460 +0.406971 +0.966769 +0.379944 +0.956361 +0.401107 +0.952093 +0.405798 +0.948310 +0.372150 +0.916331 +0.390989 +0.928165 +0.371729 +0.920376 +0.382354 +0.916805 +0.372256 +0.905838 +0.377837 +0.930085 +0.385876 +0.903650 +0.362738 +0.915525 +0.358967 +0.915541 +0.351257 +0.903164 +0.335589 +0.936120 +0.348279 +0.910942 +0.362448 +0.906502 +0.348847 +0.902924 +0.361183 +0.903387 +0.342357 +0.877552 +0.356902 +0.889821 +0.346548 +0.917174 +0.344457 +0.886207 +0.343706 +0.894849 +0.349302 +0.894849 +0.349302 +0.894849 +0.349302 +0.894849 +0.349302 +0.894849 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.896635 +0.372795 +0.968524 +0.367648 +0.887237 +0.408726 +0.885082 +0.370476 +0.917584 +0.339182 +0.887614 +0.359307 +0.919179 +0.374693 +0.888431 +0.385950 +0.903215 +0.371363 +0.897820 +0.391238 +0.917636 +0.396432 +0.926258 +0.379329 +0.919822 +0.421506 +0.933134 +0.367363 +0.916224 +0.412937 +0.921389 +0.390871 +0.925437 +0.388382 +0.925199 +0.396872 +0.924286 +0.409264 +0.892192 +0.401032 +0.922615 +0.428702 +0.934120 +0.418546 +0.935821 +0.394831 +0.933697 +0.406550 +0.940074 +0.405054 +0.934500 +0.418597 +0.946024 +0.393950 +0.943398 +0.419125 +0.954567 +0.408667 +0.943789 +0.374696 +0.962719 +0.410517 +0.969653 +0.404854 +0.967209 +0.420282 +0.958652 +0.430098 +0.982655 +0.434128 +0.983688 +0.424088 +0.966747 +0.431272 +0.976755 +0.421051 +0.953075 +0.427965 +0.960335 +0.437823 +0.972820 +0.445913 +0.947525 +0.454622 +0.966808 +0.430135 +0.971921 +0.458644 +0.987301 +0.455751 +0.975275 +0.452406 +0.952065 +0.430953 +0.968005 +0.420832 +0.958607 +0.427526 +0.988386 +0.440921 +0.975628 +0.460827 +0.979104 +0.438136 +0.983009 +0.448916 +0.966628 +0.446310 +0.954882 +0.447040 +0.969318 +0.440999 +0.971635 +0.456073 +0.968937 +0.462090 +0.974210 +0.442831 +0.977501 +0.457083 +0.973496 +0.463585 +0.980604 +0.459693 +0.968411 +0.449597 +0.981895 +0.459379 +0.982232 +0.461958 +0.989147 +0.469392 +0.982440 +0.487237 +0.987345 +0.449128 +0.973057 +0.471355 +0.993165 +0.453122 +0.988417 +0.435313 +0.982221 +0.461568 +0.976670 +0.459139 +0.985932 +0.466653 +0.974673 +0.468447 +0.965566 +0.471338 +0.968711 +0.464601 +0.972964 +0.464400 +0.949629 +0.444628 +0.953051 +0.428511 +0.949115 +0.418946 +0.960060 +0.424046 +0.957329 +0.416227 +0.918090 +0.432495 +0.956254 +0.418903 +0.918590 +0.418998 +0.963106 +0.416201 +0.942652 +0.408256 +0.941231 +0.396247 +0.937877 +0.389393 +0.929937 +0.388299 +0.928836 +0.393091 +0.937008 +0.382486 +0.916671 +0.374347 +0.928778 +0.362107 +0.934493 +0.371333 +0.922014 +0.374957 +0.909832 +0.382107 +0.900845 +0.380499 +0.913427 +0.358891 +0.906428 +0.344654 +0.907636 +0.349305 +0.897612 +0.347448 +0.901134 +0.351245 +0.929428 +0.355911 +0.911543 +0.331736 +0.929506 +0.341398 +0.904223 +0.346599 +0.906806 +0.349714 +0.885898 +0.342161 +0.902794 +0.334149 +0.880559 +0.350107 +0.898499 +0.342008 +0.898499 +0.342008 +0.898499 +0.342008 +0.898499 +0.342008 +0.898499 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.909702 +0.371277 +0.940472 +0.413972 +0.917093 +0.395247 +0.940245 +0.336285 +0.871770 +0.382298 +0.905873 +0.428114 +0.933714 +0.372022 +0.946962 +0.386188 +0.917540 +0.393322 +0.932853 +0.400695 +0.905907 +0.432769 +0.925749 +0.402208 +0.924790 +0.417289 +0.911195 +0.417966 +0.930345 +0.392464 +0.922892 +0.403707 +0.920218 +0.393574 +0.924349 +0.395888 +0.963159 +0.412136 +0.925118 +0.420128 +0.932964 +0.434200 +0.953995 +0.420795 +0.932966 +0.422115 +0.920124 +0.411600 +0.963853 +0.416981 +0.940331 +0.431331 +0.946065 +0.419277 +0.936439 +0.426558 +0.946425 +0.430059 +0.955255 +0.442965 +0.944271 +0.429749 +0.967093 +0.429529 +0.982033 +0.444751 +0.970998 +0.436495 +0.958221 +0.435517 +0.971852 +0.459126 +0.987604 +0.439876 +0.979776 +0.455059 +0.964858 +0.427761 +0.951177 +0.436346 +0.967332 +0.459649 +0.978144 +0.465018 +0.957226 +0.450993 +0.945894 +0.447623 +0.963831 +0.468001 +0.964064 +0.464648 +0.986341 +0.469349 +0.944799 +0.449070 +0.960215 +0.445307 +0.953970 +0.462018 +0.987905 +0.465110 +0.981078 +0.449875 +0.958583 +0.458359 +0.977997 +0.465627 +0.966764 +0.444848 +0.958306 +0.458336 +0.955501 +0.473200 +0.955431 +0.463559 +0.969243 +0.477536 +0.965200 +0.458362 +0.962746 +0.462918 +0.983732 +0.476648 +0.963734 +0.476217 +0.973020 +0.474102 +0.995968 +0.487901 +0.987698 +0.473155 +0.968506 +0.472143 +0.984822 +0.477267 +0.979715 +0.485201 +0.967524 +0.469187 +1.003026 +0.493429 +0.968292 +0.467264 +0.984478 +0.481645 +0.960069 +0.483662 +0.980482 +0.472672 +0.942829 +0.467163 +0.985615 +0.480217 +0.990601 +0.495467 +0.971436 +0.483916 +0.963108 +0.465763 +0.987749 +0.442908 +0.954744 +0.446165 +0.978793 +0.446303 +0.986486 +0.454453 +0.964138 +0.444488 +0.929567 +0.425091 +0.947294 +0.441627 +0.989722 +0.415729 +0.976710 +0.427977 +0.974297 +0.433900 +0.978425 +0.423906 +0.953332 +0.411819 +0.969265 +0.407577 +0.950095 +0.413463 +0.951877 +0.422503 +0.938482 +0.409150 +0.914714 +0.395571 +0.938041 +0.369164 +0.932792 +0.422410 +0.932537 +0.405887 +0.937027 +0.400266 +0.931070 +0.398693 +0.943788 +0.372540 +0.910183 +0.348177 +0.908448 +0.361951 +0.927645 +0.386786 +0.944075 +0.375174 +0.903288 +0.381064 +0.925686 +0.387398 +0.905377 +0.382139 +0.899924 +0.356378 +0.934655 +0.373056 +0.928646 +0.359965 +0.913808 +0.339809 +0.921075 +0.363133 +0.921075 +0.363133 +0.921075 +0.363133 +0.921075 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.874678 +0.390171 +0.901426 +0.412364 +0.892000 +0.385213 +0.850925 +0.372301 +0.881108 +0.412998 +0.898176 +0.396223 +0.869427 +0.402083 +0.859099 +0.389925 +0.884270 +0.418509 +0.899794 +0.400853 +0.895968 +0.403526 +0.910164 +0.372806 +0.906067 +0.405984 +0.909943 +0.412651 +0.914046 +0.378943 +0.904237 +0.398317 +0.908922 +0.398832 +0.911066 +0.406761 +0.930068 +0.442819 +0.910832 +0.412355 +0.908539 +0.439397 +0.901035 +0.441937 +0.913590 +0.429154 +0.894264 +0.410831 +0.926629 +0.431267 +0.924308 +0.431071 +0.931813 +0.431676 +0.917759 +0.433243 +0.918249 +0.437573 +0.944251 +0.423945 +0.906907 +0.438006 +0.932368 +0.446096 +0.937042 +0.442954 +0.945954 +0.451091 +0.935868 +0.446897 +0.951841 +0.454619 +0.948829 +0.443472 +0.933149 +0.450577 +0.951810 +0.453677 +0.931402 +0.448822 +0.948035 +0.455664 +0.944444 +0.464604 +0.918275 +0.463781 +0.940454 +0.448762 +0.955965 +0.461156 +0.959304 +0.468168 +0.947209 +0.467094 +0.943571 +0.449191 +0.944447 +0.441301 +0.922905 +0.455178 +0.953617 +0.458285 +0.940284 +0.458760 +0.953482 +0.461137 +0.958800 +0.446815 +0.942332 +0.460129 +0.932072 +0.448653 +0.930726 +0.476271 +0.953598 +0.473771 +0.959691 +0.467054 +0.947915 +0.466234 +0.950821 +0.476299 +0.953588 +0.477421 +0.950610 +0.473199 +0.930603 +0.478095 +0.958634 +0.489183 +0.969883 +0.466679 +0.974322 +0.488621 +0.972842 +0.473122 +0.953678 +0.466345 +0.970535 +0.462654 +0.971854 +0.476683 +0.933453 +0.468416 +0.947137 +0.462340 +0.959072 +0.474877 +0.946783 +0.448353 +0.962254 +0.463306 +0.940360 +0.477280 +0.954091 +0.464546 +0.930507 +0.466086 +0.955012 +0.482347 +0.933757 +0.473181 +0.957403 +0.462205 +0.950871 +0.469535 +0.951581 +0.466440 +0.947833 +0.463884 +0.919513 +0.455751 +0.920550 +0.457828 +0.949518 +0.462770 +0.952062 +0.465535 +0.956538 +0.424071 +0.943193 +0.437372 +0.938690 +0.432060 +0.947413 +0.416612 +0.927286 +0.410513 +0.931111 +0.419437 +0.935474 +0.411254 +0.904039 +0.411534 +0.898599 +0.399143 +0.920812 +0.411404 +0.917149 +0.391837 +0.909005 +0.403498 +0.884090 +0.392545 +0.896704 +0.393543 +0.907701 +0.371697 +0.907837 +0.384039 +0.891542 +0.366952 +0.895008 +0.385475 +0.883904 +0.378173 +0.901258 +0.395225 +0.914013 +0.394058 +0.879752 +0.377737 +0.883758 +0.380410 +0.878023 +0.371347 +0.892507 +0.384069 +0.892507 +0.384069 +0.892507 +0.384069 +0.892507 +0.384069 +0.892507 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.861589 +0.377661 +0.884523 +0.367471 +0.859931 +0.357092 +0.848276 +0.390290 +0.876561 +0.385602 +0.846538 +0.374842 +0.904621 +0.386619 +0.870137 +0.389647 +0.876479 +0.374051 +0.904842 +0.376956 +0.864890 +0.405826 +0.914888 +0.392291 +0.889684 +0.429740 +0.893628 +0.401026 +0.923624 +0.369842 +0.911402 +0.392198 +0.929056 +0.421494 +0.928590 +0.409731 +0.906537 +0.429979 +0.895369 +0.408805 +0.913934 +0.403500 +0.896340 +0.422801 +0.924652 +0.428055 +0.902508 +0.418429 +0.908900 +0.427268 +0.906782 +0.439302 +0.916796 +0.418585 +0.920575 +0.414495 +0.920435 +0.420883 +0.927019 +0.429303 +0.903824 +0.423239 +0.974479 +0.449926 +0.938506 +0.443169 +0.948851 +0.447971 +0.929877 +0.445950 +0.939534 +0.438515 +0.931638 +0.444821 +0.945689 +0.435034 +0.952948 +0.462027 +0.930315 +0.457682 +0.948543 +0.457452 +0.941134 +0.470937 +0.948985 +0.461082 +0.939474 +0.436514 +0.954037 +0.470715 +0.948466 +0.460342 +0.945862 +0.466799 +0.952077 +0.455379 +0.927181 +0.447533 +0.949740 +0.442806 +0.955876 +0.455979 +0.950467 +0.451236 +0.937410 +0.462153 +0.955322 +0.454662 +0.944419 +0.457177 +0.942395 +0.455568 +0.943306 +0.487117 +0.950757 +0.471009 +0.942197 +0.473640 +0.946501 +0.480929 +0.940446 +0.467154 +0.959744 +0.485574 +0.952053 +0.467640 +0.954342 +0.475259 +0.971637 +0.481890 +0.963305 +0.495382 +0.958589 +0.498618 +0.956567 +0.472080 +0.964560 +0.488754 +0.967084 +0.478498 +0.959464 +0.478618 +0.936086 +0.478092 +0.945469 +0.467585 +0.976117 +0.478159 +0.955270 +0.479148 +0.934664 +0.459279 +0.943829 +0.474972 +0.950799 +0.470072 +0.959387 +0.468640 +0.927211 +0.465342 +0.929999 +0.446722 +0.932775 +0.477367 +0.945358 +0.473712 +0.953168 +0.463378 +0.924173 +0.441969 +0.919273 +0.455120 +0.924141 +0.446027 +0.946042 +0.441569 +0.954390 +0.436128 +0.954323 +0.444450 +0.930532 +0.428044 +0.919348 +0.426111 +0.938283 +0.430838 +0.917565 +0.420007 +0.915327 +0.421327 +0.919302 +0.412930 +0.907031 +0.411373 +0.903193 +0.422057 +0.916770 +0.402975 +0.917816 +0.402118 +0.885140 +0.388829 +0.899132 +0.390214 +0.872299 +0.406489 +0.916973 +0.368890 +0.889009 +0.390193 +0.898300 +0.380726 +0.906851 +0.391409 +0.889113 +0.387307 +0.893643 +0.376805 +0.889556 +0.399088 +0.911650 +0.385240 +0.885972 +0.366335 +0.878586 +0.366186 +0.887529 +0.403727 +0.892069 +0.372587 +0.892069 +0.372587 +0.892069 +0.372587 +0.892069 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.896349 +0.379947 +0.866153 +0.393395 +0.910725 +0.382640 +0.909129 +0.377083 +0.869195 +0.380119 +0.898804 +0.352811 +0.869847 +0.375401 +0.897267 +0.375026 +0.918003 +0.396118 +0.911357 +0.381001 +0.907804 +0.415433 +0.895571 +0.389698 +0.894687 +0.384882 +0.893704 +0.376094 +0.914706 +0.424330 +0.912852 +0.412178 +0.922638 +0.411721 +0.900579 +0.402336 +0.907883 +0.416109 +0.922875 +0.413487 +0.897815 +0.408672 +0.917726 +0.427720 +0.904324 +0.409870 +0.915088 +0.421208 +0.927334 +0.406550 +0.923223 +0.418121 +0.914432 +0.447025 +0.931413 +0.418638 +0.934771 +0.417165 +0.906814 +0.432890 +0.961673 +0.448906 +0.936910 +0.448697 +0.949362 +0.429358 +0.948799 +0.445254 +0.949760 +0.426616 +0.948706 +0.454466 +0.941260 +0.468352 +0.945817 +0.449412 +0.940759 +0.454818 +0.944128 +0.474254 +0.960400 +0.451781 +0.960536 +0.453179 +0.942484 +0.469062 +0.951263 +0.443986 +0.941991 +0.447199 +0.951548 +0.464980 +0.957119 +0.456645 +0.938634 +0.452955 +0.950868 +0.464687 +0.957420 +0.434622 +0.938867 +0.438346 +0.936571 +0.444704 +0.942212 +0.450421 +0.950747 +0.447564 +0.937337 +0.458344 +0.948698 +0.500558 +0.955522 +0.470931 +0.945689 +0.473556 +0.943062 +0.474768 +0.944160 +0.478913 +0.949875 +0.484660 +0.953152 +0.472307 +0.953102 +0.474986 +0.966654 +0.472552 +0.967330 +0.473252 +0.954400 +0.467456 +0.975521 +0.468577 +0.963991 +0.466993 +0.964539 +0.483740 +0.964844 +0.468103 +0.944557 +0.459364 +0.941746 +0.469627 +0.945981 +0.481169 +0.958143 +0.467856 +0.948363 +0.491190 +0.957568 +0.457319 +0.942331 +0.469982 +0.930224 +0.482096 +0.942574 +0.462572 +0.945491 +0.447998 +0.953074 +0.464825 +0.946830 +0.456146 +0.958280 +0.482754 +0.936117 +0.462918 +0.929182 +0.461285 +0.936420 +0.466804 +0.953705 +0.443999 +0.943354 +0.436673 +0.942321 +0.445556 +0.925849 +0.427567 +0.925057 +0.431802 +0.947345 +0.420431 +0.932728 +0.424277 +0.929495 +0.414772 +0.914015 +0.422906 +0.921304 +0.429865 +0.897039 +0.399195 +0.914511 +0.413842 +0.916940 +0.397150 +0.923284 +0.391090 +0.883265 +0.388629 +0.897490 +0.397869 +0.912763 +0.397890 +0.901523 +0.410452 +0.899265 +0.370138 +0.902964 +0.378449 +0.897065 +0.398125 +0.895238 +0.360587 +0.884644 +0.390076 +0.890982 +0.363288 +0.897963 +0.393909 +0.880383 +0.404228 +0.877964 +0.381995 +0.889776 +0.387142 +0.889776 +0.387142 +0.889776 +0.387142 +0.889776 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.869687 +0.389896 +0.918118 +0.410945 +0.873692 +0.406409 +0.851561 +0.376525 +0.883809 +0.386755 +0.850031 +0.423889 +0.869641 +0.345956 +0.902372 +0.369400 +0.895261 +0.358829 +0.897573 +0.386582 +0.871825 +0.418044 +0.883617 +0.388932 +0.904910 +0.378460 +0.914117 +0.417649 +0.897539 +0.406816 +0.904465 +0.385992 +0.917698 +0.383066 +0.905176 +0.394882 +0.903106 +0.403307 +0.926239 +0.425121 +0.913239 +0.427673 +0.925367 +0.420946 +0.912818 +0.403739 +0.922361 +0.415906 +0.946079 +0.426081 +0.943931 +0.438138 +0.922177 +0.431447 +0.927891 +0.400208 +0.926073 +0.438398 +0.947772 +0.433715 +0.937601 +0.439449 +0.932306 +0.431373 +0.956452 +0.453469 +0.954692 +0.459812 +0.943909 +0.444522 +0.948025 +0.448105 +0.957960 +0.446039 +0.958823 +0.458026 +0.975922 +0.452961 +0.941230 +0.463817 +0.941730 +0.459760 +0.954121 +0.465751 +0.952712 +0.457534 +0.950252 +0.458816 +0.950587 +0.461130 +0.953447 +0.444895 +0.950045 +0.474408 +0.950881 +0.474612 +0.959247 +0.443947 +0.934971 +0.459893 +0.951960 +0.463218 +0.947952 +0.459694 +0.953221 +0.424023 +0.946705 +0.454761 +0.951553 +0.452596 +0.950626 +0.451097 +0.945092 +0.461403 +0.947003 +0.482503 +0.963851 +0.471050 +0.949033 +0.468044 +0.940624 +0.474881 +0.936789 +0.481472 +0.961460 +0.480627 +0.955436 +0.469452 +0.955235 +0.461710 +0.949490 +0.474579 +0.976410 +0.476040 +0.952045 +0.489780 +0.956901 +0.465274 +0.960582 +0.484957 +0.969633 +0.474006 +0.961035 +0.468884 +0.943636 +0.463598 +0.948010 +0.474110 +0.946552 +0.471016 +0.949655 +0.460416 +0.958902 +0.459986 +0.934498 +0.465848 +0.935743 +0.462499 +0.941388 +0.470364 +0.946148 +0.462717 +0.933928 +0.474810 +0.955986 +0.473383 +0.961094 +0.473899 +0.950945 +0.468386 +0.927857 +0.454255 +0.922218 +0.462111 +0.908404 +0.447794 +0.957378 +0.424093 +0.946855 +0.445273 +0.930670 +0.428023 +0.955814 +0.439990 +0.953788 +0.425174 +0.955099 +0.410965 +0.923332 +0.419748 +0.933131 +0.443020 +0.922764 +0.420764 +0.912525 +0.420202 +0.898644 +0.389445 +0.882949 +0.398328 +0.909939 +0.388915 +0.882285 +0.418929 +0.880584 +0.401713 +0.904435 +0.415018 +0.923730 +0.403576 +0.902994 +0.378267 +0.903433 +0.398309 +0.896127 +0.381498 +0.886555 +0.387247 +0.909024 +0.381999 +0.909957 +0.401522 +0.890117 +0.385943 +0.835410 +0.394799 +0.857178 +0.367476 +0.872343 +0.392479 +0.897614 +0.364137 +0.854977 +0.384918 +0.854977 +0.384918 +0.854977 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.887385 +0.415278 +0.863717 +0.368935 +0.896270 +0.392368 +0.869637 +0.421652 +0.896249 +0.431814 +0.903405 +0.382233 +0.863767 +0.389936 +0.926089 +0.439843 +0.922204 +0.431319 +0.919566 +0.411918 +0.890900 +0.421896 +0.938452 +0.424908 +0.923900 +0.445125 +0.919723 +0.427715 +0.923556 +0.421599 +0.916304 +0.410809 +0.910716 +0.424317 +0.925298 +0.449609 +0.951554 +0.438772 +0.946170 +0.440914 +0.930729 +0.421396 +0.938161 +0.459145 +0.933271 +0.455383 +0.942742 +0.443469 +0.921525 +0.422244 +0.914244 +0.479010 +0.975664 +0.436302 +0.952182 +0.456309 +0.946102 +0.456541 +0.964518 +0.468387 +0.944356 +0.443637 +0.968653 +0.479325 +0.960631 +0.475803 +0.982715 +0.448589 +0.951974 +0.475616 +0.972320 +0.476962 +0.983785 +0.480091 +0.955172 +0.480101 +0.970986 +0.501812 +0.985968 +0.493598 +0.977488 +0.486217 +0.952258 +0.485746 +0.978780 +0.471696 +0.953868 +0.483813 +0.948908 +0.494345 +0.973930 +0.481364 +0.965101 +0.487343 +0.968176 +0.494253 +0.967511 +0.485744 +0.978662 +0.496852 +0.970814 +0.476960 +0.987151 +0.495345 +0.988887 +0.479619 +0.973230 +0.496467 +0.975711 +0.502797 +0.993430 +0.489969 +0.967402 +0.496525 +0.979619 +0.506661 +0.981802 +0.517412 +0.984089 +0.521638 +0.996460 +0.517447 +0.966019 +0.501574 +0.979704 +0.511824 +0.968147 +0.495288 +0.971195 +0.477768 +0.965759 +0.520859 +0.973270 +0.506683 +0.982309 +0.495235 +0.974087 +0.508297 +0.970911 +0.496890 +0.972381 +0.481447 +0.974537 +0.507564 +0.977701 +0.492168 +0.956611 +0.491358 +0.984885 +0.470803 +0.960699 +0.491148 +0.969830 +0.503895 +0.982816 +0.479698 +0.977681 +0.510084 +0.980448 +0.490114 +0.951677 +0.485718 +0.978175 +0.481433 +0.955646 +0.488790 +0.962701 +0.492385 +0.950559 +0.490489 +0.961392 +0.483186 +0.947459 +0.455904 +0.936582 +0.480258 +0.942868 +0.415681 +0.965781 +0.426426 +0.933930 +0.428001 +0.951230 +0.415989 +0.946636 +0.438790 +0.951718 +0.449353 +0.927112 +0.412625 +0.924111 +0.409992 +0.913074 +0.398774 +0.913741 +0.406404 +0.893261 +0.410272 +0.916408 +0.399349 +0.908806 +0.425287 +0.889174 +0.408426 +0.898992 +0.411201 +0.894440 +0.396755 +0.914214 +0.375962 +0.925449 +0.365894 +0.887702 +0.379230 +0.898495 +0.392939 +0.905062 +0.382330 +0.892062 +0.388190 +0.909922 +0.372335 +0.906664 +0.357194 +0.899776 +0.398841 +0.924211 +0.402082 +0.892271 +0.373642 +0.883184 +0.419760 +0.905419 +0.391522 +0.905419 +0.391522 +0.905419 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.907744 +0.456731 +0.876784 +0.400645 +0.878523 +0.474132 +0.899943 +0.425369 +0.944766 +0.470693 +0.880748 +0.447175 +0.891006 +0.450432 +0.925190 +0.408584 +0.915234 +0.486578 +0.895733 +0.433222 +0.932452 +0.441318 +0.949713 +0.425562 +0.919507 +0.451045 +0.925738 +0.427387 +0.925612 +0.422022 +0.922223 +0.424125 +0.924306 +0.477583 +0.943350 +0.469639 +0.926746 +0.453508 +0.942428 +0.461855 +0.930230 +0.470579 +0.944967 +0.471361 +0.953211 +0.481434 +0.937833 +0.468917 +0.960948 +0.469912 +0.946712 +0.462536 +0.954761 +0.474219 +0.962088 +0.480461 +0.958181 +0.475832 +0.961190 +0.483417 +0.944508 +0.473218 +0.943874 +0.505645 +0.980358 +0.496255 +1.001852 +0.491390 +0.974755 +0.493971 +0.970006 +0.490898 +0.974244 +0.511126 +0.977239 +0.486605 +0.975421 +0.499972 +0.982770 +0.490026 +0.983433 +0.527471 +0.958205 +0.514786 +0.986780 +0.525945 +0.947250 +0.504255 +0.961591 +0.507568 +0.962868 +0.515146 +0.973774 +0.515528 +0.975413 +0.511156 +0.965528 +0.496343 +0.982028 +0.490855 +0.967270 +0.499273 +0.986270 +0.509459 +0.992153 +0.500764 +0.971480 +0.505151 +0.978324 +0.509537 +0.980995 +0.512498 +0.967039 +0.508656 +0.966787 +0.523517 +0.971780 +0.515414 +0.970378 +0.532700 +0.984889 +0.530731 +0.973638 +0.525681 +0.975332 +0.514064 +0.970562 +0.501826 +0.956755 +0.514622 +0.966237 +0.499661 +0.974529 +0.521348 +0.971887 +0.509113 +0.970994 +0.496899 +0.965365 +0.497944 +0.967949 +0.504196 +0.965289 +0.517031 +0.962875 +0.512900 +0.987346 +0.517949 +0.959926 +0.518196 +0.969824 +0.502044 +0.959317 +0.509349 +0.979454 +0.516076 +0.966802 +0.518637 +0.969017 +0.515458 +0.965325 +0.506667 +0.965555 +0.504695 +0.954197 +0.494751 +0.963705 +0.519765 +0.961170 +0.506347 +0.948149 +0.499782 +0.940328 +0.472681 +0.920145 +0.515424 +0.978848 +0.451058 +0.967965 +0.446151 +0.958256 +0.432062 +0.972008 +0.461220 +0.958929 +0.470162 +0.958959 +0.434409 +0.942410 +0.455904 +0.927037 +0.452374 +0.929392 +0.457709 +0.928100 +0.424879 +0.940527 +0.426829 +0.938030 +0.444490 +0.937904 +0.460542 +0.929760 +0.441955 +0.918214 +0.435732 +0.930980 +0.395005 +0.921352 +0.388411 +0.913458 +0.419288 +0.930444 +0.408116 +0.911170 +0.383629 +0.891493 +0.369697 +0.911428 +0.396838 +0.883216 +0.397343 +0.915228 +0.360219 +0.915322 +0.389838 +0.892380 +0.408422 +0.896317 +0.408741 +0.896217 +0.431678 +0.901339 +0.402334 +0.901339 +0.402334 +0.901339 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.890845 +0.439417 +0.854697 +0.443422 +0.912228 +0.468290 +0.874800 +0.414865 +0.885507 +0.435096 +0.874150 +0.427052 +0.897422 +0.426874 +0.889157 +0.388180 +0.878721 +0.447941 +0.901042 +0.452572 +0.910668 +0.454701 +0.917212 +0.438935 +0.909208 +0.448497 +0.914692 +0.461399 +0.906686 +0.464651 +0.905452 +0.441019 +0.901871 +0.446993 +0.893104 +0.444275 +0.908340 +0.465934 +0.903849 +0.448197 +0.919919 +0.456972 +0.926578 +0.474221 +0.918473 +0.460298 +0.897469 +0.458765 +0.923790 +0.478610 +0.941317 +0.460220 +0.923710 +0.461527 +0.938348 +0.461079 +0.927758 +0.496576 +0.940823 +0.473771 +0.930459 +0.476561 +0.917372 +0.459916 +0.945451 +0.500827 +0.935765 +0.486719 +0.944100 +0.498912 +0.960612 +0.496722 +0.966089 +0.504904 +0.955081 +0.487289 +0.982191 +0.495980 +0.946774 +0.501777 +0.952338 +0.508104 +0.938898 +0.516726 +0.969858 +0.511476 +0.953233 +0.517518 +0.946310 +0.501794 +0.962560 +0.519552 +0.962262 +0.513257 +0.955109 +0.511882 +0.949763 +0.507516 +0.947463 +0.501376 +0.946879 +0.502683 +0.953585 +0.500036 +0.944005 +0.506312 +0.968116 +0.515122 +0.953307 +0.506606 +0.954358 +0.504692 +0.947828 +0.509862 +0.952503 +0.529423 +0.943231 +0.510569 +0.945697 +0.517328 +0.955726 +0.524086 +0.952247 +0.520859 +0.953649 +0.514004 +0.941426 +0.497025 +0.929420 +0.503197 +0.959646 +0.513015 +0.956321 +0.499928 +0.956321 +0.514710 +0.944647 +0.497691 +0.950663 +0.502774 +0.950709 +0.503463 +0.949858 +0.516899 +0.953796 +0.512654 +0.922038 +0.499412 +0.946664 +0.512286 +0.951912 +0.515768 +0.956721 +0.499228 +0.925794 +0.505428 +0.936144 +0.491997 +0.936527 +0.501006 +0.906649 +0.483572 +0.929509 +0.483646 +0.926186 +0.498511 +0.954862 +0.505818 +0.935808 +0.497436 +0.926018 +0.499765 +0.915724 +0.477698 +0.919542 +0.472655 +0.933434 +0.484356 +0.949240 +0.461560 +0.944572 +0.481508 +0.922980 +0.493874 +0.940438 +0.479869 +0.911249 +0.467657 +0.931007 +0.488991 +0.933553 +0.445810 +0.918253 +0.471337 +0.882661 +0.442996 +0.921548 +0.455904 +0.903818 +0.450061 +0.895688 +0.472179 +0.911623 +0.435705 +0.889909 +0.417667 +0.920930 +0.434155 +0.892492 +0.423548 +0.914711 +0.409718 +0.859046 +0.424320 +0.903324 +0.432757 +0.894345 +0.414825 +0.901498 +0.413317 +0.899851 +0.408767 +0.906286 +0.410330 +0.901206 +0.398484 +0.877505 +0.422823 +0.863850 +0.397776 +0.869014 +0.397164 +0.880854 +0.406361 +0.880854 +0.406361 +0.880854 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.420835 +0.883574 +0.420835 +0.883574 +0.420835 +0.883574 +0.420835 +0.883574 +0.420835 +0.883574 +0.420835 +0.883574 +0.420835 +0.868541 +0.433712 +0.884482 +0.419928 +0.842309 +0.440310 +0.923930 +0.402267 +0.903713 +0.405372 +0.897173 +0.446383 +0.866845 +0.443332 +0.883452 +0.411055 +0.924263 +0.432473 +0.890177 +0.455390 +0.897218 +0.453807 +0.877042 +0.496341 +0.895798 +0.439854 +0.884677 +0.444925 +0.890137 +0.440380 +0.891596 +0.448796 +0.913244 +0.445473 +0.907530 +0.467587 +0.911996 +0.433148 +0.903258 +0.471182 +0.912343 +0.459107 +0.925778 +0.474315 +0.892648 +0.472552 +0.926151 +0.466493 +0.900518 +0.481456 +0.920840 +0.469805 +0.922209 +0.468850 +0.919784 +0.454005 +0.919942 +0.486581 +0.925105 +0.470568 +0.922813 +0.471337 +0.921160 +0.455298 +0.951231 +0.483125 +0.934410 +0.487991 +0.935818 +0.503861 +0.941070 +0.482650 +0.960854 +0.501311 +0.943379 +0.484543 +0.944218 +0.489630 +0.945588 +0.503383 +0.952297 +0.491832 +0.952455 +0.510987 +0.957843 +0.520697 +0.959188 +0.517022 +0.948046 +0.516412 +0.967046 +0.516787 +0.963019 +0.505481 +0.954039 +0.510949 +0.961552 +0.501955 +0.944759 +0.477946 +0.963201 +0.502841 +0.945836 +0.496959 +0.956803 +0.499207 +0.958694 +0.493301 +0.955435 +0.508217 +0.959471 +0.500189 +0.943140 +0.503860 +0.959174 +0.525044 +0.962743 +0.504110 +0.941346 +0.526715 +0.961222 +0.524682 +0.960560 +0.513482 +0.968553 +0.528947 +0.955973 +0.498492 +0.969682 +0.523733 +0.950494 +0.518562 +0.939938 +0.500791 +0.943126 +0.513280 +0.965145 +0.509627 +0.957374 +0.511757 +0.937245 +0.502008 +0.953614 +0.518118 +0.947348 +0.518969 +0.950032 +0.499659 +0.950449 +0.511127 +0.943744 +0.511738 +0.940141 +0.522578 +0.934698 +0.514131 +0.933514 +0.518007 +0.951230 +0.497476 +0.938187 +0.511865 +0.931973 +0.493977 +0.938593 +0.500279 +0.932578 +0.511830 +0.923785 +0.495601 +0.938188 +0.508482 +0.904512 +0.495458 +0.916813 +0.495606 +0.929964 +0.481389 +0.937356 +0.463584 +0.934342 +0.488835 +0.934660 +0.463743 +0.939323 +0.471936 +0.921768 +0.470922 +0.908466 +0.445184 +0.910795 +0.449143 +0.900487 +0.448690 +0.880430 +0.412143 +0.911191 +0.462129 +0.893767 +0.441883 +0.879823 +0.452239 +0.874509 +0.463278 +0.910279 +0.456627 +0.894664 +0.434114 +0.907512 +0.420440 +0.864302 +0.403788 +0.924494 +0.428467 +0.898818 +0.406732 +0.879386 +0.414626 +0.839339 +0.417756 +0.870312 +0.408968 +0.869193 +0.391970 +0.879174 +0.422749 +0.860907 +0.408055 +0.889085 +0.453525 +0.863699 +0.412356 +0.888193 +0.382540 +0.871230 +0.424645 +0.871230 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.895718 +0.430618 +0.901514 +0.424048 +0.893040 +0.431768 +0.934630 +0.416575 +0.859485 +0.443511 +0.864238 +0.423639 +0.918606 +0.436468 +0.916700 +0.426449 +0.921933 +0.427672 +0.890971 +0.442674 +0.912622 +0.457609 +0.919557 +0.455101 +0.898923 +0.447252 +0.910575 +0.434092 +0.937063 +0.476695 +0.928672 +0.458902 +0.934153 +0.462152 +0.916512 +0.480485 +0.915186 +0.478158 +0.928362 +0.492629 +0.933754 +0.478166 +0.935232 +0.492176 +0.922961 +0.460580 +0.919941 +0.482946 +0.931217 +0.481873 +0.942303 +0.488724 +0.913094 +0.448543 +0.949914 +0.482927 +0.952574 +0.493530 +0.975231 +0.504954 +0.953285 +0.512200 +0.957816 +0.507923 +0.957096 +0.500007 +0.967919 +0.508080 +0.955947 +0.504551 +0.951878 +0.501215 +0.944633 +0.514539 +0.954265 +0.497981 +0.953990 +0.507007 +0.947247 +0.514051 +0.974882 +0.517413 +0.948172 +0.507855 +0.944697 +0.522035 +0.957542 +0.505023 +0.953449 +0.501197 +0.954830 +0.509289 +0.949488 +0.489884 +0.949281 +0.508352 +0.955461 +0.512800 +0.962691 +0.501827 +0.948764 +0.504278 +0.927710 +0.505920 +0.967559 +0.526345 +0.951496 +0.512074 +0.956292 +0.515916 +0.949990 +0.513186 +0.938604 +0.509879 +0.957810 +0.511406 +0.936607 +0.513580 +0.960871 +0.515278 +0.961201 +0.519791 +0.935432 +0.509242 +0.951975 +0.516549 +0.955312 +0.509563 +0.954268 +0.517052 +0.950926 +0.505155 +0.940852 +0.503552 +0.949796 +0.503904 +0.954389 +0.501059 +0.944567 +0.500096 +0.944393 +0.516953 +0.931369 +0.508651 +0.945605 +0.506703 +0.929226 +0.503951 +0.928851 +0.501476 +0.945222 +0.499708 +0.941911 +0.494490 +0.936960 +0.489040 +0.926710 +0.503683 +0.928652 +0.508858 +0.917541 +0.480344 +0.921694 +0.501722 +0.928291 +0.501174 +0.942931 +0.481720 +0.938070 +0.479367 +0.943460 +0.467074 +0.940641 +0.473788 +0.946460 +0.473864 +0.924026 +0.468646 +0.910587 +0.448332 +0.908684 +0.431901 +0.910061 +0.455272 +0.886081 +0.443529 +0.896534 +0.450324 +0.906447 +0.453830 +0.875414 +0.453737 +0.899369 +0.435747 +0.889195 +0.434859 +0.900407 +0.428770 +0.894325 +0.420007 +0.850407 +0.404758 +0.905033 +0.425224 +0.892216 +0.422131 +0.882066 +0.417770 +0.881862 +0.401317 +0.909269 +0.429799 +0.888805 +0.403286 +0.880687 +0.432803 +0.916945 +0.432204 +0.850683 +0.388230 +0.855940 +0.397464 +0.893576 +0.390339 +0.874523 +0.405966 +0.874523 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.420593 +0.904650 +0.420593 +0.904650 +0.420593 +0.904650 +0.420593 +0.904650 +0.420593 +0.904650 +0.420593 +0.904650 +0.420593 +0.918646 +0.442123 +0.905653 +0.420004 +0.927878 +0.454590 +0.880419 +0.387186 +0.856958 +0.414720 +0.895302 +0.404296 +0.894887 +0.428608 +0.955956 +0.414262 +0.940455 +0.415060 +0.883094 +0.440213 +0.915835 +0.428965 +0.896798 +0.458293 +0.944800 +0.448787 +0.930148 +0.441564 +0.912223 +0.446777 +0.906316 +0.416083 +0.910894 +0.474491 +0.932957 +0.461439 +0.908868 +0.473945 +0.941283 +0.476867 +0.953995 +0.487764 +0.913652 +0.496826 +0.923300 +0.455356 +0.944152 +0.468573 +0.940576 +0.469488 +0.923236 +0.468185 +0.932648 +0.492528 +0.935770 +0.468970 +0.916671 +0.487652 +0.925100 +0.486440 +0.919342 +0.483075 +0.916805 +0.507382 +0.958705 +0.488366 +0.937980 +0.493296 +0.956878 +0.483633 +0.956130 +0.493531 +0.949902 +0.495912 +0.962611 +0.510420 +0.946807 +0.516597 +0.976521 +0.520836 +0.964018 +0.502753 +0.963683 +0.517098 +0.964831 +0.507646 +0.981681 +0.521828 +0.957690 +0.498003 +0.950282 +0.514265 +0.963802 +0.509285 +0.956029 +0.508097 +0.953257 +0.507138 +0.961246 +0.492341 +0.966507 +0.490770 +0.953013 +0.497029 +0.942375 +0.490501 +0.941885 +0.505414 +0.962131 +0.508052 +0.975872 +0.501734 +0.949468 +0.512159 +0.953513 +0.543300 +0.955258 +0.517428 +0.960145 +0.524968 +0.954943 +0.515761 +0.952045 +0.512368 +0.934862 +0.509194 +0.928012 +0.503244 +0.949816 +0.508773 +0.969128 +0.518622 +0.946959 +0.513142 +0.970376 +0.521630 +0.950534 +0.510045 +0.926589 +0.515481 +0.966048 +0.503271 +0.942258 +0.507442 +0.944834 +0.507500 +0.971507 +0.511277 +0.934678 +0.504710 +0.940488 +0.503806 +0.947840 +0.506671 +0.946099 +0.501040 +0.932294 +0.492245 +0.931804 +0.513810 +0.940284 +0.501977 +0.931978 +0.496253 +0.924708 +0.490105 +0.931414 +0.483084 +0.907786 +0.493017 +0.927247 +0.478867 +0.917384 +0.495174 +0.906147 +0.496146 +0.947563 +0.469514 +0.957392 +0.459865 +0.940903 +0.474802 +0.948089 +0.470486 +0.937973 +0.470827 +0.931431 +0.451064 +0.905624 +0.460969 +0.921656 +0.463759 +0.903828 +0.453615 +0.896938 +0.458780 +0.910036 +0.450782 +0.911721 +0.453499 +0.895415 +0.439910 +0.888617 +0.447726 +0.886197 +0.442661 +0.905655 +0.458893 +0.901572 +0.404840 +0.885856 +0.417620 +0.898061 +0.399761 +0.906006 +0.416981 +0.874817 +0.390391 +0.855025 +0.422175 +0.858491 +0.409325 +0.879290 +0.389925 +0.901629 +0.386377 +0.859052 +0.408031 +0.847854 +0.391389 +0.886408 +0.407394 +0.845536 +0.378170 +0.864438 +0.402271 +0.864438 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.862415 +0.422200 +0.843700 +0.426527 +0.864460 +0.426032 +0.846228 +0.423295 +0.876556 +0.417274 +0.875346 +0.445896 +0.874963 +0.415404 +0.879554 +0.415883 +0.876431 +0.418138 +0.890324 +0.421403 +0.851814 +0.434323 +0.898545 +0.442644 +0.878029 +0.430568 +0.892829 +0.439907 +0.906104 +0.429532 +0.900939 +0.426143 +0.882503 +0.447401 +0.901865 +0.445640 +0.893384 +0.464422 +0.880283 +0.445907 +0.913287 +0.460392 +0.898919 +0.435173 +0.916746 +0.455103 +0.912236 +0.458110 +0.901824 +0.459546 +0.899749 +0.460282 +0.911695 +0.442336 +0.924231 +0.453057 +0.919008 +0.485830 +0.922503 +0.464600 +0.913975 +0.449597 +0.906925 +0.469435 +0.920053 +0.479594 +0.925415 +0.479844 +0.925108 +0.476841 +0.950210 +0.495686 +0.945159 +0.480871 +0.930955 +0.481887 +0.941574 +0.498100 +0.953185 +0.488856 +0.925565 +0.497650 +0.935343 +0.494484 +0.935508 +0.483878 +0.932157 +0.499252 +0.942040 +0.495512 +0.933630 +0.493856 +0.938457 +0.503716 +0.964553 +0.494821 +0.938057 +0.485349 +0.932269 +0.497434 +0.953970 +0.486618 +0.947376 +0.487392 +0.957932 +0.493259 +0.943277 +0.491908 +0.938263 +0.485843 +0.944376 +0.504014 +0.936674 +0.509147 +0.938533 +0.501551 +0.960868 +0.509490 +0.940970 +0.511701 +0.939741 +0.516864 +0.938549 +0.514196 +0.940476 +0.536623 +0.956138 +0.514936 +0.954077 +0.525945 +0.958645 +0.517303 +0.967559 +0.524570 +0.964372 +0.520954 +0.950054 +0.518936 +0.971730 +0.528937 +0.957406 +0.511533 +0.953234 +0.499584 +0.942036 +0.507185 +0.933648 +0.513890 +0.943070 +0.507948 +0.952360 +0.500748 +0.945096 +0.516045 +0.951836 +0.510355 +0.942172 +0.519924 +0.934545 +0.511240 +0.956326 +0.493933 +0.935159 +0.513724 +0.926505 +0.513122 +0.920998 +0.497732 +0.923573 +0.526665 +0.922674 +0.489765 +0.915565 +0.487791 +0.905839 +0.494970 +0.932146 +0.474893 +0.937446 +0.481287 +0.923613 +0.462375 +0.936003 +0.494276 +0.938295 +0.467051 +0.923418 +0.446564 +0.918279 +0.453063 +0.927186 +0.453545 +0.912662 +0.454092 +0.898041 +0.455344 +0.889506 +0.440661 +0.918423 +0.455353 +0.918536 +0.442298 +0.881003 +0.426340 +0.881304 +0.422666 +0.912532 +0.436539 +0.919300 +0.460287 +0.883253 +0.425800 +0.891979 +0.440335 +0.893976 +0.386750 +0.886579 +0.424227 +0.871435 +0.418763 +0.853063 +0.444792 +0.871374 +0.408833 +0.819446 +0.408158 +0.843119 +0.402757 +0.874863 +0.400178 +0.844646 +0.406583 +0.844646 +0.406583 +0.844646 +0.406583 +0.844646 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.454019 +0.886841 +0.454019 +0.886841 +0.454019 +0.886841 +0.454019 +0.886841 +0.454019 +0.886841 +0.454019 +0.886841 +0.454019 +0.869676 +0.414948 +0.912295 +0.475488 +0.883872 +0.444520 +0.864357 +0.442050 +0.859071 +0.456590 +0.854645 +0.442869 +0.865009 +0.432282 +0.888754 +0.423061 +0.870159 +0.452772 +0.884050 +0.414127 +0.863136 +0.457316 +0.901704 +0.455768 +0.893542 +0.441254 +0.887477 +0.456575 +0.872280 +0.470940 +0.898610 +0.460412 +0.878897 +0.461423 +0.889029 +0.445207 +0.906027 +0.476678 +0.900698 +0.469700 +0.910092 +0.489651 +0.889841 +0.486139 +0.907552 +0.477793 +0.890590 +0.485787 +0.907822 +0.479554 +0.908151 +0.469937 +0.943301 +0.472691 +0.902476 +0.484808 +0.928510 +0.484594 +0.933353 +0.479505 +0.895266 +0.473868 +0.916519 +0.487765 +0.919854 +0.488850 +0.928517 +0.493776 +0.939868 +0.512770 +0.921305 +0.500438 +0.928999 +0.510470 +0.952489 +0.508010 +0.939581 +0.507714 +0.957069 +0.518505 +0.945865 +0.510053 +0.938626 +0.500885 +0.944809 +0.518517 +0.922477 +0.527164 +0.935571 +0.519572 +0.938867 +0.528456 +0.946712 +0.511323 +0.928474 +0.524787 +0.920722 +0.505433 +0.924057 +0.507810 +0.932971 +0.510689 +0.943884 +0.504149 +0.945584 +0.508305 +0.930823 +0.502351 +0.934688 +0.529959 +0.937225 +0.503533 +0.935630 +0.519185 +0.917997 +0.523595 +0.950884 +0.508424 +0.929320 +0.530312 +0.931809 +0.522622 +0.948090 +0.541146 +0.940902 +0.529932 +0.930834 +0.525872 +0.948403 +0.527913 +0.947745 +0.523968 +0.952071 +0.525104 +0.939378 +0.532667 +0.949570 +0.534381 +0.947088 +0.534894 +0.952645 +0.512726 +0.951374 +0.530003 +0.931415 +0.515198 +0.962564 +0.523598 +0.937418 +0.539825 +0.936214 +0.534336 +0.940249 +0.533400 +0.929441 +0.531539 +0.955896 +0.527155 +0.936710 +0.507809 +0.942778 +0.536645 +0.944350 +0.526377 +0.922204 +0.519259 +0.932681 +0.532103 +0.954579 +0.521048 +0.915921 +0.521294 +0.913306 +0.513090 +0.917953 +0.516413 +0.949989 +0.505954 +0.945740 +0.488365 +0.941719 +0.471725 +0.935138 +0.486538 +0.925686 +0.482665 +0.940748 +0.482472 +0.914014 +0.484066 +0.937116 +0.482484 +0.909699 +0.475831 +0.911398 +0.479840 +0.933947 +0.475911 +0.904691 +0.480709 +0.913648 +0.467434 +0.896115 +0.468414 +0.894188 +0.442780 +0.888872 +0.428287 +0.877560 +0.453047 +0.891081 +0.462380 +0.893246 +0.438511 +0.905618 +0.469317 +0.873661 +0.440463 +0.886160 +0.394325 +0.885286 +0.436686 +0.883904 +0.405585 +0.859524 +0.444699 +0.864110 +0.429585 +0.838699 +0.433156 +0.863143 +0.406359 +0.854111 +0.435813 +0.854111 +0.435813 +0.854111 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.440503 +0.856119 +0.440503 +0.856119 +0.440503 +0.856119 +0.440503 +0.856119 +0.440503 +0.856119 +0.440503 +0.856119 +0.440503 +0.866780 +0.423362 +0.856961 +0.436293 +0.847078 +0.439002 +0.864318 +0.446214 +0.856162 +0.441948 +0.861724 +0.426651 +0.845573 +0.402847 +0.853220 +0.438265 +0.861095 +0.456777 +0.875657 +0.450543 +0.867662 +0.422123 +0.876843 +0.446078 +0.861556 +0.413120 +0.867282 +0.446549 +0.883811 +0.451659 +0.856555 +0.445318 +0.871280 +0.464293 +0.869790 +0.459915 +0.880601 +0.452797 +0.896617 +0.479998 +0.891955 +0.475399 +0.875863 +0.485081 +0.886410 +0.469690 +0.894825 +0.476824 +0.897357 +0.475212 +0.898565 +0.478967 +0.897343 +0.488999 +0.885758 +0.476091 +0.898745 +0.480923 +0.915873 +0.493043 +0.890620 +0.468866 +0.883810 +0.483493 +0.906644 +0.489329 +0.898873 +0.492448 +0.922877 +0.486638 +0.924714 +0.505142 +0.921121 +0.494770 +0.923594 +0.493426 +0.934818 +0.506125 +0.928109 +0.507152 +0.912249 +0.523275 +0.917361 +0.515756 +0.919644 +0.508377 +0.923954 +0.513302 +0.929229 +0.512781 +0.903384 +0.510332 +0.921506 +0.516988 +0.926508 +0.528364 +0.910000 +0.510382 +0.912632 +0.515233 +0.914920 +0.509904 +0.924890 +0.505145 +0.925173 +0.515749 +0.922474 +0.498551 +0.927855 +0.521897 +0.927545 +0.512170 +0.915772 +0.514258 +0.913594 +0.521592 +0.928889 +0.515973 +0.925680 +0.527420 +0.910410 +0.532580 +0.934123 +0.536386 +0.918106 +0.516896 +0.924838 +0.523044 +0.931822 +0.528863 +0.937082 +0.528110 +0.932605 +0.541349 +0.918320 +0.524842 +0.917962 +0.515390 +0.914055 +0.528369 +0.934990 +0.530014 +0.935847 +0.522791 +0.917915 +0.524583 +0.910350 +0.523335 +0.908286 +0.533856 +0.911541 +0.538916 +0.930911 +0.519318 +0.920508 +0.531566 +0.915981 +0.529780 +0.888974 +0.513795 +0.895216 +0.519799 +0.929652 +0.529907 +0.897958 +0.518124 +0.917031 +0.524557 +0.910517 +0.516978 +0.874346 +0.510014 +0.903040 +0.502107 +0.901678 +0.511762 +0.915881 +0.466993 +0.907252 +0.490112 +0.922518 +0.474754 +0.906176 +0.482737 +0.913284 +0.482754 +0.892252 +0.476536 +0.885151 +0.455990 +0.895473 +0.471015 +0.868339 +0.461610 +0.887621 +0.468542 +0.906932 +0.467068 +0.897397 +0.457852 +0.868579 +0.455094 +0.881061 +0.460726 +0.857030 +0.461929 +0.875611 +0.452435 +0.860522 +0.457827 +0.860948 +0.451883 +0.897625 +0.431098 +0.874290 +0.414570 +0.846682 +0.410996 +0.852536 +0.405951 +0.848035 +0.416177 +0.880591 +0.427398 +0.852520 +0.409431 +0.897392 +0.399351 +0.832332 +0.430808 +0.840371 +0.446894 +0.860748 +0.413197 +0.860748 +0.413197 +0.860748 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.427241 +0.839309 +0.427241 +0.839309 +0.427241 +0.839309 +0.427241 +0.839309 +0.427241 +0.839309 +0.427241 +0.804567 +0.453402 +0.828343 +0.414947 +0.842726 +0.431537 +0.846858 +0.435238 +0.849454 +0.473009 +0.836542 +0.398712 +0.854686 +0.451789 +0.840816 +0.430463 +0.839061 +0.428866 +0.846737 +0.433317 +0.860903 +0.430579 +0.892321 +0.420354 +0.850699 +0.466656 +0.865646 +0.420862 +0.881650 +0.458295 +0.867086 +0.463913 +0.851564 +0.455859 +0.874372 +0.479546 +0.880348 +0.489094 +0.884038 +0.457618 +0.880020 +0.474220 +0.878940 +0.456685 +0.875005 +0.477710 +0.886111 +0.461302 +0.861173 +0.479366 +0.886787 +0.463738 +0.896271 +0.478288 +0.884729 +0.474315 +0.883540 +0.477879 +0.882528 +0.497057 +0.896637 +0.488660 +0.894398 +0.486929 +0.899379 +0.484232 +0.909110 +0.491333 +0.917548 +0.492920 +0.916135 +0.506297 +0.910180 +0.498284 +0.917014 +0.488595 +0.918761 +0.506614 +0.930684 +0.496373 +0.916397 +0.495313 +0.914846 +0.505875 +0.909999 +0.521064 +0.914577 +0.510309 +0.916566 +0.520260 +0.909088 +0.513039 +0.906123 +0.507313 +0.929505 +0.514990 +0.927717 +0.524252 +0.913827 +0.499886 +0.906215 +0.515933 +0.922072 +0.520835 +0.923783 +0.510172 +0.923875 +0.517876 +0.926158 +0.506879 +0.919689 +0.510669 +0.919118 +0.514380 +0.918597 +0.522991 +0.923963 +0.526384 +0.929131 +0.533254 +0.908986 +0.524403 +0.928741 +0.529625 +0.921238 +0.526245 +0.931943 +0.529769 +0.921463 +0.524010 +0.932427 +0.508926 +0.928895 +0.545222 +0.922649 +0.527968 +0.926360 +0.530846 +0.930800 +0.526585 +0.945758 +0.523324 +0.938163 +0.530066 +0.928627 +0.551335 +0.919708 +0.531612 +0.922623 +0.538444 +0.912413 +0.536192 +0.917070 +0.520567 +0.922882 +0.530203 +0.914738 +0.520587 +0.922051 +0.519697 +0.912110 +0.525921 +0.907580 +0.533467 +0.920200 +0.522460 +0.917477 +0.528587 +0.920066 +0.519672 +0.920305 +0.512118 +0.897060 +0.513724 +0.894176 +0.523962 +0.907400 +0.511305 +0.909293 +0.476309 +0.917281 +0.483149 +0.922126 +0.508434 +0.892929 +0.493704 +0.909009 +0.490232 +0.904351 +0.472273 +0.900338 +0.472820 +0.889815 +0.466784 +0.879565 +0.481449 +0.898129 +0.468459 +0.889359 +0.443184 +0.884452 +0.462095 +0.881590 +0.459453 +0.873995 +0.459834 +0.873866 +0.444711 +0.869323 +0.461559 +0.851994 +0.441050 +0.859364 +0.423081 +0.872146 +0.443493 +0.883728 +0.449624 +0.867906 +0.442001 +0.828350 +0.433716 +0.839108 +0.405569 +0.877072 +0.411960 +0.873996 +0.414904 +0.817582 +0.430234 +0.822697 +0.402470 +0.838409 +0.415207 +0.849649 +0.448128 +0.826229 +0.415970 +0.826229 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.455228 +0.846404 +0.455228 +0.846404 +0.455228 +0.846404 +0.455228 +0.846404 +0.455228 +0.846404 +0.455228 +0.809767 +0.414691 +0.829421 +0.440524 +0.826146 +0.455522 +0.883645 +0.469636 +0.863337 +0.420224 +0.872234 +0.432727 +0.830003 +0.432727 +0.851910 +0.424291 +0.886108 +0.454541 +0.849737 +0.451928 +0.867589 +0.442760 +0.866010 +0.464906 +0.869735 +0.437646 +0.899173 +0.422461 +0.864825 +0.444339 +0.855456 +0.449045 +0.868422 +0.458976 +0.873163 +0.470318 +0.886436 +0.444192 +0.870822 +0.467549 +0.880881 +0.467448 +0.884003 +0.484185 +0.884707 +0.474914 +0.880048 +0.468736 +0.882887 +0.450646 +0.895495 +0.481726 +0.885122 +0.482178 +0.883187 +0.489512 +0.899428 +0.461311 +0.889424 +0.505095 +0.908598 +0.499573 +0.891626 +0.485906 +0.899355 +0.474370 +0.906871 +0.500354 +0.915855 +0.488325 +0.920599 +0.522382 +0.906599 +0.497643 +0.935612 +0.489388 +0.910910 +0.505371 +0.927811 +0.503368 +0.917807 +0.519442 +0.905774 +0.492284 +0.901703 +0.528355 +0.918648 +0.503188 +0.908122 +0.516395 +0.907833 +0.508633 +0.925456 +0.524943 +0.922236 +0.515373 +0.925017 +0.514916 +0.912043 +0.489532 +0.908035 +0.508977 +0.915514 +0.508402 +0.929351 +0.518695 +0.927436 +0.514564 +0.915474 +0.508349 +0.912235 +0.502835 +0.911910 +0.499875 +0.915455 +0.525904 +0.922246 +0.523354 +0.916459 +0.517085 +0.915439 +0.527575 +0.926924 +0.519402 +0.924084 +0.533120 +0.923193 +0.528269 +0.914526 +0.515218 +0.930050 +0.519420 +0.924777 +0.530974 +0.931366 +0.525093 +0.923453 +0.537788 +0.931391 +0.540108 +0.929217 +0.526119 +0.929681 +0.539401 +0.932527 +0.536244 +0.921556 +0.531524 +0.924005 +0.524759 +0.916480 +0.522025 +0.926507 +0.517321 +0.919832 +0.521125 +0.921865 +0.541881 +0.930858 +0.535016 +0.911526 +0.512671 +0.906488 +0.521259 +0.916740 +0.521347 +0.910699 +0.515152 +0.900294 +0.534251 +0.904431 +0.516271 +0.886921 +0.518819 +0.905530 +0.523259 +0.906656 +0.513504 +0.910596 +0.497691 +0.921463 +0.493386 +0.925587 +0.501993 +0.921069 +0.478947 +0.907865 +0.474472 +0.911487 +0.480333 +0.910830 +0.481516 +0.886956 +0.477601 +0.878031 +0.460585 +0.878303 +0.463839 +0.885850 +0.487050 +0.899416 +0.457638 +0.891480 +0.449556 +0.881948 +0.445567 +0.873378 +0.456011 +0.864023 +0.460209 +0.860381 +0.413748 +0.877984 +0.435644 +0.866591 +0.428967 +0.874490 +0.426438 +0.838143 +0.427790 +0.890272 +0.439465 +0.875933 +0.420824 +0.880501 +0.417374 +0.835384 +0.429813 +0.846425 +0.443122 +0.856358 +0.402147 +0.862216 +0.410822 +0.846056 +0.425027 +0.846056 +0.425027 +0.846056 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.451264 +0.837978 +0.451264 +0.837978 +0.451264 +0.837978 +0.451264 +0.837978 +0.451264 +0.837978 +0.451264 +0.837978 +0.451264 +0.815681 +0.401768 +0.832478 +0.464130 +0.877686 +0.433139 +0.803772 +0.456522 +0.823738 +0.426824 +0.832087 +0.403482 +0.831646 +0.426982 +0.849016 +0.440801 +0.865556 +0.440625 +0.838706 +0.485958 +0.858891 +0.472552 +0.864907 +0.490996 +0.867093 +0.464412 +0.884260 +0.439525 +0.870886 +0.466515 +0.887326 +0.438947 +0.888068 +0.464364 +0.898983 +0.475739 +0.906555 +0.458568 +0.914694 +0.483809 +0.908272 +0.484494 +0.891029 +0.479803 +0.882011 +0.480282 +0.894439 +0.483054 +0.912471 +0.495403 +0.899218 +0.473711 +0.867943 +0.520080 +0.911690 +0.477946 +0.903245 +0.471008 +0.901954 +0.489956 +0.884202 +0.479054 +0.881054 +0.466937 +0.906128 +0.498247 +0.928888 +0.491398 +0.931226 +0.508845 +0.915865 +0.519916 +0.942608 +0.499276 +0.920357 +0.527631 +0.933503 +0.522462 +0.937127 +0.515700 +0.908844 +0.509646 +0.918554 +0.525922 +0.923957 +0.512907 +0.919313 +0.523220 +0.925170 +0.520264 +0.933242 +0.523826 +0.923026 +0.515271 +0.928379 +0.521069 +0.909946 +0.499150 +0.916350 +0.503591 +0.935126 +0.521657 +0.914257 +0.519853 +0.931593 +0.519199 +0.905055 +0.508611 +0.927224 +0.520704 +0.921827 +0.512791 +0.916393 +0.530434 +0.915277 +0.526979 +0.927600 +0.524979 +0.929978 +0.524982 +0.917763 +0.510619 +0.923941 +0.539343 +0.928030 +0.527205 +0.913562 +0.537417 +0.919673 +0.526676 +0.927981 +0.533374 +0.930810 +0.522998 +0.930703 +0.522962 +0.936791 +0.541558 +0.924346 +0.520243 +0.915518 +0.519500 +0.935926 +0.535902 +0.921737 +0.524699 +0.908101 +0.523190 +0.913866 +0.523243 +0.914567 +0.537770 +0.914051 +0.541590 +0.913992 +0.528136 +0.928592 +0.523853 +0.904395 +0.515327 +0.912720 +0.521371 +0.902355 +0.503971 +0.907640 +0.521835 +0.893656 +0.510629 +0.890945 +0.497249 +0.906331 +0.507133 +0.895054 +0.524931 +0.888757 +0.516249 +0.907751 +0.484970 +0.909127 +0.495356 +0.900868 +0.480510 +0.915680 +0.486074 +0.913881 +0.520740 +0.888713 +0.474900 +0.907234 +0.480850 +0.888267 +0.478763 +0.876210 +0.475067 +0.875407 +0.460487 +0.895509 +0.475080 +0.881933 +0.470013 +0.885209 +0.451955 +0.863776 +0.434441 +0.880660 +0.461468 +0.881308 +0.454089 +0.885383 +0.424927 +0.884618 +0.434011 +0.859611 +0.447962 +0.851821 +0.418941 +0.865117 +0.437824 +0.842766 +0.435602 +0.858020 +0.412709 +0.871447 +0.409015 +0.848880 +0.424614 +0.805862 +0.434213 +0.845699 +0.439805 +0.822672 +0.395899 +0.784566 +0.384615 +0.824744 +0.423306 +0.824744 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.426498 +0.861227 +0.426498 +0.861227 +0.426498 +0.861227 +0.426498 +0.861227 +0.426498 +0.861227 +0.426498 +0.861227 +0.426498 +0.855123 +0.426667 +0.837503 +0.411291 +0.875119 +0.437267 +0.871060 +0.430937 +0.862304 +0.443222 +0.835601 +0.444833 +0.832347 +0.431070 +0.864401 +0.427187 +0.854663 +0.469381 +0.859544 +0.472198 +0.852037 +0.465917 +0.870153 +0.455290 +0.851087 +0.467123 +0.895849 +0.465318 +0.872387 +0.472757 +0.852410 +0.460499 +0.875180 +0.454314 +0.877640 +0.453924 +0.891073 +0.489952 +0.903186 +0.487494 +0.900345 +0.496932 +0.887320 +0.486740 +0.908242 +0.493924 +0.890004 +0.473158 +0.885591 +0.484512 +0.905218 +0.508035 +0.903274 +0.501529 +0.902446 +0.503778 +0.912229 +0.492127 +0.910116 +0.505804 +0.928638 +0.503600 +0.912041 +0.498353 +0.914936 +0.507303 +0.938765 +0.516689 +0.932782 +0.501356 +0.929598 +0.512426 +0.915466 +0.528835 +0.921632 +0.526858 +0.937984 +0.538340 +0.921512 +0.520481 +0.927509 +0.521262 +0.926084 +0.521352 +0.938980 +0.534778 +0.918169 +0.530217 +0.929243 +0.525532 +0.909264 +0.532515 +0.933964 +0.535839 +0.943332 +0.519999 +0.928143 +0.516975 +0.928744 +0.515396 +0.943638 +0.530608 +0.929650 +0.518566 +0.932540 +0.527617 +0.921296 +0.534368 +0.925062 +0.517711 +0.919104 +0.523764 +0.923576 +0.537888 +0.925306 +0.541114 +0.937561 +0.548313 +0.923324 +0.541581 +0.927131 +0.544041 +0.926609 +0.537960 +0.948580 +0.557375 +0.936741 +0.537619 +0.937412 +0.546353 +0.941548 +0.560544 +0.944620 +0.534270 +0.945420 +0.565592 +0.933987 +0.536550 +0.959394 +0.534898 +0.942238 +0.542043 +0.940856 +0.555635 +0.954620 +0.550671 +0.932425 +0.549054 +0.940989 +0.546305 +0.931440 +0.546301 +0.948209 +0.545342 +0.932700 +0.546455 +0.931481 +0.544949 +0.914522 +0.537500 +0.930792 +0.528046 +0.917561 +0.535243 +0.925528 +0.529280 +0.916471 +0.527593 +0.916947 +0.513025 +0.894271 +0.527018 +0.902292 +0.522076 +0.927545 +0.523928 +0.936754 +0.490942 +0.916459 +0.500953 +0.911589 +0.492449 +0.919450 +0.504830 +0.936598 +0.501084 +0.934069 +0.506302 +0.928528 +0.496432 +0.888797 +0.472213 +0.886842 +0.491125 +0.895227 +0.489042 +0.894034 +0.505608 +0.888439 +0.487796 +0.886611 +0.500779 +0.881305 +0.450399 +0.886537 +0.457806 +0.862381 +0.476379 +0.886226 +0.444312 +0.878699 +0.467244 +0.901631 +0.452954 +0.891905 +0.443608 +0.857698 +0.402645 +0.884639 +0.435470 +0.870537 +0.439889 +0.867211 +0.473888 +0.853363 +0.445461 +0.872412 +0.475859 +0.887061 +0.433011 +0.868134 +0.416658 +0.847991 +0.399374 +0.840716 +0.418485 +0.867729 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.463706 +0.852609 +0.463706 +0.852609 +0.463706 +0.852609 +0.463706 +0.852609 +0.463706 +0.852609 +0.463706 +0.816403 +0.471267 +0.820164 +0.451710 +0.864373 +0.484808 +0.873290 +0.454601 +0.877909 +0.460347 +0.862405 +0.479760 +0.860921 +0.462649 +0.839341 +0.461471 +0.885639 +0.457693 +0.892830 +0.481058 +0.848155 +0.460717 +0.847761 +0.504658 +0.871336 +0.482399 +0.867161 +0.489745 +0.901907 +0.489051 +0.890150 +0.483813 +0.875697 +0.497552 +0.877886 +0.495937 +0.886330 +0.498622 +0.892477 +0.499403 +0.902373 +0.490422 +0.900444 +0.512741 +0.883430 +0.494169 +0.893142 +0.516020 +0.890107 +0.500528 +0.882486 +0.513250 +0.887630 +0.515574 +0.899379 +0.530075 +0.910198 +0.519129 +0.902830 +0.507283 +0.895351 +0.528819 +0.912880 +0.510391 +0.899844 +0.521317 +0.920289 +0.541840 +0.934425 +0.530023 +0.930386 +0.522959 +0.923010 +0.533893 +0.928341 +0.534146 +0.928059 +0.534470 +0.931745 +0.542485 +0.922369 +0.556639 +0.930948 +0.539356 +0.918559 +0.532116 +0.931956 +0.535647 +0.929674 +0.554372 +0.931083 +0.558024 +0.917381 +0.537541 +0.943881 +0.550131 +0.929939 +0.555818 +0.921668 +0.540450 +0.922504 +0.543306 +0.936748 +0.553749 +0.927695 +0.549426 +0.942357 +0.552373 +0.907008 +0.533437 +0.913881 +0.550105 +0.923202 +0.536062 +0.932003 +0.554834 +0.926102 +0.558127 +0.927231 +0.553496 +0.915788 +0.567035 +0.927099 +0.556781 +0.943560 +0.565345 +0.930429 +0.574008 +0.931773 +0.548159 +0.923879 +0.557649 +0.938548 +0.561719 +0.929646 +0.562465 +0.925825 +0.558773 +0.938685 +0.549284 +0.939840 +0.559392 +0.931165 +0.553764 +0.938868 +0.567398 +0.940825 +0.561420 +0.928537 +0.569169 +0.937957 +0.555728 +0.929291 +0.556902 +0.939572 +0.552687 +0.946323 +0.584386 +0.932063 +0.556504 +0.925090 +0.562467 +0.908588 +0.550357 +0.915276 +0.558008 +0.912653 +0.560737 +0.908027 +0.551110 +0.909731 +0.544989 +0.891809 +0.542062 +0.903643 +0.533925 +0.903331 +0.540561 +0.922203 +0.514842 +0.927279 +0.515532 +0.918633 +0.520810 +0.910215 +0.538406 +0.932028 +0.509776 +0.908179 +0.511360 +0.925402 +0.508038 +0.897301 +0.501846 +0.909515 +0.520772 +0.892796 +0.483932 +0.895889 +0.500793 +0.895548 +0.506762 +0.892868 +0.508116 +0.883202 +0.506488 +0.905153 +0.475767 +0.880523 +0.468392 +0.872034 +0.469134 +0.868424 +0.469788 +0.865218 +0.478675 +0.853466 +0.445838 +0.860649 +0.472697 +0.877780 +0.476450 +0.835503 +0.474868 +0.878865 +0.440924 +0.841438 +0.432243 +0.824480 +0.448905 +0.885832 +0.447979 +0.839648 +0.440193 +0.830289 +0.470763 +0.754289 +0.439265 +0.851923 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.456477 +0.822466 +0.456477 +0.822466 +0.456477 +0.822466 +0.456477 +0.822466 +0.456477 +0.779228 +0.457722 +0.835467 +0.425869 +0.814321 +0.461165 +0.817611 +0.482396 +0.870384 +0.454702 +0.858098 +0.465350 +0.832460 +0.500334 +0.835051 +0.470321 +0.838435 +0.471217 +0.844374 +0.477587 +0.832700 +0.469426 +0.840340 +0.487860 +0.847501 +0.478820 +0.837350 +0.508131 +0.874038 +0.478204 +0.841053 +0.485673 +0.838767 +0.491304 +0.847689 +0.480228 +0.871001 +0.486631 +0.869247 +0.479479 +0.864256 +0.493302 +0.860345 +0.505530 +0.860791 +0.509442 +0.876706 +0.505119 +0.861472 +0.513748 +0.862873 +0.497230 +0.864860 +0.514238 +0.901469 +0.516811 +0.869291 +0.507379 +0.886601 +0.521057 +0.889481 +0.503222 +0.884703 +0.517184 +0.898806 +0.521981 +0.888421 +0.521888 +0.915690 +0.551775 +0.910635 +0.553907 +0.915338 +0.529142 +0.920029 +0.545574 +0.914495 +0.540046 +0.917326 +0.540898 +0.911123 +0.535044 +0.915401 +0.545944 +0.899174 +0.537240 +0.895820 +0.542058 +0.902225 +0.551932 +0.915825 +0.550979 +0.918917 +0.553372 +0.904398 +0.545465 +0.916909 +0.556886 +0.918284 +0.555283 +0.899239 +0.548315 +0.916506 +0.537797 +0.903692 +0.535607 +0.907835 +0.543045 +0.909057 +0.542377 +0.906067 +0.550855 +0.906829 +0.541043 +0.903709 +0.530241 +0.898150 +0.560227 +0.911665 +0.566156 +0.913147 +0.564440 +0.893726 +0.561190 +0.904851 +0.560747 +0.907133 +0.557104 +0.913525 +0.584941 +0.904613 +0.544966 +0.907268 +0.546072 +0.907211 +0.556906 +0.918287 +0.575845 +0.917957 +0.537735 +0.902536 +0.549650 +0.919859 +0.559072 +0.925148 +0.548195 +0.901784 +0.571804 +0.898289 +0.534565 +0.905117 +0.538075 +0.885962 +0.542575 +0.912301 +0.548035 +0.913889 +0.549908 +0.899326 +0.555825 +0.910553 +0.552610 +0.901834 +0.551734 +0.893817 +0.546164 +0.902497 +0.548794 +0.885556 +0.557436 +0.885762 +0.544209 +0.896019 +0.547996 +0.872150 +0.534021 +0.896575 +0.533655 +0.876796 +0.541536 +0.904594 +0.513171 +0.892495 +0.530069 +0.889204 +0.514741 +0.887473 +0.506004 +0.910249 +0.519467 +0.903900 +0.512629 +0.895046 +0.502114 +0.883758 +0.498474 +0.857548 +0.494301 +0.865807 +0.491944 +0.875803 +0.510086 +0.859622 +0.500612 +0.872414 +0.478035 +0.849118 +0.501376 +0.866057 +0.490539 +0.860825 +0.495237 +0.852545 +0.473018 +0.832705 +0.464065 +0.855612 +0.450628 +0.815568 +0.448712 +0.847959 +0.442972 +0.828181 +0.436676 +0.845112 +0.460808 +0.838143 +0.432592 +0.836243 +0.424152 +0.838838 +0.446574 +0.815064 +0.434497 +0.806554 +0.395243 +0.830048 +0.435074 +0.830048 +0.435074 +0.830048 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.457720 +0.833225 +0.457720 +0.833225 +0.457720 +0.833225 +0.457720 +0.833225 +0.457720 +0.833225 +0.457720 +0.798843 +0.467625 +0.845520 +0.465183 +0.824160 +0.437904 +0.829995 +0.470072 +0.829250 +0.484856 +0.816413 +0.482034 +0.844246 +0.430608 +0.835282 +0.482849 +0.824910 +0.492483 +0.844473 +0.450384 +0.861764 +0.475520 +0.849847 +0.500280 +0.837676 +0.458568 +0.851589 +0.475751 +0.856710 +0.491610 +0.864007 +0.486445 +0.831477 +0.463063 +0.873656 +0.491367 +0.878347 +0.512504 +0.856393 +0.455261 +0.876084 +0.524146 +0.858268 +0.503702 +0.897192 +0.528055 +0.883784 +0.490285 +0.876782 +0.492220 +0.872358 +0.499569 +0.887497 +0.499074 +0.876517 +0.495560 +0.867623 +0.517220 +0.873374 +0.499346 +0.872701 +0.511363 +0.890349 +0.512437 +0.871852 +0.500360 +0.901805 +0.541042 +0.911504 +0.529304 +0.888839 +0.533148 +0.905932 +0.543678 +0.924072 +0.528832 +0.892033 +0.527544 +0.916469 +0.532339 +0.891511 +0.553951 +0.894944 +0.531051 +0.885120 +0.543875 +0.893243 +0.540159 +0.915756 +0.554694 +0.911193 +0.542980 +0.892332 +0.532845 +0.903770 +0.556163 +0.901575 +0.556159 +0.892896 +0.545846 +0.912193 +0.547577 +0.909620 +0.545086 +0.910167 +0.545967 +0.909382 +0.557294 +0.898574 +0.535806 +0.912925 +0.534464 +0.893050 +0.542139 +0.902188 +0.570413 +0.912222 +0.559435 +0.910905 +0.555865 +0.900671 +0.562479 +0.902857 +0.558107 +0.918568 +0.564612 +0.911806 +0.554880 +0.909114 +0.553176 +0.905037 +0.570305 +0.920736 +0.557497 +0.915950 +0.577602 +0.928642 +0.561544 +0.906212 +0.562123 +0.913976 +0.562658 +0.926433 +0.560030 +0.913148 +0.558283 +0.892752 +0.561109 +0.915858 +0.558856 +0.896173 +0.557346 +0.911631 +0.547797 +0.901024 +0.544794 +0.899788 +0.554217 +0.907128 +0.558539 +0.899561 +0.543065 +0.888610 +0.539615 +0.909447 +0.544649 +0.890542 +0.542953 +0.906300 +0.544664 +0.890089 +0.552168 +0.874110 +0.541558 +0.870450 +0.552887 +0.888399 +0.546731 +0.910843 +0.531025 +0.902564 +0.512157 +0.886486 +0.523417 +0.894568 +0.507017 +0.905008 +0.517608 +0.896730 +0.494681 +0.873844 +0.528505 +0.895551 +0.515272 +0.855257 +0.505292 +0.874928 +0.485746 +0.870463 +0.501318 +0.856430 +0.508846 +0.840481 +0.498436 +0.885389 +0.494532 +0.858490 +0.480067 +0.863651 +0.480932 +0.846322 +0.465907 +0.848861 +0.468120 +0.865885 +0.460769 +0.854143 +0.494297 +0.851714 +0.468562 +0.852225 +0.462322 +0.849265 +0.445115 +0.834756 +0.440245 +0.806812 +0.462062 +0.830081 +0.457405 +0.834948 +0.480986 +0.826249 +0.431525 +0.808376 +0.451042 +0.816092 +0.438104 +0.823191 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.459600 +0.825020 +0.459600 +0.825020 +0.459600 +0.825020 +0.459600 +0.825020 +0.459600 +0.825020 +0.459600 +0.825020 +0.459600 +0.856975 +0.467512 +0.804931 +0.492009 +0.819523 +0.455845 +0.850607 +0.430946 +0.843935 +0.451604 +0.807501 +0.464413 +0.839724 +0.467376 +0.815752 +0.445916 +0.835549 +0.474600 +0.833044 +0.473122 +0.865117 +0.474568 +0.843069 +0.496047 +0.853000 +0.471916 +0.831077 +0.469269 +0.850131 +0.503736 +0.825077 +0.494945 +0.864658 +0.485544 +0.883028 +0.505315 +0.868050 +0.471676 +0.881889 +0.507956 +0.878170 +0.498390 +0.892394 +0.508752 +0.888726 +0.531879 +0.893989 +0.503263 +0.879271 +0.513481 +0.882358 +0.509125 +0.869135 +0.514250 +0.885784 +0.517304 +0.878513 +0.523814 +0.888732 +0.518002 +0.897696 +0.523891 +0.897249 +0.495405 +0.907776 +0.531679 +0.910436 +0.540612 +0.902157 +0.539706 +0.910557 +0.525972 +0.907533 +0.543077 +0.904760 +0.530531 +0.921826 +0.555296 +0.904031 +0.563485 +0.903937 +0.543423 +0.901786 +0.556891 +0.911167 +0.548979 +0.910779 +0.565158 +0.901906 +0.543445 +0.906582 +0.554914 +0.895982 +0.550264 +0.906088 +0.547416 +0.897145 +0.539073 +0.917466 +0.542613 +0.902906 +0.543013 +0.894495 +0.547030 +0.915794 +0.549427 +0.908262 +0.534138 +0.915721 +0.542016 +0.905336 +0.532104 +0.899934 +0.551588 +0.906740 +0.554362 +0.907856 +0.557365 +0.911277 +0.557512 +0.895595 +0.561266 +0.904723 +0.552723 +0.909929 +0.571412 +0.914939 +0.560274 +0.924668 +0.548298 +0.914813 +0.558794 +0.927522 +0.562475 +0.908516 +0.558369 +0.924176 +0.561771 +0.908797 +0.561630 +0.901907 +0.554164 +0.910375 +0.547560 +0.904909 +0.560563 +0.904659 +0.554794 +0.901002 +0.554971 +0.899368 +0.557016 +0.908267 +0.567740 +0.911192 +0.568309 +0.906903 +0.558116 +0.899384 +0.551636 +0.876081 +0.555785 +0.900926 +0.551447 +0.889992 +0.536994 +0.899487 +0.550687 +0.900576 +0.539902 +0.883664 +0.536524 +0.864660 +0.557057 +0.884156 +0.526971 +0.903855 +0.533422 +0.901165 +0.531844 +0.890985 +0.519736 +0.906187 +0.519480 +0.901060 +0.515061 +0.894219 +0.508399 +0.875973 +0.515663 +0.872409 +0.494357 +0.873027 +0.502782 +0.874089 +0.497713 +0.876151 +0.495781 +0.880780 +0.484789 +0.880855 +0.497874 +0.862513 +0.481183 +0.872510 +0.497262 +0.862492 +0.480324 +0.851115 +0.492445 +0.866440 +0.471896 +0.861306 +0.474574 +0.838448 +0.439051 +0.830277 +0.479392 +0.855244 +0.443603 +0.818929 +0.449601 +0.819872 +0.449066 +0.808253 +0.450358 +0.830085 +0.448689 +0.853475 +0.444268 +0.838932 +0.448992 +0.827491 +0.400049 +0.776982 +0.409170 +0.839966 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.451048 +0.836464 +0.451048 +0.836464 +0.451048 +0.836464 +0.451048 +0.836464 +0.451048 +0.827616 +0.491423 +0.829401 +0.433937 +0.819382 +0.474358 +0.860608 +0.444848 +0.820457 +0.461590 +0.824751 +0.493755 +0.848879 +0.482523 +0.815741 +0.457999 +0.840969 +0.486088 +0.856319 +0.486535 +0.861953 +0.467419 +0.851926 +0.474051 +0.866298 +0.480523 +0.842168 +0.497989 +0.868501 +0.458822 +0.829804 +0.475101 +0.855791 +0.500618 +0.867500 +0.497259 +0.872861 +0.492466 +0.880104 +0.505764 +0.884101 +0.487496 +0.886122 +0.532571 +0.881435 +0.520719 +0.883314 +0.498906 +0.888501 +0.506529 +0.880195 +0.511125 +0.883583 +0.521289 +0.903841 +0.518168 +0.891924 +0.514679 +0.884995 +0.523630 +0.885771 +0.517595 +0.882183 +0.537182 +0.885041 +0.530425 +0.890180 +0.518399 +0.895095 +0.540247 +0.908414 +0.532126 +0.915664 +0.551687 +0.906455 +0.546045 +0.917341 +0.538119 +0.905838 +0.533584 +0.931167 +0.550268 +0.900439 +0.548839 +0.915053 +0.548748 +0.916583 +0.546919 +0.912137 +0.561332 +0.912182 +0.579360 +0.907559 +0.547633 +0.903440 +0.558512 +0.905109 +0.537696 +0.910281 +0.556074 +0.908258 +0.549833 +0.915290 +0.550784 +0.914418 +0.538844 +0.904957 +0.548858 +0.920733 +0.563187 +0.906728 +0.537683 +0.917409 +0.563557 +0.911655 +0.545252 +0.912975 +0.563087 +0.903883 +0.560066 +0.915797 +0.577456 +0.914257 +0.552915 +0.895468 +0.557130 +0.909618 +0.556465 +0.904729 +0.557009 +0.913022 +0.552072 +0.909872 +0.561682 +0.912128 +0.546421 +0.916660 +0.561346 +0.926441 +0.560456 +0.920260 +0.553256 +0.904954 +0.571730 +0.911443 +0.556805 +0.919795 +0.545260 +0.915051 +0.557888 +0.890987 +0.562494 +0.911315 +0.550844 +0.909332 +0.546983 +0.916051 +0.551138 +0.899463 +0.532865 +0.888117 +0.543946 +0.898304 +0.549747 +0.896613 +0.564094 +0.893498 +0.544399 +0.909312 +0.541141 +0.893114 +0.550727 +0.883304 +0.544275 +0.889338 +0.528935 +0.874807 +0.547161 +0.881518 +0.542750 +0.899946 +0.512574 +0.909315 +0.530683 +0.896511 +0.507357 +0.899344 +0.537719 +0.892555 +0.508450 +0.895438 +0.512245 +0.890935 +0.523610 +0.889481 +0.513950 +0.869751 +0.496171 +0.861734 +0.492340 +0.865867 +0.493827 +0.882844 +0.489189 +0.854571 +0.491137 +0.868684 +0.490586 +0.876560 +0.490373 +0.859942 +0.485421 +0.853625 +0.482735 +0.841569 +0.443781 +0.849831 +0.462561 +0.847898 +0.471260 +0.857335 +0.455877 +0.876458 +0.434419 +0.841135 +0.452884 +0.854187 +0.450711 +0.815680 +0.421933 +0.790530 +0.441118 +0.821867 +0.434534 +0.809571 +0.425745 +0.809359 +0.432528 +0.809359 +0.432528 +0.809359 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.481564 +0.832137 +0.481564 +0.832137 +0.481564 +0.832137 +0.481564 +0.832137 +0.481564 +0.867010 +0.481224 +0.821758 +0.470882 +0.822112 +0.475703 +0.852542 +0.498106 +0.830718 +0.482682 +0.835628 +0.464213 +0.854984 +0.445763 +0.840692 +0.500175 +0.822799 +0.455082 +0.859561 +0.508991 +0.836024 +0.484717 +0.856922 +0.479498 +0.836585 +0.486289 +0.858011 +0.479914 +0.876880 +0.502257 +0.851573 +0.498253 +0.839333 +0.498454 +0.859519 +0.485273 +0.868192 +0.503410 +0.887930 +0.493876 +0.863894 +0.515250 +0.873714 +0.504429 +0.871846 +0.499428 +0.877567 +0.514106 +0.874485 +0.522655 +0.884821 +0.515279 +0.882273 +0.513688 +0.872579 +0.499153 +0.902408 +0.532010 +0.893096 +0.519420 +0.904121 +0.529503 +0.891588 +0.521550 +0.887916 +0.505680 +0.893450 +0.537814 +0.873998 +0.529733 +0.920609 +0.549351 +0.913224 +0.540700 +0.910186 +0.540905 +0.898236 +0.551861 +0.926817 +0.542142 +0.918817 +0.544546 +0.917152 +0.548893 +0.907223 +0.556460 +0.915988 +0.544335 +0.913676 +0.554750 +0.923710 +0.553004 +0.917042 +0.562479 +0.904327 +0.542600 +0.928529 +0.581258 +0.922968 +0.552315 +0.916136 +0.536616 +0.911102 +0.546708 +0.921839 +0.545359 +0.918137 +0.553062 +0.909618 +0.547315 +0.903899 +0.554914 +0.913677 +0.562514 +0.923457 +0.547857 +0.898348 +0.566621 +0.922244 +0.570904 +0.908155 +0.557043 +0.903973 +0.563942 +0.922720 +0.579075 +0.914478 +0.573842 +0.911398 +0.582961 +0.931810 +0.566524 +0.930977 +0.562796 +0.915015 +0.563743 +0.916280 +0.557459 +0.919138 +0.579054 +0.917891 +0.572358 +0.929847 +0.580280 +0.914677 +0.577087 +0.913595 +0.552311 +0.907198 +0.568239 +0.912918 +0.568640 +0.924458 +0.576000 +0.911624 +0.580414 +0.905550 +0.554387 +0.919399 +0.567088 +0.921443 +0.587721 +0.918786 +0.566856 +0.907154 +0.562027 +0.896347 +0.562910 +0.898671 +0.554648 +0.902350 +0.550073 +0.898652 +0.545938 +0.885836 +0.560871 +0.883808 +0.540618 +0.867255 +0.547355 +0.899146 +0.526007 +0.897976 +0.509437 +0.918290 +0.528843 +0.905290 +0.513521 +0.906455 +0.517152 +0.913670 +0.527110 +0.903389 +0.535358 +0.895729 +0.516569 +0.868014 +0.515723 +0.879523 +0.517347 +0.892120 +0.487480 +0.874860 +0.508288 +0.876631 +0.502899 +0.878027 +0.503887 +0.858916 +0.518148 +0.851948 +0.484931 +0.845180 +0.491132 +0.869157 +0.498934 +0.864562 +0.478906 +0.881746 +0.450875 +0.814007 +0.468887 +0.860140 +0.480352 +0.836201 +0.468495 +0.831181 +0.440592 +0.825428 +0.442961 +0.847092 +0.453774 +0.838177 +0.407410 +0.835248 +0.463143 +0.826266 +0.490763 +0.817777 +0.404810 +0.833230 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.494282 +0.841025 +0.494282 +0.841025 +0.494282 +0.841025 +0.494282 +0.841025 +0.494282 +0.841025 +0.494282 +0.841025 +0.494282 +0.827192 +0.472003 +0.866880 +0.500215 +0.834736 +0.480778 +0.821459 +0.501852 +0.868378 +0.496126 +0.803821 +0.510473 +0.849702 +0.519211 +0.863237 +0.523711 +0.843377 +0.504019 +0.846527 +0.508603 +0.866902 +0.501466 +0.853894 +0.540188 +0.869946 +0.502190 +0.875639 +0.504854 +0.857822 +0.507269 +0.838351 +0.512423 +0.852122 +0.505959 +0.870206 +0.496165 +0.872059 +0.511080 +0.886929 +0.498748 +0.873679 +0.509365 +0.868302 +0.511179 +0.885064 +0.529777 +0.871254 +0.522684 +0.884020 +0.516396 +0.872836 +0.523031 +0.888610 +0.511827 +0.886314 +0.541187 +0.891518 +0.516136 +0.894467 +0.527255 +0.910563 +0.532345 +0.881973 +0.538459 +0.890587 +0.525238 +0.928459 +0.548728 +0.912118 +0.542941 +0.898650 +0.536544 +0.906644 +0.545212 +0.917157 +0.547256 +0.916479 +0.547799 +0.914179 +0.557509 +0.906926 +0.563002 +0.909004 +0.553208 +0.913233 +0.548877 +0.911033 +0.555695 +0.900429 +0.564752 +0.891968 +0.548487 +0.930962 +0.557884 +0.907193 +0.552559 +0.904175 +0.549948 +0.910933 +0.560589 +0.898354 +0.549938 +0.926233 +0.558412 +0.915022 +0.558620 +0.916972 +0.553584 +0.905471 +0.562823 +0.916131 +0.559467 +0.922579 +0.585947 +0.913025 +0.581692 +0.916616 +0.593897 +0.902070 +0.587169 +0.905069 +0.579930 +0.915644 +0.584178 +0.913314 +0.596904 +0.905807 +0.597625 +0.901902 +0.581360 +0.918270 +0.576591 +0.901607 +0.585564 +0.912647 +0.596239 +0.922543 +0.578606 +0.907740 +0.593152 +0.918899 +0.586219 +0.908920 +0.572588 +0.888965 +0.571079 +0.899959 +0.562618 +0.888844 +0.579287 +0.918056 +0.598707 +0.913089 +0.587893 +0.896752 +0.581296 +0.921599 +0.593947 +0.916570 +0.578247 +0.882313 +0.576311 +0.888743 +0.575439 +0.913999 +0.578317 +0.891939 +0.565948 +0.918372 +0.575302 +0.893885 +0.570497 +0.884346 +0.557183 +0.877371 +0.540744 +0.907395 +0.546605 +0.907560 +0.557897 +0.920302 +0.555997 +0.892292 +0.549280 +0.904885 +0.564631 +0.905023 +0.543112 +0.896997 +0.545837 +0.875218 +0.522655 +0.872932 +0.518103 +0.884457 +0.524586 +0.867782 +0.511396 +0.882992 +0.549389 +0.856485 +0.512898 +0.854167 +0.503694 +0.851268 +0.488798 +0.859988 +0.509396 +0.851662 +0.512351 +0.866539 +0.489644 +0.866054 +0.500006 +0.838338 +0.509497 +0.843230 +0.496036 +0.858655 +0.469907 +0.833976 +0.484313 +0.854070 +0.502288 +0.841937 +0.468456 +0.839642 +0.459223 +0.855269 +0.444614 +0.820065 +0.438834 +0.767398 +0.483634 +0.815818 +0.474169 +0.814244 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.486358 +0.816619 +0.486358 +0.816619 +0.486358 +0.816619 +0.486358 +0.816619 +0.486358 +0.795006 +0.481254 +0.803037 +0.476868 +0.833106 +0.470392 +0.813713 +0.511813 +0.836402 +0.480767 +0.819691 +0.513022 +0.820118 +0.465861 +0.812563 +0.493163 +0.794454 +0.498307 +0.857587 +0.500333 +0.847454 +0.505538 +0.814232 +0.504020 +0.846371 +0.494394 +0.820676 +0.508689 +0.813707 +0.493454 +0.835128 +0.514429 +0.834156 +0.515466 +0.842942 +0.514036 +0.846820 +0.527950 +0.860114 +0.526752 +0.842250 +0.546293 +0.851076 +0.521512 +0.864778 +0.544658 +0.872722 +0.537904 +0.865750 +0.536622 +0.855305 +0.549971 +0.854110 +0.530876 +0.860763 +0.550966 +0.874180 +0.548402 +0.869546 +0.546127 +0.851944 +0.544716 +0.860044 +0.537537 +0.872988 +0.541851 +0.874857 +0.541314 +0.888322 +0.556886 +0.903325 +0.543965 +0.888404 +0.564474 +0.883138 +0.563655 +0.896285 +0.571011 +0.896202 +0.549242 +0.903037 +0.572701 +0.896035 +0.569429 +0.881533 +0.577822 +0.885610 +0.574857 +0.884352 +0.591632 +0.891999 +0.573769 +0.888133 +0.571677 +0.888972 +0.572236 +0.903636 +0.585036 +0.900476 +0.570526 +0.903327 +0.565624 +0.895527 +0.571143 +0.891165 +0.568786 +0.907457 +0.569776 +0.884987 +0.577353 +0.893744 +0.571684 +0.897086 +0.567273 +0.883305 +0.564755 +0.895269 +0.582307 +0.899853 +0.590694 +0.905658 +0.598673 +0.894788 +0.586353 +0.900515 +0.600500 +0.873490 +0.583337 +0.880193 +0.591433 +0.895289 +0.586629 +0.891584 +0.584206 +0.898161 +0.568136 +0.871960 +0.583343 +0.904461 +0.600546 +0.916285 +0.582629 +0.891028 +0.590901 +0.898585 +0.583903 +0.874025 +0.571827 +0.880755 +0.577754 +0.883288 +0.573566 +0.883961 +0.574688 +0.884405 +0.580565 +0.894191 +0.583464 +0.873401 +0.572397 +0.885111 +0.586635 +0.880302 +0.588832 +0.870280 +0.570620 +0.856830 +0.575154 +0.875832 +0.566136 +0.870883 +0.562535 +0.860762 +0.571967 +0.857319 +0.571979 +0.872515 +0.575045 +0.861954 +0.540361 +0.877791 +0.522811 +0.886778 +0.542199 +0.881824 +0.534225 +0.876850 +0.530187 +0.874317 +0.542428 +0.870309 +0.541976 +0.878031 +0.520869 +0.840599 +0.509267 +0.858224 +0.504923 +0.844895 +0.532187 +0.842726 +0.519223 +0.851564 +0.529569 +0.830397 +0.505800 +0.824787 +0.488166 +0.843917 +0.501460 +0.828946 +0.507487 +0.823543 +0.491535 +0.822904 +0.476934 +0.820712 +0.479893 +0.836188 +0.495881 +0.833983 +0.488728 +0.809335 +0.478138 +0.828687 +0.462016 +0.807088 +0.478490 +0.805112 +0.473525 +0.806514 +0.479939 +0.806062 +0.441617 +0.790965 +0.451647 +0.793752 +0.463573 +0.794435 +0.450122 +0.796926 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.474644 +0.799487 +0.474644 +0.799487 +0.474644 +0.799487 +0.474644 +0.799487 +0.474644 +0.802909 +0.496836 +0.757055 +0.467797 +0.800065 +0.470336 +0.841341 +0.485799 +0.797888 +0.513514 +0.832509 +0.475572 +0.851223 +0.520149 +0.817414 +0.512742 +0.802041 +0.486857 +0.834186 +0.476593 +0.842651 +0.501455 +0.815528 +0.503261 +0.811494 +0.510418 +0.815347 +0.494890 +0.870629 +0.511191 +0.821836 +0.509666 +0.835994 +0.520042 +0.826054 +0.502621 +0.856604 +0.501663 +0.862618 +0.538185 +0.869425 +0.542567 +0.856584 +0.531015 +0.862086 +0.540443 +0.869792 +0.539491 +0.865531 +0.511731 +0.856181 +0.533165 +0.877050 +0.544280 +0.862857 +0.531892 +0.885698 +0.546321 +0.859423 +0.539796 +0.857498 +0.542613 +0.873914 +0.539606 +0.869282 +0.540096 +0.881111 +0.551137 +0.900129 +0.548033 +0.891702 +0.559584 +0.882014 +0.566582 +0.889113 +0.563260 +0.886897 +0.572191 +0.890791 +0.570926 +0.899908 +0.577124 +0.884973 +0.570251 +0.889795 +0.568178 +0.879581 +0.573807 +0.882735 +0.573889 +0.896405 +0.577765 +0.882996 +0.567883 +0.896892 +0.597197 +0.904786 +0.585900 +0.892120 +0.570618 +0.884518 +0.567095 +0.876642 +0.565270 +0.894409 +0.581103 +0.913441 +0.580554 +0.896975 +0.571852 +0.898109 +0.584132 +0.901826 +0.570120 +0.878990 +0.570192 +0.890104 +0.577335 +0.885085 +0.593078 +0.895395 +0.590515 +0.887822 +0.596093 +0.881618 +0.596511 +0.895656 +0.588498 +0.884981 +0.597563 +0.894758 +0.607121 +0.899604 +0.588909 +0.911821 +0.587551 +0.889098 +0.587477 +0.904815 +0.582279 +0.904995 +0.569936 +0.891243 +0.591375 +0.894157 +0.586536 +0.891627 +0.579903 +0.883029 +0.581046 +0.885913 +0.581787 +0.884895 +0.570498 +0.888527 +0.599849 +0.887993 +0.580188 +0.880833 +0.579484 +0.878312 +0.583283 +0.887420 +0.587336 +0.870123 +0.568019 +0.877228 +0.576083 +0.872441 +0.589082 +0.870193 +0.551375 +0.869199 +0.566286 +0.857979 +0.573420 +0.868867 +0.556263 +0.857178 +0.553410 +0.881040 +0.537784 +0.874814 +0.546181 +0.897249 +0.546944 +0.865309 +0.532415 +0.886182 +0.560848 +0.854690 +0.535981 +0.887301 +0.514518 +0.867401 +0.522343 +0.836224 +0.526276 +0.846947 +0.542558 +0.844438 +0.539575 +0.845372 +0.538224 +0.838497 +0.505465 +0.846720 +0.491696 +0.854948 +0.517072 +0.839916 +0.467767 +0.837347 +0.496193 +0.833920 +0.508702 +0.825910 +0.482244 +0.792039 +0.456758 +0.852150 +0.503384 +0.866506 +0.468604 +0.816118 +0.477322 +0.796761 +0.467603 +0.821066 +0.480390 +0.771115 +0.460217 +0.816517 +0.494327 +0.813013 +0.489066 +0.780062 +0.448004 +0.779615 +0.439794 +0.803197 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.481254 +0.788996 +0.481254 +0.788996 +0.481254 +0.788996 +0.481254 +0.799359 +0.462265 +0.800323 +0.458499 +0.771747 +0.457465 +0.794919 +0.527798 +0.833237 +0.509039 +0.859492 +0.508630 +0.830888 +0.466132 +0.863772 +0.495203 +0.841273 +0.479038 +0.824289 +0.495938 +0.840214 +0.490222 +0.861099 +0.514333 +0.815485 +0.474417 +0.835460 +0.519663 +0.839167 +0.521630 +0.835166 +0.513616 +0.845724 +0.529909 +0.860352 +0.510166 +0.828493 +0.518699 +0.845534 +0.521330 +0.850779 +0.528537 +0.871569 +0.539845 +0.846135 +0.534092 +0.867561 +0.516542 +0.867745 +0.544508 +0.866635 +0.514508 +0.859510 +0.523459 +0.862840 +0.537275 +0.870127 +0.553270 +0.865573 +0.544184 +0.884411 +0.544633 +0.863102 +0.547852 +0.884481 +0.546022 +0.863232 +0.537332 +0.880180 +0.540250 +0.884959 +0.571388 +0.896370 +0.555413 +0.893272 +0.567126 +0.890949 +0.548037 +0.891160 +0.558726 +0.904911 +0.559834 +0.886937 +0.561258 +0.901897 +0.575574 +0.895720 +0.572781 +0.870556 +0.579417 +0.893467 +0.575240 +0.884006 +0.576872 +0.884940 +0.559065 +0.897436 +0.586337 +0.893499 +0.574005 +0.892142 +0.577277 +0.886373 +0.560241 +0.887720 +0.553934 +0.881568 +0.570518 +0.893781 +0.580093 +0.891591 +0.549562 +0.882014 +0.572557 +0.893262 +0.565957 +0.877745 +0.569850 +0.881601 +0.575574 +0.894347 +0.589620 +0.883736 +0.581247 +0.902651 +0.586341 +0.893524 +0.593219 +0.888894 +0.573903 +0.888711 +0.579415 +0.884058 +0.576529 +0.911784 +0.588892 +0.899308 +0.590194 +0.894412 +0.560504 +0.883342 +0.578688 +0.891108 +0.583389 +0.905425 +0.582701 +0.891304 +0.578775 +0.899053 +0.570255 +0.875169 +0.589336 +0.896745 +0.595303 +0.896240 +0.585294 +0.891278 +0.597893 +0.893931 +0.577600 +0.881712 +0.586549 +0.885495 +0.584542 +0.880190 +0.574888 +0.870841 +0.573409 +0.883252 +0.560934 +0.889979 +0.569270 +0.882297 +0.567696 +0.877088 +0.556877 +0.855924 +0.563904 +0.863683 +0.565061 +0.867253 +0.554218 +0.878279 +0.543436 +0.874032 +0.538219 +0.877507 +0.549838 +0.873633 +0.537202 +0.864965 +0.541673 +0.880145 +0.524914 +0.865580 +0.524116 +0.860901 +0.530279 +0.854503 +0.511779 +0.844509 +0.519346 +0.851086 +0.498448 +0.868751 +0.510823 +0.838593 +0.491975 +0.854542 +0.517017 +0.844521 +0.506373 +0.834933 +0.511553 +0.830720 +0.526399 +0.842948 +0.493055 +0.847206 +0.491430 +0.850273 +0.474508 +0.824500 +0.490255 +0.815436 +0.471939 +0.814710 +0.506803 +0.818903 +0.480942 +0.794918 +0.486445 +0.801387 +0.456692 +0.821278 +0.486950 +0.792750 +0.434213 +0.765734 +0.473450 +0.783231 +0.486624 +0.793254 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.503572 +0.819105 +0.503572 +0.819105 +0.503572 +0.819105 +0.503572 +0.819105 +0.503572 +0.819105 +0.503572 +0.787238 +0.512306 +0.841250 +0.514064 +0.800762 +0.491308 +0.815301 +0.505343 +0.816260 +0.495972 +0.826594 +0.461761 +0.813968 +0.504920 +0.849659 +0.493854 +0.836437 +0.463102 +0.824470 +0.518145 +0.812524 +0.534553 +0.829957 +0.521371 +0.816802 +0.502067 +0.853196 +0.509743 +0.842497 +0.517371 +0.853089 +0.516624 +0.841277 +0.523521 +0.841777 +0.507194 +0.877303 +0.532998 +0.849523 +0.522336 +0.861503 +0.546551 +0.844730 +0.527223 +0.877562 +0.532419 +0.850300 +0.520101 +0.871489 +0.539389 +0.872144 +0.528112 +0.887644 +0.545981 +0.862259 +0.540979 +0.881891 +0.533938 +0.881879 +0.552462 +0.861890 +0.545840 +0.866846 +0.545868 +0.872975 +0.541990 +0.886270 +0.569049 +0.896790 +0.563671 +0.894275 +0.550771 +0.884423 +0.557014 +0.909982 +0.570063 +0.903306 +0.571659 +0.903241 +0.578370 +0.909747 +0.582057 +0.897128 +0.578062 +0.884479 +0.580639 +0.905832 +0.587134 +0.894478 +0.577188 +0.889404 +0.575160 +0.890552 +0.572722 +0.897144 +0.576436 +0.898768 +0.593614 +0.892147 +0.574088 +0.885679 +0.565641 +0.890968 +0.566932 +0.907732 +0.576365 +0.897768 +0.569134 +0.904168 +0.577008 +0.901008 +0.578175 +0.899495 +0.581102 +0.898374 +0.581883 +0.885181 +0.586518 +0.891466 +0.598550 +0.891817 +0.594336 +0.900410 +0.582496 +0.889857 +0.579202 +0.885156 +0.590090 +0.890684 +0.589597 +0.895180 +0.581822 +0.896782 +0.585716 +0.890989 +0.583739 +0.892347 +0.560301 +0.886086 +0.578014 +0.890042 +0.588662 +0.892628 +0.576124 +0.893773 +0.576854 +0.890543 +0.572659 +0.884452 +0.594017 +0.890736 +0.588650 +0.884414 +0.576227 +0.882379 +0.578301 +0.891096 +0.574186 +0.884434 +0.570727 +0.873281 +0.573132 +0.879141 +0.573728 +0.867393 +0.559188 +0.885959 +0.569835 +0.887644 +0.562319 +0.860106 +0.559894 +0.858294 +0.551750 +0.855326 +0.564425 +0.846325 +0.563466 +0.870270 +0.540074 +0.877381 +0.548314 +0.889200 +0.538039 +0.889691 +0.528538 +0.867147 +0.519941 +0.879996 +0.529743 +0.860161 +0.535438 +0.849894 +0.519952 +0.842953 +0.506053 +0.847084 +0.510020 +0.831245 +0.493905 +0.833120 +0.502775 +0.841400 +0.497721 +0.847766 +0.503192 +0.842580 +0.507656 +0.820349 +0.516273 +0.839641 +0.485563 +0.834365 +0.487419 +0.835320 +0.469593 +0.831155 +0.480168 +0.844104 +0.493263 +0.832522 +0.458492 +0.828140 +0.479080 +0.816615 +0.482894 +0.841629 +0.468072 +0.805834 +0.475806 +0.831207 +0.463249 +0.810265 +0.466772 +0.800441 +0.499398 +0.813559 +0.467986 +0.813971 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.506630 +0.791082 +0.506630 +0.791082 +0.506630 +0.791082 +0.506630 +0.791082 +0.506630 +0.796252 +0.548578 +0.781857 +0.468928 +0.791808 +0.510223 +0.799582 +0.540738 +0.832460 +0.494421 +0.822224 +0.480606 +0.826364 +0.513352 +0.852007 +0.533478 +0.826864 +0.486091 +0.829679 +0.470212 +0.841881 +0.542617 +0.848174 +0.518934 +0.851186 +0.530936 +0.848329 +0.532106 +0.862692 +0.533071 +0.870702 +0.504102 +0.843259 +0.530732 +0.893049 +0.536949 +0.863137 +0.537164 +0.850853 +0.535688 +0.844277 +0.547657 +0.873111 +0.518205 +0.857057 +0.554648 +0.866472 +0.554207 +0.887678 +0.570023 +0.885870 +0.553774 +0.861825 +0.551146 +0.883444 +0.559339 +0.881365 +0.572883 +0.884298 +0.569917 +0.884318 +0.549760 +0.874471 +0.547961 +0.874332 +0.542875 +0.886605 +0.540362 +0.892866 +0.566235 +0.901406 +0.580809 +0.904481 +0.565117 +0.913719 +0.564548 +0.889632 +0.576671 +0.911562 +0.589760 +0.908802 +0.572290 +0.901573 +0.593279 +0.905244 +0.580838 +0.911615 +0.600978 +0.915018 +0.593342 +0.910856 +0.591104 +0.913181 +0.596688 +0.894922 +0.581145 +0.912921 +0.591359 +0.902861 +0.586337 +0.895788 +0.592792 +0.901934 +0.595632 +0.914518 +0.595568 +0.902042 +0.593596 +0.919160 +0.589258 +0.908759 +0.578437 +0.907723 +0.579343 +0.918591 +0.592255 +0.893630 +0.602257 +0.900236 +0.611495 +0.895041 +0.588058 +0.909601 +0.602226 +0.891144 +0.610721 +0.918722 +0.613350 +0.899476 +0.608260 +0.895097 +0.589720 +0.894914 +0.580650 +0.892222 +0.584604 +0.904744 +0.573080 +0.905991 +0.592227 +0.895995 +0.583383 +0.912681 +0.588601 +0.897879 +0.598353 +0.915895 +0.590401 +0.899794 +0.581496 +0.876572 +0.594311 +0.895427 +0.592707 +0.884357 +0.577974 +0.885078 +0.589566 +0.886217 +0.587641 +0.898350 +0.592364 +0.887782 +0.600472 +0.890268 +0.586733 +0.885861 +0.593250 +0.884068 +0.578581 +0.871606 +0.576652 +0.895481 +0.568989 +0.870428 +0.571585 +0.854539 +0.559964 +0.840171 +0.579528 +0.877643 +0.551425 +0.894563 +0.546875 +0.891219 +0.567497 +0.895984 +0.548706 +0.893508 +0.550562 +0.900596 +0.554138 +0.878789 +0.537973 +0.861001 +0.531971 +0.856607 +0.534018 +0.839532 +0.529117 +0.851632 +0.506165 +0.837183 +0.518769 +0.847403 +0.532920 +0.877467 +0.521608 +0.852550 +0.535365 +0.819289 +0.504540 +0.809756 +0.487433 +0.835743 +0.485442 +0.857618 +0.477986 +0.818146 +0.513717 +0.841404 +0.534308 +0.796485 +0.484482 +0.820884 +0.480458 +0.825699 +0.461061 +0.773640 +0.532569 +0.826286 +0.459469 +0.786122 +0.436511 +0.811607 +0.479513 +0.794762 +0.475722 +0.722344 +0.500510 +0.797497 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.527473 +0.821826 +0.527473 +0.821826 +0.527473 +0.821826 +0.527473 +0.821826 +0.527473 +0.774999 +0.528359 +0.838851 +0.515237 +0.799824 +0.543988 +0.826802 +0.523196 +0.840092 +0.501205 +0.797196 +0.537181 +0.820034 +0.542938 +0.816584 +0.527582 +0.840675 +0.501017 +0.837760 +0.548698 +0.844211 +0.552980 +0.838517 +0.521633 +0.853432 +0.537298 +0.841997 +0.547094 +0.852072 +0.548924 +0.844038 +0.519528 +0.860336 +0.553169 +0.866457 +0.561439 +0.872104 +0.547273 +0.855053 +0.542432 +0.871079 +0.528191 +0.858008 +0.559258 +0.861317 +0.565600 +0.863730 +0.549375 +0.867127 +0.571647 +0.878464 +0.543624 +0.884012 +0.558543 +0.870014 +0.557398 +0.882494 +0.549620 +0.877684 +0.556272 +0.877526 +0.544495 +0.888369 +0.562647 +0.886994 +0.551070 +0.881729 +0.562197 +0.892253 +0.571579 +0.901446 +0.574804 +0.888201 +0.565950 +0.914305 +0.581353 +0.897349 +0.570724 +0.902548 +0.582899 +0.897577 +0.587101 +0.914330 +0.580652 +0.889134 +0.594555 +0.897006 +0.594443 +0.911741 +0.595196 +0.895009 +0.577584 +0.900546 +0.594661 +0.892872 +0.587569 +0.920658 +0.586373 +0.892224 +0.582898 +0.894792 +0.570633 +0.898613 +0.591257 +0.900150 +0.563857 +0.892059 +0.584518 +0.910889 +0.594995 +0.901628 +0.577258 +0.894405 +0.577599 +0.886057 +0.577170 +0.890019 +0.607036 +0.903621 +0.606483 +0.899963 +0.624081 +0.895891 +0.625282 +0.904032 +0.621808 +0.917607 +0.621818 +0.897871 +0.600606 +0.884347 +0.605988 +0.879133 +0.600023 +0.888461 +0.608257 +0.895859 +0.610533 +0.891048 +0.594835 +0.899681 +0.617344 +0.894010 +0.608546 +0.899045 +0.609385 +0.892713 +0.608937 +0.890188 +0.619266 +0.889014 +0.606600 +0.886717 +0.611448 +0.894480 +0.607619 +0.881184 +0.609792 +0.872473 +0.606787 +0.892611 +0.600142 +0.880800 +0.595830 +0.885097 +0.596283 +0.884733 +0.600631 +0.885183 +0.594420 +0.887360 +0.599159 +0.887162 +0.595473 +0.870689 +0.589006 +0.859733 +0.576101 +0.861423 +0.574473 +0.885036 +0.563969 +0.878019 +0.565879 +0.888385 +0.558881 +0.869272 +0.572531 +0.869926 +0.577906 +0.875745 +0.558974 +0.890347 +0.556314 +0.878871 +0.558161 +0.857039 +0.549613 +0.862010 +0.549928 +0.867146 +0.536725 +0.845740 +0.545728 +0.856047 +0.560155 +0.869104 +0.549977 +0.833349 +0.530482 +0.829993 +0.508258 +0.839725 +0.516298 +0.862992 +0.558786 +0.863093 +0.515734 +0.823537 +0.523148 +0.818605 +0.509621 +0.844091 +0.521420 +0.826584 +0.484131 +0.816879 +0.516376 +0.814640 +0.496312 +0.808882 +0.485929 +0.761780 +0.469061 +0.814923 +0.503692 +0.786113 +0.476769 +0.786094 +0.479427 +0.787605 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.510349 +0.807520 +0.510349 +0.807520 +0.510349 +0.807520 +0.510349 +0.807520 +0.510349 +0.790376 +0.500138 +0.827333 +0.492818 +0.791067 +0.525307 +0.804160 +0.512921 +0.794244 +0.558736 +0.794394 +0.537018 +0.825680 +0.509091 +0.819896 +0.530190 +0.789925 +0.512517 +0.821081 +0.535378 +0.823521 +0.551572 +0.832311 +0.562296 +0.816960 +0.525928 +0.842614 +0.549628 +0.834257 +0.532137 +0.853821 +0.518017 +0.836794 +0.549239 +0.833032 +0.533979 +0.845937 +0.549437 +0.839414 +0.562727 +0.850738 +0.558789 +0.832514 +0.558938 +0.864721 +0.577974 +0.849137 +0.582608 +0.857243 +0.567543 +0.848220 +0.567750 +0.861048 +0.575151 +0.856664 +0.581516 +0.853412 +0.575631 +0.860892 +0.589762 +0.847850 +0.565222 +0.863837 +0.575012 +0.874974 +0.590879 +0.857340 +0.579647 +0.880529 +0.617871 +0.867401 +0.594972 +0.885143 +0.595763 +0.891921 +0.597928 +0.881960 +0.603418 +0.899090 +0.596238 +0.893194 +0.608336 +0.883842 +0.602348 +0.868882 +0.596529 +0.874189 +0.619603 +0.893853 +0.605858 +0.872461 +0.612975 +0.873691 +0.608278 +0.882021 +0.606054 +0.890020 +0.606470 +0.882062 +0.606583 +0.878224 +0.595168 +0.881251 +0.617485 +0.891857 +0.594151 +0.870668 +0.601865 +0.893053 +0.613903 +0.900321 +0.602944 +0.881422 +0.605320 +0.869445 +0.602429 +0.875529 +0.625022 +0.883415 +0.622051 +0.888272 +0.621249 +0.889142 +0.633088 +0.885482 +0.612746 +0.884677 +0.621598 +0.873288 +0.608372 +0.877011 +0.604194 +0.873146 +0.601613 +0.887465 +0.602596 +0.875757 +0.612082 +0.858208 +0.593150 +0.877893 +0.603857 +0.858403 +0.605705 +0.876587 +0.594957 +0.858243 +0.610039 +0.866160 +0.602565 +0.873890 +0.617963 +0.862301 +0.592071 +0.871876 +0.604324 +0.863741 +0.597876 +0.852856 +0.595147 +0.869284 +0.592474 +0.844490 +0.591070 +0.847198 +0.583712 +0.848649 +0.605642 +0.849997 +0.585181 +0.854650 +0.583533 +0.840806 +0.596644 +0.832027 +0.580290 +0.852997 +0.565514 +0.817930 +0.565213 +0.850244 +0.552859 +0.871146 +0.579751 +0.845959 +0.570224 +0.843061 +0.560291 +0.854243 +0.545168 +0.850740 +0.541175 +0.841868 +0.549114 +0.836821 +0.559935 +0.823808 +0.534508 +0.832181 +0.534521 +0.825047 +0.531291 +0.831908 +0.540914 +0.838457 +0.546134 +0.814198 +0.527498 +0.809776 +0.524683 +0.816088 +0.528564 +0.824784 +0.515888 +0.824392 +0.525433 +0.821979 +0.514016 +0.830880 +0.497181 +0.810678 +0.509788 +0.798288 +0.505388 +0.755960 +0.503931 +0.802265 +0.510706 +0.765204 +0.516315 +0.823562 +0.497365 +0.788101 +0.473783 +0.774401 +0.516898 +0.741565 +0.428917 +0.783959 +0.463558 +0.768022 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.535516 +0.797545 +0.535516 +0.797545 +0.535516 +0.797545 +0.535516 +0.797545 +0.535516 +0.783527 +0.498739 +0.798543 +0.517068 +0.799614 +0.537761 +0.794477 +0.551718 +0.786137 +0.536662 +0.796291 +0.516883 +0.800905 +0.535351 +0.809364 +0.535204 +0.827802 +0.523788 +0.796862 +0.529670 +0.841219 +0.533064 +0.821800 +0.549478 +0.822994 +0.540153 +0.826224 +0.523914 +0.828898 +0.535173 +0.816019 +0.536105 +0.834512 +0.549304 +0.843964 +0.527585 +0.843815 +0.544539 +0.848402 +0.558785 +0.856910 +0.569146 +0.852243 +0.568193 +0.843877 +0.571445 +0.855179 +0.572258 +0.859357 +0.569057 +0.829454 +0.566892 +0.868635 +0.557620 +0.839066 +0.572711 +0.834386 +0.567495 +0.863291 +0.577348 +0.872702 +0.574826 +0.867994 +0.586130 +0.856748 +0.581040 +0.871786 +0.586666 +0.870495 +0.601749 +0.875507 +0.586858 +0.884703 +0.594879 +0.882869 +0.594554 +0.880489 +0.616821 +0.882027 +0.608898 +0.893583 +0.593424 +0.880061 +0.605249 +0.876572 +0.590552 +0.866032 +0.624808 +0.874496 +0.610855 +0.870409 +0.608922 +0.868315 +0.614621 +0.890016 +0.614314 +0.893952 +0.616551 +0.892913 +0.587592 +0.865987 +0.596768 +0.887185 +0.607984 +0.878202 +0.601922 +0.871755 +0.606267 +0.894354 +0.608767 +0.886797 +0.612616 +0.889232 +0.603705 +0.887007 +0.609764 +0.878956 +0.625997 +0.877008 +0.614105 +0.873574 +0.608458 +0.893350 +0.636248 +0.878259 +0.619736 +0.881346 +0.609239 +0.874190 +0.606533 +0.871727 +0.614669 +0.884568 +0.599686 +0.868878 +0.605097 +0.870527 +0.605131 +0.869324 +0.601089 +0.875732 +0.602332 +0.875635 +0.604888 +0.870821 +0.605881 +0.871691 +0.601672 +0.866334 +0.608264 +0.876345 +0.611084 +0.861283 +0.584285 +0.866580 +0.600890 +0.860606 +0.608729 +0.873804 +0.608360 +0.864416 +0.597091 +0.858757 +0.612963 +0.864403 +0.591152 +0.850258 +0.610868 +0.869926 +0.590259 +0.861199 +0.602208 +0.845778 +0.593782 +0.861352 +0.601973 +0.842722 +0.581819 +0.841127 +0.583284 +0.855550 +0.558014 +0.876536 +0.574395 +0.872474 +0.573047 +0.854287 +0.550201 +0.863101 +0.566217 +0.843136 +0.545635 +0.842141 +0.550375 +0.838959 +0.557806 +0.836972 +0.546468 +0.836310 +0.545056 +0.817501 +0.551436 +0.832899 +0.547387 +0.821249 +0.531261 +0.810595 +0.527656 +0.802348 +0.533920 +0.804015 +0.531300 +0.812329 +0.505388 +0.815924 +0.526523 +0.815521 +0.525149 +0.810041 +0.512667 +0.814515 +0.493441 +0.819076 +0.488475 +0.786665 +0.516210 +0.814903 +0.484886 +0.829529 +0.508496 +0.793795 +0.503665 +0.795703 +0.504212 +0.786588 +0.479643 +0.778767 +0.443967 +0.766364 +0.477293 +0.787019 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.519259 +0.791723 +0.519259 +0.791723 +0.519259 +0.791723 +0.519259 +0.778083 +0.508218 +0.803825 +0.478947 +0.773301 +0.509617 +0.798042 +0.569212 +0.796315 +0.513645 +0.786700 +0.514449 +0.826283 +0.529935 +0.824675 +0.540882 +0.802536 +0.520194 +0.812254 +0.515028 +0.807198 +0.517869 +0.795719 +0.527612 +0.801898 +0.560958 +0.838008 +0.541356 +0.820184 +0.534356 +0.843016 +0.554075 +0.847102 +0.547877 +0.848499 +0.533855 +0.824681 +0.553117 +0.837607 +0.553836 +0.837646 +0.555892 +0.855343 +0.558445 +0.834625 +0.574295 +0.848559 +0.543272 +0.844172 +0.566447 +0.841238 +0.570909 +0.846815 +0.578073 +0.854859 +0.563019 +0.847539 +0.571008 +0.846643 +0.581699 +0.860289 +0.598405 +0.861136 +0.577949 +0.874656 +0.565692 +0.867153 +0.590455 +0.874648 +0.578595 +0.873988 +0.607107 +0.880722 +0.587791 +0.879136 +0.593954 +0.889384 +0.588316 +0.885485 +0.613766 +0.882954 +0.596025 +0.895556 +0.601757 +0.884464 +0.587155 +0.875419 +0.588230 +0.874934 +0.606284 +0.881461 +0.604217 +0.887781 +0.614557 +0.874391 +0.615040 +0.887949 +0.633023 +0.886788 +0.609659 +0.872290 +0.597390 +0.878669 +0.602506 +0.886013 +0.603655 +0.878170 +0.588618 +0.881388 +0.596219 +0.888725 +0.609252 +0.896805 +0.604631 +0.895887 +0.598406 +0.865579 +0.602514 +0.878871 +0.621174 +0.875336 +0.613273 +0.878396 +0.613199 +0.871576 +0.602329 +0.883569 +0.622451 +0.889611 +0.606614 +0.880370 +0.609171 +0.877555 +0.601929 +0.869423 +0.578871 +0.852635 +0.603236 +0.882802 +0.606252 +0.866559 +0.589224 +0.868783 +0.594904 +0.866976 +0.593815 +0.867278 +0.605441 +0.874261 +0.590573 +0.845154 +0.596727 +0.855886 +0.596731 +0.856922 +0.587000 +0.852742 +0.597829 +0.866048 +0.614617 +0.870269 +0.605151 +0.869219 +0.601480 +0.861160 +0.595987 +0.852325 +0.581510 +0.848874 +0.587881 +0.868729 +0.589169 +0.845767 +0.599156 +0.871938 +0.605157 +0.845650 +0.578678 +0.839854 +0.593619 +0.847446 +0.566756 +0.853023 +0.568013 +0.876228 +0.562531 +0.861929 +0.562705 +0.863177 +0.555976 +0.864825 +0.564431 +0.848810 +0.569506 +0.858674 +0.556184 +0.829151 +0.551715 +0.833185 +0.553759 +0.833683 +0.547137 +0.816686 +0.543214 +0.829263 +0.521147 +0.833123 +0.543176 +0.824318 +0.534974 +0.794293 +0.513919 +0.809260 +0.545767 +0.824565 +0.508166 +0.835173 +0.509292 +0.800088 +0.531955 +0.819091 +0.504794 +0.807031 +0.518171 +0.778406 +0.506128 +0.800141 +0.486351 +0.788575 +0.471902 +0.757240 +0.495789 +0.794971 +0.472672 +0.779917 +0.492089 +0.796541 +0.472308 +0.765095 +0.458567 +0.778726 +0.483046 +0.780518 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.507969 +0.788705 +0.507969 +0.788705 +0.507969 +0.788705 +0.507969 +0.787604 +0.532967 +0.787255 +0.538030 +0.769186 +0.485813 +0.809673 +0.500063 +0.797753 +0.518654 +0.834696 +0.555307 +0.808972 +0.540842 +0.820167 +0.551579 +0.820315 +0.495047 +0.794127 +0.524007 +0.823807 +0.508633 +0.856138 +0.530528 +0.808183 +0.553178 +0.831932 +0.525657 +0.823735 +0.552525 +0.844259 +0.533180 +0.855238 +0.570443 +0.809795 +0.534084 +0.845749 +0.567312 +0.837766 +0.566779 +0.871193 +0.573560 +0.860627 +0.560063 +0.859326 +0.571869 +0.854347 +0.580376 +0.856581 +0.587570 +0.851648 +0.561761 +0.872154 +0.576147 +0.858019 +0.568880 +0.890224 +0.597931 +0.847442 +0.577970 +0.874161 +0.585594 +0.841388 +0.592121 +0.875088 +0.573067 +0.872269 +0.588798 +0.873917 +0.569801 +0.883522 +0.601791 +0.892931 +0.592138 +0.901898 +0.611092 +0.880702 +0.591562 +0.899298 +0.612492 +0.898557 +0.602171 +0.901357 +0.622290 +0.888053 +0.604749 +0.867694 +0.610906 +0.867604 +0.603229 +0.892975 +0.623509 +0.884195 +0.614518 +0.884156 +0.619051 +0.891014 +0.627902 +0.914278 +0.618037 +0.885810 +0.596042 +0.883239 +0.612474 +0.905993 +0.632935 +0.910046 +0.611620 +0.880202 +0.599868 +0.881679 +0.610578 +0.892901 +0.607336 +0.881519 +0.614872 +0.873563 +0.604570 +0.886077 +0.628148 +0.892859 +0.621556 +0.887329 +0.616539 +0.876606 +0.621315 +0.900469 +0.626467 +0.876801 +0.628953 +0.875713 +0.610302 +0.881885 +0.612871 +0.880730 +0.601225 +0.856512 +0.606520 +0.880912 +0.608298 +0.870976 +0.596107 +0.860499 +0.597779 +0.860711 +0.603225 +0.869763 +0.594786 +0.878939 +0.595547 +0.866159 +0.595617 +0.850024 +0.599124 +0.853455 +0.591087 +0.866394 +0.600250 +0.881608 +0.612529 +0.874267 +0.598585 +0.854318 +0.595579 +0.862256 +0.594296 +0.853413 +0.591084 +0.844200 +0.589972 +0.859699 +0.587579 +0.851635 +0.597193 +0.844975 +0.588432 +0.844224 +0.585416 +0.849549 +0.589429 +0.844896 +0.562231 +0.843476 +0.573147 +0.854900 +0.566430 +0.867855 +0.554361 +0.848741 +0.559544 +0.843207 +0.560570 +0.840618 +0.551460 +0.837305 +0.566164 +0.834066 +0.537511 +0.817992 +0.529273 +0.802185 +0.554989 +0.822212 +0.531679 +0.822236 +0.540903 +0.816871 +0.533178 +0.824525 +0.516216 +0.821786 +0.519512 +0.815181 +0.535056 +0.814370 +0.512835 +0.819059 +0.496164 +0.807748 +0.502296 +0.807336 +0.501911 +0.821441 +0.508748 +0.796283 +0.504463 +0.798421 +0.471814 +0.794616 +0.492927 +0.797592 +0.460600 +0.802521 +0.513052 +0.797138 +0.488752 +0.816029 +0.485064 +0.760887 +0.456740 +0.758442 +0.466834 +0.791352 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.524891 +0.793765 +0.524891 +0.793765 +0.524891 +0.793765 +0.524891 +0.793765 +0.524891 +0.757441 +0.505419 +0.783383 +0.525226 +0.786787 +0.508672 +0.811124 +0.540774 +0.792732 +0.522292 +0.827406 +0.567731 +0.826342 +0.537154 +0.799577 +0.530550 +0.839024 +0.538838 +0.824593 +0.522797 +0.817747 +0.502314 +0.837195 +0.546691 +0.817885 +0.524534 +0.815509 +0.539660 +0.809849 +0.525798 +0.831132 +0.557720 +0.819324 +0.564679 +0.836408 +0.527030 +0.836902 +0.538025 +0.851165 +0.550812 +0.840046 +0.534004 +0.842953 +0.567342 +0.844446 +0.552275 +0.858211 +0.560681 +0.845793 +0.584252 +0.859780 +0.560470 +0.854020 +0.578852 +0.866090 +0.587744 +0.863807 +0.566095 +0.853639 +0.584803 +0.853827 +0.576489 +0.874351 +0.572645 +0.868758 +0.582472 +0.860357 +0.590199 +0.870652 +0.585040 +0.874920 +0.604490 +0.888322 +0.573835 +0.865650 +0.596280 +0.882836 +0.591476 +0.887727 +0.606547 +0.890357 +0.613884 +0.889569 +0.619098 +0.854526 +0.598315 +0.875753 +0.612499 +0.875899 +0.593901 +0.884085 +0.621283 +0.880000 +0.614890 +0.888378 +0.612065 +0.897102 +0.622989 +0.889292 +0.610832 +0.890162 +0.605148 +0.877903 +0.598614 +0.903663 +0.607745 +0.875648 +0.610871 +0.884281 +0.615367 +0.895663 +0.616650 +0.894574 +0.611137 +0.892976 +0.605421 +0.871811 +0.617053 +0.866494 +0.625485 +0.881366 +0.633477 +0.897408 +0.640850 +0.890215 +0.624716 +0.889916 +0.617789 +0.888311 +0.634174 +0.879410 +0.625791 +0.876287 +0.623311 +0.887112 +0.613942 +0.898369 +0.627997 +0.882125 +0.618712 +0.874251 +0.614490 +0.882677 +0.629214 +0.868930 +0.612574 +0.894997 +0.611062 +0.869600 +0.609623 +0.859772 +0.623812 +0.882068 +0.618074 +0.879817 +0.616310 +0.874570 +0.598229 +0.878576 +0.604345 +0.879988 +0.612042 +0.894217 +0.615070 +0.846522 +0.608157 +0.864185 +0.597275 +0.865057 +0.609604 +0.869802 +0.601617 +0.867885 +0.597715 +0.857183 +0.612780 +0.853168 +0.584068 +0.831108 +0.600672 +0.864472 +0.577701 +0.870578 +0.577455 +0.882770 +0.589563 +0.866264 +0.583337 +0.889110 +0.567265 +0.881512 +0.571008 +0.858265 +0.566020 +0.862460 +0.564276 +0.841097 +0.568804 +0.850149 +0.567323 +0.838734 +0.556347 +0.817815 +0.542207 +0.812263 +0.539862 +0.842740 +0.548010 +0.824007 +0.529665 +0.814221 +0.543682 +0.803551 +0.538308 +0.825522 +0.524011 +0.828234 +0.518866 +0.828040 +0.520267 +0.807239 +0.513940 +0.822683 +0.510626 +0.814735 +0.507284 +0.795035 +0.468781 +0.765332 +0.510786 +0.799664 +0.508140 +0.806415 +0.512768 +0.789252 +0.496151 +0.772733 +0.516312 +0.810538 +0.494681 +0.789467 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.569353 +0.790073 +0.569353 +0.790073 +0.569353 +0.790073 +0.569353 +0.790073 +0.569353 +0.800408 +0.546375 +0.810368 +0.562138 +0.797801 +0.555970 +0.762051 +0.589951 +0.810772 +0.553337 +0.836549 +0.555450 +0.824743 +0.559936 +0.832528 +0.571899 +0.839135 +0.522328 +0.825747 +0.544985 +0.824058 +0.541487 +0.835148 +0.549506 +0.814163 +0.525066 +0.828980 +0.545261 +0.829710 +0.533379 +0.831654 +0.563481 +0.824871 +0.549289 +0.828458 +0.548418 +0.825972 +0.552799 +0.833770 +0.558788 +0.850284 +0.576044 +0.856298 +0.576869 +0.843379 +0.570594 +0.833129 +0.571159 +0.855970 +0.556195 +0.833032 +0.572964 +0.857660 +0.568840 +0.866192 +0.589537 +0.864993 +0.567894 +0.856947 +0.567995 +0.871427 +0.579376 +0.863006 +0.583285 +0.871506 +0.573351 +0.852116 +0.591984 +0.872423 +0.579753 +0.866015 +0.620969 +0.875569 +0.592247 +0.877753 +0.594389 +0.886636 +0.593275 +0.885050 +0.608516 +0.899376 +0.607262 +0.884827 +0.598429 +0.863482 +0.609257 +0.891659 +0.602883 +0.856538 +0.607822 +0.872886 +0.611375 +0.866191 +0.617927 +0.886753 +0.622262 +0.875921 +0.624364 +0.877630 +0.617488 +0.877974 +0.611822 +0.879170 +0.614603 +0.890315 +0.615675 +0.881855 +0.603636 +0.873503 +0.599272 +0.887479 +0.608574 +0.857617 +0.604411 +0.880268 +0.593546 +0.868003 +0.625420 +0.862833 +0.609987 +0.882760 +0.630643 +0.876763 +0.621851 +0.889431 +0.631915 +0.888446 +0.624648 +0.882738 +0.612408 +0.889275 +0.612760 +0.868271 +0.604674 +0.888567 +0.605999 +0.869101 +0.618258 +0.879511 +0.613912 +0.888653 +0.620219 +0.874965 +0.622383 +0.875336 +0.604828 +0.889924 +0.607546 +0.854228 +0.604610 +0.860278 +0.607247 +0.872317 +0.604460 +0.877853 +0.605910 +0.864305 +0.597021 +0.859064 +0.597669 +0.893694 +0.621077 +0.882701 +0.611028 +0.854901 +0.612806 +0.856844 +0.623389 +0.862687 +0.622228 +0.875175 +0.618350 +0.852020 +0.612202 +0.846899 +0.614348 +0.853232 +0.601125 +0.829029 +0.600709 +0.870648 +0.607437 +0.868611 +0.599780 +0.855297 +0.589497 +0.865426 +0.600512 +0.862294 +0.595696 +0.859353 +0.583925 +0.866810 +0.582738 +0.856945 +0.582414 +0.821409 +0.559833 +0.832629 +0.578303 +0.841548 +0.573366 +0.811072 +0.556109 +0.844694 +0.558357 +0.820989 +0.574053 +0.808612 +0.541967 +0.813265 +0.550230 +0.816340 +0.542548 +0.830292 +0.528358 +0.830782 +0.537091 +0.821167 +0.546277 +0.807432 +0.515688 +0.804198 +0.532050 +0.791792 +0.528758 +0.827627 +0.510924 +0.778269 +0.525230 +0.786062 +0.525222 +0.763147 +0.497701 +0.766205 +0.507277 +0.778062 +0.518069 +0.785335 +0.507000 +0.769138 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.545471 +0.773750 +0.545471 +0.773750 +0.545471 +0.773750 +0.545471 +0.773750 +0.545471 +0.799090 +0.557294 +0.780912 +0.541750 +0.784786 +0.565857 +0.755551 +0.528806 +0.790582 +0.570377 +0.815171 +0.543631 +0.800618 +0.552263 +0.784694 +0.562627 +0.791137 +0.544555 +0.778231 +0.561728 +0.778869 +0.556431 +0.804274 +0.572413 +0.821360 +0.596773 +0.818543 +0.581134 +0.820556 +0.583714 +0.826140 +0.557908 +0.806538 +0.568897 +0.824735 +0.540187 +0.816787 +0.586411 +0.818342 +0.579812 +0.834504 +0.577495 +0.811980 +0.579909 +0.839133 +0.578128 +0.808407 +0.584697 +0.833227 +0.584591 +0.809988 +0.583007 +0.815807 +0.603071 +0.838228 +0.592676 +0.849991 +0.596995 +0.842734 +0.591633 +0.833135 +0.593952 +0.831618 +0.610547 +0.835026 +0.589490 +0.831433 +0.597799 +0.862446 +0.613265 +0.869245 +0.618803 +0.862294 +0.629893 +0.856968 +0.619942 +0.864971 +0.605294 +0.855533 +0.626081 +0.876048 +0.622000 +0.875527 +0.627902 +0.862871 +0.632498 +0.853964 +0.630738 +0.852169 +0.626301 +0.853876 +0.619174 +0.859937 +0.627526 +0.865911 +0.624536 +0.854900 +0.630397 +0.852297 +0.620236 +0.854556 +0.615595 +0.856470 +0.633031 +0.861231 +0.621867 +0.860208 +0.623038 +0.866862 +0.629003 +0.868304 +0.622605 +0.872215 +0.632852 +0.867537 +0.635396 +0.848969 +0.634668 +0.845929 +0.640076 +0.854882 +0.646249 +0.848861 +0.642135 +0.870175 +0.632139 +0.859229 +0.641896 +0.855323 +0.635771 +0.859432 +0.633089 +0.845248 +0.618443 +0.861697 +0.615781 +0.840659 +0.635884 +0.873125 +0.643290 +0.863034 +0.629566 +0.852290 +0.626875 +0.853033 +0.620210 +0.835790 +0.619119 +0.835143 +0.610633 +0.839984 +0.619864 +0.836151 +0.628959 +0.841944 +0.622927 +0.841754 +0.618664 +0.831132 +0.614560 +0.853913 +0.626597 +0.833670 +0.619645 +0.839828 +0.595276 +0.825638 +0.626334 +0.847520 +0.603837 +0.839130 +0.620494 +0.822797 +0.607297 +0.830030 +0.610130 +0.832842 +0.602698 +0.832357 +0.614596 +0.843626 +0.579037 +0.843875 +0.600109 +0.842766 +0.581228 +0.824941 +0.572112 +0.822258 +0.595477 +0.836635 +0.593643 +0.816710 +0.570547 +0.824380 +0.566774 +0.802109 +0.559491 +0.818229 +0.575421 +0.801089 +0.564028 +0.800693 +0.550285 +0.809137 +0.564815 +0.781287 +0.550793 +0.798785 +0.553876 +0.788311 +0.539904 +0.776041 +0.541693 +0.797358 +0.540472 +0.806096 +0.537585 +0.773173 +0.536560 +0.813906 +0.532955 +0.785106 +0.529605 +0.776693 +0.524333 +0.765989 +0.506133 +0.765697 +0.530930 +0.776050 +0.515198 +0.780260 +0.504508 +0.746569 +0.481716 +0.759688 +0.521502 +0.757965 +0.492347 +0.762172 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.534166 +0.760756 +0.534166 +0.760756 +0.534166 +0.760756 +0.534166 +0.760756 +0.534166 +0.784918 +0.570257 +0.769523 +0.538691 +0.777229 +0.523586 +0.735516 +0.540221 +0.724556 +0.531559 +0.783646 +0.560050 +0.769913 +0.530753 +0.798848 +0.549145 +0.786744 +0.538968 +0.779811 +0.530653 +0.778100 +0.553289 +0.788854 +0.561054 +0.837788 +0.574874 +0.827965 +0.579536 +0.824143 +0.568361 +0.780567 +0.547518 +0.810458 +0.579933 +0.807460 +0.574384 +0.824087 +0.585861 +0.820040 +0.586078 +0.836638 +0.578099 +0.816944 +0.601944 +0.837578 +0.580311 +0.834341 +0.597079 +0.810706 +0.578163 +0.828434 +0.586201 +0.818270 +0.589245 +0.840910 +0.591361 +0.838922 +0.580637 +0.829178 +0.594436 +0.830298 +0.592812 +0.831151 +0.590457 +0.836374 +0.605734 +0.841655 +0.597209 +0.856491 +0.612380 +0.843225 +0.615337 +0.862967 +0.619551 +0.857017 +0.635404 +0.866155 +0.606009 +0.856146 +0.621061 +0.858803 +0.628669 +0.851140 +0.628971 +0.855912 +0.630011 +0.851200 +0.628045 +0.860257 +0.631623 +0.856155 +0.624879 +0.853362 +0.626924 +0.859214 +0.634869 +0.868069 +0.626790 +0.848081 +0.621120 +0.847220 +0.619461 +0.847660 +0.627298 +0.878448 +0.621813 +0.861559 +0.623720 +0.861542 +0.635658 +0.862725 +0.628113 +0.863853 +0.631850 +0.859556 +0.633000 +0.854257 +0.638746 +0.866685 +0.648973 +0.862864 +0.625951 +0.854611 +0.640266 +0.855673 +0.641484 +0.865331 +0.635243 +0.851912 +0.631818 +0.846717 +0.628953 +0.846531 +0.626723 +0.860248 +0.627907 +0.856509 +0.634877 +0.850667 +0.629023 +0.854955 +0.632610 +0.863567 +0.635496 +0.850133 +0.633768 +0.854443 +0.627820 +0.842820 +0.619080 +0.846174 +0.623400 +0.858117 +0.633905 +0.847743 +0.631327 +0.848796 +0.629344 +0.847581 +0.613746 +0.842295 +0.624261 +0.837950 +0.627958 +0.838209 +0.626986 +0.836094 +0.604070 +0.831015 +0.618612 +0.833092 +0.614368 +0.813162 +0.608461 +0.820780 +0.610770 +0.832672 +0.606863 +0.811262 +0.598539 +0.847178 +0.593948 +0.838384 +0.587092 +0.833049 +0.604700 +0.831535 +0.585646 +0.835665 +0.569653 +0.826093 +0.581387 +0.826663 +0.568404 +0.827177 +0.580422 +0.809071 +0.576369 +0.802513 +0.580461 +0.816087 +0.561770 +0.813224 +0.566144 +0.817003 +0.574570 +0.804939 +0.574103 +0.795245 +0.568586 +0.796598 +0.549578 +0.799025 +0.541363 +0.805452 +0.544193 +0.811135 +0.559869 +0.795545 +0.544645 +0.811674 +0.530223 +0.783759 +0.545710 +0.783021 +0.536999 +0.777696 +0.508022 +0.767337 +0.512217 +0.773824 +0.511887 +0.771163 +0.502687 +0.746335 +0.544448 +0.770704 +0.497107 +0.749755 +0.503043 +0.762734 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.539370 +0.780796 +0.539370 +0.780796 +0.539370 +0.780796 +0.539370 +0.780796 +0.539370 +0.763257 +0.551402 +0.765615 +0.534343 +0.779336 +0.549098 +0.797437 +0.534670 +0.768694 +0.541012 +0.784947 +0.559315 +0.803409 +0.560739 +0.778274 +0.553043 +0.751833 +0.553866 +0.789567 +0.518847 +0.789280 +0.574818 +0.771574 +0.571518 +0.830308 +0.594150 +0.802970 +0.576223 +0.824261 +0.577604 +0.794877 +0.571625 +0.821238 +0.584089 +0.804838 +0.587597 +0.817408 +0.576201 +0.816307 +0.600930 +0.844872 +0.567686 +0.830361 +0.597632 +0.824374 +0.572625 +0.813208 +0.588290 +0.833243 +0.582357 +0.824674 +0.606559 +0.832466 +0.571317 +0.833134 +0.612346 +0.845400 +0.600970 +0.830969 +0.588960 +0.828069 +0.595695 +0.838539 +0.594465 +0.836257 +0.598898 +0.848143 +0.592572 +0.864036 +0.616301 +0.853524 +0.606450 +0.854365 +0.623386 +0.859237 +0.623077 +0.856953 +0.609612 +0.864563 +0.597501 +0.859082 +0.623967 +0.862838 +0.611266 +0.860457 +0.634827 +0.866049 +0.631541 +0.862880 +0.638600 +0.857575 +0.635298 +0.860071 +0.623301 +0.857021 +0.642552 +0.872463 +0.632550 +0.855447 +0.616996 +0.860638 +0.618980 +0.859467 +0.628603 +0.865402 +0.618678 +0.872470 +0.618655 +0.857790 +0.628164 +0.853184 +0.629473 +0.865844 +0.618968 +0.854483 +0.616934 +0.848817 +0.649260 +0.866755 +0.637548 +0.869379 +0.651093 +0.850843 +0.638135 +0.865977 +0.632472 +0.861854 +0.630296 +0.849654 +0.639295 +0.859598 +0.621142 +0.850736 +0.615970 +0.855615 +0.636360 +0.854722 +0.626967 +0.839886 +0.618445 +0.846204 +0.636070 +0.867117 +0.634757 +0.862493 +0.631761 +0.848373 +0.613253 +0.832526 +0.622708 +0.854124 +0.622715 +0.858725 +0.642525 +0.850749 +0.629466 +0.857231 +0.629261 +0.845681 +0.613593 +0.835921 +0.607755 +0.833864 +0.623206 +0.832963 +0.621477 +0.830039 +0.609162 +0.832525 +0.611643 +0.832353 +0.617105 +0.826507 +0.613872 +0.823339 +0.601434 +0.819830 +0.601692 +0.830473 +0.602781 +0.834405 +0.603622 +0.839378 +0.586291 +0.849635 +0.580465 +0.841508 +0.581868 +0.835469 +0.588638 +0.848453 +0.591681 +0.822598 +0.585729 +0.832789 +0.584500 +0.813108 +0.566465 +0.819954 +0.576304 +0.805541 +0.571888 +0.802671 +0.566636 +0.813602 +0.580547 +0.805231 +0.557372 +0.785781 +0.549071 +0.776535 +0.563492 +0.790837 +0.551438 +0.799632 +0.560505 +0.787890 +0.516399 +0.807417 +0.529710 +0.808972 +0.536466 +0.797580 +0.535127 +0.781304 +0.537078 +0.770232 +0.528182 +0.792721 +0.533112 +0.732115 +0.529516 +0.783191 +0.491946 +0.760342 +0.498669 +0.752845 +0.509858 +0.742687 +0.517371 +0.765459 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.564354 +0.770783 +0.564354 +0.770783 +0.564354 +0.770783 +0.564354 +0.770783 +0.564354 +0.770783 +0.564354 +0.757666 +0.529683 +0.769404 +0.550787 +0.767793 +0.567441 +0.775150 +0.574833 +0.792702 +0.562619 +0.802097 +0.569423 +0.767034 +0.527442 +0.814962 +0.542017 +0.790241 +0.545696 +0.783386 +0.573384 +0.823786 +0.587227 +0.781296 +0.586656 +0.813618 +0.568403 +0.840375 +0.561594 +0.790389 +0.590237 +0.799940 +0.547211 +0.806947 +0.576113 +0.813594 +0.559198 +0.825236 +0.594985 +0.840487 +0.584351 +0.830423 +0.598093 +0.831778 +0.581990 +0.827720 +0.602845 +0.853065 +0.578444 +0.830938 +0.606134 +0.826296 +0.581823 +0.844834 +0.607723 +0.848695 +0.597715 +0.837748 +0.594217 +0.838378 +0.588487 +0.849573 +0.617189 +0.840204 +0.612801 +0.853793 +0.588242 +0.844662 +0.613620 +0.862417 +0.613226 +0.864893 +0.621026 +0.855901 +0.624901 +0.857456 +0.613889 +0.864618 +0.607085 +0.872419 +0.638593 +0.858742 +0.618927 +0.871967 +0.641387 +0.874799 +0.623677 +0.865622 +0.646644 +0.855341 +0.643027 +0.859851 +0.625537 +0.868094 +0.632318 +0.874789 +0.640684 +0.859278 +0.630974 +0.851608 +0.611638 +0.876233 +0.629563 +0.865134 +0.638811 +0.870970 +0.628051 +0.865479 +0.637708 +0.871389 +0.638448 +0.868030 +0.616660 +0.843114 +0.616577 +0.868886 +0.643459 +0.858164 +0.639681 +0.866834 +0.654640 +0.867634 +0.639713 +0.861102 +0.643402 +0.859678 +0.639371 +0.847921 +0.637677 +0.863255 +0.622173 +0.867779 +0.627705 +0.855992 +0.631704 +0.849761 +0.618112 +0.852828 +0.629244 +0.849487 +0.624927 +0.861203 +0.641018 +0.861791 +0.633336 +0.851431 +0.606935 +0.841511 +0.613319 +0.845773 +0.637653 +0.860170 +0.643595 +0.859726 +0.627355 +0.847748 +0.646278 +0.842805 +0.616540 +0.834609 +0.600983 +0.840098 +0.632502 +0.836941 +0.611596 +0.829985 +0.620074 +0.844817 +0.621159 +0.857560 +0.614153 +0.823020 +0.618699 +0.829836 +0.617999 +0.813708 +0.604161 +0.837582 +0.605810 +0.827080 +0.597940 +0.840088 +0.575101 +0.846284 +0.575382 +0.835055 +0.580952 +0.838533 +0.591467 +0.834454 +0.584558 +0.832053 +0.577640 +0.807362 +0.576529 +0.834594 +0.561417 +0.812421 +0.587792 +0.795578 +0.554461 +0.803951 +0.560629 +0.802255 +0.557440 +0.796703 +0.555373 +0.807155 +0.550085 +0.783516 +0.543365 +0.807489 +0.524896 +0.816717 +0.522345 +0.798495 +0.528821 +0.800196 +0.542085 +0.775277 +0.530370 +0.785451 +0.507518 +0.757752 +0.510407 +0.766342 +0.514147 +0.773430 +0.492791 +0.777303 +0.534047 +0.736299 +0.536158 +0.733320 +0.530791 +0.743693 +0.542959 +0.722694 +0.534548 +0.737771 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.563416 +0.773974 +0.563416 +0.773974 +0.563416 +0.773974 +0.563416 +0.773974 +0.563416 +0.781158 +0.537879 +0.757676 +0.561272 +0.788481 +0.560034 +0.775765 +0.568942 +0.781519 +0.547171 +0.794768 +0.531355 +0.784231 +0.541761 +0.763246 +0.550756 +0.769528 +0.525667 +0.815426 +0.571095 +0.794976 +0.533112 +0.791361 +0.564980 +0.813915 +0.556037 +0.801881 +0.579470 +0.797644 +0.569218 +0.805122 +0.585572 +0.806105 +0.566167 +0.826026 +0.557368 +0.815712 +0.576082 +0.833083 +0.607358 +0.816700 +0.583632 +0.828985 +0.567703 +0.834259 +0.590750 +0.840250 +0.595282 +0.837956 +0.622464 +0.850432 +0.605651 +0.839417 +0.611682 +0.827496 +0.601362 +0.835433 +0.592069 +0.826654 +0.600836 +0.833574 +0.612056 +0.846866 +0.605382 +0.849160 +0.601162 +0.841547 +0.613700 +0.860970 +0.614324 +0.861792 +0.611782 +0.866639 +0.628544 +0.865119 +0.625408 +0.867606 +0.620682 +0.863741 +0.621660 +0.862295 +0.617335 +0.871718 +0.636959 +0.866397 +0.648289 +0.860945 +0.635994 +0.854907 +0.626340 +0.858999 +0.633742 +0.864681 +0.646216 +0.865263 +0.637877 +0.867459 +0.650517 +0.872558 +0.640965 +0.861190 +0.617201 +0.848956 +0.632747 +0.877786 +0.640891 +0.868897 +0.624649 +0.872076 +0.632607 +0.851686 +0.640469 +0.875000 +0.632061 +0.868922 +0.631860 +0.857012 +0.641321 +0.880016 +0.641046 +0.859880 +0.636151 +0.866868 +0.655530 +0.868903 +0.643156 +0.867724 +0.652292 +0.866775 +0.650889 +0.860892 +0.654570 +0.880628 +0.642805 +0.862775 +0.628589 +0.864784 +0.642974 +0.859152 +0.637402 +0.860386 +0.632191 +0.870244 +0.647099 +0.855541 +0.647999 +0.877209 +0.648450 +0.856636 +0.646867 +0.845854 +0.644496 +0.865516 +0.632638 +0.864679 +0.631757 +0.861149 +0.639439 +0.863081 +0.643027 +0.847361 +0.617513 +0.859439 +0.641650 +0.857696 +0.640087 +0.851006 +0.634783 +0.853615 +0.653935 +0.848261 +0.626318 +0.851224 +0.619656 +0.821929 +0.634925 +0.850115 +0.619255 +0.853519 +0.618223 +0.866915 +0.616303 +0.861044 +0.599802 +0.861302 +0.607930 +0.863519 +0.611395 +0.863393 +0.598289 +0.857124 +0.583970 +0.828476 +0.602694 +0.827218 +0.575028 +0.803035 +0.579038 +0.830310 +0.583027 +0.826966 +0.581433 +0.824947 +0.582947 +0.814509 +0.561680 +0.802375 +0.572139 +0.808876 +0.568863 +0.818402 +0.563266 +0.819898 +0.575348 +0.794993 +0.539726 +0.804802 +0.548830 +0.818719 +0.542824 +0.800062 +0.525236 +0.772158 +0.525692 +0.782792 +0.546591 +0.784592 +0.528573 +0.780000 +0.525095 +0.787448 +0.528741 +0.788020 +0.550520 +0.782978 +0.532146 +0.783835 +0.515195 +0.727614 +0.510387 +0.784944 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.580243 +0.773722 +0.580243 +0.773722 +0.580243 +0.773722 +0.580243 +0.773722 +0.580243 +0.736170 +0.583152 +0.778205 +0.591484 +0.776007 +0.562294 +0.766955 +0.586952 +0.808172 +0.549553 +0.784959 +0.566351 +0.785513 +0.588637 +0.778347 +0.596031 +0.795376 +0.568872 +0.803408 +0.576150 +0.806017 +0.548864 +0.790222 +0.551232 +0.804523 +0.587007 +0.827927 +0.577835 +0.809727 +0.560608 +0.809439 +0.578454 +0.813629 +0.573159 +0.831157 +0.555381 +0.829505 +0.587239 +0.835412 +0.584437 +0.815920 +0.601159 +0.827858 +0.581956 +0.825826 +0.585850 +0.806079 +0.590192 +0.820809 +0.610978 +0.844863 +0.605129 +0.854144 +0.610875 +0.827398 +0.595527 +0.836060 +0.603196 +0.833238 +0.593778 +0.853454 +0.617244 +0.833889 +0.611623 +0.843257 +0.596135 +0.835675 +0.611561 +0.873039 +0.619605 +0.867537 +0.621955 +0.884586 +0.624340 +0.848868 +0.626365 +0.853671 +0.612448 +0.853156 +0.618188 +0.877718 +0.619790 +0.863996 +0.636701 +0.851957 +0.637318 +0.868839 +0.631006 +0.858531 +0.650286 +0.845343 +0.610937 +0.862162 +0.658659 +0.854847 +0.643752 +0.868230 +0.642003 +0.869562 +0.643958 +0.850326 +0.608250 +0.847066 +0.623455 +0.869504 +0.634163 +0.865318 +0.634671 +0.863736 +0.629161 +0.866141 +0.633416 +0.867002 +0.627697 +0.870541 +0.616825 +0.842347 +0.642063 +0.857843 +0.652991 +0.864290 +0.649106 +0.850455 +0.647522 +0.868631 +0.647703 +0.854101 +0.649876 +0.858258 +0.635579 +0.854905 +0.639500 +0.867077 +0.651491 +0.857518 +0.622269 +0.849071 +0.647009 +0.863266 +0.616899 +0.841010 +0.643502 +0.856642 +0.639607 +0.857875 +0.635917 +0.852127 +0.629471 +0.852872 +0.633533 +0.853128 +0.629521 +0.838341 +0.631458 +0.860703 +0.640864 +0.857530 +0.643468 +0.853459 +0.627928 +0.855347 +0.626676 +0.854099 +0.631411 +0.863673 +0.623166 +0.848630 +0.625671 +0.833455 +0.625835 +0.846609 +0.619930 +0.832867 +0.618259 +0.831833 +0.631307 +0.823361 +0.603683 +0.838113 +0.627479 +0.869457 +0.606790 +0.840254 +0.592587 +0.841122 +0.609418 +0.846678 +0.607533 +0.852758 +0.601801 +0.843759 +0.596240 +0.833887 +0.583750 +0.838329 +0.594531 +0.809321 +0.580195 +0.829421 +0.583602 +0.811796 +0.592440 +0.827165 +0.575300 +0.807504 +0.561134 +0.813534 +0.578582 +0.802642 +0.566777 +0.797451 +0.574568 +0.800471 +0.568959 +0.808560 +0.537865 +0.801539 +0.527383 +0.787033 +0.521462 +0.792251 +0.521571 +0.810192 +0.525339 +0.762338 +0.530124 +0.781998 +0.527526 +0.761714 +0.542486 +0.748171 +0.525863 +0.764094 +0.522765 +0.753800 +0.531375 +0.731600 +0.506414 +0.729614 +0.481693 +0.749831 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.579862 +0.766028 +0.579862 +0.766028 +0.579862 +0.766028 +0.579862 +0.766028 +0.579862 +0.794939 +0.587242 +0.775129 +0.589417 +0.740726 +0.535018 +0.782229 +0.615150 +0.788916 +0.571677 +0.759099 +0.580182 +0.743611 +0.569161 +0.762600 +0.589566 +0.764637 +0.572692 +0.805748 +0.564343 +0.766761 +0.570803 +0.787099 +0.596067 +0.790299 +0.603863 +0.797927 +0.600466 +0.777184 +0.587239 +0.809623 +0.598767 +0.795668 +0.593863 +0.799109 +0.591480 +0.802165 +0.613297 +0.788196 +0.608690 +0.818086 +0.637951 +0.814067 +0.613974 +0.835330 +0.607434 +0.801248 +0.615590 +0.792088 +0.599712 +0.807007 +0.633584 +0.819654 +0.620704 +0.816736 +0.608396 +0.815418 +0.623780 +0.811780 +0.625122 +0.822469 +0.614405 +0.827270 +0.609500 +0.835721 +0.617193 +0.826950 +0.611248 +0.846456 +0.633079 +0.851361 +0.651599 +0.855293 +0.637645 +0.839377 +0.644830 +0.839026 +0.633419 +0.837907 +0.635540 +0.853991 +0.644459 +0.850037 +0.649158 +0.834621 +0.657974 +0.836781 +0.646985 +0.848873 +0.661342 +0.843167 +0.652331 +0.836365 +0.664638 +0.851957 +0.664302 +0.855179 +0.667379 +0.855726 +0.647282 +0.830673 +0.639887 +0.834326 +0.643394 +0.846290 +0.651669 +0.845101 +0.648882 +0.846680 +0.665516 +0.844672 +0.656271 +0.845117 +0.667208 +0.846245 +0.652274 +0.832826 +0.659993 +0.831966 +0.660084 +0.844152 +0.675795 +0.832618 +0.664492 +0.828183 +0.668819 +0.840364 +0.672358 +0.839043 +0.658093 +0.822498 +0.645788 +0.843630 +0.669703 +0.839027 +0.645566 +0.833760 +0.658140 +0.845035 +0.647780 +0.823007 +0.660859 +0.838933 +0.653573 +0.834967 +0.638587 +0.810350 +0.646017 +0.829512 +0.636128 +0.828368 +0.644815 +0.823197 +0.649752 +0.837663 +0.663801 +0.841850 +0.664654 +0.830415 +0.637061 +0.818456 +0.639668 +0.822415 +0.655068 +0.828309 +0.632285 +0.817079 +0.639245 +0.817555 +0.638892 +0.821322 +0.637554 +0.806922 +0.646751 +0.796537 +0.623811 +0.803812 +0.636862 +0.807459 +0.632118 +0.822895 +0.606982 +0.825445 +0.601113 +0.813725 +0.628313 +0.815377 +0.608126 +0.820696 +0.604874 +0.818588 +0.613204 +0.814469 +0.591317 +0.798025 +0.588766 +0.775374 +0.587169 +0.804393 +0.583129 +0.797634 +0.589661 +0.783147 +0.588447 +0.791563 +0.579540 +0.777371 +0.581708 +0.768743 +0.559341 +0.755955 +0.569337 +0.781977 +0.565688 +0.787122 +0.560923 +0.771815 +0.548462 +0.774888 +0.543117 +0.789526 +0.549330 +0.776861 +0.562312 +0.771431 +0.518533 +0.767485 +0.546631 +0.743994 +0.539520 +0.758119 +0.534704 +0.750280 +0.550434 +0.748137 +0.548113 +0.714571 +0.517084 +0.723546 +0.529264 +0.737663 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.569188 +0.744851 +0.569188 +0.744851 +0.569188 +0.744851 +0.569188 +0.744851 +0.569188 +0.728853 +0.540023 +0.739034 +0.572021 +0.739716 +0.558153 +0.755804 +0.577391 +0.779562 +0.549799 +0.757579 +0.559181 +0.766011 +0.583996 +0.755376 +0.568554 +0.791392 +0.572599 +0.798228 +0.567924 +0.770923 +0.587228 +0.766318 +0.564767 +0.773776 +0.609829 +0.788392 +0.576396 +0.787119 +0.573837 +0.757113 +0.584928 +0.795329 +0.598035 +0.786156 +0.576302 +0.798874 +0.616639 +0.783128 +0.604354 +0.812288 +0.608799 +0.817209 +0.640586 +0.822239 +0.614593 +0.810719 +0.616660 +0.795756 +0.611393 +0.805833 +0.627199 +0.831841 +0.618539 +0.805840 +0.626103 +0.812494 +0.614625 +0.822834 +0.618479 +0.835013 +0.618751 +0.823779 +0.620641 +0.808717 +0.605443 +0.811857 +0.614193 +0.836799 +0.631718 +0.845994 +0.641394 +0.845667 +0.641155 +0.841077 +0.625283 +0.832617 +0.647314 +0.849299 +0.648202 +0.841850 +0.647842 +0.839789 +0.645135 +0.835192 +0.654507 +0.827353 +0.648537 +0.836469 +0.658049 +0.835520 +0.654988 +0.849881 +0.664740 +0.848496 +0.658631 +0.833777 +0.652013 +0.847443 +0.666147 +0.832495 +0.643031 +0.843565 +0.638622 +0.849117 +0.659410 +0.841138 +0.644059 +0.848446 +0.657269 +0.846185 +0.652932 +0.859761 +0.662702 +0.848654 +0.661660 +0.839572 +0.659070 +0.842058 +0.667956 +0.846509 +0.671846 +0.845672 +0.671562 +0.836194 +0.660775 +0.836637 +0.659591 +0.843977 +0.652233 +0.836822 +0.645364 +0.841552 +0.663085 +0.848116 +0.651597 +0.833554 +0.664002 +0.832381 +0.657838 +0.850040 +0.655526 +0.841147 +0.667737 +0.833957 +0.655490 +0.825218 +0.652956 +0.823841 +0.663266 +0.835023 +0.640751 +0.834177 +0.653826 +0.835797 +0.659153 +0.843194 +0.657694 +0.833964 +0.652157 +0.834713 +0.646858 +0.814298 +0.655559 +0.830913 +0.647864 +0.822569 +0.662172 +0.823883 +0.638651 +0.812637 +0.650281 +0.811740 +0.638766 +0.803813 +0.639644 +0.814633 +0.649119 +0.824994 +0.637388 +0.848216 +0.621759 +0.833758 +0.614971 +0.824260 +0.613795 +0.820740 +0.616260 +0.817057 +0.608523 +0.826236 +0.610127 +0.800723 +0.607139 +0.788801 +0.592057 +0.804126 +0.600585 +0.792329 +0.595137 +0.796833 +0.586241 +0.792410 +0.572177 +0.795130 +0.587658 +0.800907 +0.604997 +0.790071 +0.579440 +0.773685 +0.584320 +0.790870 +0.578283 +0.804581 +0.557423 +0.767972 +0.561704 +0.772502 +0.558263 +0.766682 +0.543184 +0.771347 +0.543615 +0.775286 +0.547305 +0.742553 +0.539484 +0.746909 +0.559666 +0.743903 +0.538782 +0.746164 +0.546250 +0.738600 +0.539874 +0.720444 +0.525375 +0.701985 +0.571250 +0.735069 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.551070 +0.737293 +0.551070 +0.737293 +0.551070 +0.737293 +0.551070 +0.778354 +0.556718 +0.757733 +0.531123 +0.701756 +0.544656 +0.752391 +0.577431 +0.757609 +0.591359 +0.771557 +0.582845 +0.790618 +0.574664 +0.755691 +0.582809 +0.783762 +0.554296 +0.758875 +0.566949 +0.775288 +0.571449 +0.757384 +0.576523 +0.786221 +0.597240 +0.797256 +0.578310 +0.813628 +0.579070 +0.798893 +0.594563 +0.786550 +0.597162 +0.802816 +0.599004 +0.778905 +0.577535 +0.796782 +0.598088 +0.814137 +0.606437 +0.812315 +0.608983 +0.790541 +0.623750 +0.815599 +0.606522 +0.815197 +0.602800 +0.784263 +0.588923 +0.801659 +0.616562 +0.804712 +0.617957 +0.821208 +0.627509 +0.820753 +0.630705 +0.822664 +0.644621 +0.826499 +0.617683 +0.820135 +0.623917 +0.804098 +0.611772 +0.820790 +0.610659 +0.834434 +0.630982 +0.845468 +0.644200 +0.828883 +0.656941 +0.853492 +0.629886 +0.843307 +0.647121 +0.858609 +0.644299 +0.826075 +0.649567 +0.847605 +0.654338 +0.838989 +0.651102 +0.839298 +0.645446 +0.839825 +0.661125 +0.848681 +0.659997 +0.840763 +0.656817 +0.843326 +0.646158 +0.836941 +0.656061 +0.854816 +0.673130 +0.837648 +0.660008 +0.850893 +0.651593 +0.836090 +0.655733 +0.845969 +0.631272 +0.844435 +0.645164 +0.835705 +0.652263 +0.863755 +0.660317 +0.844944 +0.653889 +0.833505 +0.643513 +0.838936 +0.658856 +0.837985 +0.665414 +0.844906 +0.668439 +0.839357 +0.665841 +0.857723 +0.674880 +0.826946 +0.660897 +0.817391 +0.646651 +0.830518 +0.651859 +0.837066 +0.658636 +0.845689 +0.647270 +0.828594 +0.658925 +0.839173 +0.654251 +0.846453 +0.651285 +0.822249 +0.642415 +0.837339 +0.642579 +0.817378 +0.655008 +0.831625 +0.640190 +0.825018 +0.644345 +0.832655 +0.662869 +0.831587 +0.650505 +0.820454 +0.654810 +0.827515 +0.641346 +0.821575 +0.642778 +0.817663 +0.649829 +0.818837 +0.661035 +0.826571 +0.632987 +0.810520 +0.656714 +0.821137 +0.637500 +0.790708 +0.627317 +0.814511 +0.640703 +0.804618 +0.628886 +0.827183 +0.610089 +0.825769 +0.609498 +0.819318 +0.625373 +0.834371 +0.608265 +0.817288 +0.623071 +0.822506 +0.611064 +0.797197 +0.595850 +0.804666 +0.597170 +0.803175 +0.597314 +0.820888 +0.601860 +0.798956 +0.584982 +0.799478 +0.577895 +0.802932 +0.584245 +0.783060 +0.573698 +0.797489 +0.573409 +0.768763 +0.570239 +0.794208 +0.565482 +0.768171 +0.571720 +0.776647 +0.567536 +0.784792 +0.574529 +0.763950 +0.547329 +0.742888 +0.546764 +0.760711 +0.555124 +0.777672 +0.538515 +0.739502 +0.546092 +0.771514 +0.528236 +0.727864 +0.573100 +0.766042 +0.533951 +0.727268 +0.530088 +0.718892 +0.547460 +0.740391 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.561805 +0.757939 +0.561805 +0.757939 +0.561805 +0.757939 +0.561805 +0.757939 +0.561805 +0.781541 +0.559563 +0.769592 +0.559344 +0.758014 +0.579613 +0.746212 +0.546458 +0.769586 +0.560621 +0.774883 +0.576577 +0.769052 +0.625192 +0.749012 +0.552668 +0.772503 +0.568493 +0.811418 +0.615612 +0.787455 +0.595535 +0.768793 +0.597661 +0.775136 +0.594834 +0.792805 +0.587265 +0.812899 +0.609077 +0.783143 +0.582529 +0.786052 +0.605389 +0.795645 +0.602266 +0.801979 +0.614409 +0.804009 +0.600748 +0.806064 +0.625704 +0.797427 +0.612056 +0.824470 +0.615860 +0.813595 +0.624643 +0.814226 +0.609277 +0.811392 +0.649750 +0.821139 +0.633722 +0.824833 +0.615534 +0.814772 +0.620486 +0.824378 +0.633089 +0.836683 +0.609451 +0.837382 +0.632398 +0.814929 +0.625349 +0.818407 +0.615268 +0.837882 +0.646722 +0.865713 +0.636148 +0.853700 +0.653060 +0.864125 +0.649233 +0.850742 +0.666719 +0.859820 +0.660910 +0.859000 +0.665944 +0.851267 +0.665345 +0.848226 +0.659783 +0.838972 +0.646940 +0.845002 +0.668092 +0.840094 +0.655680 +0.838691 +0.662736 +0.847569 +0.655150 +0.845137 +0.660180 +0.857793 +0.661700 +0.860857 +0.649898 +0.846335 +0.652052 +0.847273 +0.657075 +0.846626 +0.650660 +0.846320 +0.657125 +0.843058 +0.672362 +0.854020 +0.666136 +0.846635 +0.656338 +0.844724 +0.664406 +0.837532 +0.658405 +0.864767 +0.673487 +0.845272 +0.664463 +0.837941 +0.674580 +0.845770 +0.678486 +0.836343 +0.661584 +0.826704 +0.648173 +0.826505 +0.657291 +0.841574 +0.660160 +0.829604 +0.658782 +0.836599 +0.666647 +0.846312 +0.655358 +0.845831 +0.648932 +0.836354 +0.662132 +0.848227 +0.644050 +0.821687 +0.651605 +0.831187 +0.651097 +0.821397 +0.646579 +0.830648 +0.652278 +0.841009 +0.650258 +0.824931 +0.657587 +0.826506 +0.652332 +0.820628 +0.639582 +0.825347 +0.648533 +0.817511 +0.643103 +0.820302 +0.647138 +0.835563 +0.653007 +0.809854 +0.623298 +0.803019 +0.620238 +0.812955 +0.628824 +0.790659 +0.622091 +0.825998 +0.611381 +0.811766 +0.609493 +0.820481 +0.602797 +0.828874 +0.625110 +0.825641 +0.612958 +0.807149 +0.609122 +0.808837 +0.602535 +0.808394 +0.586574 +0.806822 +0.589608 +0.807557 +0.590980 +0.783139 +0.584245 +0.778861 +0.596062 +0.781523 +0.592980 +0.783229 +0.598857 +0.790406 +0.568006 +0.773271 +0.555696 +0.787493 +0.576198 +0.770236 +0.563560 +0.779307 +0.592673 +0.797142 +0.560773 +0.756306 +0.561399 +0.756970 +0.524704 +0.749089 +0.536629 +0.756942 +0.511492 +0.734146 +0.520259 +0.783763 +0.568169 +0.749569 +0.529171 +0.729347 +0.502990 +0.747186 +0.546788 +0.744648 +0.545916 +0.742034 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.581311 +0.735594 +0.581311 +0.735594 +0.581311 +0.735594 +0.581311 +0.735594 +0.581311 +0.773436 +0.540018 +0.720703 +0.585820 +0.738598 +0.557684 +0.747481 +0.600430 +0.784847 +0.577739 +0.754006 +0.574659 +0.766933 +0.583015 +0.765058 +0.562063 +0.772963 +0.564698 +0.780302 +0.570665 +0.767387 +0.562886 +0.765267 +0.565233 +0.783710 +0.605036 +0.796406 +0.588085 +0.768653 +0.579459 +0.797799 +0.575291 +0.768776 +0.589582 +0.777826 +0.573623 +0.787298 +0.603463 +0.790174 +0.594258 +0.810132 +0.611567 +0.810928 +0.619277 +0.813912 +0.612601 +0.809493 +0.605472 +0.797088 +0.622071 +0.827680 +0.608079 +0.807784 +0.600102 +0.787651 +0.600077 +0.809560 +0.634990 +0.822071 +0.627136 +0.821298 +0.599637 +0.807250 +0.613041 +0.818895 +0.617739 +0.813411 +0.625379 +0.824359 +0.644584 +0.848397 +0.642713 +0.842270 +0.650183 +0.839182 +0.635234 +0.819464 +0.632315 +0.846476 +0.653994 +0.847676 +0.636062 +0.848787 +0.667621 +0.833469 +0.638436 +0.838857 +0.666356 +0.844252 +0.659736 +0.829842 +0.649170 +0.844263 +0.640508 +0.838611 +0.653714 +0.835338 +0.660862 +0.849550 +0.658912 +0.836133 +0.649363 +0.830005 +0.633419 +0.836089 +0.648199 +0.838027 +0.644426 +0.840158 +0.658952 +0.831469 +0.657214 +0.838288 +0.651834 +0.849917 +0.659838 +0.837531 +0.656522 +0.820216 +0.667931 +0.827316 +0.661833 +0.844663 +0.691022 +0.851864 +0.663698 +0.828970 +0.662665 +0.843296 +0.651610 +0.832816 +0.642726 +0.832601 +0.645448 +0.828386 +0.647487 +0.825032 +0.656386 +0.830655 +0.648651 +0.829682 +0.654241 +0.841361 +0.658257 +0.843453 +0.640799 +0.830254 +0.646465 +0.832394 +0.646015 +0.821460 +0.640248 +0.823772 +0.651807 +0.814047 +0.638351 +0.822228 +0.646237 +0.821815 +0.652023 +0.810881 +0.646027 +0.814627 +0.635216 +0.796172 +0.645060 +0.809107 +0.648711 +0.815941 +0.644794 +0.816587 +0.637490 +0.815641 +0.635129 +0.799032 +0.638090 +0.808496 +0.632174 +0.806173 +0.645025 +0.812979 +0.611086 +0.825806 +0.601077 +0.818110 +0.603358 +0.804097 +0.604261 +0.825727 +0.616595 +0.829230 +0.604103 +0.818350 +0.605670 +0.820759 +0.600936 +0.770099 +0.570627 +0.778999 +0.590842 +0.782730 +0.599107 +0.786950 +0.594630 +0.768666 +0.560588 +0.783476 +0.576641 +0.774059 +0.584768 +0.756712 +0.588827 +0.783749 +0.559028 +0.769193 +0.565653 +0.769327 +0.553864 +0.804622 +0.544288 +0.769458 +0.525848 +0.754092 +0.551240 +0.734453 +0.554549 +0.754509 +0.544736 +0.753637 +0.514188 +0.735958 +0.546294 +0.716129 +0.537196 +0.732776 +0.539445 +0.730285 +0.522297 +0.733404 +0.533053 +0.726397 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.576156 +0.766689 +0.576156 +0.766689 +0.576156 +0.766689 +0.576156 +0.766689 +0.576156 +0.738537 +0.572310 +0.781366 +0.587831 +0.762308 +0.558711 +0.756392 +0.581927 +0.766052 +0.582216 +0.754384 +0.608238 +0.760008 +0.583712 +0.779022 +0.588003 +0.739102 +0.591807 +0.734215 +0.584899 +0.787530 +0.581004 +0.761286 +0.588214 +0.771030 +0.579741 +0.778493 +0.586685 +0.772833 +0.593665 +0.790650 +0.604911 +0.763008 +0.607899 +0.789749 +0.595659 +0.810350 +0.621205 +0.790763 +0.600705 +0.792260 +0.596425 +0.791704 +0.609834 +0.823461 +0.619342 +0.810618 +0.598683 +0.788057 +0.622240 +0.801932 +0.631102 +0.806455 +0.609860 +0.792875 +0.620865 +0.827857 +0.631335 +0.820819 +0.632624 +0.815916 +0.612408 +0.800648 +0.618762 +0.833190 +0.613110 +0.801470 +0.617902 +0.826223 +0.636994 +0.834800 +0.651389 +0.857657 +0.653209 +0.850023 +0.644712 +0.827858 +0.624553 +0.844516 +0.641787 +0.828825 +0.648727 +0.842408 +0.641516 +0.814795 +0.654773 +0.847076 +0.656978 +0.831884 +0.657914 +0.825877 +0.641864 +0.833460 +0.650508 +0.819405 +0.647644 +0.834650 +0.657492 +0.832501 +0.652259 +0.835903 +0.650320 +0.838237 +0.644094 +0.828331 +0.638890 +0.851607 +0.656530 +0.837443 +0.651346 +0.831299 +0.652620 +0.834485 +0.646378 +0.841310 +0.655185 +0.831155 +0.651888 +0.830551 +0.657942 +0.834710 +0.662013 +0.853991 +0.683400 +0.851064 +0.651285 +0.822912 +0.647731 +0.842467 +0.659826 +0.820409 +0.656577 +0.823653 +0.651702 +0.829140 +0.639393 +0.825870 +0.658794 +0.824771 +0.633420 +0.825260 +0.651723 +0.825307 +0.644745 +0.822363 +0.657482 +0.822487 +0.642396 +0.820006 +0.640359 +0.808232 +0.644835 +0.814005 +0.643864 +0.812741 +0.640780 +0.822284 +0.646825 +0.825631 +0.648604 +0.833412 +0.640921 +0.808868 +0.649244 +0.810661 +0.643785 +0.800186 +0.637835 +0.806994 +0.636023 +0.816539 +0.639657 +0.809608 +0.634661 +0.786770 +0.629542 +0.805338 +0.633135 +0.790446 +0.629995 +0.819091 +0.616483 +0.823224 +0.608216 +0.798320 +0.605235 +0.804196 +0.608180 +0.797854 +0.600470 +0.811032 +0.595491 +0.804867 +0.602507 +0.779917 +0.593733 +0.766780 +0.567808 +0.791736 +0.577090 +0.777929 +0.584013 +0.785565 +0.597466 +0.770160 +0.578740 +0.774797 +0.574688 +0.745814 +0.592644 +0.774121 +0.583502 +0.760370 +0.561991 +0.761013 +0.574241 +0.774931 +0.561895 +0.769466 +0.578488 +0.774683 +0.551260 +0.762188 +0.516556 +0.726925 +0.519052 +0.741340 +0.554502 +0.735229 +0.553913 +0.757481 +0.557588 +0.774384 +0.505278 +0.739542 +0.524875 +0.726591 +0.552527 +0.730452 +0.504983 +0.746839 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.590874 +0.701901 +0.590874 +0.701901 +0.590874 +0.701901 +0.590874 +0.701901 +0.590874 +0.739445 +0.556539 +0.677044 +0.597394 +0.698345 +0.564273 +0.730316 +0.610956 +0.734692 +0.602734 +0.767079 +0.592196 +0.739852 +0.611997 +0.759004 +0.611232 +0.752561 +0.597367 +0.747067 +0.572086 +0.766577 +0.588782 +0.746145 +0.589754 +0.772278 +0.606954 +0.770624 +0.609865 +0.760998 +0.607325 +0.771095 +0.625713 +0.776936 +0.591107 +0.775469 +0.624940 +0.759827 +0.623585 +0.795415 +0.616999 +0.774834 +0.630117 +0.768867 +0.624695 +0.796009 +0.632725 +0.774116 +0.626105 +0.784852 +0.639149 +0.794598 +0.647860 +0.783534 +0.633803 +0.778954 +0.654943 +0.782075 +0.644481 +0.790490 +0.642437 +0.791532 +0.623486 +0.787111 +0.627402 +0.799276 +0.633786 +0.797135 +0.640242 +0.801738 +0.639958 +0.806158 +0.657231 +0.825739 +0.659241 +0.808212 +0.650035 +0.818661 +0.653100 +0.818210 +0.676334 +0.821523 +0.660470 +0.812895 +0.664952 +0.801117 +0.662997 +0.820175 +0.673672 +0.797654 +0.680376 +0.809313 +0.666553 +0.814867 +0.680427 +0.813926 +0.665449 +0.825628 +0.681844 +0.820160 +0.677662 +0.809323 +0.660142 +0.814787 +0.663093 +0.812304 +0.672661 +0.826675 +0.658553 +0.807793 +0.668274 +0.831871 +0.669508 +0.823615 +0.668091 +0.819806 +0.672697 +0.812140 +0.673440 +0.817951 +0.670610 +0.809027 +0.679547 +0.832395 +0.688501 +0.821940 +0.671286 +0.799134 +0.684241 +0.818197 +0.678486 +0.797385 +0.659243 +0.804986 +0.662530 +0.804605 +0.653168 +0.791217 +0.665766 +0.812608 +0.658856 +0.799500 +0.672323 +0.802829 +0.660243 +0.814997 +0.654970 +0.799037 +0.658378 +0.794571 +0.647426 +0.793389 +0.669409 +0.791719 +0.653843 +0.794817 +0.665813 +0.792982 +0.668259 +0.801482 +0.651948 +0.788427 +0.666998 +0.787445 +0.658727 +0.791535 +0.645852 +0.774717 +0.650982 +0.777448 +0.653756 +0.798159 +0.649304 +0.767176 +0.644917 +0.765683 +0.622645 +0.788841 +0.642495 +0.772245 +0.641441 +0.792328 +0.621092 +0.789265 +0.620702 +0.779186 +0.605350 +0.785663 +0.630880 +0.771803 +0.612841 +0.777236 +0.597817 +0.778074 +0.596670 +0.761520 +0.609348 +0.753253 +0.579127 +0.766656 +0.596675 +0.771112 +0.608890 +0.745494 +0.609818 +0.761379 +0.576015 +0.742967 +0.588706 +0.757107 +0.590669 +0.742853 +0.560680 +0.733409 +0.577382 +0.749187 +0.583360 +0.745063 +0.566385 +0.722394 +0.559394 +0.740424 +0.553869 +0.724235 +0.531608 +0.725794 +0.565802 +0.736828 +0.555449 +0.722113 +0.543105 +0.724272 +0.572906 +0.726991 +0.543902 +0.716709 +0.540490 +0.700997 +0.547873 +0.687175 +0.551515 +0.714899 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.578454 +0.720781 +0.578454 +0.720781 +0.578454 +0.720781 +0.578454 +0.720781 +0.578454 +0.727904 +0.578478 +0.725611 +0.588847 +0.723753 +0.553825 +0.712978 +0.592689 +0.721220 +0.557108 +0.733868 +0.599215 +0.735153 +0.595665 +0.760043 +0.594099 +0.739904 +0.586279 +0.779785 +0.610843 +0.738840 +0.622409 +0.752680 +0.584477 +0.764292 +0.610798 +0.748966 +0.597420 +0.764818 +0.614823 +0.769750 +0.615526 +0.741969 +0.605290 +0.783547 +0.594499 +0.787163 +0.632485 +0.753649 +0.617762 +0.767359 +0.622037 +0.780742 +0.628639 +0.768284 +0.639472 +0.771808 +0.634809 +0.775755 +0.617940 +0.784578 +0.619172 +0.791820 +0.627228 +0.797588 +0.637447 +0.790079 +0.639762 +0.786012 +0.633564 +0.788472 +0.640841 +0.795808 +0.642689 +0.794632 +0.624383 +0.800521 +0.639703 +0.810955 +0.636701 +0.809923 +0.671191 +0.813529 +0.659931 +0.807887 +0.645772 +0.822563 +0.656241 +0.822422 +0.656651 +0.806607 +0.672572 +0.823020 +0.667247 +0.813942 +0.658051 +0.812623 +0.669738 +0.806386 +0.669543 +0.804365 +0.665997 +0.824187 +0.671068 +0.816054 +0.681127 +0.818417 +0.672516 +0.817252 +0.663064 +0.814737 +0.651232 +0.809229 +0.671735 +0.819455 +0.666125 +0.826152 +0.670070 +0.829047 +0.659990 +0.812626 +0.663285 +0.811729 +0.677397 +0.822072 +0.679481 +0.820810 +0.680582 +0.809855 +0.670454 +0.808231 +0.686024 +0.817218 +0.684558 +0.811781 +0.676653 +0.815549 +0.683179 +0.822667 +0.674000 +0.800260 +0.660089 +0.790985 +0.663836 +0.811132 +0.651774 +0.805543 +0.668664 +0.811323 +0.665768 +0.798570 +0.661640 +0.812339 +0.670338 +0.799519 +0.655355 +0.795447 +0.655474 +0.786985 +0.655728 +0.793869 +0.654895 +0.794364 +0.661485 +0.794560 +0.658988 +0.803576 +0.667592 +0.793575 +0.663552 +0.791094 +0.660890 +0.786658 +0.652727 +0.783981 +0.646349 +0.781821 +0.656598 +0.789362 +0.668577 +0.788919 +0.655001 +0.775185 +0.654478 +0.761060 +0.640238 +0.787285 +0.653563 +0.768140 +0.635033 +0.777541 +0.623320 +0.789349 +0.616847 +0.788709 +0.620457 +0.788554 +0.634160 +0.786239 +0.609230 +0.770844 +0.630253 +0.775452 +0.620516 +0.770416 +0.601415 +0.749928 +0.587689 +0.766905 +0.600687 +0.753444 +0.598206 +0.742233 +0.606410 +0.748526 +0.592075 +0.749430 +0.592640 +0.735427 +0.585363 +0.751435 +0.580263 +0.752485 +0.593372 +0.756936 +0.583601 +0.749073 +0.572626 +0.737161 +0.577916 +0.747603 +0.555087 +0.720384 +0.563024 +0.722427 +0.539009 +0.733848 +0.543867 +0.731100 +0.544701 +0.726250 +0.535355 +0.719168 +0.564059 +0.698050 +0.521346 +0.689501 +0.535062 +0.697150 +0.533792 +0.702240 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.571464 +0.706761 +0.571464 +0.706761 +0.571464 +0.706761 +0.571464 +0.717971 +0.579496 +0.715734 +0.573471 +0.700297 +0.585149 +0.704250 +0.555773 +0.728317 +0.610349 +0.734984 +0.596370 +0.722151 +0.584396 +0.756248 +0.594814 +0.729703 +0.601369 +0.765777 +0.592534 +0.756576 +0.608182 +0.742314 +0.631546 +0.776674 +0.578316 +0.756795 +0.608978 +0.741608 +0.592862 +0.754939 +0.623165 +0.755162 +0.617003 +0.770512 +0.595244 +0.754947 +0.593435 +0.781447 +0.628010 +0.766689 +0.616816 +0.764335 +0.616280 +0.784637 +0.638029 +0.810565 +0.649384 +0.781078 +0.632919 +0.778980 +0.622527 +0.754324 +0.620952 +0.779650 +0.625297 +0.792035 +0.637239 +0.787061 +0.627568 +0.787701 +0.646033 +0.798201 +0.639655 +0.798936 +0.647761 +0.790660 +0.631883 +0.795898 +0.645058 +0.811379 +0.643644 +0.816415 +0.678422 +0.821528 +0.679656 +0.825783 +0.640469 +0.817497 +0.643549 +0.825938 +0.650920 +0.800990 +0.650075 +0.820717 +0.669450 +0.803608 +0.657645 +0.806306 +0.659962 +0.806896 +0.663678 +0.808650 +0.669300 +0.820722 +0.676646 +0.823033 +0.670684 +0.827612 +0.676150 +0.818132 +0.667143 +0.807471 +0.648753 +0.799932 +0.669551 +0.820842 +0.674748 +0.804311 +0.672532 +0.826405 +0.678692 +0.806099 +0.662961 +0.816242 +0.676992 +0.823520 +0.667905 +0.807011 +0.667570 +0.813497 +0.679138 +0.814869 +0.678843 +0.816635 +0.689605 +0.806701 +0.667646 +0.819589 +0.669739 +0.818993 +0.674593 +0.798287 +0.668363 +0.800181 +0.657718 +0.802127 +0.661402 +0.817231 +0.662170 +0.791990 +0.654027 +0.801171 +0.671103 +0.809064 +0.656236 +0.794066 +0.645227 +0.793394 +0.656882 +0.793684 +0.650902 +0.788373 +0.643592 +0.805062 +0.668962 +0.798247 +0.659855 +0.789938 +0.653844 +0.777065 +0.664256 +0.783984 +0.653553 +0.795492 +0.657028 +0.778452 +0.653760 +0.780010 +0.639817 +0.783997 +0.645797 +0.785044 +0.647664 +0.784494 +0.648397 +0.758290 +0.654597 +0.772897 +0.665009 +0.771707 +0.636565 +0.778364 +0.625347 +0.793266 +0.623756 +0.790086 +0.632721 +0.785706 +0.628611 +0.781453 +0.617039 +0.779152 +0.625035 +0.785246 +0.626251 +0.751726 +0.600603 +0.749138 +0.592578 +0.780743 +0.611449 +0.775641 +0.595703 +0.763856 +0.593795 +0.755239 +0.612020 +0.737547 +0.595630 +0.744221 +0.579003 +0.731647 +0.592698 +0.741483 +0.564280 +0.761114 +0.573182 +0.735756 +0.568853 +0.732745 +0.562370 +0.738599 +0.569472 +0.713674 +0.556027 +0.742087 +0.546317 +0.717462 +0.557635 +0.705522 +0.544882 +0.723808 +0.550911 +0.705147 +0.534029 +0.711598 +0.529694 +0.670730 +0.527573 +0.706859 +0.539621 +0.695825 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.584642 +0.717906 +0.584642 +0.717906 +0.584642 +0.717906 +0.584642 +0.717906 +0.584642 +0.717906 +0.584642 +0.704693 +0.558149 +0.705983 +0.625860 +0.716639 +0.553136 +0.731096 +0.574928 +0.718821 +0.579001 +0.754091 +0.590831 +0.743026 +0.595107 +0.712211 +0.590332 +0.741158 +0.581732 +0.779232 +0.625868 +0.753138 +0.604497 +0.773086 +0.601788 +0.772325 +0.610478 +0.785692 +0.617800 +0.764693 +0.617010 +0.776524 +0.626018 +0.757654 +0.612385 +0.783242 +0.619953 +0.789928 +0.618520 +0.758702 +0.613875 +0.785339 +0.625628 +0.786052 +0.654914 +0.778364 +0.644449 +0.783691 +0.628606 +0.789003 +0.644137 +0.790181 +0.631156 +0.804473 +0.637329 +0.779420 +0.637424 +0.800658 +0.662403 +0.799039 +0.639553 +0.808193 +0.653390 +0.799536 +0.649554 +0.805106 +0.644939 +0.813577 +0.641626 +0.813422 +0.675183 +0.837654 +0.669392 +0.818245 +0.656502 +0.814339 +0.675682 +0.821944 +0.652768 +0.818042 +0.670501 +0.824347 +0.680827 +0.822718 +0.673327 +0.822865 +0.659000 +0.805006 +0.690099 +0.817299 +0.673065 +0.814700 +0.674221 +0.821868 +0.667613 +0.830205 +0.682187 +0.818264 +0.674660 +0.817822 +0.653486 +0.818001 +0.672970 +0.816313 +0.667539 +0.822373 +0.679593 +0.830173 +0.679016 +0.837951 +0.673104 +0.821314 +0.675117 +0.833099 +0.674709 +0.825817 +0.686102 +0.822547 +0.675417 +0.811072 +0.694432 +0.821052 +0.684485 +0.826777 +0.693825 +0.822842 +0.695692 +0.817056 +0.664063 +0.807820 +0.678375 +0.805016 +0.665697 +0.821976 +0.674014 +0.815207 +0.678330 +0.810998 +0.661837 +0.804970 +0.667613 +0.802702 +0.671772 +0.810020 +0.664571 +0.794717 +0.647470 +0.795966 +0.666701 +0.802673 +0.661824 +0.789431 +0.667311 +0.806235 +0.669845 +0.799123 +0.651530 +0.797151 +0.655076 +0.779717 +0.659181 +0.798595 +0.662635 +0.785764 +0.659577 +0.784446 +0.644675 +0.775558 +0.643655 +0.783033 +0.661512 +0.780843 +0.659683 +0.773944 +0.639249 +0.784737 +0.656134 +0.767611 +0.640415 +0.791893 +0.626155 +0.780520 +0.627563 +0.789283 +0.623834 +0.784963 +0.629747 +0.786292 +0.616686 +0.787035 +0.620574 +0.771186 +0.598199 +0.766642 +0.608386 +0.775623 +0.593803 +0.761443 +0.606287 +0.761328 +0.587383 +0.748520 +0.583047 +0.756063 +0.599575 +0.760245 +0.575383 +0.746238 +0.588426 +0.734258 +0.587285 +0.740310 +0.582025 +0.755473 +0.587334 +0.757997 +0.551488 +0.738824 +0.569286 +0.735536 +0.565623 +0.714448 +0.569380 +0.745977 +0.534780 +0.716205 +0.560448 +0.708947 +0.552272 +0.711559 +0.557311 +0.712510 +0.536863 +0.728178 +0.540408 +0.704056 +0.542558 +0.697700 +0.533860 +0.714914 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.594497 +0.717309 +0.594497 +0.717309 +0.594497 +0.717309 +0.594497 +0.717309 +0.594497 +0.727675 +0.574918 +0.689099 +0.570370 +0.726533 +0.605281 +0.736296 +0.607838 +0.726896 +0.549825 +0.738259 +0.613419 +0.752452 +0.592374 +0.736605 +0.594949 +0.743509 +0.587225 +0.751451 +0.597837 +0.768683 +0.610272 +0.735577 +0.585407 +0.726109 +0.601757 +0.777604 +0.637704 +0.756355 +0.605182 +0.769905 +0.593768 +0.752183 +0.589845 +0.778737 +0.605126 +0.758848 +0.609393 +0.758985 +0.625768 +0.774557 +0.615611 +0.784836 +0.622141 +0.775392 +0.631832 +0.774214 +0.624783 +0.787589 +0.644230 +0.793926 +0.641836 +0.783192 +0.638496 +0.782442 +0.647749 +0.769484 +0.634030 +0.793923 +0.636972 +0.791500 +0.631031 +0.796308 +0.636038 +0.794831 +0.651629 +0.801305 +0.646966 +0.799018 +0.647224 +0.789080 +0.649490 +0.825608 +0.666681 +0.826405 +0.670050 +0.815563 +0.660080 +0.819144 +0.671654 +0.816435 +0.658085 +0.821020 +0.675576 +0.811042 +0.660163 +0.809695 +0.680620 +0.822368 +0.684232 +0.826616 +0.702526 +0.826878 +0.673349 +0.809125 +0.675442 +0.793214 +0.667406 +0.821727 +0.662151 +0.805422 +0.666360 +0.820486 +0.669682 +0.833548 +0.679949 +0.836147 +0.679971 +0.823658 +0.672198 +0.819255 +0.669110 +0.821210 +0.677807 +0.821010 +0.674756 +0.814461 +0.689505 +0.825501 +0.690343 +0.817569 +0.676904 +0.799900 +0.680596 +0.805334 +0.686035 +0.822274 +0.683125 +0.815902 +0.679090 +0.804719 +0.662255 +0.800460 +0.678571 +0.818564 +0.666665 +0.820443 +0.689656 +0.812687 +0.669113 +0.811099 +0.664241 +0.821466 +0.678714 +0.807974 +0.656152 +0.810843 +0.669718 +0.792338 +0.655071 +0.798405 +0.665775 +0.812293 +0.680911 +0.808074 +0.668340 +0.801604 +0.654190 +0.801029 +0.673911 +0.807124 +0.679956 +0.802294 +0.682971 +0.786616 +0.665978 +0.802364 +0.656574 +0.780782 +0.652474 +0.787267 +0.658859 +0.795456 +0.654673 +0.773232 +0.660061 +0.783437 +0.640949 +0.770617 +0.650984 +0.798450 +0.620527 +0.810480 +0.633545 +0.796962 +0.637920 +0.800607 +0.645439 +0.801207 +0.646874 +0.802446 +0.641896 +0.795175 +0.629418 +0.775891 +0.631423 +0.762790 +0.623309 +0.754471 +0.619961 +0.766961 +0.607073 +0.756000 +0.616195 +0.762082 +0.604141 +0.763107 +0.605503 +0.758115 +0.596980 +0.769210 +0.613754 +0.758369 +0.581680 +0.754378 +0.565850 +0.744840 +0.567841 +0.755820 +0.552943 +0.753046 +0.562333 +0.734852 +0.549400 +0.721057 +0.567222 +0.729336 +0.593114 +0.713561 +0.564855 +0.731358 +0.554470 +0.723257 +0.526843 +0.703054 +0.563808 +0.694727 +0.557004 +0.709111 +0.534316 +0.707013 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.598766 +0.725844 +0.598766 +0.725844 +0.598766 +0.725844 +0.598766 +0.725844 +0.598766 +0.733830 +0.595524 +0.724545 +0.607276 +0.725514 +0.614891 +0.727473 +0.574131 +0.738542 +0.565295 +0.712870 +0.581613 +0.748869 +0.576157 +0.753641 +0.603001 +0.759809 +0.573470 +0.758632 +0.610550 +0.735872 +0.588816 +0.740469 +0.590167 +0.752114 +0.605892 +0.753494 +0.623354 +0.770575 +0.620451 +0.774555 +0.595441 +0.744399 +0.604296 +0.772321 +0.601324 +0.754674 +0.622631 +0.777607 +0.636007 +0.778920 +0.636894 +0.773262 +0.610632 +0.770231 +0.645094 +0.780245 +0.634905 +0.784590 +0.663872 +0.780676 +0.628050 +0.774536 +0.627731 +0.785666 +0.645292 +0.778815 +0.633640 +0.806383 +0.645574 +0.809220 +0.649568 +0.803893 +0.652800 +0.810847 +0.633282 +0.797598 +0.644532 +0.798454 +0.650169 +0.797115 +0.672575 +0.823927 +0.663626 +0.825184 +0.658242 +0.814542 +0.653595 +0.829694 +0.662072 +0.812973 +0.667140 +0.811371 +0.673025 +0.802662 +0.669691 +0.800605 +0.669264 +0.806321 +0.691030 +0.835986 +0.696962 +0.825637 +0.680233 +0.805441 +0.685812 +0.814449 +0.680020 +0.823194 +0.665497 +0.800666 +0.658769 +0.816877 +0.677563 +0.815392 +0.675066 +0.819348 +0.671617 +0.820309 +0.657204 +0.805142 +0.670887 +0.816189 +0.668822 +0.818005 +0.660136 +0.804366 +0.700746 +0.824158 +0.686208 +0.811149 +0.689510 +0.812480 +0.674004 +0.810814 +0.686029 +0.810375 +0.688098 +0.821795 +0.679119 +0.815532 +0.682673 +0.816864 +0.664654 +0.806649 +0.680012 +0.794600 +0.674387 +0.811950 +0.668688 +0.804737 +0.680818 +0.804763 +0.663660 +0.814179 +0.674751 +0.793057 +0.657343 +0.790292 +0.653926 +0.780533 +0.674954 +0.812966 +0.680027 +0.818637 +0.671125 +0.788042 +0.659848 +0.791574 +0.649133 +0.790713 +0.657014 +0.790285 +0.667012 +0.781870 +0.656808 +0.783494 +0.662351 +0.785147 +0.647857 +0.785921 +0.645550 +0.789249 +0.653485 +0.761594 +0.647165 +0.761911 +0.639592 +0.770832 +0.668274 +0.790094 +0.631116 +0.797994 +0.632048 +0.794490 +0.635681 +0.794320 +0.625873 +0.798367 +0.632477 +0.769329 +0.620213 +0.786257 +0.626848 +0.789774 +0.637650 +0.778466 +0.604973 +0.768200 +0.606192 +0.762778 +0.609505 +0.767494 +0.607374 +0.757672 +0.591678 +0.748002 +0.602063 +0.744000 +0.593726 +0.742260 +0.607598 +0.753614 +0.570116 +0.755165 +0.589524 +0.747016 +0.561176 +0.755323 +0.581793 +0.748006 +0.553162 +0.747201 +0.556820 +0.739046 +0.570290 +0.712275 +0.562511 +0.720374 +0.573203 +0.715836 +0.579844 +0.737897 +0.542023 +0.719718 +0.550319 +0.693312 +0.555170 +0.717922 +0.541233 +0.716976 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.614591 +0.707838 +0.614591 +0.707838 +0.614591 +0.707838 +0.614591 +0.707838 +0.614591 +0.682299 +0.613245 +0.699133 +0.609637 +0.698114 +0.620454 +0.726265 +0.613682 +0.732256 +0.577020 +0.718352 +0.605368 +0.718711 +0.608289 +0.704684 +0.625937 +0.727519 +0.612506 +0.732928 +0.610428 +0.743837 +0.640745 +0.751899 +0.618666 +0.731002 +0.624247 +0.747411 +0.642770 +0.764374 +0.629827 +0.723718 +0.635117 +0.738397 +0.652856 +0.751224 +0.641692 +0.745803 +0.655762 +0.755758 +0.635606 +0.757639 +0.638491 +0.749494 +0.662079 +0.769417 +0.663100 +0.769408 +0.655628 +0.749097 +0.663857 +0.760586 +0.633532 +0.756478 +0.658722 +0.772509 +0.666462 +0.771294 +0.645928 +0.749392 +0.659563 +0.773160 +0.669316 +0.775521 +0.653097 +0.778382 +0.677411 +0.769227 +0.655819 +0.780304 +0.653765 +0.775138 +0.680722 +0.804566 +0.698161 +0.790393 +0.680241 +0.787572 +0.681936 +0.799920 +0.676209 +0.813023 +0.678754 +0.781730 +0.682817 +0.788896 +0.676692 +0.792127 +0.683002 +0.789169 +0.698356 +0.813132 +0.718355 +0.800021 +0.685347 +0.801499 +0.697697 +0.792847 +0.688720 +0.792364 +0.677998 +0.778976 +0.669968 +0.791040 +0.690054 +0.796229 +0.691321 +0.801267 +0.691337 +0.804857 +0.685849 +0.793496 +0.691126 +0.802556 +0.680916 +0.795332 +0.687563 +0.788394 +0.704480 +0.796233 +0.703143 +0.796740 +0.704664 +0.792728 +0.700034 +0.802007 +0.702049 +0.803676 +0.705664 +0.791260 +0.696510 +0.790253 +0.682658 +0.782593 +0.676958 +0.787165 +0.698448 +0.791818 +0.687399 +0.796241 +0.690641 +0.783368 +0.688748 +0.794621 +0.694192 +0.789035 +0.685112 +0.776027 +0.682009 +0.767813 +0.682072 +0.765447 +0.678618 +0.781025 +0.688182 +0.782670 +0.686385 +0.762106 +0.693250 +0.773550 +0.683032 +0.778004 +0.696183 +0.764252 +0.685400 +0.771192 +0.670750 +0.755405 +0.675819 +0.763206 +0.672257 +0.774558 +0.677659 +0.768744 +0.674724 +0.752694 +0.661847 +0.750387 +0.678402 +0.761139 +0.683571 +0.776478 +0.636599 +0.784869 +0.649062 +0.762750 +0.642932 +0.762457 +0.653195 +0.763070 +0.643427 +0.758467 +0.638815 +0.764433 +0.633828 +0.770824 +0.632569 +0.731971 +0.616681 +0.750919 +0.625720 +0.739292 +0.636025 +0.737735 +0.617079 +0.727874 +0.617887 +0.729289 +0.616563 +0.728730 +0.601120 +0.736207 +0.617077 +0.736474 +0.590921 +0.734721 +0.593413 +0.711230 +0.590286 +0.729232 +0.583865 +0.712082 +0.589006 +0.706435 +0.590726 +0.713409 +0.586080 +0.729661 +0.571938 +0.693961 +0.570781 +0.703799 +0.587984 +0.708120 +0.569933 +0.683839 +0.554759 +0.715197 +0.571732 +0.683069 +0.556828 +0.702385 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.581922 +0.696235 +0.581922 +0.696235 +0.581922 +0.696235 +0.581922 +0.699323 +0.610498 +0.684425 +0.565680 +0.706214 +0.578338 +0.698066 +0.601748 +0.725764 +0.610642 +0.720360 +0.577595 +0.716425 +0.579431 +0.743908 +0.616922 +0.731977 +0.608028 +0.718299 +0.618189 +0.753098 +0.615450 +0.744917 +0.617043 +0.720768 +0.611772 +0.738401 +0.634540 +0.725038 +0.642191 +0.743990 +0.634385 +0.736200 +0.636918 +0.747206 +0.640422 +0.741362 +0.639437 +0.739840 +0.632824 +0.745728 +0.664835 +0.775875 +0.655198 +0.760028 +0.639365 +0.760828 +0.646511 +0.761160 +0.656633 +0.762870 +0.650474 +0.727750 +0.650671 +0.763603 +0.661476 +0.775861 +0.664391 +0.788092 +0.655283 +0.758762 +0.666899 +0.774679 +0.657325 +0.778776 +0.660509 +0.782177 +0.648553 +0.771282 +0.663800 +0.782072 +0.660534 +0.784364 +0.691071 +0.794533 +0.674018 +0.783422 +0.674863 +0.792562 +0.693791 +0.802610 +0.683415 +0.792265 +0.693701 +0.793858 +0.686535 +0.780387 +0.687519 +0.785015 +0.685904 +0.783746 +0.698092 +0.797420 +0.694665 +0.786348 +0.676645 +0.792466 +0.701313 +0.806795 +0.696950 +0.800088 +0.682982 +0.784106 +0.672229 +0.787893 +0.683659 +0.798308 +0.699775 +0.811877 +0.690315 +0.804093 +0.692857 +0.779763 +0.676827 +0.790357 +0.679813 +0.795265 +0.688554 +0.795595 +0.704947 +0.788419 +0.706423 +0.786717 +0.698008 +0.789823 +0.704289 +0.797500 +0.708058 +0.797849 +0.701700 +0.785622 +0.706560 +0.784736 +0.677620 +0.781682 +0.687837 +0.785995 +0.689038 +0.777789 +0.692529 +0.780283 +0.688526 +0.789347 +0.688423 +0.791274 +0.691577 +0.778265 +0.680674 +0.793000 +0.691068 +0.780416 +0.693905 +0.769036 +0.676487 +0.775320 +0.689752 +0.780604 +0.690684 +0.777956 +0.684232 +0.766898 +0.687226 +0.780438 +0.687041 +0.775072 +0.686773 +0.759872 +0.678592 +0.778708 +0.677399 +0.775323 +0.681115 +0.770359 +0.695992 +0.776481 +0.682576 +0.736943 +0.660931 +0.739806 +0.674791 +0.753708 +0.666277 +0.771832 +0.658035 +0.789698 +0.674940 +0.767683 +0.636740 +0.758593 +0.651322 +0.763361 +0.653371 +0.761915 +0.647459 +0.757127 +0.633717 +0.738088 +0.618534 +0.734705 +0.629122 +0.748247 +0.637525 +0.741275 +0.633520 +0.741746 +0.632806 +0.723765 +0.601872 +0.740488 +0.616647 +0.737858 +0.622911 +0.753099 +0.624587 +0.708291 +0.604757 +0.736660 +0.585289 +0.716772 +0.592745 +0.728434 +0.586416 +0.718127 +0.592082 +0.725772 +0.598012 +0.698947 +0.575930 +0.714002 +0.575026 +0.702294 +0.583835 +0.695827 +0.580962 +0.691981 +0.572797 +0.660382 +0.556367 +0.674723 +0.568147 +0.698414 +0.592905 +0.675696 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.605600 +0.689801 +0.605600 +0.689801 +0.605600 +0.689801 +0.605600 +0.689801 +0.605600 +0.695339 +0.610520 +0.674068 +0.580775 +0.683590 +0.612510 +0.711745 +0.623515 +0.732459 +0.625112 +0.735219 +0.592347 +0.715140 +0.616121 +0.735980 +0.634923 +0.709740 +0.627204 +0.744703 +0.597183 +0.738387 +0.601040 +0.719492 +0.609590 +0.735273 +0.615042 +0.730476 +0.634117 +0.741445 +0.628018 +0.736028 +0.633937 +0.751406 +0.651556 +0.735301 +0.629655 +0.747329 +0.621360 +0.738921 +0.651912 +0.778589 +0.641060 +0.751134 +0.630426 +0.775145 +0.654677 +0.763687 +0.647928 +0.742703 +0.640621 +0.743342 +0.633432 +0.761901 +0.659572 +0.771298 +0.669352 +0.779188 +0.653629 +0.757394 +0.667120 +0.764540 +0.652541 +0.778254 +0.670570 +0.752003 +0.654749 +0.796228 +0.665105 +0.783416 +0.669601 +0.793595 +0.693016 +0.797991 +0.676699 +0.784832 +0.665832 +0.778084 +0.673317 +0.808357 +0.693556 +0.787726 +0.690709 +0.805735 +0.676544 +0.779701 +0.697737 +0.778898 +0.673641 +0.784967 +0.697070 +0.790908 +0.692441 +0.773520 +0.696821 +0.803831 +0.712299 +0.806473 +0.700367 +0.800211 +0.700717 +0.785810 +0.668109 +0.777017 +0.681159 +0.790041 +0.693835 +0.795660 +0.685520 +0.794666 +0.689515 +0.776730 +0.670912 +0.792424 +0.685963 +0.790746 +0.681940 +0.785915 +0.701322 +0.804254 +0.710572 +0.782328 +0.687479 +0.792648 +0.708613 +0.791277 +0.706378 +0.791168 +0.693592 +0.785920 +0.707824 +0.789322 +0.696705 +0.784174 +0.693165 +0.762631 +0.674241 +0.783645 +0.686115 +0.777657 +0.681420 +0.790167 +0.685376 +0.785079 +0.677821 +0.772167 +0.678611 +0.788765 +0.686852 +0.791487 +0.688770 +0.779347 +0.670215 +0.771620 +0.686989 +0.776590 +0.682627 +0.781260 +0.686231 +0.779090 +0.685702 +0.776965 +0.689620 +0.776910 +0.678488 +0.754538 +0.659143 +0.774685 +0.673503 +0.780608 +0.675223 +0.762953 +0.683710 +0.773909 +0.665994 +0.742898 +0.670139 +0.762921 +0.657969 +0.761745 +0.652259 +0.766025 +0.648096 +0.785694 +0.660022 +0.777520 +0.650514 +0.759859 +0.637982 +0.754237 +0.658358 +0.771385 +0.650771 +0.763100 +0.641974 +0.755223 +0.626217 +0.738843 +0.639347 +0.752673 +0.630102 +0.752588 +0.632296 +0.727852 +0.615903 +0.723505 +0.603216 +0.738121 +0.619165 +0.731832 +0.611480 +0.732494 +0.610197 +0.725181 +0.603293 +0.709201 +0.608103 +0.708586 +0.592216 +0.748179 +0.590011 +0.692804 +0.574817 +0.716986 +0.584164 +0.724802 +0.587538 +0.698334 +0.568131 +0.719290 +0.584846 +0.704859 +0.567485 +0.696705 +0.586896 +0.688071 +0.573109 +0.667050 +0.597990 +0.678167 +0.549825 +0.683942 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.599602 +0.697250 +0.599602 +0.697250 +0.599602 +0.697250 +0.599602 +0.697250 +0.599602 +0.670608 +0.620575 +0.688462 +0.557815 +0.666874 +0.594624 +0.736414 +0.646366 +0.701439 +0.575914 +0.728900 +0.600165 +0.713495 +0.592866 +0.728052 +0.607441 +0.731296 +0.601155 +0.736131 +0.606639 +0.750001 +0.619976 +0.729117 +0.634420 +0.741958 +0.627771 +0.721143 +0.623224 +0.726651 +0.629633 +0.734680 +0.647198 +0.765599 +0.655104 +0.747695 +0.642502 +0.741261 +0.616089 +0.742494 +0.658327 +0.757289 +0.637438 +0.755211 +0.658376 +0.768933 +0.630165 +0.759357 +0.668171 +0.771139 +0.651046 +0.758128 +0.651490 +0.761515 +0.655566 +0.761727 +0.679772 +0.775060 +0.637437 +0.766504 +0.673277 +0.782974 +0.670281 +0.778202 +0.669598 +0.772075 +0.660067 +0.779750 +0.655550 +0.783703 +0.684268 +0.803473 +0.675317 +0.798317 +0.673132 +0.789639 +0.665778 +0.794198 +0.695689 +0.811720 +0.687362 +0.808516 +0.695054 +0.812755 +0.680613 +0.778040 +0.687640 +0.770408 +0.686427 +0.785063 +0.676944 +0.776145 +0.672263 +0.773589 +0.672644 +0.788023 +0.680333 +0.792282 +0.682426 +0.779951 +0.668779 +0.790772 +0.677650 +0.778926 +0.687275 +0.809957 +0.702688 +0.802319 +0.705897 +0.807904 +0.693246 +0.796965 +0.687021 +0.790548 +0.679887 +0.785308 +0.689959 +0.781226 +0.693107 +0.770925 +0.699060 +0.787979 +0.697643 +0.785916 +0.702363 +0.793338 +0.698192 +0.789264 +0.711473 +0.788785 +0.694200 +0.776657 +0.692609 +0.781317 +0.678187 +0.781914 +0.676250 +0.791526 +0.689139 +0.794226 +0.695000 +0.785909 +0.683032 +0.777641 +0.678274 +0.769823 +0.675597 +0.786553 +0.685133 +0.774847 +0.679309 +0.764371 +0.680083 +0.774757 +0.689617 +0.763736 +0.670182 +0.775701 +0.684100 +0.774649 +0.674941 +0.783590 +0.681764 +0.775378 +0.678739 +0.756942 +0.667931 +0.762007 +0.667681 +0.753082 +0.679459 +0.755904 +0.676538 +0.767653 +0.676833 +0.740801 +0.667813 +0.735430 +0.661928 +0.737248 +0.675551 +0.763253 +0.643682 +0.767883 +0.643146 +0.764936 +0.655566 +0.748193 +0.631682 +0.769290 +0.635302 +0.767048 +0.629265 +0.758476 +0.630990 +0.748998 +0.649456 +0.727823 +0.620896 +0.755756 +0.631265 +0.740261 +0.630119 +0.727248 +0.610263 +0.713182 +0.602121 +0.737087 +0.604860 +0.702264 +0.618727 +0.733882 +0.578157 +0.725759 +0.590850 +0.695412 +0.584524 +0.713449 +0.581697 +0.694442 +0.605787 +0.716305 +0.584095 +0.721891 +0.594345 +0.704798 +0.573400 +0.712263 +0.555906 +0.686032 +0.600840 +0.672748 +0.589717 +0.699472 +0.574252 +0.666881 +0.547548 +0.646789 +0.549591 +0.693221 +0.571027 +0.671047 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.569128 +0.693388 +0.569128 +0.693388 +0.569128 +0.693388 +0.569128 +0.693388 +0.569128 +0.720791 +0.548528 +0.693991 +0.557075 +0.702802 +0.572593 +0.683370 +0.577715 +0.731427 +0.602612 +0.692979 +0.606266 +0.706948 +0.576249 +0.688368 +0.602424 +0.693496 +0.605575 +0.706551 +0.588731 +0.739475 +0.612107 +0.705740 +0.596772 +0.724357 +0.605464 +0.726271 +0.597474 +0.716372 +0.615525 +0.711948 +0.623489 +0.712928 +0.603532 +0.745089 +0.626301 +0.727041 +0.618189 +0.749337 +0.633070 +0.747563 +0.645951 +0.766226 +0.659151 +0.750125 +0.643854 +0.748170 +0.638921 +0.753840 +0.662018 +0.748326 +0.642181 +0.743575 +0.646022 +0.751744 +0.639030 +0.759393 +0.647176 +0.745472 +0.636413 +0.768430 +0.662859 +0.757595 +0.647786 +0.763826 +0.642265 +0.765905 +0.646130 +0.754314 +0.668213 +0.781784 +0.670906 +0.781269 +0.671960 +0.804999 +0.686593 +0.774499 +0.674560 +0.786065 +0.665370 +0.799289 +0.661807 +0.805180 +0.680007 +0.760642 +0.686337 +0.789143 +0.678371 +0.785604 +0.687276 +0.776636 +0.670234 +0.789002 +0.682831 +0.772090 +0.691839 +0.786566 +0.683424 +0.759944 +0.665254 +0.776486 +0.676685 +0.786644 +0.675470 +0.779668 +0.677639 +0.786586 +0.679693 +0.785289 +0.676116 +0.775529 +0.676915 +0.775783 +0.679204 +0.772907 +0.671116 +0.774932 +0.674229 +0.775196 +0.682804 +0.764531 +0.699980 +0.782023 +0.682193 +0.776251 +0.713179 +0.783465 +0.697506 +0.787477 +0.685155 +0.757225 +0.685351 +0.770344 +0.683802 +0.777108 +0.677761 +0.785691 +0.688555 +0.771664 +0.685795 +0.769975 +0.677758 +0.773898 +0.675328 +0.767749 +0.664920 +0.756629 +0.676455 +0.763910 +0.662331 +0.756281 +0.680504 +0.771163 +0.684369 +0.761866 +0.671080 +0.763989 +0.672787 +0.762858 +0.681090 +0.773330 +0.678444 +0.761068 +0.664872 +0.762014 +0.677016 +0.751989 +0.664071 +0.760043 +0.667851 +0.748803 +0.670339 +0.758796 +0.665683 +0.737303 +0.654570 +0.734368 +0.647605 +0.750733 +0.678388 +0.754666 +0.626641 +0.759285 +0.652635 +0.747084 +0.632370 +0.748450 +0.633661 +0.762896 +0.647874 +0.755435 +0.642435 +0.737612 +0.628466 +0.750982 +0.638604 +0.729238 +0.622096 +0.720442 +0.633415 +0.735507 +0.629049 +0.709828 +0.622635 +0.739033 +0.597172 +0.708174 +0.610137 +0.719735 +0.605141 +0.720150 +0.613750 +0.705849 +0.582308 +0.705333 +0.590205 +0.731236 +0.583179 +0.707663 +0.570725 +0.702362 +0.586299 +0.688083 +0.590841 +0.696070 +0.564554 +0.697154 +0.545964 +0.688769 +0.586079 +0.681338 +0.557157 +0.658671 +0.569675 +0.643978 +0.567566 +0.655290 +0.537755 +0.677246 +0.540429 +0.652646 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.588371 +0.669447 +0.588371 +0.669447 +0.588371 +0.669447 +0.588371 +0.669447 +0.588371 +0.659052 +0.609872 +0.655081 +0.586918 +0.676007 +0.596412 +0.677251 +0.581783 +0.688511 +0.611595 +0.691814 +0.581316 +0.719126 +0.576800 +0.725528 +0.609889 +0.712319 +0.591450 +0.725175 +0.625059 +0.714139 +0.616291 +0.714060 +0.600828 +0.713872 +0.614888 +0.720322 +0.611083 +0.722165 +0.622042 +0.727571 +0.613142 +0.737302 +0.627340 +0.741040 +0.610873 +0.712232 +0.621818 +0.733037 +0.609138 +0.722710 +0.660193 +0.740749 +0.648227 +0.740102 +0.654641 +0.735032 +0.655238 +0.735572 +0.638729 +0.739801 +0.641249 +0.751245 +0.642180 +0.751947 +0.639433 +0.738100 +0.649865 +0.764675 +0.633884 +0.745773 +0.628718 +0.760797 +0.657610 +0.770483 +0.652706 +0.752760 +0.644190 +0.769056 +0.664031 +0.788971 +0.670907 +0.780806 +0.662305 +0.780777 +0.675401 +0.771809 +0.655084 +0.779048 +0.675742 +0.783524 +0.671176 +0.782451 +0.674148 +0.766479 +0.692077 +0.782901 +0.689235 +0.778561 +0.699854 +0.775460 +0.693021 +0.771555 +0.701662 +0.768751 +0.686580 +0.787105 +0.698157 +0.762240 +0.687799 +0.770984 +0.677149 +0.771342 +0.677556 +0.787850 +0.672371 +0.777337 +0.697535 +0.778104 +0.663416 +0.782290 +0.677195 +0.777519 +0.673888 +0.771808 +0.672229 +0.765536 +0.704747 +0.771606 +0.702513 +0.775639 +0.720614 +0.781900 +0.704362 +0.782463 +0.716042 +0.784222 +0.724709 +0.784128 +0.711185 +0.761666 +0.702495 +0.783811 +0.699759 +0.759372 +0.691698 +0.772190 +0.702492 +0.765395 +0.686460 +0.763991 +0.696464 +0.770917 +0.690999 +0.775425 +0.682271 +0.765666 +0.690700 +0.757049 +0.685128 +0.751655 +0.690490 +0.776658 +0.698405 +0.766769 +0.702951 +0.755328 +0.690908 +0.750336 +0.689827 +0.762857 +0.704138 +0.762440 +0.696904 +0.763639 +0.691331 +0.753318 +0.677796 +0.742475 +0.684411 +0.736898 +0.689348 +0.753817 +0.677392 +0.730625 +0.671603 +0.737841 +0.672156 +0.728754 +0.677457 +0.747508 +0.643383 +0.754419 +0.652882 +0.761282 +0.660234 +0.741931 +0.661611 +0.750845 +0.656407 +0.758436 +0.678555 +0.741611 +0.652243 +0.751957 +0.644655 +0.730420 +0.612692 +0.730682 +0.629993 +0.740004 +0.628717 +0.718787 +0.632656 +0.737383 +0.635559 +0.688633 +0.617031 +0.728220 +0.628997 +0.712709 +0.613643 +0.703073 +0.598606 +0.714170 +0.602459 +0.700896 +0.615013 +0.693209 +0.604732 +0.693853 +0.596852 +0.684793 +0.590102 +0.709842 +0.573071 +0.680144 +0.585202 +0.663906 +0.603068 +0.690746 +0.604105 +0.675293 +0.575667 +0.657135 +0.601781 +0.678225 +0.564354 +0.657949 +0.583771 +0.670218 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.600955 +0.651436 +0.600955 +0.651436 +0.600955 +0.651436 +0.600955 +0.663111 +0.587882 +0.673236 +0.603926 +0.640614 +0.594366 +0.640457 +0.604573 +0.653657 +0.606266 +0.694082 +0.616079 +0.674562 +0.600860 +0.694740 +0.589357 +0.676742 +0.586901 +0.697756 +0.612983 +0.692568 +0.634617 +0.716046 +0.640428 +0.695980 +0.630066 +0.702980 +0.618361 +0.699333 +0.645055 +0.698746 +0.650965 +0.715133 +0.644576 +0.707968 +0.638420 +0.728333 +0.636897 +0.705124 +0.635586 +0.714744 +0.640031 +0.714663 +0.651196 +0.715706 +0.671488 +0.728069 +0.675941 +0.729014 +0.663377 +0.720657 +0.651889 +0.719396 +0.657270 +0.731216 +0.674519 +0.736993 +0.658959 +0.734379 +0.661859 +0.722013 +0.651270 +0.731290 +0.664724 +0.733217 +0.680363 +0.745922 +0.670328 +0.732477 +0.675060 +0.742953 +0.679576 +0.771092 +0.691502 +0.765595 +0.690059 +0.754102 +0.685064 +0.751264 +0.676933 +0.766504 +0.705433 +0.756841 +0.684433 +0.764840 +0.687702 +0.742920 +0.689275 +0.750193 +0.698237 +0.775988 +0.708514 +0.753520 +0.701319 +0.766143 +0.702393 +0.761916 +0.707218 +0.757594 +0.699344 +0.758887 +0.709245 +0.750220 +0.686436 +0.766085 +0.689696 +0.767913 +0.694859 +0.754417 +0.705941 +0.762805 +0.694858 +0.757026 +0.691016 +0.766864 +0.698685 +0.750686 +0.693519 +0.754887 +0.714355 +0.759307 +0.717237 +0.746787 +0.711411 +0.756071 +0.715796 +0.749720 +0.708024 +0.759566 +0.715535 +0.755654 +0.717581 +0.758101 +0.701385 +0.751505 +0.692911 +0.754175 +0.695073 +0.753323 +0.696500 +0.746688 +0.705023 +0.755914 +0.701100 +0.763638 +0.698080 +0.748473 +0.697158 +0.755772 +0.695290 +0.749707 +0.692326 +0.736351 +0.695318 +0.746596 +0.695918 +0.753765 +0.694288 +0.746118 +0.691286 +0.732657 +0.699702 +0.737126 +0.709131 +0.741627 +0.701241 +0.742407 +0.681786 +0.739303 +0.687450 +0.731131 +0.679536 +0.722619 +0.690308 +0.728000 +0.678867 +0.722010 +0.674360 +0.720047 +0.668010 +0.706923 +0.682890 +0.726861 +0.662325 +0.741663 +0.664984 +0.742976 +0.662093 +0.735731 +0.666147 +0.733195 +0.643693 +0.726088 +0.653656 +0.728938 +0.639994 +0.722057 +0.643131 +0.704939 +0.623500 +0.697040 +0.647392 +0.718502 +0.638685 +0.696850 +0.630576 +0.705423 +0.620962 +0.703647 +0.615627 +0.700919 +0.639418 +0.690852 +0.617277 +0.698398 +0.610243 +0.691022 +0.602519 +0.677227 +0.607887 +0.689982 +0.602847 +0.685856 +0.606307 +0.689076 +0.591844 +0.674851 +0.602441 +0.655138 +0.575503 +0.674482 +0.592661 +0.673497 +0.608908 +0.651030 +0.571757 +0.640658 +0.580037 +0.641401 +0.583869 +0.646040 +0.596604 +0.644363 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.625718 +0.677760 +0.625718 +0.677760 +0.625718 +0.677760 +0.625718 +0.677760 +0.625718 +0.667400 +0.616113 +0.682333 +0.612432 +0.664458 +0.625467 +0.686488 +0.639255 +0.654823 +0.617528 +0.651708 +0.592335 +0.669496 +0.629266 +0.668912 +0.609320 +0.689060 +0.631012 +0.699667 +0.619371 +0.694221 +0.618256 +0.683549 +0.614450 +0.705960 +0.620513 +0.718083 +0.621634 +0.704097 +0.637810 +0.714290 +0.633964 +0.740149 +0.642176 +0.711501 +0.634361 +0.712602 +0.637308 +0.725296 +0.652792 +0.732053 +0.666781 +0.721741 +0.671291 +0.720665 +0.667373 +0.720098 +0.670678 +0.710206 +0.651085 +0.709561 +0.661529 +0.729600 +0.668043 +0.736658 +0.666702 +0.734685 +0.653363 +0.718550 +0.663692 +0.741205 +0.659192 +0.738859 +0.676959 +0.748159 +0.676379 +0.745696 +0.662434 +0.761944 +0.679854 +0.773373 +0.693304 +0.749462 +0.689978 +0.751824 +0.681387 +0.749307 +0.681492 +0.762283 +0.690407 +0.754459 +0.687845 +0.754163 +0.698258 +0.758513 +0.702942 +0.750252 +0.694739 +0.757332 +0.704216 +0.760025 +0.703782 +0.745988 +0.698171 +0.758570 +0.690329 +0.765947 +0.710514 +0.748333 +0.718468 +0.748540 +0.692321 +0.750023 +0.696120 +0.757808 +0.696595 +0.761576 +0.693172 +0.761696 +0.699120 +0.761047 +0.694811 +0.760289 +0.702056 +0.753391 +0.692141 +0.755735 +0.704075 +0.758255 +0.718393 +0.756095 +0.717401 +0.750944 +0.702293 +0.748314 +0.698369 +0.748697 +0.697068 +0.734064 +0.707659 +0.758950 +0.704547 +0.766368 +0.700747 +0.754427 +0.695969 +0.751883 +0.695625 +0.735738 +0.699898 +0.767868 +0.711968 +0.760016 +0.693892 +0.752329 +0.694882 +0.753991 +0.690903 +0.741743 +0.698681 +0.751401 +0.698413 +0.754327 +0.700739 +0.749905 +0.701410 +0.746018 +0.699964 +0.740756 +0.696639 +0.740078 +0.692029 +0.741093 +0.703818 +0.740563 +0.688423 +0.739363 +0.695311 +0.734602 +0.681666 +0.737383 +0.686343 +0.723391 +0.672448 +0.722475 +0.670856 +0.715871 +0.681961 +0.723956 +0.670013 +0.728753 +0.658377 +0.740197 +0.670070 +0.739049 +0.644276 +0.736821 +0.659039 +0.738490 +0.648242 +0.725052 +0.658040 +0.725719 +0.650685 +0.725767 +0.630059 +0.692894 +0.632806 +0.706140 +0.628926 +0.709092 +0.633446 +0.711621 +0.628861 +0.709377 +0.645030 +0.684455 +0.630217 +0.700242 +0.622344 +0.682154 +0.617670 +0.690659 +0.602606 +0.695227 +0.602441 +0.698084 +0.615385 +0.690415 +0.606475 +0.679544 +0.599143 +0.672554 +0.597850 +0.675873 +0.605237 +0.681105 +0.562609 +0.646483 +0.585839 +0.671749 +0.586528 +0.663095 +0.576797 +0.669713 +0.556537 +0.653416 +0.570807 +0.653241 +0.567151 +0.662075 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.606504 +0.660061 +0.606504 +0.660061 +0.606504 +0.660061 +0.606504 +0.660061 +0.606504 +0.654772 +0.595581 +0.656418 +0.573495 +0.657318 +0.622440 +0.666448 +0.623576 +0.675821 +0.607652 +0.674792 +0.596535 +0.674133 +0.629226 +0.679353 +0.626546 +0.670900 +0.666138 +0.692439 +0.630132 +0.683109 +0.613540 +0.672356 +0.628930 +0.714204 +0.630060 +0.708580 +0.621990 +0.700167 +0.621787 +0.696768 +0.607169 +0.715828 +0.626663 +0.703140 +0.643196 +0.705460 +0.651161 +0.699047 +0.638638 +0.707848 +0.654512 +0.717164 +0.657591 +0.730451 +0.657641 +0.710148 +0.647041 +0.714091 +0.656143 +0.708594 +0.657114 +0.727998 +0.670881 +0.716941 +0.676527 +0.744160 +0.658089 +0.729200 +0.682473 +0.740562 +0.663062 +0.731785 +0.677065 +0.740713 +0.682895 +0.750032 +0.659339 +0.751509 +0.685686 +0.756102 +0.677491 +0.748704 +0.683804 +0.757097 +0.676388 +0.745364 +0.678840 +0.765539 +0.689430 +0.772147 +0.686523 +0.755731 +0.689475 +0.750180 +0.697789 +0.758600 +0.707574 +0.768008 +0.708121 +0.768848 +0.700082 +0.759672 +0.703445 +0.758804 +0.703092 +0.768870 +0.703114 +0.762558 +0.706951 +0.769941 +0.691158 +0.745315 +0.693375 +0.747988 +0.699345 +0.764099 +0.700453 +0.757439 +0.716205 +0.762967 +0.688956 +0.770542 +0.693713 +0.742517 +0.691979 +0.761530 +0.715634 +0.759065 +0.726604 +0.761203 +0.711028 +0.750736 +0.721788 +0.760903 +0.702400 +0.750248 +0.704886 +0.742829 +0.705882 +0.759134 +0.706873 +0.763743 +0.698847 +0.750240 +0.703352 +0.744087 +0.692274 +0.739844 +0.702585 +0.775378 +0.723237 +0.764398 +0.701286 +0.746960 +0.699762 +0.750653 +0.691846 +0.734558 +0.691386 +0.755351 +0.700442 +0.749650 +0.707533 +0.759966 +0.707422 +0.743879 +0.702082 +0.745313 +0.690274 +0.730222 +0.685415 +0.744887 +0.706828 +0.736239 +0.701388 +0.736630 +0.697002 +0.735561 +0.691319 +0.726794 +0.688723 +0.712337 +0.683244 +0.733498 +0.679150 +0.722374 +0.684514 +0.711790 +0.672884 +0.750100 +0.671518 +0.750583 +0.665208 +0.736378 +0.649307 +0.752528 +0.657832 +0.726953 +0.648288 +0.719491 +0.662074 +0.734352 +0.654617 +0.736994 +0.643807 +0.713463 +0.650822 +0.724988 +0.643368 +0.693716 +0.631471 +0.714198 +0.634498 +0.689924 +0.637131 +0.730643 +0.612457 +0.693132 +0.632339 +0.672377 +0.617019 +0.686764 +0.597375 +0.707722 +0.618871 +0.696708 +0.635731 +0.697975 +0.604968 +0.696370 +0.618453 +0.672352 +0.576669 +0.666973 +0.601183 +0.646751 +0.583788 +0.652821 +0.570412 +0.648758 +0.580334 +0.648974 +0.570237 +0.671500 +0.582222 +0.627786 +0.572958 +0.643365 +0.555446 +0.649420 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.598075 +0.661577 +0.598075 +0.661577 +0.598075 +0.661577 +0.598075 +0.661577 +0.598075 +0.664114 +0.582196 +0.640399 +0.582835 +0.653096 +0.610713 +0.691235 +0.600677 +0.638637 +0.612747 +0.684503 +0.621121 +0.658113 +0.635386 +0.676966 +0.613376 +0.682736 +0.641581 +0.687834 +0.632842 +0.708924 +0.642225 +0.687179 +0.630935 +0.700534 +0.649908 +0.692460 +0.631267 +0.705155 +0.629803 +0.713690 +0.634094 +0.717178 +0.647164 +0.714719 +0.612199 +0.717704 +0.661612 +0.710800 +0.648838 +0.741236 +0.670505 +0.718029 +0.653880 +0.712362 +0.642125 +0.715825 +0.658645 +0.725524 +0.665999 +0.723684 +0.656942 +0.737830 +0.661823 +0.723233 +0.666583 +0.742570 +0.659816 +0.730492 +0.696954 +0.739313 +0.649593 +0.740319 +0.672159 +0.738065 +0.679270 +0.750254 +0.681569 +0.741166 +0.666579 +0.742999 +0.672530 +0.758176 +0.691084 +0.734789 +0.672115 +0.743512 +0.673112 +0.738691 +0.696052 +0.752081 +0.689117 +0.767125 +0.695124 +0.739384 +0.672061 +0.740140 +0.694277 +0.751409 +0.682946 +0.740553 +0.707554 +0.752724 +0.709708 +0.747563 +0.705035 +0.747635 +0.683995 +0.754772 +0.693226 +0.736209 +0.691431 +0.753468 +0.683184 +0.756432 +0.694857 +0.764801 +0.686046 +0.749043 +0.687279 +0.752742 +0.688956 +0.760192 +0.696278 +0.749589 +0.675280 +0.735739 +0.690420 +0.748196 +0.709093 +0.761198 +0.712778 +0.756704 +0.701767 +0.749969 +0.710977 +0.760915 +0.707274 +0.747523 +0.704040 +0.743663 +0.710888 +0.760330 +0.702080 +0.753452 +0.702655 +0.743927 +0.706877 +0.747619 +0.704932 +0.758182 +0.706251 +0.754411 +0.702492 +0.743802 +0.695914 +0.753535 +0.702436 +0.742089 +0.688733 +0.748826 +0.714583 +0.736795 +0.699715 +0.748732 +0.691066 +0.745860 +0.691622 +0.738928 +0.697296 +0.749981 +0.704634 +0.738380 +0.704546 +0.730103 +0.688226 +0.724514 +0.693518 +0.736170 +0.693221 +0.730986 +0.675287 +0.731685 +0.678210 +0.718373 +0.676036 +0.716632 +0.662465 +0.698637 +0.684737 +0.738535 +0.673445 +0.739393 +0.651577 +0.727250 +0.659501 +0.739033 +0.665656 +0.730742 +0.645485 +0.734630 +0.659045 +0.717471 +0.645709 +0.740003 +0.639200 +0.706730 +0.640813 +0.718816 +0.641869 +0.715264 +0.653869 +0.684325 +0.630004 +0.707925 +0.622995 +0.694095 +0.635737 +0.686274 +0.611641 +0.691944 +0.620526 +0.692815 +0.606857 +0.690685 +0.585932 +0.705793 +0.613608 +0.686248 +0.596181 +0.690255 +0.588461 +0.681212 +0.579181 +0.665662 +0.589790 +0.672809 +0.592475 +0.659869 +0.590780 +0.679473 +0.572668 +0.642322 +0.595105 +0.647126 +0.585622 +0.660775 +0.576016 +0.616479 +0.579910 +0.650074 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.607519 +0.654617 +0.607519 +0.654617 +0.607519 +0.654617 +0.607519 +0.654617 +0.607519 +0.643787 +0.586386 +0.652554 +0.581052 +0.676334 +0.590127 +0.634961 +0.651379 +0.694106 +0.607908 +0.684299 +0.604209 +0.679077 +0.607302 +0.677409 +0.605833 +0.659526 +0.604981 +0.692192 +0.629299 +0.691281 +0.615892 +0.674361 +0.655390 +0.681585 +0.621336 +0.671570 +0.638012 +0.681761 +0.600938 +0.686593 +0.636262 +0.679908 +0.627423 +0.706030 +0.622810 +0.707057 +0.638115 +0.719252 +0.637517 +0.726196 +0.661343 +0.716068 +0.644000 +0.699098 +0.645918 +0.716927 +0.654474 +0.710681 +0.664228 +0.736599 +0.651941 +0.704636 +0.654266 +0.725663 +0.654658 +0.721001 +0.668978 +0.722798 +0.664133 +0.729244 +0.655559 +0.728274 +0.663338 +0.727415 +0.658869 +0.717721 +0.673219 +0.743393 +0.674319 +0.735549 +0.674840 +0.754832 +0.672842 +0.733857 +0.680096 +0.759677 +0.687769 +0.746682 +0.682805 +0.741187 +0.683351 +0.758052 +0.697941 +0.739676 +0.690040 +0.734175 +0.678491 +0.741122 +0.688761 +0.730108 +0.674782 +0.740666 +0.700299 +0.740608 +0.697953 +0.750916 +0.697237 +0.754322 +0.688094 +0.738781 +0.676802 +0.753917 +0.685103 +0.755563 +0.692649 +0.741103 +0.695012 +0.742982 +0.664708 +0.752229 +0.698554 +0.760192 +0.703128 +0.754445 +0.679311 +0.724168 +0.688673 +0.740674 +0.704265 +0.743316 +0.712894 +0.740659 +0.690437 +0.743193 +0.713176 +0.738062 +0.681515 +0.740514 +0.688646 +0.739552 +0.702661 +0.754670 +0.696431 +0.744243 +0.687247 +0.740556 +0.693110 +0.745682 +0.700409 +0.742330 +0.706209 +0.738740 +0.687846 +0.739015 +0.693941 +0.741351 +0.693266 +0.722489 +0.677964 +0.738573 +0.697931 +0.746262 +0.695285 +0.747929 +0.697343 +0.734035 +0.706955 +0.742279 +0.691437 +0.745712 +0.692016 +0.731044 +0.696447 +0.747059 +0.697386 +0.723031 +0.688044 +0.723855 +0.701659 +0.729971 +0.679139 +0.733071 +0.679600 +0.725942 +0.678277 +0.713214 +0.667234 +0.696140 +0.662777 +0.739716 +0.668946 +0.736799 +0.649861 +0.727987 +0.660702 +0.725019 +0.638445 +0.728096 +0.658348 +0.723340 +0.666219 +0.714124 +0.643188 +0.720381 +0.649013 +0.696562 +0.641635 +0.705422 +0.627648 +0.710123 +0.639143 +0.698632 +0.625677 +0.703044 +0.624374 +0.678671 +0.638422 +0.699252 +0.623932 +0.680713 +0.608481 +0.688443 +0.601955 +0.692082 +0.605823 +0.691392 +0.604957 +0.693039 +0.597867 +0.692246 +0.588002 +0.666242 +0.610480 +0.667414 +0.569712 +0.650594 +0.564203 +0.665765 +0.587836 +0.668846 +0.559582 +0.651539 +0.583974 +0.653959 +0.582815 +0.619941 +0.575004 +0.655437 +0.544723 +0.641813 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.592734 +0.654518 +0.592734 +0.654518 +0.592734 +0.654518 +0.592734 +0.654518 +0.592734 +0.660862 +0.623231 +0.653386 +0.582365 +0.646920 +0.599593 +0.663248 +0.596244 +0.662871 +0.607451 +0.668109 +0.615994 +0.672153 +0.601245 +0.653629 +0.606379 +0.669456 +0.606886 +0.656160 +0.608181 +0.685371 +0.620933 +0.663427 +0.627289 +0.677185 +0.625194 +0.687734 +0.629137 +0.658545 +0.621367 +0.691614 +0.620645 +0.685435 +0.624837 +0.710583 +0.635673 +0.688932 +0.644842 +0.709057 +0.642132 +0.687267 +0.644727 +0.722443 +0.657589 +0.703433 +0.660082 +0.721210 +0.647043 +0.717526 +0.668551 +0.719662 +0.650304 +0.709147 +0.639612 +0.707236 +0.656502 +0.719458 +0.664208 +0.727279 +0.671675 +0.725686 +0.663671 +0.735042 +0.667189 +0.730493 +0.665938 +0.719647 +0.651439 +0.741583 +0.679040 +0.736301 +0.686528 +0.748300 +0.689617 +0.734371 +0.701575 +0.749612 +0.711948 +0.738994 +0.689891 +0.744783 +0.704966 +0.754234 +0.712660 +0.744342 +0.697724 +0.734930 +0.701355 +0.746204 +0.719466 +0.752474 +0.706644 +0.733342 +0.707861 +0.736288 +0.713043 +0.754470 +0.706864 +0.742067 +0.708627 +0.745083 +0.702047 +0.749774 +0.705191 +0.748819 +0.708192 +0.751618 +0.710526 +0.737384 +0.714098 +0.756068 +0.710565 +0.760671 +0.693642 +0.746735 +0.693245 +0.737926 +0.718365 +0.742589 +0.722422 +0.747866 +0.727606 +0.736921 +0.708537 +0.743282 +0.729526 +0.731626 +0.722395 +0.739284 +0.712565 +0.735321 +0.728460 +0.757878 +0.714300 +0.741308 +0.708500 +0.737300 +0.712624 +0.747129 +0.712104 +0.735772 +0.718732 +0.747321 +0.722777 +0.739444 +0.719824 +0.735864 +0.712179 +0.733375 +0.706943 +0.740224 +0.724507 +0.748550 +0.715559 +0.743455 +0.724622 +0.730465 +0.713092 +0.734336 +0.715026 +0.745478 +0.708376 +0.724868 +0.705669 +0.737370 +0.711148 +0.727145 +0.710738 +0.742860 +0.709913 +0.739012 +0.706393 +0.719620 +0.701436 +0.716369 +0.689245 +0.718079 +0.690342 +0.704727 +0.693905 +0.739128 +0.676992 +0.745628 +0.676767 +0.725551 +0.670560 +0.724104 +0.677887 +0.728734 +0.664549 +0.726998 +0.676469 +0.717408 +0.646368 +0.725751 +0.662140 +0.681931 +0.643170 +0.712647 +0.645989 +0.703111 +0.652343 +0.699470 +0.651130 +0.700215 +0.636703 +0.693411 +0.632814 +0.691492 +0.635690 +0.691493 +0.625437 +0.696951 +0.635164 +0.692836 +0.622734 +0.685230 +0.621916 +0.675483 +0.634151 +0.695659 +0.619201 +0.676117 +0.609675 +0.681471 +0.599007 +0.654457 +0.604608 +0.655771 +0.602175 +0.681403 +0.608266 +0.668111 +0.620145 +0.648624 +0.598099 +0.660826 +0.573111 +0.655199 +0.601195 +0.659187 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.611509 +0.637187 +0.611509 +0.637187 +0.611509 +0.637187 +0.611509 +0.637187 +0.611509 +0.639261 +0.637947 +0.628828 +0.600789 +0.635206 +0.612369 +0.647527 +0.621368 +0.664082 +0.622568 +0.643883 +0.630019 +0.637779 +0.640515 +0.636767 +0.622775 +0.663447 +0.626298 +0.652279 +0.605619 +0.673683 +0.625667 +0.654023 +0.652502 +0.659135 +0.635793 +0.669959 +0.639361 +0.655593 +0.653938 +0.672871 +0.654737 +0.662095 +0.651848 +0.685761 +0.649110 +0.672733 +0.660822 +0.694073 +0.657416 +0.675407 +0.671818 +0.686629 +0.680658 +0.690306 +0.676075 +0.708680 +0.674102 +0.684521 +0.669417 +0.701544 +0.666484 +0.691632 +0.675539 +0.692489 +0.657730 +0.698867 +0.688396 +0.711343 +0.681929 +0.697796 +0.678214 +0.706238 +0.676364 +0.722513 +0.681059 +0.702327 +0.681803 +0.715134 +0.680763 +0.722665 +0.702135 +0.729256 +0.691189 +0.724023 +0.690696 +0.722020 +0.703237 +0.727418 +0.680401 +0.724803 +0.708204 +0.718373 +0.714312 +0.728675 +0.695253 +0.726699 +0.708292 +0.726070 +0.717693 +0.732975 +0.719096 +0.719100 +0.723110 +0.720597 +0.711814 +0.732801 +0.711034 +0.735063 +0.714669 +0.728081 +0.695387 +0.725686 +0.702679 +0.736396 +0.706484 +0.744628 +0.704333 +0.732326 +0.710929 +0.725860 +0.713962 +0.745227 +0.715909 +0.728169 +0.709833 +0.720323 +0.723964 +0.720095 +0.728690 +0.735990 +0.727482 +0.728806 +0.727799 +0.733703 +0.733858 +0.741795 +0.723755 +0.724042 +0.723356 +0.729125 +0.722400 +0.739491 +0.721546 +0.725557 +0.705443 +0.723183 +0.717425 +0.729594 +0.723109 +0.736892 +0.719196 +0.728262 +0.720717 +0.726667 +0.721382 +0.723099 +0.712622 +0.718933 +0.714556 +0.723668 +0.713868 +0.721496 +0.713204 +0.716005 +0.707295 +0.720809 +0.720499 +0.711414 +0.711514 +0.713556 +0.709129 +0.717098 +0.711811 +0.716610 +0.715387 +0.710057 +0.710409 +0.712912 +0.707480 +0.695182 +0.704249 +0.700338 +0.706869 +0.693530 +0.694519 +0.688523 +0.684918 +0.691733 +0.690376 +0.719697 +0.678760 +0.719799 +0.672487 +0.715258 +0.661965 +0.720787 +0.673481 +0.711444 +0.690311 +0.705221 +0.670621 +0.696292 +0.667041 +0.702565 +0.659024 +0.685707 +0.643436 +0.682160 +0.659978 +0.688908 +0.651717 +0.677482 +0.651898 +0.675281 +0.637843 +0.680666 +0.642763 +0.673720 +0.648433 +0.637447 +0.652006 +0.669985 +0.649111 +0.669918 +0.625852 +0.665469 +0.621367 +0.661838 +0.623565 +0.668453 +0.596398 +0.662681 +0.616823 +0.662809 +0.619348 +0.631568 +0.589765 +0.657216 +0.615078 +0.652623 +0.597865 +0.630083 +0.581220 +0.635486 +0.588290 +0.615590 +0.584950 +0.605405 +0.578900 +0.627053 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.619416 +0.622964 +0.619416 +0.622964 +0.619416 +0.622964 +0.619416 +0.612884 +0.608875 +0.628947 +0.628571 +0.622281 +0.599007 +0.617664 +0.630670 +0.642298 +0.654015 +0.656225 +0.632287 +0.671717 +0.625671 +0.651733 +0.628356 +0.649844 +0.633728 +0.632823 +0.649858 +0.664658 +0.625824 +0.659807 +0.629066 +0.677642 +0.653548 +0.681158 +0.656832 +0.657204 +0.640405 +0.657212 +0.637526 +0.670435 +0.642127 +0.666607 +0.655603 +0.675614 +0.655040 +0.679558 +0.642210 +0.671071 +0.675426 +0.690435 +0.671186 +0.683531 +0.657317 +0.685245 +0.667069 +0.701887 +0.648912 +0.693482 +0.673486 +0.691950 +0.680058 +0.688642 +0.666963 +0.693324 +0.681398 +0.704185 +0.672025 +0.706827 +0.680520 +0.692686 +0.668498 +0.707829 +0.686507 +0.709899 +0.681158 +0.683817 +0.687974 +0.708743 +0.675963 +0.730831 +0.708380 +0.734300 +0.711941 +0.726211 +0.699115 +0.717120 +0.706663 +0.719512 +0.685723 +0.733346 +0.702945 +0.731604 +0.713811 +0.715589 +0.692878 +0.707025 +0.716156 +0.728290 +0.733510 +0.744861 +0.719701 +0.718236 +0.712574 +0.728371 +0.706023 +0.737180 +0.709919 +0.724489 +0.714138 +0.731258 +0.693016 +0.720035 +0.696302 +0.727566 +0.717247 +0.716595 +0.706256 +0.715052 +0.713825 +0.712818 +0.695749 +0.728579 +0.710209 +0.709625 +0.688622 +0.711003 +0.721890 +0.723216 +0.729025 +0.727008 +0.707946 +0.732126 +0.744415 +0.731043 +0.727289 +0.732264 +0.719885 +0.716121 +0.722054 +0.735523 +0.716458 +0.723936 +0.728143 +0.722203 +0.712221 +0.724750 +0.725764 +0.729145 +0.727240 +0.724145 +0.713614 +0.721488 +0.708278 +0.728230 +0.731638 +0.723751 +0.716186 +0.703894 +0.716467 +0.722507 +0.724496 +0.725080 +0.712816 +0.711001 +0.706940 +0.725696 +0.717996 +0.723077 +0.716851 +0.707700 +0.708655 +0.703285 +0.718610 +0.713798 +0.710101 +0.709644 +0.693796 +0.703891 +0.714263 +0.705283 +0.699651 +0.711965 +0.690455 +0.711394 +0.706265 +0.695816 +0.694253 +0.696228 +0.682458 +0.712551 +0.682628 +0.715136 +0.680117 +0.712032 +0.676190 +0.718043 +0.683800 +0.715122 +0.680325 +0.708464 +0.680903 +0.687067 +0.651324 +0.693774 +0.659069 +0.683662 +0.651591 +0.675834 +0.656746 +0.687614 +0.655609 +0.689126 +0.658377 +0.678314 +0.641712 +0.684592 +0.658513 +0.689903 +0.633060 +0.677158 +0.633616 +0.667140 +0.640827 +0.665075 +0.637753 +0.679910 +0.619004 +0.670072 +0.603974 +0.675328 +0.610519 +0.654335 +0.606270 +0.639696 +0.604393 +0.654627 +0.610325 +0.638941 +0.608521 +0.650415 +0.620708 +0.633737 +0.588802 +0.625715 +0.581636 +0.622746 +0.591954 +0.610539 +0.593425 +0.627399 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.632801 +0.644170 +0.632801 +0.644170 +0.632801 +0.644170 +0.632801 +0.644170 +0.632801 +0.644170 +0.632801 +0.640045 +0.578181 +0.608189 +0.633497 +0.661816 +0.642192 +0.662507 +0.622713 +0.651464 +0.601566 +0.648712 +0.632162 +0.652277 +0.637103 +0.673476 +0.647320 +0.648243 +0.610068 +0.658357 +0.655688 +0.679791 +0.631014 +0.684187 +0.650313 +0.664606 +0.634060 +0.679998 +0.652700 +0.664767 +0.619535 +0.688858 +0.642762 +0.673476 +0.638956 +0.672273 +0.649520 +0.656508 +0.660613 +0.683456 +0.676772 +0.683161 +0.682285 +0.701648 +0.683523 +0.690764 +0.663738 +0.686575 +0.682009 +0.687510 +0.675497 +0.694154 +0.667210 +0.692827 +0.670762 +0.718948 +0.681909 +0.700684 +0.673991 +0.704356 +0.672825 +0.718698 +0.700939 +0.712414 +0.676107 +0.700178 +0.660719 +0.709583 +0.676902 +0.741183 +0.704993 +0.735541 +0.707701 +0.722732 +0.699932 +0.731678 +0.698400 +0.722587 +0.698905 +0.719971 +0.711594 +0.744311 +0.715517 +0.715185 +0.697911 +0.722072 +0.699641 +0.738043 +0.733836 +0.738501 +0.719434 +0.722852 +0.712560 +0.718431 +0.712557 +0.738225 +0.719542 +0.739577 +0.734750 +0.736477 +0.697018 +0.723379 +0.696372 +0.737565 +0.701808 +0.721554 +0.708083 +0.737934 +0.713816 +0.733541 +0.710050 +0.732548 +0.711068 +0.716707 +0.698157 +0.719471 +0.728704 +0.726457 +0.732590 +0.729506 +0.723105 +0.724241 +0.738583 +0.740335 +0.727207 +0.723096 +0.726637 +0.732114 +0.731837 +0.734593 +0.720404 +0.734989 +0.720261 +0.726411 +0.718902 +0.727750 +0.723909 +0.742421 +0.734425 +0.729523 +0.718636 +0.736341 +0.720826 +0.731911 +0.718810 +0.731125 +0.712967 +0.712210 +0.713210 +0.723907 +0.726417 +0.727055 +0.715063 +0.720604 +0.716822 +0.723119 +0.719245 +0.723654 +0.710965 +0.716240 +0.705253 +0.720747 +0.709144 +0.712983 +0.706996 +0.713875 +0.713891 +0.706548 +0.714428 +0.702300 +0.717609 +0.691804 +0.705008 +0.703308 +0.706590 +0.691030 +0.701242 +0.695259 +0.693468 +0.721997 +0.678363 +0.728589 +0.693536 +0.731596 +0.668193 +0.712753 +0.681981 +0.713369 +0.675522 +0.710767 +0.672298 +0.692853 +0.667403 +0.702624 +0.662705 +0.714991 +0.658211 +0.691175 +0.670447 +0.678512 +0.668115 +0.698640 +0.657113 +0.674206 +0.661823 +0.681525 +0.645472 +0.685010 +0.649386 +0.669727 +0.639694 +0.677376 +0.627471 +0.683346 +0.633212 +0.679071 +0.631464 +0.669219 +0.637808 +0.657183 +0.618590 +0.653825 +0.613951 +0.665497 +0.614105 +0.628711 +0.603910 +0.642178 +0.622601 +0.640937 +0.621006 +0.625803 +0.574859 +0.585254 +0.574975 +0.632839 +0.577628 +0.622800 +0.610794 +0.614632 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.616099 +0.623220 +0.616099 +0.623220 +0.616099 +0.623220 +0.616099 +0.623220 +0.616099 +0.619320 +0.617530 +0.623693 +0.601600 +0.619818 +0.635471 +0.626150 +0.611227 +0.642333 +0.653313 +0.663151 +0.641571 +0.676268 +0.650523 +0.688097 +0.655933 +0.645869 +0.624638 +0.673782 +0.605355 +0.656779 +0.659230 +0.671108 +0.636120 +0.669952 +0.670780 +0.664500 +0.630817 +0.681422 +0.637538 +0.680421 +0.624376 +0.673370 +0.662836 +0.679149 +0.647774 +0.689237 +0.687771 +0.681716 +0.647048 +0.693857 +0.680040 +0.679268 +0.672169 +0.705355 +0.677656 +0.695734 +0.672602 +0.708039 +0.688220 +0.672103 +0.679494 +0.701227 +0.654424 +0.693841 +0.682229 +0.708520 +0.684053 +0.707741 +0.673906 +0.688882 +0.667828 +0.702893 +0.699156 +0.721596 +0.688825 +0.726506 +0.679285 +0.738418 +0.697723 +0.721509 +0.697330 +0.717517 +0.697230 +0.722284 +0.697755 +0.730187 +0.721890 +0.738127 +0.715104 +0.740088 +0.710948 +0.738250 +0.720453 +0.720169 +0.716411 +0.726240 +0.724139 +0.726692 +0.727866 +0.728273 +0.726356 +0.724402 +0.723351 +0.727061 +0.733369 +0.724157 +0.726875 +0.737394 +0.726672 +0.729613 +0.706720 +0.738123 +0.705214 +0.737735 +0.704944 +0.718767 +0.711493 +0.745276 +0.724378 +0.744288 +0.735961 +0.747585 +0.728993 +0.741292 +0.719585 +0.723926 +0.739086 +0.731849 +0.741750 +0.741487 +0.734128 +0.734159 +0.743266 +0.744093 +0.742391 +0.738645 +0.737405 +0.748302 +0.735703 +0.736861 +0.722091 +0.733323 +0.729407 +0.725932 +0.719116 +0.725034 +0.720321 +0.728724 +0.727914 +0.726960 +0.714124 +0.718215 +0.702674 +0.723522 +0.732073 +0.728058 +0.720090 +0.706788 +0.702966 +0.707315 +0.718167 +0.720252 +0.708619 +0.717821 +0.712429 +0.711546 +0.709149 +0.695069 +0.704696 +0.700444 +0.706948 +0.712613 +0.701843 +0.696964 +0.715005 +0.713442 +0.725955 +0.702297 +0.706377 +0.704673 +0.701072 +0.706551 +0.711983 +0.679794 +0.705872 +0.693954 +0.686738 +0.680536 +0.687543 +0.711866 +0.673248 +0.714454 +0.686203 +0.701217 +0.676588 +0.705004 +0.671964 +0.706198 +0.679700 +0.701725 +0.678865 +0.703065 +0.668645 +0.693373 +0.674905 +0.667741 +0.656893 +0.691774 +0.641872 +0.671940 +0.642005 +0.677605 +0.645932 +0.671606 +0.648079 +0.667362 +0.659669 +0.665336 +0.648109 +0.654184 +0.644130 +0.661606 +0.624835 +0.669776 +0.618349 +0.665048 +0.638651 +0.643643 +0.627825 +0.661014 +0.612602 +0.655128 +0.623084 +0.644949 +0.627592 +0.651090 +0.584721 +0.633820 +0.614783 +0.631500 +0.609184 +0.628095 +0.583877 +0.634924 +0.596568 +0.593450 +0.564478 +0.615781 +0.587134 +0.618823 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.633772 +0.612375 +0.633772 +0.612375 +0.633772 +0.612375 +0.633772 +0.612375 +0.633772 +0.652555 +0.617847 +0.616695 +0.609780 +0.612913 +0.635567 +0.607518 +0.655967 +0.649766 +0.638638 +0.615091 +0.650865 +0.640149 +0.642549 +0.640318 +0.624469 +0.652568 +0.613585 +0.676759 +0.649806 +0.678124 +0.640830 +0.674219 +0.651278 +0.703794 +0.638754 +0.670887 +0.655910 +0.683014 +0.654262 +0.686558 +0.669920 +0.672962 +0.652612 +0.663196 +0.663234 +0.676573 +0.648900 +0.693963 +0.693221 +0.697307 +0.668956 +0.691447 +0.667807 +0.696783 +0.669445 +0.713716 +0.686987 +0.707909 +0.704774 +0.695622 +0.682427 +0.702410 +0.679304 +0.700004 +0.692934 +0.721236 +0.690667 +0.722872 +0.682090 +0.700525 +0.687146 +0.711236 +0.690890 +0.718998 +0.676356 +0.718109 +0.689091 +0.719668 +0.690911 +0.726123 +0.703396 +0.735104 +0.699213 +0.727118 +0.699955 +0.726557 +0.706955 +0.746380 +0.708776 +0.720378 +0.724586 +0.735011 +0.717174 +0.715317 +0.711116 +0.738784 +0.710108 +0.725173 +0.714428 +0.717606 +0.716973 +0.724400 +0.720600 +0.731346 +0.725202 +0.728928 +0.721322 +0.735842 +0.724734 +0.737107 +0.702744 +0.716598 +0.711645 +0.736580 +0.712718 +0.726229 +0.711961 +0.732236 +0.713214 +0.735889 +0.729225 +0.732188 +0.712205 +0.734637 +0.714803 +0.711986 +0.729597 +0.729761 +0.732358 +0.722217 +0.736199 +0.725494 +0.720967 +0.733808 +0.726810 +0.717355 +0.723302 +0.738183 +0.724830 +0.711564 +0.711267 +0.725393 +0.711565 +0.731202 +0.712838 +0.717423 +0.709450 +0.723821 +0.709180 +0.707629 +0.716734 +0.718544 +0.712558 +0.707859 +0.712019 +0.712335 +0.712791 +0.714969 +0.714305 +0.708687 +0.709195 +0.720980 +0.716339 +0.709708 +0.717970 +0.710974 +0.707581 +0.700807 +0.702455 +0.703882 +0.699022 +0.709790 +0.698891 +0.702574 +0.712993 +0.698778 +0.704848 +0.697097 +0.702463 +0.707085 +0.701317 +0.703703 +0.694090 +0.697115 +0.713073 +0.685726 +0.696306 +0.685159 +0.688055 +0.692620 +0.682681 +0.717983 +0.672863 +0.703237 +0.658578 +0.687080 +0.678830 +0.693122 +0.670252 +0.696827 +0.668437 +0.706925 +0.671691 +0.689261 +0.662828 +0.681949 +0.662092 +0.681443 +0.639539 +0.675324 +0.636823 +0.661646 +0.644840 +0.675411 +0.658241 +0.670358 +0.623913 +0.667285 +0.613426 +0.659748 +0.646178 +0.654851 +0.629077 +0.648009 +0.624784 +0.645446 +0.627243 +0.648343 +0.625612 +0.667087 +0.608889 +0.641916 +0.612729 +0.646074 +0.614639 +0.641985 +0.586653 +0.629671 +0.621378 +0.611537 +0.605734 +0.646876 +0.606789 +0.628204 +0.588070 +0.588814 +0.580684 +0.633768 +0.577809 +0.621298 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.623817 +0.637753 +0.623817 +0.637753 +0.623817 +0.637753 +0.623817 +0.637753 +0.623817 +0.621695 +0.618441 +0.617743 +0.640477 +0.665502 +0.628305 +0.630013 +0.602670 +0.652015 +0.633324 +0.622232 +0.658076 +0.653972 +0.643347 +0.662803 +0.620230 +0.660040 +0.644491 +0.683960 +0.630444 +0.674637 +0.652394 +0.670598 +0.660033 +0.662259 +0.670517 +0.657002 +0.664810 +0.669648 +0.652609 +0.677107 +0.667086 +0.684229 +0.657462 +0.678271 +0.661505 +0.694543 +0.661643 +0.703150 +0.665867 +0.681241 +0.675589 +0.700700 +0.674139 +0.678205 +0.675478 +0.675198 +0.681653 +0.693617 +0.680161 +0.718287 +0.672121 +0.696260 +0.680291 +0.694401 +0.699298 +0.721664 +0.684365 +0.704834 +0.680820 +0.701356 +0.696748 +0.705051 +0.697963 +0.711582 +0.701057 +0.713270 +0.687183 +0.729674 +0.704056 +0.714801 +0.714227 +0.734259 +0.716464 +0.710246 +0.729216 +0.728525 +0.725666 +0.733856 +0.734459 +0.726156 +0.733952 +0.740692 +0.735400 +0.731178 +0.733694 +0.721214 +0.733778 +0.729658 +0.741402 +0.727967 +0.732472 +0.721765 +0.744580 +0.728666 +0.737251 +0.737708 +0.739141 +0.736746 +0.733928 +0.725206 +0.723714 +0.720486 +0.738281 +0.743009 +0.738729 +0.723699 +0.722317 +0.732802 +0.742132 +0.738949 +0.736552 +0.734165 +0.735035 +0.730783 +0.734031 +0.724008 +0.759174 +0.734679 +0.743094 +0.723462 +0.746854 +0.731378 +0.758602 +0.739911 +0.752871 +0.726681 +0.738199 +0.740678 +0.746327 +0.710042 +0.735398 +0.728691 +0.734839 +0.731671 +0.734253 +0.719268 +0.732409 +0.722171 +0.732110 +0.715483 +0.717795 +0.719394 +0.736071 +0.715453 +0.737332 +0.715827 +0.742569 +0.705454 +0.727314 +0.704861 +0.730136 +0.717615 +0.728816 +0.719392 +0.743032 +0.710015 +0.725728 +0.701067 +0.722507 +0.705951 +0.726428 +0.707238 +0.729016 +0.693996 +0.715652 +0.712410 +0.720190 +0.694450 +0.709369 +0.702682 +0.723836 +0.696902 +0.713621 +0.692891 +0.724962 +0.680957 +0.703595 +0.675630 +0.690605 +0.709397 +0.692447 +0.710648 +0.700619 +0.702440 +0.686884 +0.694345 +0.685807 +0.695000 +0.693204 +0.706469 +0.693244 +0.704862 +0.676040 +0.665640 +0.672468 +0.678243 +0.674908 +0.695810 +0.670348 +0.664003 +0.656153 +0.668678 +0.663618 +0.668993 +0.626400 +0.656727 +0.652881 +0.667856 +0.666959 +0.662531 +0.657270 +0.663611 +0.654262 +0.662679 +0.632864 +0.662421 +0.624334 +0.662540 +0.632680 +0.652644 +0.620785 +0.649177 +0.624768 +0.668522 +0.618007 +0.664330 +0.638654 +0.625865 +0.616922 +0.639318 +0.637411 +0.634425 +0.608679 +0.635275 +0.628410 +0.603808 +0.617242 +0.626034 +0.589658 +0.624503 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.632077 +0.626761 +0.632077 +0.626761 +0.632077 +0.626761 +0.632077 +0.626761 +0.632077 +0.585250 +0.621531 +0.602080 +0.622796 +0.644819 +0.625823 +0.633382 +0.647612 +0.642404 +0.666445 +0.639812 +0.633746 +0.625503 +0.654613 +0.640222 +0.656886 +0.633262 +0.676798 +0.672415 +0.668214 +0.654311 +0.666310 +0.650582 +0.669330 +0.637929 +0.674848 +0.632689 +0.655906 +0.665537 +0.675796 +0.654478 +0.671771 +0.672870 +0.669148 +0.664699 +0.666649 +0.650740 +0.690674 +0.659764 +0.695750 +0.676885 +0.690648 +0.677747 +0.694694 +0.682588 +0.703244 +0.674839 +0.700412 +0.672345 +0.719030 +0.669694 +0.686818 +0.678088 +0.692863 +0.666112 +0.689962 +0.690586 +0.711010 +0.683424 +0.703855 +0.688905 +0.721028 +0.695215 +0.716104 +0.698195 +0.708316 +0.690295 +0.705154 +0.720846 +0.721532 +0.708123 +0.718170 +0.709128 +0.721260 +0.703592 +0.722209 +0.711016 +0.727445 +0.719414 +0.728106 +0.711963 +0.743523 +0.724661 +0.733094 +0.727756 +0.740373 +0.697295 +0.727642 +0.713653 +0.743712 +0.715720 +0.737672 +0.703150 +0.741622 +0.731946 +0.744834 +0.717396 +0.733314 +0.704276 +0.729876 +0.708505 +0.736384 +0.711170 +0.729318 +0.729874 +0.737324 +0.720970 +0.730314 +0.726028 +0.747733 +0.712634 +0.736099 +0.716794 +0.742348 +0.707446 +0.743628 +0.692167 +0.742305 +0.705691 +0.738663 +0.707265 +0.740121 +0.718332 +0.757669 +0.719861 +0.743452 +0.697533 +0.758006 +0.707198 +0.757984 +0.705198 +0.735514 +0.695477 +0.735389 +0.708462 +0.733016 +0.702406 +0.729351 +0.700603 +0.732338 +0.697212 +0.722437 +0.703573 +0.733461 +0.698629 +0.724831 +0.711273 +0.731151 +0.690035 +0.726281 +0.689809 +0.732442 +0.700032 +0.747478 +0.682026 +0.728408 +0.692250 +0.728092 +0.680805 +0.721070 +0.697853 +0.735218 +0.690813 +0.727744 +0.677381 +0.721439 +0.677217 +0.727735 +0.674903 +0.715007 +0.684756 +0.732814 +0.674533 +0.714603 +0.678519 +0.719290 +0.669279 +0.709233 +0.660488 +0.701081 +0.672833 +0.698226 +0.697556 +0.692912 +0.683660 +0.684659 +0.683319 +0.693101 +0.676285 +0.697883 +0.694396 +0.694970 +0.671450 +0.676103 +0.657308 +0.686221 +0.654653 +0.674305 +0.643684 +0.655045 +0.654768 +0.652062 +0.677473 +0.673367 +0.659737 +0.658448 +0.649068 +0.648061 +0.662122 +0.653990 +0.636691 +0.678067 +0.652086 +0.642520 +0.645934 +0.655893 +0.638689 +0.641372 +0.642352 +0.631917 +0.649670 +0.626978 +0.631390 +0.601963 +0.631062 +0.609833 +0.634368 +0.610714 +0.602073 +0.620153 +0.593040 +0.612341 +0.613484 +0.632698 +0.622617 +0.637991 +0.589663 +0.608919 +0.577344 +0.622858 +0.608588 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.637313 +0.621802 +0.637313 +0.621802 +0.637313 +0.621802 +0.637313 +0.621802 +0.637313 +0.607491 +0.636646 +0.612095 +0.626788 +0.626422 +0.643696 +0.626890 +0.641455 +0.630769 +0.656844 +0.641370 +0.636789 +0.635744 +0.651881 +0.644383 +0.644237 +0.629117 +0.668818 +0.656366 +0.639477 +0.644614 +0.656660 +0.667856 +0.664869 +0.647499 +0.667284 +0.660282 +0.679059 +0.647899 +0.697101 +0.659739 +0.676665 +0.651967 +0.685227 +0.658208 +0.697097 +0.660501 +0.690149 +0.674803 +0.694996 +0.669005 +0.704639 +0.679194 +0.711551 +0.686260 +0.711694 +0.674484 +0.713574 +0.686772 +0.713622 +0.676849 +0.682545 +0.663952 +0.696316 +0.676364 +0.706953 +0.686288 +0.697696 +0.693780 +0.705034 +0.682216 +0.705044 +0.696367 +0.706257 +0.676836 +0.694925 +0.687247 +0.715425 +0.707071 +0.734361 +0.708412 +0.722423 +0.709646 +0.719936 +0.713119 +0.722815 +0.713627 +0.725009 +0.704174 +0.725504 +0.698882 +0.740194 +0.718985 +0.743119 +0.706391 +0.726140 +0.683466 +0.723088 +0.709364 +0.739915 +0.721136 +0.738155 +0.710165 +0.742971 +0.715507 +0.747259 +0.698681 +0.733634 +0.696191 +0.730830 +0.710658 +0.722906 +0.700535 +0.735195 +0.718424 +0.744772 +0.713457 +0.732420 +0.710388 +0.740644 +0.708967 +0.737558 +0.720619 +0.738782 +0.710078 +0.727616 +0.712479 +0.739495 +0.693955 +0.752326 +0.713710 +0.741713 +0.712639 +0.761907 +0.703348 +0.741045 +0.725135 +0.759565 +0.712870 +0.733837 +0.695675 +0.726252 +0.692139 +0.733901 +0.695472 +0.717156 +0.718623 +0.743027 +0.688218 +0.727317 +0.702676 +0.729707 +0.706526 +0.729719 +0.691252 +0.728727 +0.703484 +0.738733 +0.685030 +0.722596 +0.690535 +0.726216 +0.693624 +0.737809 +0.695147 +0.724741 +0.686119 +0.726617 +0.684493 +0.738914 +0.689788 +0.732326 +0.697094 +0.740756 +0.694425 +0.717861 +0.685307 +0.725860 +0.686299 +0.717142 +0.678823 +0.718024 +0.679775 +0.714147 +0.661542 +0.709151 +0.672304 +0.730830 +0.680618 +0.703754 +0.679026 +0.691869 +0.684191 +0.694861 +0.682535 +0.698170 +0.690772 +0.682040 +0.671448 +0.684703 +0.687989 +0.689780 +0.660523 +0.685133 +0.668597 +0.672926 +0.657810 +0.674693 +0.650611 +0.673926 +0.641776 +0.667818 +0.659614 +0.674875 +0.660994 +0.672250 +0.647946 +0.648036 +0.658769 +0.659155 +0.631911 +0.660417 +0.632791 +0.631751 +0.630809 +0.640006 +0.641317 +0.629512 +0.637192 +0.633138 +0.644642 +0.643697 +0.625172 +0.624933 +0.619135 +0.630876 +0.615866 +0.610930 +0.621938 +0.614984 +0.607630 +0.631637 +0.594663 +0.605505 +0.605965 +0.609572 +0.586691 +0.599061 +0.579687 +0.621879 +0.595773 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.646016 +0.623064 +0.646016 +0.623064 +0.646016 +0.623064 +0.646016 +0.623064 +0.646016 +0.609685 +0.657974 +0.631445 +0.656507 +0.625183 +0.641324 +0.612563 +0.640218 +0.633113 +0.650669 +0.636529 +0.678711 +0.621271 +0.652039 +0.654895 +0.658955 +0.653856 +0.653372 +0.671193 +0.646926 +0.634716 +0.669397 +0.644756 +0.651037 +0.658782 +0.684509 +0.663257 +0.665090 +0.659791 +0.684826 +0.652707 +0.669471 +0.656726 +0.657781 +0.646258 +0.678415 +0.666485 +0.694984 +0.678766 +0.696620 +0.671391 +0.704703 +0.685083 +0.705675 +0.680831 +0.693232 +0.657660 +0.682047 +0.672413 +0.708398 +0.673298 +0.699154 +0.673141 +0.680942 +0.658365 +0.696068 +0.695757 +0.709824 +0.690008 +0.714874 +0.695229 +0.702699 +0.698593 +0.696049 +0.677057 +0.686725 +0.684452 +0.706554 +0.712946 +0.737265 +0.725018 +0.722104 +0.721280 +0.733751 +0.719673 +0.736251 +0.716544 +0.735648 +0.697271 +0.718266 +0.703313 +0.747407 +0.721650 +0.760056 +0.698176 +0.731330 +0.690420 +0.739845 +0.711294 +0.746509 +0.709951 +0.733339 +0.722967 +0.746607 +0.718880 +0.755916 +0.711380 +0.738035 +0.710978 +0.741102 +0.700420 +0.740603 +0.725927 +0.749730 +0.726114 +0.734677 +0.708840 +0.737195 +0.718342 +0.729265 +0.717573 +0.737437 +0.700405 +0.740819 +0.721633 +0.741594 +0.715493 +0.757684 +0.722477 +0.756886 +0.706262 +0.745558 +0.712738 +0.760165 +0.716513 +0.752652 +0.716566 +0.757265 +0.716867 +0.743906 +0.701131 +0.728440 +0.706668 +0.732481 +0.709198 +0.736764 +0.713516 +0.744654 +0.705309 +0.738274 +0.710058 +0.750359 +0.696966 +0.725165 +0.697902 +0.731761 +0.707080 +0.734994 +0.695992 +0.730651 +0.692482 +0.740195 +0.689071 +0.732991 +0.690252 +0.742216 +0.692839 +0.734215 +0.691724 +0.736230 +0.699328 +0.747694 +0.695734 +0.738999 +0.684804 +0.722454 +0.675015 +0.726623 +0.698582 +0.717723 +0.692080 +0.728195 +0.682099 +0.734631 +0.669841 +0.721271 +0.674431 +0.731234 +0.674303 +0.703949 +0.689427 +0.694944 +0.705709 +0.699570 +0.702348 +0.692122 +0.678583 +0.697850 +0.677733 +0.686954 +0.673816 +0.683718 +0.667049 +0.697473 +0.679152 +0.689107 +0.658775 +0.668211 +0.653869 +0.682290 +0.668401 +0.682985 +0.670428 +0.672988 +0.628164 +0.676547 +0.665687 +0.661980 +0.658550 +0.648001 +0.651672 +0.638142 +0.636456 +0.667289 +0.629587 +0.675307 +0.638802 +0.664441 +0.628545 +0.631171 +0.628497 +0.648673 +0.621821 +0.637741 +0.615070 +0.656140 +0.610154 +0.607490 +0.628593 +0.611298 +0.614554 +0.613512 +0.594510 +0.608720 +0.618131 +0.643088 +0.583401 +0.616607 +0.591791 +0.626957 +0.598680 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.637744 +0.610511 +0.637744 +0.610511 +0.637744 +0.610511 +0.637744 +0.610511 +0.637744 +0.613095 +0.643367 +0.609557 +0.629360 +0.601971 +0.613415 +0.620006 +0.670456 +0.622237 +0.642512 +0.652864 +0.672944 +0.623449 +0.642411 +0.636519 +0.656481 +0.626786 +0.658542 +0.645500 +0.640009 +0.643552 +0.669622 +0.628868 +0.667730 +0.629062 +0.702000 +0.647690 +0.646541 +0.663734 +0.692702 +0.641720 +0.678197 +0.655753 +0.679868 +0.650687 +0.670795 +0.682039 +0.701638 +0.669563 +0.697540 +0.687982 +0.685053 +0.679113 +0.708582 +0.660893 +0.697825 +0.646572 +0.692795 +0.681141 +0.708145 +0.683273 +0.693027 +0.687295 +0.712469 +0.705706 +0.714813 +0.681556 +0.731174 +0.681892 +0.714079 +0.701433 +0.727984 +0.708673 +0.711284 +0.712483 +0.724317 +0.703524 +0.722132 +0.712482 +0.725067 +0.702721 +0.733821 +0.721706 +0.753569 +0.726163 +0.756400 +0.737041 +0.760088 +0.727046 +0.743582 +0.730905 +0.746375 +0.723308 +0.758169 +0.722385 +0.744849 +0.717420 +0.758314 +0.721171 +0.752567 +0.727183 +0.764116 +0.709058 +0.747633 +0.715048 +0.744714 +0.726826 +0.771580 +0.744313 +0.758870 +0.710021 +0.747465 +0.718198 +0.751820 +0.728079 +0.748240 +0.734018 +0.767300 +0.745893 +0.760193 +0.715046 +0.740145 +0.718352 +0.743228 +0.723731 +0.746192 +0.726210 +0.764473 +0.727927 +0.771412 +0.728688 +0.758373 +0.726516 +0.764842 +0.724030 +0.776992 +0.733148 +0.769644 +0.726126 +0.749346 +0.702477 +0.765665 +0.728351 +0.755526 +0.730413 +0.773820 +0.725816 +0.762653 +0.730292 +0.762771 +0.723483 +0.748879 +0.725281 +0.761041 +0.726909 +0.742925 +0.724410 +0.746280 +0.709448 +0.755820 +0.721458 +0.754194 +0.709963 +0.771237 +0.725301 +0.761394 +0.728330 +0.754175 +0.706003 +0.747241 +0.699571 +0.741744 +0.709512 +0.767512 +0.705360 +0.750871 +0.697923 +0.737207 +0.713991 +0.777752 +0.723774 +0.744427 +0.701916 +0.731587 +0.687071 +0.739652 +0.695236 +0.725984 +0.684030 +0.744964 +0.711603 +0.721318 +0.721418 +0.737436 +0.708861 +0.716367 +0.706353 +0.716416 +0.696672 +0.724465 +0.698813 +0.696794 +0.699136 +0.702277 +0.695717 +0.711344 +0.681924 +0.682850 +0.665522 +0.692945 +0.663868 +0.710413 +0.688455 +0.697782 +0.693476 +0.700021 +0.672846 +0.686334 +0.667692 +0.671669 +0.667102 +0.672443 +0.671720 +0.689923 +0.655710 +0.647751 +0.645009 +0.658922 +0.644534 +0.657459 +0.656971 +0.639752 +0.659733 +0.652387 +0.652932 +0.660788 +0.668600 +0.645099 +0.613683 +0.646234 +0.624406 +0.653413 +0.660451 +0.626100 +0.639539 +0.664684 +0.595484 +0.650740 +0.610136 +0.629971 +0.631825 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.660954 +0.636751 +0.660954 +0.636751 +0.660954 +0.636751 +0.660954 +0.636751 +0.660954 +0.666563 +0.661473 +0.608230 +0.666486 +0.660076 +0.667770 +0.641946 +0.648604 +0.646259 +0.653858 +0.621221 +0.649563 +0.638131 +0.650502 +0.646011 +0.681009 +0.655349 +0.681246 +0.663281 +0.649508 +0.653358 +0.671747 +0.642112 +0.678879 +0.667189 +0.670969 +0.632930 +0.685202 +0.673297 +0.697876 +0.667019 +0.690970 +0.657887 +0.662961 +0.665930 +0.700273 +0.693189 +0.692693 +0.698772 +0.698971 +0.665125 +0.707101 +0.675685 +0.708403 +0.684078 +0.708157 +0.714100 +0.723290 +0.686192 +0.718797 +0.691580 +0.695936 +0.674946 +0.714518 +0.716135 +0.717511 +0.696714 +0.727359 +0.700751 +0.713753 +0.702192 +0.721697 +0.706114 +0.720527 +0.707232 +0.721804 +0.701166 +0.717067 +0.710919 +0.738124 +0.712348 +0.733817 +0.724643 +0.736892 +0.717980 +0.732531 +0.726876 +0.740897 +0.720051 +0.745444 +0.730227 +0.749960 +0.726193 +0.758134 +0.716995 +0.758500 +0.731535 +0.758954 +0.717305 +0.743395 +0.735622 +0.758503 +0.723810 +0.748454 +0.716053 +0.739341 +0.721766 +0.761151 +0.737812 +0.759369 +0.721381 +0.735235 +0.698795 +0.740560 +0.724395 +0.750881 +0.722977 +0.754005 +0.731838 +0.745803 +0.730392 +0.740658 +0.712776 +0.733137 +0.717077 +0.747939 +0.710676 +0.757443 +0.722144 +0.755217 +0.719011 +0.769753 +0.721736 +0.766210 +0.726129 +0.781296 +0.717114 +0.769914 +0.734868 +0.752663 +0.694532 +0.761200 +0.719050 +0.741319 +0.714655 +0.769131 +0.709852 +0.758841 +0.722126 +0.745444 +0.709976 +0.751730 +0.721457 +0.751065 +0.719962 +0.747731 +0.713804 +0.763921 +0.706564 +0.741520 +0.704590 +0.751053 +0.707859 +0.744616 +0.714674 +0.765264 +0.714431 +0.745389 +0.707014 +0.757938 +0.712512 +0.738345 +0.695435 +0.750277 +0.706167 +0.743937 +0.698836 +0.750193 +0.724795 +0.773360 +0.712882 +0.742446 +0.685858 +0.728924 +0.675808 +0.732163 +0.674274 +0.732888 +0.689924 +0.738451 +0.717751 +0.717409 +0.708415 +0.722986 +0.700052 +0.710381 +0.699739 +0.728165 +0.705795 +0.719017 +0.689728 +0.694732 +0.678899 +0.715670 +0.676692 +0.695088 +0.672493 +0.694554 +0.665060 +0.687039 +0.676854 +0.701343 +0.675239 +0.691515 +0.687146 +0.696502 +0.663821 +0.665675 +0.660979 +0.674965 +0.656646 +0.683907 +0.667853 +0.664238 +0.663246 +0.652821 +0.679096 +0.639282 +0.641398 +0.663220 +0.667469 +0.632085 +0.648584 +0.675621 +0.648074 +0.654472 +0.666156 +0.656609 +0.605120 +0.630611 +0.636237 +0.628291 +0.628059 +0.635215 +0.600709 +0.633590 +0.608977 +0.617806 +0.624664 +0.641046 +0.612582 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.657853 +0.635518 +0.657853 +0.635518 +0.657853 +0.635518 +0.657853 +0.635518 +0.657853 +0.597703 +0.654583 +0.645974 +0.656458 +0.636970 +0.664253 +0.623611 +0.652847 +0.655510 +0.676943 +0.642444 +0.628538 +0.658265 +0.671821 +0.625917 +0.681338 +0.658605 +0.666841 +0.642716 +0.657542 +0.652116 +0.667126 +0.654146 +0.689451 +0.668219 +0.695748 +0.653226 +0.693438 +0.677410 +0.688383 +0.663492 +0.687289 +0.661992 +0.672278 +0.663436 +0.706045 +0.677860 +0.700355 +0.696008 +0.712811 +0.672579 +0.708545 +0.680130 +0.699968 +0.666964 +0.717116 +0.687743 +0.702495 +0.687702 +0.717954 +0.692930 +0.706307 +0.688547 +0.732945 +0.695520 +0.739227 +0.691302 +0.736407 +0.706737 +0.749550 +0.698832 +0.750545 +0.688989 +0.747263 +0.713768 +0.753834 +0.716844 +0.736714 +0.720606 +0.741137 +0.715784 +0.762975 +0.723007 +0.755198 +0.715252 +0.761779 +0.701987 +0.758092 +0.714161 +0.754518 +0.728577 +0.751952 +0.728846 +0.767971 +0.714113 +0.781908 +0.723570 +0.773714 +0.717464 +0.759628 +0.716554 +0.783532 +0.716990 +0.762442 +0.724124 +0.779638 +0.727300 +0.786793 +0.723441 +0.778005 +0.712722 +0.743363 +0.705208 +0.774922 +0.723610 +0.760192 +0.729822 +0.778059 +0.736239 +0.773924 +0.709381 +0.762348 +0.703483 +0.769644 +0.724292 +0.764100 +0.734193 +0.793280 +0.719840 +0.776919 +0.719751 +0.780136 +0.720905 +0.794690 +0.730976 +0.783721 +0.723770 +0.779502 +0.716385 +0.774329 +0.703959 +0.769691 +0.715466 +0.765423 +0.717272 +0.781521 +0.716842 +0.789237 +0.722880 +0.775239 +0.706484 +0.756424 +0.719321 +0.776145 +0.718471 +0.770416 +0.713823 +0.772007 +0.704645 +0.773818 +0.703514 +0.765646 +0.716375 +0.763970 +0.697655 +0.778034 +0.723659 +0.776038 +0.712205 +0.767343 +0.706406 +0.755771 +0.693376 +0.778484 +0.697557 +0.748866 +0.693240 +0.757805 +0.717929 +0.782075 +0.711239 +0.762965 +0.687560 +0.760121 +0.690255 +0.739403 +0.688208 +0.748355 +0.678818 +0.751166 +0.711384 +0.738209 +0.696510 +0.729313 +0.711591 +0.734244 +0.690990 +0.732547 +0.715007 +0.734058 +0.700270 +0.715606 +0.682095 +0.720971 +0.693046 +0.711201 +0.673176 +0.703746 +0.662466 +0.719428 +0.684304 +0.719214 +0.679720 +0.715060 +0.671585 +0.705258 +0.660282 +0.681636 +0.659967 +0.697497 +0.650968 +0.692990 +0.672269 +0.692517 +0.658024 +0.702908 +0.651305 +0.678322 +0.662760 +0.686117 +0.651761 +0.676179 +0.622215 +0.664552 +0.639371 +0.647356 +0.646982 +0.659769 +0.623976 +0.646592 +0.627446 +0.667327 +0.605658 +0.636595 +0.605367 +0.636085 +0.594056 +0.657328 +0.596361 +0.619508 +0.601694 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.668235 +0.600030 +0.668235 +0.600030 +0.668235 +0.600030 +0.668235 +0.621365 +0.672044 +0.604349 +0.680805 +0.594627 +0.668226 +0.601113 +0.655674 +0.604860 +0.703815 +0.597803 +0.692407 +0.611967 +0.668664 +0.640007 +0.692383 +0.607615 +0.663582 +0.638168 +0.706439 +0.659074 +0.667178 +0.652186 +0.698531 +0.629504 +0.710232 +0.633338 +0.709652 +0.654146 +0.735449 +0.650081 +0.713511 +0.648427 +0.706767 +0.651853 +0.711932 +0.663880 +0.728032 +0.667161 +0.717260 +0.659918 +0.721600 +0.651651 +0.730092 +0.673948 +0.726206 +0.662924 +0.725452 +0.677949 +0.732118 +0.667596 +0.752472 +0.667781 +0.731491 +0.682275 +0.742107 +0.671802 +0.749716 +0.667571 +0.735729 +0.687031 +0.738225 +0.685472 +0.754660 +0.684863 +0.754582 +0.690613 +0.740231 +0.676009 +0.740772 +0.702642 +0.752544 +0.701206 +0.759455 +0.702162 +0.759463 +0.702864 +0.757018 +0.701709 +0.751999 +0.706005 +0.761058 +0.697980 +0.758147 +0.695939 +0.774092 +0.699885 +0.777874 +0.704432 +0.764618 +0.687159 +0.767080 +0.688245 +0.765990 +0.684809 +0.771908 +0.699670 +0.777114 +0.711488 +0.790175 +0.715101 +0.780720 +0.688372 +0.751752 +0.674293 +0.764173 +0.719971 +0.769175 +0.715986 +0.781522 +0.715447 +0.766430 +0.710046 +0.762102 +0.687083 +0.769021 +0.712756 +0.789435 +0.707241 +0.775543 +0.705887 +0.780448 +0.703151 +0.768568 +0.686778 +0.783261 +0.689169 +0.782326 +0.700201 +0.790127 +0.705056 +0.773872 +0.694393 +0.775174 +0.689588 +0.782737 +0.710485 +0.778959 +0.704794 +0.786975 +0.699593 +0.782380 +0.706864 +0.769819 +0.694532 +0.777585 +0.703843 +0.766265 +0.688548 +0.773336 +0.678623 +0.774565 +0.691020 +0.769732 +0.687532 +0.761464 +0.677684 +0.770204 +0.694385 +0.781683 +0.687884 +0.778446 +0.686615 +0.754288 +0.665335 +0.779780 +0.687151 +0.755151 +0.678502 +0.768957 +0.685506 +0.777622 +0.691013 +0.760569 +0.665482 +0.761311 +0.674089 +0.756311 +0.671988 +0.756689 +0.653651 +0.752176 +0.694180 +0.732715 +0.667793 +0.725840 +0.678382 +0.740084 +0.680559 +0.727307 +0.675962 +0.739090 +0.666711 +0.728287 +0.660811 +0.722841 +0.678645 +0.718417 +0.663297 +0.716630 +0.661570 +0.707010 +0.651424 +0.725753 +0.642004 +0.704049 +0.645945 +0.699711 +0.645540 +0.701755 +0.655330 +0.707236 +0.644881 +0.679032 +0.631124 +0.677679 +0.660584 +0.687947 +0.645185 +0.682236 +0.639249 +0.687464 +0.636703 +0.677882 +0.637411 +0.667723 +0.614801 +0.651392 +0.600754 +0.645284 +0.627741 +0.645825 +0.603192 +0.671064 +0.594240 +0.651936 +0.608268 +0.658260 +0.586596 +0.669056 +0.594414 +0.659732 +0.596368 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.667088 +0.597957 +0.667088 +0.597957 +0.667088 +0.597957 +0.667088 +0.597957 +0.667088 +0.613741 +0.676793 +0.601870 +0.650039 +0.603959 +0.669852 +0.588041 +0.681371 +0.609525 +0.697321 +0.630083 +0.688209 +0.638738 +0.705662 +0.653870 +0.690763 +0.625613 +0.707867 +0.646171 +0.677047 +0.617411 +0.691123 +0.649761 +0.684973 +0.642262 +0.717542 +0.646196 +0.709031 +0.648649 +0.692599 +0.653909 +0.709754 +0.637089 +0.699649 +0.656135 +0.708665 +0.652085 +0.736596 +0.661966 +0.723536 +0.641718 +0.714915 +0.673424 +0.733888 +0.659838 +0.742533 +0.670908 +0.721844 +0.677155 +0.723452 +0.669164 +0.738996 +0.668773 +0.730718 +0.661260 +0.732695 +0.670726 +0.732347 +0.682166 +0.751151 +0.672988 +0.740971 +0.676939 +0.733752 +0.661777 +0.749747 +0.688091 +0.724746 +0.693481 +0.746166 +0.708819 +0.756410 +0.702427 +0.761670 +0.706869 +0.764564 +0.703644 +0.754245 +0.701801 +0.763097 +0.679813 +0.752894 +0.710278 +0.774835 +0.690968 +0.771605 +0.702484 +0.770614 +0.691595 +0.777064 +0.697567 +0.766767 +0.700132 +0.789503 +0.719564 +0.780596 +0.693610 +0.775332 +0.697721 +0.781468 +0.692625 +0.755774 +0.694263 +0.769379 +0.706184 +0.761691 +0.707514 +0.772451 +0.698525 +0.776704 +0.703451 +0.760361 +0.697577 +0.763889 +0.711435 +0.786341 +0.708823 +0.774689 +0.699134 +0.774613 +0.694012 +0.764031 +0.690840 +0.774149 +0.685629 +0.778373 +0.706103 +0.778739 +0.687482 +0.777887 +0.687374 +0.757441 +0.698951 +0.788983 +0.700147 +0.773923 +0.703862 +0.783322 +0.681598 +0.779765 +0.701692 +0.756494 +0.689870 +0.775048 +0.693206 +0.783618 +0.699384 +0.781587 +0.683736 +0.767068 +0.698198 +0.769877 +0.691134 +0.762550 +0.689537 +0.755481 +0.684022 +0.777507 +0.682892 +0.784194 +0.683386 +0.762154 +0.689323 +0.774088 +0.679731 +0.755222 +0.695257 +0.769759 +0.672792 +0.758493 +0.673462 +0.758031 +0.680699 +0.764122 +0.686980 +0.745779 +0.670437 +0.753945 +0.666161 +0.760930 +0.703173 +0.732918 +0.684779 +0.732028 +0.673093 +0.737352 +0.681255 +0.724951 +0.682950 +0.719020 +0.685898 +0.734431 +0.680262 +0.734831 +0.676927 +0.719865 +0.671839 +0.711037 +0.648220 +0.707937 +0.641961 +0.690712 +0.635601 +0.709046 +0.654299 +0.716653 +0.635511 +0.706115 +0.650461 +0.711853 +0.647793 +0.678004 +0.640929 +0.692819 +0.645343 +0.710291 +0.650153 +0.683496 +0.649179 +0.684461 +0.619704 +0.662583 +0.624581 +0.704760 +0.625020 +0.661770 +0.598511 +0.656814 +0.586903 +0.650199 +0.596155 +0.687056 +0.610594 +0.641295 +0.601767 +0.648867 +0.590969 +0.638536 +0.584363 +0.645793 +0.601110 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.699216 +0.623766 +0.699216 +0.623766 +0.699216 +0.623766 +0.699216 +0.623766 +0.699216 +0.623766 +0.699216 +0.579418 +0.681968 +0.612490 +0.669112 +0.619033 +0.688792 +0.639776 +0.739743 +0.602350 +0.713300 +0.614154 +0.686938 +0.631243 +0.695271 +0.643375 +0.725184 +0.649965 +0.678605 +0.633409 +0.693179 +0.634509 +0.695417 +0.636817 +0.697999 +0.624619 +0.710183 +0.671311 +0.732423 +0.662346 +0.713114 +0.639157 +0.714641 +0.640255 +0.690369 +0.667893 +0.737508 +0.660289 +0.711473 +0.647471 +0.731743 +0.671332 +0.735807 +0.681028 +0.732278 +0.655299 +0.711639 +0.672431 +0.739843 +0.656227 +0.747267 +0.680570 +0.736883 +0.675311 +0.761391 +0.681606 +0.730675 +0.685747 +0.748938 +0.687327 +0.736011 +0.674262 +0.740904 +0.660144 +0.748909 +0.677932 +0.743683 +0.689764 +0.750848 +0.698554 +0.758150 +0.706160 +0.754967 +0.712250 +0.762934 +0.702414 +0.748835 +0.704420 +0.754604 +0.698330 +0.761032 +0.700687 +0.772876 +0.692010 +0.776952 +0.708317 +0.761184 +0.700511 +0.773252 +0.685591 +0.765798 +0.701862 +0.780419 +0.719374 +0.776854 +0.714642 +0.776445 +0.701429 +0.769014 +0.706062 +0.759747 +0.690226 +0.769001 +0.705290 +0.766393 +0.707817 +0.776337 +0.708763 +0.779368 +0.714680 +0.778998 +0.702494 +0.787302 +0.711724 +0.781214 +0.705296 +0.799180 +0.707581 +0.784247 +0.707821 +0.793592 +0.701856 +0.775289 +0.705761 +0.781799 +0.721580 +0.813017 +0.701225 +0.793285 +0.699927 +0.775889 +0.711069 +0.782735 +0.702695 +0.782179 +0.705308 +0.779073 +0.694265 +0.785506 +0.704295 +0.780863 +0.710493 +0.790245 +0.700058 +0.783981 +0.712027 +0.772527 +0.691099 +0.783174 +0.702223 +0.778650 +0.697136 +0.776640 +0.702198 +0.779734 +0.690852 +0.788739 +0.706156 +0.789747 +0.692803 +0.767927 +0.687509 +0.777798 +0.690293 +0.775123 +0.683899 +0.768697 +0.686369 +0.739935 +0.668240 +0.751656 +0.659570 +0.767309 +0.680366 +0.763378 +0.664182 +0.766465 +0.668388 +0.742494 +0.707951 +0.769200 +0.694117 +0.740972 +0.698133 +0.736703 +0.688487 +0.736091 +0.680593 +0.737711 +0.690339 +0.728795 +0.680204 +0.734912 +0.665500 +0.717992 +0.654652 +0.708476 +0.656440 +0.718758 +0.657097 +0.709010 +0.660817 +0.709637 +0.648333 +0.709162 +0.645837 +0.714949 +0.661382 +0.697475 +0.640000 +0.691990 +0.639136 +0.685195 +0.639408 +0.681973 +0.654129 +0.688749 +0.647381 +0.674116 +0.642316 +0.677800 +0.648865 +0.665897 +0.628368 +0.672348 +0.651761 +0.648659 +0.616214 +0.684235 +0.591956 +0.643671 +0.592686 +0.634412 +0.605973 +0.652420 +0.590870 +0.668730 +0.584078 +0.652917 +0.596510 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.682335 +0.589622 +0.682335 +0.589622 +0.682335 +0.589622 +0.682335 +0.589622 +0.682335 +0.589151 +0.687932 +0.544852 +0.649303 +0.601025 +0.689507 +0.622991 +0.708194 +0.636819 +0.689746 +0.619596 +0.694834 +0.609112 +0.712558 +0.616809 +0.689788 +0.659546 +0.703907 +0.660665 +0.685678 +0.647435 +0.711596 +0.641179 +0.691820 +0.625635 +0.728800 +0.652440 +0.684296 +0.655924 +0.745900 +0.664931 +0.715851 +0.645234 +0.709736 +0.653615 +0.691064 +0.667195 +0.748660 +0.654956 +0.723661 +0.674892 +0.741180 +0.665290 +0.730847 +0.672255 +0.745492 +0.668695 +0.736759 +0.677556 +0.737799 +0.660934 +0.739169 +0.672447 +0.743127 +0.683305 +0.746851 +0.683206 +0.741747 +0.686921 +0.741396 +0.674299 +0.751360 +0.674647 +0.742142 +0.689770 +0.766232 +0.673682 +0.736810 +0.695943 +0.764597 +0.696332 +0.760060 +0.697087 +0.771451 +0.700571 +0.761504 +0.712577 +0.764824 +0.694115 +0.778550 +0.723431 +0.780539 +0.709953 +0.777038 +0.702154 +0.760669 +0.688952 +0.788315 +0.721827 +0.788905 +0.705210 +0.778176 +0.695608 +0.777321 +0.692558 +0.788769 +0.688083 +0.775665 +0.711076 +0.792233 +0.693381 +0.770868 +0.712734 +0.773571 +0.707470 +0.778821 +0.703275 +0.772704 +0.707902 +0.772072 +0.707383 +0.773888 +0.717401 +0.786039 +0.698578 +0.779679 +0.720616 +0.790994 +0.704206 +0.791925 +0.712561 +0.789301 +0.705237 +0.797815 +0.703171 +0.792907 +0.694946 +0.793873 +0.705323 +0.781563 +0.677587 +0.779318 +0.698022 +0.786462 +0.703589 +0.779232 +0.689533 +0.778630 +0.691904 +0.781621 +0.700812 +0.768163 +0.679132 +0.773475 +0.706051 +0.769855 +0.702451 +0.766054 +0.687574 +0.764927 +0.685992 +0.767884 +0.682601 +0.755332 +0.670750 +0.775487 +0.691982 +0.781219 +0.684838 +0.768300 +0.686987 +0.773433 +0.684448 +0.774052 +0.670061 +0.757403 +0.665534 +0.747975 +0.673421 +0.767274 +0.675874 +0.747534 +0.670856 +0.751327 +0.651170 +0.752281 +0.658635 +0.757568 +0.677565 +0.771396 +0.689141 +0.732167 +0.691262 +0.702090 +0.659716 +0.727424 +0.669825 +0.718871 +0.684343 +0.729592 +0.679466 +0.715674 +0.665742 +0.725271 +0.655886 +0.724137 +0.643326 +0.719998 +0.673657 +0.714278 +0.640990 +0.714424 +0.654677 +0.705472 +0.665747 +0.716741 +0.651055 +0.698379 +0.649130 +0.698032 +0.635924 +0.705370 +0.641007 +0.695603 +0.646223 +0.697769 +0.633393 +0.699641 +0.626010 +0.671892 +0.627330 +0.667060 +0.641478 +0.663907 +0.634087 +0.654115 +0.586970 +0.638593 +0.606334 +0.664526 +0.613649 +0.640601 +0.610685 +0.680257 +0.634544 +0.656359 +0.627948 +0.646803 +0.572640 +0.673547 +0.624392 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.695078 +0.603678 +0.695078 +0.603678 +0.695078 +0.603678 +0.695078 +0.603678 +0.695078 +0.617857 +0.681422 +0.580673 +0.686970 +0.614503 +0.688842 +0.615859 +0.709422 +0.636560 +0.675816 +0.605736 +0.687948 +0.620558 +0.712646 +0.633294 +0.717051 +0.637833 +0.695697 +0.624986 +0.703994 +0.652944 +0.693385 +0.624762 +0.711590 +0.639171 +0.718400 +0.642239 +0.714186 +0.663761 +0.684740 +0.669662 +0.728467 +0.660793 +0.717101 +0.650509 +0.737879 +0.658637 +0.721132 +0.665219 +0.746727 +0.643046 +0.715269 +0.659343 +0.749163 +0.648606 +0.744485 +0.690218 +0.755117 +0.666010 +0.735182 +0.684535 +0.735661 +0.672614 +0.742651 +0.678909 +0.739997 +0.682813 +0.754977 +0.667170 +0.746025 +0.666536 +0.739201 +0.702562 +0.757784 +0.694232 +0.757677 +0.678130 +0.740232 +0.684602 +0.763159 +0.689755 +0.752399 +0.703987 +0.768565 +0.710630 +0.768225 +0.698256 +0.766355 +0.703175 +0.784470 +0.727734 +0.783029 +0.707869 +0.777196 +0.689540 +0.773203 +0.688931 +0.769056 +0.704262 +0.777780 +0.678567 +0.776721 +0.702990 +0.781234 +0.694639 +0.772747 +0.701466 +0.772457 +0.711022 +0.785028 +0.698259 +0.774047 +0.703534 +0.773422 +0.711832 +0.786085 +0.709943 +0.775844 +0.702393 +0.772283 +0.702293 +0.787139 +0.705116 +0.775521 +0.704617 +0.764652 +0.697093 +0.780701 +0.693576 +0.780994 +0.706229 +0.801416 +0.689888 +0.778908 +0.702722 +0.792110 +0.697185 +0.788931 +0.705155 +0.784856 +0.681063 +0.776433 +0.698170 +0.774629 +0.701793 +0.782158 +0.694240 +0.771480 +0.685964 +0.765161 +0.689448 +0.761581 +0.681296 +0.770228 +0.694855 +0.778533 +0.694925 +0.775299 +0.677697 +0.768170 +0.677344 +0.759106 +0.666330 +0.753060 +0.680192 +0.772688 +0.686894 +0.764490 +0.684939 +0.768405 +0.679858 +0.768322 +0.686743 +0.765956 +0.676833 +0.753618 +0.668069 +0.767569 +0.673452 +0.760772 +0.678910 +0.763189 +0.682542 +0.765697 +0.658801 +0.751282 +0.645515 +0.744005 +0.664862 +0.762580 +0.671189 +0.729963 +0.681749 +0.712963 +0.666292 +0.728502 +0.678337 +0.740965 +0.674753 +0.725471 +0.672202 +0.703473 +0.661436 +0.725815 +0.670439 +0.736330 +0.677111 +0.728640 +0.639881 +0.730672 +0.664270 +0.723810 +0.645683 +0.711902 +0.649054 +0.698539 +0.647867 +0.716436 +0.638313 +0.688554 +0.648775 +0.700276 +0.641024 +0.679652 +0.652609 +0.694376 +0.643370 +0.682115 +0.621039 +0.661513 +0.636805 +0.678400 +0.628173 +0.665641 +0.615746 +0.660433 +0.606088 +0.643866 +0.600684 +0.652612 +0.624642 +0.637636 +0.616002 +0.631885 +0.576739 +0.652282 +0.598954 +0.642093 +0.601165 +0.677460 +0.597232 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.687370 +0.616458 +0.687370 +0.616458 +0.687370 +0.616458 +0.687370 +0.616458 +0.687370 +0.580179 +0.667153 +0.593421 +0.684503 +0.635942 +0.701355 +0.620010 +0.676252 +0.627410 +0.674019 +0.616865 +0.667039 +0.633196 +0.697853 +0.629149 +0.696295 +0.640686 +0.698809 +0.622686 +0.714787 +0.639333 +0.695429 +0.619811 +0.704080 +0.636295 +0.706287 +0.639789 +0.693421 +0.653218 +0.704119 +0.633533 +0.731871 +0.642384 +0.692129 +0.656791 +0.718650 +0.645874 +0.724501 +0.650863 +0.715674 +0.654767 +0.710210 +0.669565 +0.741745 +0.658116 +0.730635 +0.683478 +0.745320 +0.684577 +0.748305 +0.668338 +0.723516 +0.671405 +0.753802 +0.670227 +0.764182 +0.681455 +0.734519 +0.664037 +0.763075 +0.671358 +0.767351 +0.687852 +0.783019 +0.696545 +0.762748 +0.672090 +0.756541 +0.693767 +0.778159 +0.681830 +0.771928 +0.705315 +0.784345 +0.710630 +0.781468 +0.692835 +0.772014 +0.711088 +0.786240 +0.713376 +0.783759 +0.700851 +0.792908 +0.688896 +0.784766 +0.698917 +0.799974 +0.691417 +0.786161 +0.699523 +0.787914 +0.701085 +0.788851 +0.702037 +0.806601 +0.712736 +0.810139 +0.711041 +0.786352 +0.689944 +0.772672 +0.693669 +0.782709 +0.693530 +0.805327 +0.710368 +0.791180 +0.693560 +0.796644 +0.699178 +0.795020 +0.706874 +0.790882 +0.701464 +0.797329 +0.698900 +0.798702 +0.698788 +0.798358 +0.694956 +0.812088 +0.706592 +0.802328 +0.708104 +0.797026 +0.690121 +0.800781 +0.710645 +0.796860 +0.687146 +0.795981 +0.701404 +0.791806 +0.703029 +0.788518 +0.693099 +0.796784 +0.691876 +0.786284 +0.683943 +0.776902 +0.684168 +0.793445 +0.700080 +0.783442 +0.689318 +0.780723 +0.688532 +0.796157 +0.685689 +0.783916 +0.673991 +0.772197 +0.684186 +0.793835 +0.686630 +0.791373 +0.685503 +0.788338 +0.678964 +0.792858 +0.684694 +0.797607 +0.664858 +0.771194 +0.664685 +0.778014 +0.673312 +0.774623 +0.682553 +0.778858 +0.670927 +0.771865 +0.663243 +0.771672 +0.658875 +0.773573 +0.670080 +0.775760 +0.678234 +0.763667 +0.684384 +0.745761 +0.675394 +0.750340 +0.680851 +0.763209 +0.682080 +0.738431 +0.667630 +0.742063 +0.654297 +0.724760 +0.649572 +0.747464 +0.646161 +0.705363 +0.650473 +0.706676 +0.628523 +0.709644 +0.651377 +0.719884 +0.665004 +0.702367 +0.631662 +0.681791 +0.629711 +0.690480 +0.625660 +0.711258 +0.646740 +0.694931 +0.656975 +0.692137 +0.628616 +0.678492 +0.623146 +0.672550 +0.621156 +0.666253 +0.616880 +0.671077 +0.613712 +0.653626 +0.610507 +0.663377 +0.612217 +0.674169 +0.580694 +0.660263 +0.608840 +0.658733 +0.574990 +0.651249 +0.581276 +0.672401 +0.576922 +0.631296 +0.588369 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.689735 +0.595833 +0.689735 +0.595833 +0.689735 +0.595833 +0.689735 +0.595833 +0.689735 +0.585361 +0.698023 +0.611644 +0.662171 +0.581658 +0.713303 +0.594199 +0.693731 +0.594678 +0.709360 +0.601696 +0.734285 +0.612330 +0.730015 +0.589774 +0.714906 +0.639871 +0.726104 +0.639559 +0.703867 +0.602954 +0.705326 +0.606433 +0.720459 +0.611432 +0.711700 +0.628413 +0.742908 +0.618934 +0.710151 +0.645148 +0.718399 +0.624878 +0.719426 +0.641437 +0.739296 +0.631035 +0.734174 +0.633209 +0.730316 +0.627554 +0.737024 +0.652238 +0.756958 +0.646402 +0.746186 +0.662840 +0.755638 +0.658915 +0.761921 +0.627510 +0.754803 +0.645526 +0.766440 +0.654206 +0.773263 +0.672742 +0.761303 +0.647049 +0.760365 +0.655297 +0.771589 +0.657854 +0.767603 +0.664477 +0.771613 +0.653486 +0.761912 +0.680033 +0.777704 +0.681133 +0.773022 +0.674742 +0.789756 +0.688152 +0.786041 +0.671771 +0.782546 +0.672080 +0.777558 +0.676676 +0.787762 +0.681606 +0.784003 +0.664305 +0.791645 +0.680651 +0.804655 +0.675284 +0.796445 +0.684947 +0.809999 +0.689651 +0.801495 +0.698959 +0.797965 +0.669312 +0.792364 +0.688157 +0.791636 +0.668534 +0.790238 +0.679062 +0.787918 +0.678120 +0.806617 +0.679195 +0.791735 +0.677432 +0.798555 +0.670673 +0.792135 +0.687715 +0.808083 +0.689938 +0.801407 +0.685804 +0.802520 +0.683652 +0.802407 +0.688667 +0.808154 +0.677256 +0.814459 +0.688467 +0.814688 +0.667801 +0.802769 +0.676031 +0.813009 +0.673633 +0.808165 +0.671305 +0.796619 +0.689647 +0.792688 +0.677791 +0.790904 +0.669722 +0.781692 +0.674389 +0.782758 +0.659708 +0.793566 +0.678384 +0.789953 +0.672901 +0.794925 +0.668286 +0.778558 +0.657487 +0.786892 +0.661130 +0.775754 +0.662490 +0.792364 +0.672440 +0.784454 +0.643600 +0.783842 +0.658474 +0.779188 +0.659988 +0.800159 +0.663999 +0.783571 +0.659638 +0.787646 +0.654874 +0.772280 +0.643926 +0.786143 +0.635732 +0.785160 +0.648330 +0.766853 +0.643885 +0.771042 +0.643802 +0.769824 +0.661641 +0.746705 +0.653768 +0.754142 +0.648469 +0.747418 +0.655614 +0.758471 +0.658285 +0.762494 +0.650216 +0.733128 +0.643609 +0.738884 +0.633646 +0.732837 +0.626284 +0.736611 +0.626095 +0.743181 +0.633406 +0.727050 +0.618381 +0.716236 +0.620341 +0.727590 +0.614273 +0.721360 +0.630466 +0.701703 +0.614933 +0.731474 +0.633968 +0.674754 +0.623134 +0.701650 +0.606917 +0.677051 +0.625861 +0.699200 +0.624448 +0.673196 +0.610402 +0.686703 +0.608504 +0.688154 +0.586124 +0.677277 +0.599531 +0.669993 +0.588782 +0.668025 +0.590426 +0.659282 +0.569759 +0.682625 +0.538914 +0.666954 +0.569280 +0.693110 +0.566366 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.698781 +0.597537 +0.698781 +0.597537 +0.698781 +0.597537 +0.698781 +0.597537 +0.698781 +0.574597 +0.681619 +0.584388 +0.678064 +0.601750 +0.696863 +0.606472 +0.721416 +0.600452 +0.716580 +0.600749 +0.708141 +0.629631 +0.712031 +0.606143 +0.715111 +0.604700 +0.731992 +0.608422 +0.719108 +0.595340 +0.711508 +0.611935 +0.737810 +0.610679 +0.736781 +0.650970 +0.728559 +0.628038 +0.740567 +0.612921 +0.724121 +0.639773 +0.740726 +0.638766 +0.745677 +0.617319 +0.746547 +0.646893 +0.768948 +0.645784 +0.739048 +0.650073 +0.760564 +0.652379 +0.758237 +0.634282 +0.779587 +0.648888 +0.760819 +0.633377 +0.745271 +0.641251 +0.749726 +0.651965 +0.771433 +0.666083 +0.757910 +0.646459 +0.747139 +0.663630 +0.764630 +0.667122 +0.783180 +0.670139 +0.752430 +0.645124 +0.744305 +0.669512 +0.785355 +0.683791 +0.789263 +0.680569 +0.787080 +0.681510 +0.785424 +0.680165 +0.784603 +0.677589 +0.790105 +0.683295 +0.778763 +0.678718 +0.780158 +0.674112 +0.796201 +0.682409 +0.797888 +0.684390 +0.794562 +0.681680 +0.803082 +0.689949 +0.799284 +0.685742 +0.802085 +0.680727 +0.797097 +0.671874 +0.799805 +0.687132 +0.790911 +0.688252 +0.787220 +0.676624 +0.800779 +0.691312 +0.794898 +0.678836 +0.785182 +0.665425 +0.780247 +0.688501 +0.813432 +0.684512 +0.797841 +0.683734 +0.800839 +0.680937 +0.792225 +0.680667 +0.796289 +0.680559 +0.809951 +0.687190 +0.808127 +0.681741 +0.806723 +0.658188 +0.802134 +0.667137 +0.789028 +0.669369 +0.804332 +0.669117 +0.787368 +0.669303 +0.794377 +0.661095 +0.775789 +0.676679 +0.789633 +0.681975 +0.791032 +0.664669 +0.778769 +0.668595 +0.796170 +0.659675 +0.788066 +0.659250 +0.794894 +0.657907 +0.804139 +0.657954 +0.788583 +0.663820 +0.792733 +0.647393 +0.770206 +0.655050 +0.772228 +0.652817 +0.794014 +0.656069 +0.789501 +0.659102 +0.786530 +0.651100 +0.788852 +0.654154 +0.784226 +0.646033 +0.767579 +0.644702 +0.767974 +0.624802 +0.781122 +0.638393 +0.762964 +0.661237 +0.772216 +0.654100 +0.746256 +0.661399 +0.738617 +0.669222 +0.767819 +0.666170 +0.743562 +0.654156 +0.744010 +0.635396 +0.760786 +0.641408 +0.728929 +0.640531 +0.727590 +0.637020 +0.717556 +0.619426 +0.731277 +0.631512 +0.724041 +0.624032 +0.721920 +0.632880 +0.719032 +0.625294 +0.702811 +0.616131 +0.725190 +0.622056 +0.686091 +0.632603 +0.705578 +0.602319 +0.699424 +0.619886 +0.691341 +0.591989 +0.690674 +0.621202 +0.681665 +0.600086 +0.675104 +0.586732 +0.682834 +0.604008 +0.687072 +0.582215 +0.687428 +0.597301 +0.665048 +0.559231 +0.673612 +0.585873 +0.664920 +0.589314 +0.667381 +0.580801 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.716993 +0.589571 +0.716993 +0.589571 +0.716993 +0.589571 +0.716993 +0.589571 +0.716993 +0.588584 +0.711857 +0.589894 +0.712646 +0.576479 +0.707311 +0.602340 +0.731021 +0.596868 +0.718149 +0.608555 +0.682401 +0.606665 +0.734794 +0.600138 +0.704854 +0.596990 +0.698398 +0.630674 +0.711486 +0.611007 +0.742055 +0.622160 +0.744590 +0.620795 +0.727769 +0.629779 +0.722003 +0.611614 +0.706043 +0.620525 +0.707004 +0.621915 +0.710872 +0.638317 +0.735185 +0.635676 +0.742394 +0.630684 +0.746931 +0.649276 +0.748237 +0.651325 +0.759446 +0.650102 +0.741360 +0.636504 +0.755980 +0.622129 +0.746986 +0.632063 +0.745226 +0.652848 +0.750629 +0.653286 +0.768859 +0.661621 +0.759507 +0.641595 +0.755578 +0.660206 +0.763268 +0.658228 +0.790344 +0.662339 +0.777625 +0.670120 +0.754907 +0.691508 +0.770116 +0.686701 +0.781587 +0.681532 +0.784010 +0.690757 +0.787503 +0.682896 +0.773263 +0.666477 +0.768332 +0.694963 +0.792378 +0.681276 +0.787526 +0.687234 +0.798982 +0.701186 +0.800877 +0.675976 +0.799696 +0.692150 +0.813649 +0.692418 +0.799442 +0.693041 +0.792413 +0.687327 +0.801243 +0.688877 +0.805380 +0.680725 +0.786895 +0.685396 +0.800379 +0.693338 +0.802020 +0.678106 +0.799375 +0.689305 +0.793321 +0.673394 +0.793893 +0.688345 +0.812024 +0.691248 +0.798900 +0.690290 +0.810628 +0.692869 +0.808783 +0.690118 +0.811283 +0.680188 +0.798357 +0.685961 +0.807752 +0.695110 +0.811077 +0.661443 +0.794144 +0.695420 +0.788926 +0.673676 +0.804768 +0.682637 +0.807823 +0.674396 +0.785477 +0.673173 +0.798596 +0.674821 +0.801410 +0.685430 +0.793286 +0.673565 +0.792313 +0.684331 +0.804885 +0.668907 +0.786219 +0.667043 +0.798664 +0.669440 +0.807740 +0.667029 +0.803773 +0.672144 +0.791118 +0.657576 +0.789275 +0.657583 +0.793043 +0.670220 +0.788599 +0.655656 +0.802330 +0.649957 +0.791440 +0.666447 +0.792577 +0.664249 +0.787365 +0.639561 +0.789466 +0.659169 +0.777109 +0.636498 +0.777685 +0.637941 +0.770457 +0.668071 +0.779799 +0.665188 +0.769140 +0.668442 +0.753814 +0.675361 +0.772218 +0.669989 +0.752650 +0.664620 +0.749663 +0.648767 +0.769956 +0.632955 +0.747893 +0.640267 +0.732267 +0.636771 +0.736529 +0.619536 +0.724197 +0.643553 +0.719304 +0.608183 +0.721646 +0.632803 +0.711574 +0.619549 +0.738907 +0.612383 +0.725637 +0.631764 +0.692854 +0.602621 +0.730778 +0.600355 +0.697441 +0.622774 +0.675620 +0.597382 +0.698009 +0.587559 +0.688458 +0.626932 +0.681582 +0.598572 +0.694157 +0.580553 +0.692936 +0.588098 +0.690008 +0.599499 +0.683719 +0.585444 +0.662633 +0.543503 +0.672513 +0.580244 +0.689730 +0.576149 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.684866 +0.574933 +0.684866 +0.574933 +0.684866 +0.574933 +0.684866 +0.574885 +0.719835 +0.572724 +0.681490 +0.576853 +0.698519 +0.575222 +0.674589 +0.593848 +0.725546 +0.596713 +0.712809 +0.594226 +0.720088 +0.608632 +0.719314 +0.594192 +0.698404 +0.572563 +0.723766 +0.616227 +0.717086 +0.622823 +0.727745 +0.607486 +0.742540 +0.607526 +0.740943 +0.632777 +0.731556 +0.620648 +0.736793 +0.603727 +0.732081 +0.615188 +0.742752 +0.635695 +0.724716 +0.648095 +0.760023 +0.628861 +0.732694 +0.647664 +0.763067 +0.644159 +0.760251 +0.649239 +0.753280 +0.640460 +0.753521 +0.654850 +0.757792 +0.617053 +0.737352 +0.645746 +0.785724 +0.676707 +0.773704 +0.663167 +0.771196 +0.676023 +0.780119 +0.671903 +0.778789 +0.660681 +0.772229 +0.683568 +0.777779 +0.654317 +0.757704 +0.675105 +0.784008 +0.680635 +0.794622 +0.705052 +0.804342 +0.700438 +0.795437 +0.692048 +0.785312 +0.680239 +0.806586 +0.703736 +0.801893 +0.687469 +0.795686 +0.683549 +0.796887 +0.683224 +0.820397 +0.690479 +0.799497 +0.689958 +0.812877 +0.685691 +0.804225 +0.675185 +0.795375 +0.680553 +0.819190 +0.702079 +0.807652 +0.680777 +0.792798 +0.694390 +0.799637 +0.689914 +0.801095 +0.696162 +0.801209 +0.689763 +0.799795 +0.694709 +0.812185 +0.688480 +0.819063 +0.679571 +0.798219 +0.693859 +0.811128 +0.683368 +0.801934 +0.688222 +0.801457 +0.688653 +0.814174 +0.676726 +0.817440 +0.698974 +0.805436 +0.684663 +0.823182 +0.693616 +0.818065 +0.701921 +0.820099 +0.691729 +0.802566 +0.690201 +0.793367 +0.670428 +0.797558 +0.680254 +0.807957 +0.689992 +0.821496 +0.691576 +0.798282 +0.673246 +0.799083 +0.665253 +0.803419 +0.664925 +0.802872 +0.666097 +0.811272 +0.690390 +0.796649 +0.674982 +0.804387 +0.678197 +0.803187 +0.682429 +0.807298 +0.677391 +0.806428 +0.672278 +0.796544 +0.669761 +0.796098 +0.665345 +0.797223 +0.661986 +0.791123 +0.670021 +0.799397 +0.665549 +0.802299 +0.650062 +0.782629 +0.641789 +0.789344 +0.657743 +0.747181 +0.674606 +0.767479 +0.663479 +0.765906 +0.674475 +0.774202 +0.681300 +0.765558 +0.673478 +0.759153 +0.657702 +0.759493 +0.657210 +0.752038 +0.633410 +0.749381 +0.668978 +0.745814 +0.631905 +0.739404 +0.626872 +0.736899 +0.633670 +0.727423 +0.629490 +0.749041 +0.621473 +0.717586 +0.620449 +0.730042 +0.620311 +0.699472 +0.621103 +0.706327 +0.599176 +0.692161 +0.612172 +0.699577 +0.634746 +0.683853 +0.606996 +0.694180 +0.603650 +0.682563 +0.607558 +0.678044 +0.604078 +0.700980 +0.583770 +0.667182 +0.597075 +0.715824 +0.587688 +0.667324 +0.582473 +0.671653 +0.582286 +0.685463 +0.589078 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.710794 +0.598895 +0.710794 +0.598895 +0.710794 +0.598895 +0.710794 +0.598895 +0.710794 +0.551409 +0.697600 +0.608475 +0.684768 +0.583056 +0.747136 +0.605155 +0.700478 +0.585210 +0.732452 +0.577666 +0.747692 +0.606561 +0.739820 +0.583166 +0.710063 +0.607000 +0.708295 +0.614750 +0.715205 +0.632443 +0.753008 +0.630155 +0.721733 +0.635287 +0.738674 +0.609840 +0.736248 +0.635203 +0.736840 +0.644257 +0.742503 +0.625683 +0.735906 +0.647943 +0.759450 +0.630547 +0.767555 +0.651291 +0.757065 +0.630929 +0.734065 +0.660644 +0.777756 +0.636426 +0.760206 +0.656258 +0.755861 +0.637126 +0.762254 +0.666959 +0.772037 +0.645123 +0.780931 +0.667397 +0.771105 +0.650870 +0.769543 +0.654372 +0.773951 +0.662413 +0.769384 +0.653573 +0.761170 +0.685262 +0.779096 +0.665284 +0.768423 +0.657874 +0.778854 +0.668512 +0.793294 +0.680952 +0.789040 +0.688842 +0.797662 +0.691830 +0.793493 +0.683111 +0.786849 +0.697242 +0.797158 +0.682425 +0.799722 +0.684114 +0.800513 +0.671890 +0.803723 +0.680638 +0.802369 +0.684051 +0.815411 +0.689070 +0.817811 +0.686994 +0.810031 +0.681923 +0.813601 +0.690785 +0.812005 +0.684447 +0.798264 +0.690082 +0.800088 +0.681821 +0.788705 +0.688865 +0.803341 +0.687726 +0.805206 +0.694462 +0.803927 +0.685565 +0.798554 +0.687531 +0.803326 +0.671951 +0.805195 +0.690101 +0.799655 +0.667861 +0.809031 +0.679830 +0.812905 +0.669963 +0.813586 +0.690853 +0.815490 +0.693903 +0.818324 +0.686619 +0.824186 +0.699224 +0.803597 +0.671075 +0.811009 +0.668405 +0.797620 +0.667408 +0.797428 +0.672118 +0.810403 +0.680167 +0.798855 +0.678428 +0.798779 +0.668369 +0.809667 +0.670763 +0.786818 +0.674176 +0.800311 +0.675905 +0.805172 +0.682581 +0.808433 +0.665763 +0.803559 +0.670996 +0.793134 +0.663061 +0.794506 +0.660783 +0.799998 +0.666700 +0.799526 +0.667796 +0.791872 +0.661061 +0.786168 +0.660433 +0.791689 +0.656428 +0.797884 +0.654424 +0.793904 +0.637114 +0.774471 +0.642931 +0.769475 +0.660270 +0.755674 +0.660304 +0.744777 +0.652248 +0.749170 +0.675291 +0.762295 +0.677153 +0.772946 +0.658937 +0.764191 +0.660135 +0.761439 +0.656005 +0.753667 +0.632872 +0.747534 +0.649569 +0.750406 +0.630610 +0.743540 +0.645549 +0.741688 +0.647701 +0.748715 +0.658184 +0.756169 +0.615004 +0.725695 +0.626110 +0.726171 +0.603518 +0.733839 +0.631787 +0.732108 +0.647696 +0.737515 +0.610436 +0.714226 +0.615405 +0.703424 +0.612640 +0.701636 +0.587670 +0.676613 +0.607197 +0.718533 +0.596126 +0.700181 +0.592355 +0.702285 +0.577617 +0.697348 +0.594336 +0.705690 +0.564342 +0.665687 +0.555732 +0.684216 +0.578765 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.698321 +0.583081 +0.698321 +0.583081 +0.698321 +0.583081 +0.698321 +0.583081 +0.698321 +0.576672 +0.724233 +0.591083 +0.699380 +0.602834 +0.696137 +0.555326 +0.699446 +0.611163 +0.728509 +0.613785 +0.725742 +0.614490 +0.722230 +0.613866 +0.721708 +0.599943 +0.719668 +0.605604 +0.720084 +0.631574 +0.735661 +0.616103 +0.720695 +0.618329 +0.727971 +0.603989 +0.754639 +0.651977 +0.738803 +0.635004 +0.739901 +0.637562 +0.723261 +0.648625 +0.735224 +0.628123 +0.740053 +0.635620 +0.763399 +0.648148 +0.742650 +0.658579 +0.768156 +0.655570 +0.767637 +0.647252 +0.760703 +0.641385 +0.770718 +0.651766 +0.778713 +0.647131 +0.797684 +0.653600 +0.790844 +0.660223 +0.791098 +0.663163 +0.783359 +0.664855 +0.784302 +0.650974 +0.786844 +0.675145 +0.794091 +0.661276 +0.780919 +0.689292 +0.788868 +0.672364 +0.818118 +0.693224 +0.811723 +0.695729 +0.814250 +0.671693 +0.793606 +0.679052 +0.820813 +0.691071 +0.815756 +0.702172 +0.826277 +0.678075 +0.814117 +0.684311 +0.810582 +0.685833 +0.828138 +0.687998 +0.828942 +0.675137 +0.816153 +0.690412 +0.812689 +0.682550 +0.822653 +0.665779 +0.831164 +0.682569 +0.809558 +0.679778 +0.823257 +0.687148 +0.813937 +0.669824 +0.825417 +0.681784 +0.817816 +0.692478 +0.820802 +0.679114 +0.822525 +0.693202 +0.825865 +0.679070 +0.829272 +0.677998 +0.830340 +0.679701 +0.844117 +0.689029 +0.833137 +0.680054 +0.832381 +0.675504 +0.825879 +0.683735 +0.836373 +0.680055 +0.837385 +0.684209 +0.832925 +0.680297 +0.826740 +0.674595 +0.825778 +0.678869 +0.828988 +0.675056 +0.822917 +0.679052 +0.817263 +0.683045 +0.812773 +0.674708 +0.827177 +0.675947 +0.821411 +0.665819 +0.816830 +0.663099 +0.817745 +0.668297 +0.817166 +0.671370 +0.816051 +0.673622 +0.818308 +0.671237 +0.813775 +0.650624 +0.803100 +0.664702 +0.793559 +0.671988 +0.792805 +0.660964 +0.794625 +0.681806 +0.778482 +0.654088 +0.785250 +0.646090 +0.788665 +0.637083 +0.785656 +0.651064 +0.784159 +0.668328 +0.756897 +0.670610 +0.775387 +0.664742 +0.751587 +0.678389 +0.790945 +0.669903 +0.767354 +0.655870 +0.759578 +0.643017 +0.755708 +0.659714 +0.754150 +0.640812 +0.751426 +0.643588 +0.729479 +0.634769 +0.751086 +0.648213 +0.742298 +0.628228 +0.726731 +0.629000 +0.733148 +0.619474 +0.732223 +0.607912 +0.727023 +0.609256 +0.718470 +0.634972 +0.722104 +0.635145 +0.704983 +0.611169 +0.713260 +0.624550 +0.709514 +0.613855 +0.696825 +0.579544 +0.691450 +0.598035 +0.698082 +0.577741 +0.699490 +0.591059 +0.694840 +0.611491 +0.709242 +0.591929 +0.709786 +0.573356 +0.711547 +0.573164 +0.690391 +0.592259 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.729679 +0.576433 +0.729679 +0.576433 +0.729679 +0.576433 +0.729679 +0.576433 +0.729679 +0.585831 +0.755817 +0.547251 +0.726906 +0.605213 +0.724773 +0.576836 +0.737357 +0.614365 +0.747300 +0.576755 +0.745761 +0.596676 +0.753939 +0.584731 +0.743047 +0.589304 +0.710417 +0.589767 +0.760906 +0.595827 +0.736638 +0.607835 +0.756514 +0.582926 +0.733718 +0.592229 +0.757046 +0.626757 +0.751655 +0.614091 +0.757874 +0.611774 +0.778553 +0.608437 +0.752418 +0.623223 +0.746960 +0.623958 +0.785265 +0.636464 +0.786998 +0.633802 +0.780088 +0.643014 +0.781307 +0.629375 +0.782089 +0.641859 +0.790222 +0.627618 +0.795166 +0.625325 +0.796979 +0.641057 +0.791628 +0.645667 +0.792050 +0.642402 +0.783739 +0.637732 +0.786870 +0.645261 +0.790542 +0.637582 +0.786288 +0.651799 +0.789778 +0.662203 +0.796668 +0.663220 +0.801152 +0.670272 +0.808050 +0.675610 +0.812383 +0.654870 +0.808698 +0.672903 +0.811453 +0.681905 +0.833340 +0.674194 +0.819371 +0.665398 +0.816242 +0.655189 +0.813049 +0.663294 +0.814664 +0.671359 +0.826905 +0.676962 +0.822779 +0.658250 +0.816981 +0.667094 +0.818994 +0.657591 +0.812271 +0.657636 +0.822707 +0.669533 +0.819288 +0.666791 +0.834348 +0.656112 +0.806295 +0.667232 +0.823281 +0.666953 +0.819165 +0.667799 +0.824933 +0.668323 +0.813600 +0.673099 +0.836979 +0.671494 +0.839508 +0.668285 +0.833677 +0.665269 +0.843891 +0.669100 +0.823036 +0.657682 +0.836051 +0.663091 +0.839651 +0.657224 +0.825761 +0.664017 +0.828268 +0.656404 +0.833432 +0.655825 +0.823101 +0.671509 +0.832311 +0.674915 +0.819207 +0.657772 +0.825854 +0.646104 +0.818202 +0.648190 +0.817345 +0.645822 +0.817535 +0.663658 +0.819823 +0.647967 +0.829119 +0.647809 +0.821473 +0.654773 +0.810321 +0.655005 +0.817035 +0.641870 +0.809445 +0.635009 +0.808756 +0.650028 +0.814036 +0.649012 +0.821495 +0.647799 +0.820119 +0.645850 +0.805442 +0.628467 +0.813532 +0.628738 +0.795085 +0.613629 +0.808351 +0.633926 +0.799125 +0.654882 +0.775941 +0.648950 +0.776928 +0.645099 +0.777698 +0.652687 +0.794734 +0.655296 +0.764168 +0.624141 +0.764251 +0.617732 +0.788234 +0.639888 +0.754712 +0.629314 +0.768834 +0.610122 +0.761471 +0.605577 +0.757549 +0.626491 +0.763367 +0.598998 +0.756061 +0.596435 +0.757867 +0.597796 +0.772036 +0.608358 +0.748204 +0.603490 +0.741794 +0.602981 +0.738484 +0.611990 +0.707613 +0.611080 +0.718925 +0.584027 +0.709681 +0.604698 +0.710749 +0.587086 +0.707237 +0.573205 +0.701097 +0.576241 +0.696890 +0.564127 +0.716648 +0.554336 +0.702496 +0.576043 +0.714824 +0.584875 +0.718822 +0.529911 +0.704905 +0.571751 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.727812 +0.574260 +0.727812 +0.574260 +0.727812 +0.574260 +0.727812 +0.521220 +0.747923 +0.580207 +0.703951 +0.565753 +0.749037 +0.576818 +0.730449 +0.604177 +0.732908 +0.579899 +0.733176 +0.595236 +0.729022 +0.575700 +0.748807 +0.592764 +0.731005 +0.599373 +0.732545 +0.625817 +0.734081 +0.604797 +0.747456 +0.622671 +0.750273 +0.608963 +0.737149 +0.602598 +0.744372 +0.606530 +0.761256 +0.625804 +0.752081 +0.627124 +0.762612 +0.607957 +0.761306 +0.604960 +0.773664 +0.611983 +0.791012 +0.620551 +0.787850 +0.634739 +0.779451 +0.630071 +0.771641 +0.620437 +0.779965 +0.623593 +0.789882 +0.629388 +0.774963 +0.623206 +0.785076 +0.634173 +0.787519 +0.634437 +0.783782 +0.634656 +0.783091 +0.651468 +0.792364 +0.638557 +0.801817 +0.636814 +0.786417 +0.652653 +0.793142 +0.668170 +0.795814 +0.662557 +0.807424 +0.659889 +0.817149 +0.681278 +0.815421 +0.656584 +0.802885 +0.662442 +0.811582 +0.670776 +0.820483 +0.664610 +0.818751 +0.656589 +0.810088 +0.642961 +0.819623 +0.665866 +0.831308 +0.666516 +0.822467 +0.667144 +0.832417 +0.662682 +0.825376 +0.654891 +0.824080 +0.649341 +0.825669 +0.658703 +0.820044 +0.668698 +0.826424 +0.664882 +0.823054 +0.659116 +0.807235 +0.657765 +0.828632 +0.662710 +0.820322 +0.663238 +0.820590 +0.657692 +0.809730 +0.666050 +0.833179 +0.660172 +0.834279 +0.674175 +0.844121 +0.670319 +0.839630 +0.670760 +0.831400 +0.657663 +0.830503 +0.633391 +0.822774 +0.653315 +0.800372 +0.666177 +0.821577 +0.661963 +0.817146 +0.664196 +0.826833 +0.660732 +0.821724 +0.666680 +0.815493 +0.664303 +0.831327 +0.653732 +0.809686 +0.657857 +0.835688 +0.654251 +0.827195 +0.677336 +0.817113 +0.653256 +0.829604 +0.655079 +0.817515 +0.648944 +0.830809 +0.652377 +0.820723 +0.643072 +0.810399 +0.629364 +0.819062 +0.652009 +0.808717 +0.641575 +0.820334 +0.642344 +0.810669 +0.639729 +0.805447 +0.631838 +0.807794 +0.627047 +0.804445 +0.625558 +0.804756 +0.624730 +0.806641 +0.648110 +0.783794 +0.653584 +0.793939 +0.642048 +0.782101 +0.660878 +0.783343 +0.640455 +0.767443 +0.619162 +0.779071 +0.615268 +0.781129 +0.634452 +0.765049 +0.619666 +0.754751 +0.611163 +0.760572 +0.608961 +0.753103 +0.622067 +0.755800 +0.578218 +0.761682 +0.607053 +0.743193 +0.611482 +0.768216 +0.621431 +0.742128 +0.626187 +0.732942 +0.599100 +0.716867 +0.594985 +0.738651 +0.606130 +0.734320 +0.594515 +0.730303 +0.591168 +0.715797 +0.583412 +0.714765 +0.569795 +0.754108 +0.573481 +0.700801 +0.558449 +0.738559 +0.564971 +0.731353 +0.586598 +0.690928 +0.556013 +0.710041 +0.570113 +0.686032 +0.569194 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.723128 +0.570078 +0.723128 +0.570078 +0.723128 +0.570078 +0.723128 +0.553477 +0.725066 +0.568819 +0.713973 +0.553493 +0.735126 +0.587921 +0.720284 +0.570511 +0.743634 +0.566417 +0.710960 +0.579427 +0.736722 +0.587577 +0.753317 +0.598568 +0.727209 +0.586897 +0.740632 +0.599107 +0.755128 +0.590724 +0.746018 +0.617933 +0.757311 +0.629774 +0.752176 +0.588840 +0.743672 +0.601157 +0.744164 +0.598501 +0.758588 +0.612855 +0.764631 +0.621794 +0.765063 +0.615782 +0.760709 +0.601445 +0.774177 +0.618644 +0.774551 +0.615571 +0.777928 +0.640977 +0.776026 +0.616553 +0.768591 +0.620121 +0.778218 +0.625545 +0.778242 +0.635808 +0.791315 +0.650402 +0.797143 +0.649985 +0.783026 +0.654195 +0.786888 +0.646417 +0.795492 +0.631662 +0.781708 +0.630668 +0.787390 +0.661861 +0.809988 +0.661619 +0.808806 +0.655146 +0.826309 +0.666577 +0.810575 +0.687991 +0.814247 +0.658481 +0.806089 +0.671344 +0.809041 +0.667028 +0.814636 +0.665522 +0.829314 +0.671907 +0.817344 +0.675601 +0.818185 +0.661529 +0.837318 +0.662784 +0.820843 +0.662731 +0.827999 +0.665639 +0.819830 +0.662793 +0.832498 +0.666621 +0.817248 +0.664894 +0.814173 +0.670049 +0.834097 +0.691257 +0.828630 +0.669156 +0.826035 +0.664845 +0.823089 +0.665560 +0.828119 +0.678186 +0.824686 +0.665839 +0.820502 +0.681524 +0.841788 +0.675717 +0.848610 +0.690399 +0.842579 +0.673887 +0.839416 +0.668454 +0.838065 +0.660221 +0.826802 +0.654871 +0.824290 +0.651317 +0.819160 +0.653501 +0.822803 +0.662129 +0.829675 +0.658942 +0.824251 +0.665039 +0.834197 +0.677589 +0.831826 +0.678041 +0.822806 +0.658417 +0.825028 +0.656520 +0.844217 +0.666529 +0.832352 +0.661065 +0.831328 +0.651472 +0.814560 +0.672244 +0.827313 +0.658919 +0.828794 +0.663066 +0.836162 +0.656561 +0.822859 +0.645003 +0.824088 +0.662618 +0.825535 +0.654457 +0.821176 +0.653486 +0.815904 +0.647857 +0.813080 +0.637445 +0.807970 +0.631589 +0.811094 +0.640810 +0.822656 +0.639740 +0.803878 +0.644289 +0.792167 +0.660840 +0.786777 +0.656265 +0.778337 +0.654298 +0.788934 +0.643680 +0.779838 +0.644815 +0.783536 +0.625393 +0.805765 +0.636186 +0.776826 +0.631541 +0.778412 +0.621674 +0.771347 +0.622937 +0.755008 +0.643168 +0.774678 +0.601551 +0.772576 +0.615148 +0.756031 +0.607501 +0.783515 +0.602391 +0.759685 +0.592647 +0.735941 +0.605649 +0.715096 +0.575109 +0.748528 +0.587000 +0.731084 +0.609743 +0.707910 +0.582478 +0.745063 +0.567887 +0.716949 +0.597120 +0.707358 +0.577314 +0.724047 +0.570005 +0.698212 +0.564833 +0.718690 +0.564153 +0.717863 +0.583245 +0.721456 +0.533611 +0.719259 +0.570744 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.718809 +0.547085 +0.718809 +0.547085 +0.718809 +0.547085 +0.718809 +0.547085 +0.718809 +0.558813 +0.734892 +0.561831 +0.730114 +0.549020 +0.713357 +0.530405 +0.712956 +0.560833 +0.717242 +0.574255 +0.751610 +0.577052 +0.722099 +0.605198 +0.723584 +0.552905 +0.732509 +0.606801 +0.725873 +0.580498 +0.755115 +0.615292 +0.757327 +0.591189 +0.780283 +0.606335 +0.763039 +0.606312 +0.748401 +0.612466 +0.759031 +0.628408 +0.771130 +0.620038 +0.757265 +0.596447 +0.772213 +0.626757 +0.774528 +0.618865 +0.788105 +0.621525 +0.778835 +0.632800 +0.789100 +0.612372 +0.796220 +0.637813 +0.791159 +0.608606 +0.768904 +0.628226 +0.797761 +0.633210 +0.786780 +0.635365 +0.805978 +0.641964 +0.806049 +0.640217 +0.791289 +0.651754 +0.793005 +0.651966 +0.791088 +0.635807 +0.777843 +0.657311 +0.831292 +0.673376 +0.810986 +0.659523 +0.818130 +0.657328 +0.812673 +0.663718 +0.813432 +0.665090 +0.814906 +0.672263 +0.817294 +0.682391 +0.840050 +0.670994 +0.821465 +0.658386 +0.833471 +0.661388 +0.822938 +0.654010 +0.831931 +0.666732 +0.833902 +0.664109 +0.838802 +0.670707 +0.837885 +0.669310 +0.819417 +0.659915 +0.811528 +0.662277 +0.815057 +0.672342 +0.824875 +0.669838 +0.828436 +0.674375 +0.831824 +0.665821 +0.820138 +0.677036 +0.819160 +0.671266 +0.819835 +0.656886 +0.823356 +0.651812 +0.835199 +0.665086 +0.833809 +0.667396 +0.834005 +0.682968 +0.843815 +0.665898 +0.837773 +0.662565 +0.820934 +0.657056 +0.829467 +0.667360 +0.835245 +0.670177 +0.814777 +0.662390 +0.822644 +0.666714 +0.824491 +0.656117 +0.823990 +0.669503 +0.834397 +0.666914 +0.829698 +0.648562 +0.836710 +0.654247 +0.827691 +0.645328 +0.826991 +0.653430 +0.823936 +0.659892 +0.828230 +0.663722 +0.822159 +0.663695 +0.831989 +0.661449 +0.812371 +0.643467 +0.813326 +0.654757 +0.832257 +0.645796 +0.826096 +0.654856 +0.819606 +0.641674 +0.810195 +0.632656 +0.810910 +0.635133 +0.810019 +0.610369 +0.803783 +0.615484 +0.804110 +0.636310 +0.770903 +0.647558 +0.790562 +0.657682 +0.789051 +0.649019 +0.768332 +0.635779 +0.787402 +0.624298 +0.785137 +0.644062 +0.782037 +0.635520 +0.765164 +0.614848 +0.771124 +0.610806 +0.760953 +0.614533 +0.783016 +0.615196 +0.756602 +0.619817 +0.747357 +0.604770 +0.735326 +0.597214 +0.734415 +0.605899 +0.738613 +0.596891 +0.716654 +0.593797 +0.717316 +0.585705 +0.735214 +0.593806 +0.695176 +0.597737 +0.711222 +0.594103 +0.725737 +0.573487 +0.709170 +0.598755 +0.720425 +0.588447 +0.718627 +0.577493 +0.733523 +0.565495 +0.683306 +0.549424 +0.706158 +0.566104 +0.700741 +0.557436 +0.689467 +0.560341 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.733874 +0.547631 +0.733874 +0.547631 +0.733874 +0.547631 +0.733874 +0.547631 +0.733874 +0.540275 +0.683568 +0.534517 +0.707802 +0.565347 +0.740278 +0.543029 +0.753541 +0.567770 +0.741644 +0.550012 +0.750470 +0.584626 +0.745644 +0.570727 +0.710873 +0.572615 +0.717668 +0.624962 +0.742132 +0.596024 +0.725147 +0.596069 +0.754694 +0.596235 +0.735264 +0.607740 +0.758784 +0.589738 +0.767597 +0.608254 +0.771888 +0.611002 +0.775016 +0.621491 +0.775297 +0.586780 +0.768979 +0.608692 +0.771731 +0.630406 +0.778849 +0.618303 +0.799482 +0.630315 +0.775186 +0.611616 +0.776786 +0.618631 +0.779371 +0.645740 +0.798579 +0.625067 +0.779765 +0.623362 +0.779022 +0.632725 +0.802587 +0.640461 +0.785517 +0.625116 +0.802427 +0.655623 +0.795182 +0.657190 +0.801613 +0.626393 +0.793059 +0.647179 +0.810080 +0.660975 +0.809097 +0.673729 +0.808061 +0.651674 +0.816802 +0.666787 +0.810277 +0.657882 +0.812712 +0.655216 +0.809869 +0.669768 +0.833630 +0.661289 +0.831895 +0.650218 +0.811341 +0.651464 +0.818552 +0.652170 +0.832727 +0.664972 +0.833071 +0.654673 +0.830881 +0.670313 +0.845577 +0.645809 +0.807228 +0.658118 +0.809742 +0.669795 +0.813242 +0.667509 +0.826641 +0.668001 +0.839484 +0.667020 +0.825678 +0.660167 +0.830443 +0.673059 +0.818949 +0.673362 +0.823553 +0.637335 +0.820169 +0.650015 +0.826585 +0.657207 +0.839788 +0.658072 +0.819489 +0.669129 +0.839925 +0.663688 +0.830534 +0.665186 +0.832875 +0.645353 +0.822942 +0.657005 +0.825924 +0.661306 +0.825398 +0.644595 +0.820249 +0.664687 +0.823405 +0.663303 +0.815335 +0.658463 +0.815011 +0.653950 +0.835753 +0.663799 +0.830692 +0.658925 +0.818284 +0.644791 +0.825073 +0.663213 +0.808613 +0.649234 +0.833111 +0.643933 +0.825229 +0.654386 +0.810842 +0.657600 +0.816020 +0.637082 +0.808414 +0.632053 +0.818936 +0.640801 +0.813072 +0.646706 +0.812870 +0.637141 +0.815227 +0.635402 +0.816127 +0.633959 +0.799335 +0.624927 +0.807341 +0.624608 +0.806627 +0.653642 +0.773012 +0.633788 +0.781624 +0.634446 +0.781835 +0.648663 +0.768876 +0.640521 +0.775119 +0.628109 +0.791349 +0.634004 +0.776214 +0.632503 +0.770190 +0.619568 +0.755323 +0.606140 +0.773528 +0.609159 +0.759823 +0.620406 +0.770670 +0.616167 +0.758858 +0.613314 +0.743514 +0.603689 +0.753830 +0.611934 +0.725070 +0.604093 +0.751060 +0.609639 +0.740080 +0.599536 +0.739598 +0.616005 +0.730234 +0.569617 +0.724883 +0.582079 +0.714009 +0.595232 +0.708726 +0.569180 +0.711564 +0.557857 +0.713161 +0.593365 +0.701825 +0.555780 +0.689040 +0.564430 +0.695075 +0.562640 +0.705392 +0.541596 +0.727533 +0.560950 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.720550 +0.555675 +0.720550 +0.555675 +0.720550 +0.555675 +0.720550 +0.555675 +0.720550 +0.563048 +0.720739 +0.563307 +0.679051 +0.541473 +0.759907 +0.562246 +0.722693 +0.597956 +0.739625 +0.571642 +0.746118 +0.573912 +0.741582 +0.591265 +0.741406 +0.586921 +0.732118 +0.583260 +0.751430 +0.585296 +0.707189 +0.600206 +0.762006 +0.596032 +0.738279 +0.581793 +0.742219 +0.600458 +0.751596 +0.604308 +0.770481 +0.614855 +0.766255 +0.615763 +0.743839 +0.600602 +0.740921 +0.620853 +0.773568 +0.618279 +0.775092 +0.620777 +0.802243 +0.629504 +0.787965 +0.617760 +0.773803 +0.615378 +0.786150 +0.638185 +0.795424 +0.625164 +0.815644 +0.626191 +0.811274 +0.642138 +0.804126 +0.630120 +0.809420 +0.640730 +0.810016 +0.648288 +0.820687 +0.647399 +0.804198 +0.630955 +0.805243 +0.665914 +0.809473 +0.656300 +0.832425 +0.661998 +0.820256 +0.658172 +0.811843 +0.660084 +0.827433 +0.662374 +0.823630 +0.667571 +0.832688 +0.654353 +0.831157 +0.673266 +0.826597 +0.668514 +0.841328 +0.655648 +0.830691 +0.647644 +0.851373 +0.662712 +0.852048 +0.655853 +0.839496 +0.675145 +0.848062 +0.638304 +0.829044 +0.650593 +0.830890 +0.666624 +0.841953 +0.656911 +0.836552 +0.667444 +0.846887 +0.662371 +0.837859 +0.668245 +0.848444 +0.675391 +0.845944 +0.667194 +0.840015 +0.646265 +0.849827 +0.663599 +0.838953 +0.660014 +0.859788 +0.659012 +0.844075 +0.655547 +0.865009 +0.648927 +0.842082 +0.659339 +0.846027 +0.650285 +0.852639 +0.662878 +0.851405 +0.647198 +0.841235 +0.655576 +0.842201 +0.657301 +0.850304 +0.661729 +0.852255 +0.664032 +0.840649 +0.654736 +0.835964 +0.662341 +0.840741 +0.641735 +0.817900 +0.642654 +0.828457 +0.652303 +0.813843 +0.647646 +0.824993 +0.650115 +0.820591 +0.646702 +0.828723 +0.648368 +0.813958 +0.645573 +0.810514 +0.641339 +0.815658 +0.646360 +0.813723 +0.639058 +0.812494 +0.639977 +0.817947 +0.628899 +0.804889 +0.617743 +0.799941 +0.627160 +0.814874 +0.627140 +0.809819 +0.638547 +0.781615 +0.643730 +0.769466 +0.643072 +0.783736 +0.629565 +0.788005 +0.632183 +0.770511 +0.630670 +0.765479 +0.633522 +0.771963 +0.642573 +0.764569 +0.608854 +0.770128 +0.613977 +0.745398 +0.614781 +0.757890 +0.608103 +0.759447 +0.604306 +0.759828 +0.604430 +0.741838 +0.579432 +0.729555 +0.570164 +0.752556 +0.605536 +0.775601 +0.599662 +0.763360 +0.594431 +0.761836 +0.627734 +0.760905 +0.610245 +0.753740 +0.616225 +0.755270 +0.592630 +0.735437 +0.588090 +0.747041 +0.577825 +0.707759 +0.585030 +0.751967 +0.575366 +0.727932 +0.562911 +0.714224 +0.541742 +0.705169 +0.534750 +0.726569 +0.560006 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.735106 +0.553820 +0.735106 +0.553820 +0.735106 +0.553820 +0.735106 +0.553820 +0.735106 +0.536036 +0.752860 +0.549329 +0.707720 +0.572720 +0.752868 +0.539410 +0.744730 +0.584457 +0.774934 +0.547481 +0.783214 +0.553710 +0.750284 +0.574158 +0.755629 +0.579456 +0.750238 +0.563992 +0.750330 +0.582301 +0.736553 +0.586951 +0.789172 +0.582563 +0.737413 +0.569491 +0.764809 +0.607220 +0.765486 +0.584336 +0.786643 +0.587024 +0.768877 +0.587219 +0.794950 +0.587405 +0.788034 +0.609614 +0.782812 +0.590834 +0.782547 +0.604523 +0.799450 +0.613076 +0.805507 +0.605241 +0.797328 +0.597740 +0.791354 +0.608955 +0.817003 +0.612578 +0.814310 +0.612329 +0.802341 +0.619287 +0.810682 +0.610483 +0.809169 +0.621822 +0.810263 +0.623944 +0.826117 +0.632538 +0.805760 +0.617275 +0.815635 +0.639296 +0.816113 +0.643256 +0.826776 +0.656672 +0.813336 +0.634068 +0.822771 +0.642827 +0.834192 +0.647128 +0.830405 +0.642380 +0.842379 +0.647559 +0.835040 +0.649423 +0.824308 +0.631043 +0.833415 +0.637607 +0.836738 +0.637286 +0.850414 +0.633490 +0.846340 +0.644845 +0.845852 +0.645344 +0.838611 +0.640740 +0.838396 +0.635662 +0.851226 +0.647543 +0.847790 +0.658771 +0.848659 +0.650728 +0.842804 +0.653313 +0.839931 +0.650431 +0.837299 +0.636989 +0.844315 +0.662302 +0.843281 +0.631541 +0.853945 +0.636449 +0.847681 +0.643699 +0.852955 +0.639022 +0.859421 +0.634285 +0.845760 +0.627957 +0.844574 +0.635923 +0.846352 +0.638212 +0.851312 +0.634973 +0.846560 +0.641390 +0.855044 +0.637134 +0.846828 +0.640079 +0.852108 +0.647294 +0.830359 +0.647668 +0.842889 +0.622579 +0.820954 +0.638848 +0.845375 +0.629949 +0.844397 +0.634849 +0.840754 +0.630287 +0.834349 +0.626113 +0.848478 +0.640182 +0.855170 +0.632768 +0.852911 +0.620600 +0.826993 +0.621803 +0.840265 +0.620422 +0.833285 +0.629755 +0.835683 +0.626791 +0.840239 +0.624909 +0.842257 +0.604769 +0.831787 +0.611985 +0.822006 +0.606140 +0.830969 +0.611939 +0.821416 +0.634661 +0.807241 +0.639533 +0.800872 +0.626676 +0.806594 +0.621433 +0.815187 +0.634706 +0.808019 +0.619344 +0.793101 +0.616770 +0.805107 +0.616758 +0.776890 +0.598554 +0.800493 +0.587806 +0.772072 +0.584153 +0.784549 +0.596824 +0.775725 +0.589580 +0.800299 +0.583935 +0.776279 +0.570654 +0.780804 +0.584090 +0.768300 +0.613222 +0.767914 +0.610010 +0.731086 +0.575487 +0.753257 +0.597334 +0.751722 +0.591496 +0.731433 +0.584922 +0.755810 +0.558852 +0.713995 +0.565025 +0.734161 +0.553418 +0.752256 +0.554443 +0.726547 +0.507486 +0.728881 +0.560568 +0.732018 +0.540243 +0.710204 +0.548856 +0.750283 +0.536099 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.749164 +0.532901 +0.749164 +0.532901 +0.749164 +0.532901 +0.749164 +0.532901 +0.749164 +0.560609 +0.716198 +0.514056 +0.743528 +0.526357 +0.741830 +0.558289 +0.762136 +0.533867 +0.749393 +0.553139 +0.768637 +0.550837 +0.748629 +0.560329 +0.765445 +0.574048 +0.750057 +0.564121 +0.795637 +0.568000 +0.767262 +0.581988 +0.786688 +0.576957 +0.776174 +0.596540 +0.753595 +0.587144 +0.784530 +0.575378 +0.794800 +0.575322 +0.787248 +0.587658 +0.775713 +0.581830 +0.800732 +0.604129 +0.815275 +0.582947 +0.785806 +0.611074 +0.798373 +0.599000 +0.795130 +0.617935 +0.801797 +0.593473 +0.802927 +0.596972 +0.814909 +0.616995 +0.807699 +0.615964 +0.796698 +0.616407 +0.797286 +0.607482 +0.805684 +0.607301 +0.825770 +0.622352 +0.811291 +0.614572 +0.810946 +0.625095 +0.829250 +0.636488 +0.830327 +0.651380 +0.832056 +0.647806 +0.834150 +0.656961 +0.819912 +0.641794 +0.826680 +0.656464 +0.835680 +0.642151 +0.849435 +0.643435 +0.851099 +0.637608 +0.836096 +0.627024 +0.832339 +0.636018 +0.838717 +0.631283 +0.844947 +0.642197 +0.849357 +0.653603 +0.850241 +0.637020 +0.835916 +0.634836 +0.849894 +0.638327 +0.828282 +0.647557 +0.836784 +0.642708 +0.842129 +0.654894 +0.840923 +0.636904 +0.836554 +0.644192 +0.833257 +0.625171 +0.842227 +0.637321 +0.837258 +0.643131 +0.841074 +0.638651 +0.849795 +0.646431 +0.866592 +0.644544 +0.855275 +0.630197 +0.840658 +0.640607 +0.857051 +0.629664 +0.851970 +0.636785 +0.841720 +0.634901 +0.838083 +0.627419 +0.850472 +0.641345 +0.856820 +0.631541 +0.841263 +0.647084 +0.849083 +0.630535 +0.841090 +0.629421 +0.838289 +0.629035 +0.848159 +0.628839 +0.834253 +0.619009 +0.834175 +0.624966 +0.845391 +0.623693 +0.843994 +0.646389 +0.840445 +0.635309 +0.850002 +0.615916 +0.823266 +0.622248 +0.843851 +0.625973 +0.846166 +0.634665 +0.837173 +0.614344 +0.824908 +0.633257 +0.848651 +0.613628 +0.821617 +0.598642 +0.821352 +0.617113 +0.826578 +0.606443 +0.828942 +0.632858 +0.809509 +0.614061 +0.809384 +0.633480 +0.807211 +0.629867 +0.808601 +0.622827 +0.799790 +0.627544 +0.799682 +0.608147 +0.804375 +0.602044 +0.784224 +0.621847 +0.776244 +0.585583 +0.779065 +0.594693 +0.782990 +0.591999 +0.771524 +0.600687 +0.792743 +0.587463 +0.753806 +0.582826 +0.771052 +0.587876 +0.768112 +0.587289 +0.802503 +0.582580 +0.768050 +0.571232 +0.781278 +0.579215 +0.749107 +0.599622 +0.745566 +0.599757 +0.735659 +0.569628 +0.744355 +0.566127 +0.740377 +0.552029 +0.733560 +0.557759 +0.746354 +0.541600 +0.747525 +0.576351 +0.731332 +0.532406 +0.736087 +0.539089 +0.740672 +0.550119 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.749532 +0.537724 +0.749532 +0.537724 +0.749532 +0.537724 +0.749532 +0.537724 +0.749532 +0.549069 +0.759077 +0.525330 +0.756991 +0.546389 +0.741773 +0.541454 +0.749831 +0.551202 +0.737574 +0.565157 +0.770058 +0.566611 +0.769235 +0.558022 +0.746618 +0.538449 +0.775511 +0.574634 +0.765745 +0.575111 +0.770038 +0.583119 +0.766237 +0.576993 +0.779549 +0.583219 +0.762500 +0.579039 +0.763971 +0.588964 +0.773903 +0.588164 +0.787469 +0.585972 +0.778033 +0.602749 +0.811698 +0.597189 +0.792666 +0.597135 +0.791513 +0.610609 +0.813870 +0.610275 +0.795585 +0.596017 +0.801801 +0.592619 +0.797582 +0.612000 +0.818301 +0.611161 +0.818035 +0.609164 +0.807996 +0.605843 +0.796582 +0.605182 +0.827542 +0.627490 +0.827945 +0.608963 +0.805506 +0.607246 +0.810332 +0.638870 +0.809614 +0.637858 +0.819215 +0.645175 +0.818194 +0.642692 +0.832475 +0.623388 +0.827536 +0.641805 +0.827930 +0.658056 +0.827534 +0.648562 +0.850915 +0.639932 +0.838746 +0.640124 +0.834780 +0.631920 +0.829080 +0.623452 +0.843727 +0.636325 +0.851775 +0.646145 +0.838031 +0.637872 +0.845784 +0.645458 +0.847010 +0.643267 +0.844864 +0.642248 +0.833968 +0.649749 +0.845057 +0.634814 +0.839399 +0.648343 +0.853676 +0.637791 +0.837547 +0.637689 +0.839228 +0.634226 +0.835699 +0.638850 +0.845775 +0.639403 +0.870365 +0.640322 +0.862293 +0.654328 +0.865016 +0.652043 +0.865318 +0.641357 +0.846077 +0.650113 +0.865721 +0.649768 +0.856929 +0.647309 +0.855470 +0.642893 +0.849735 +0.624766 +0.855426 +0.645746 +0.858963 +0.646470 +0.851904 +0.648416 +0.851781 +0.644445 +0.847166 +0.633363 +0.834598 +0.639540 +0.848885 +0.640633 +0.834142 +0.637674 +0.848548 +0.626250 +0.845774 +0.637612 +0.854766 +0.629429 +0.842356 +0.632653 +0.848702 +0.624847 +0.838353 +0.635190 +0.841941 +0.648692 +0.854944 +0.616288 +0.832102 +0.621266 +0.829190 +0.635852 +0.843876 +0.614689 +0.817177 +0.603520 +0.820303 +0.631608 +0.843214 +0.606523 +0.828858 +0.647170 +0.815620 +0.640612 +0.803772 +0.628570 +0.810978 +0.608127 +0.806234 +0.628055 +0.802811 +0.613677 +0.816004 +0.607487 +0.791806 +0.596457 +0.782566 +0.605577 +0.791640 +0.592121 +0.776125 +0.596683 +0.785665 +0.581411 +0.784960 +0.592341 +0.773994 +0.574212 +0.774052 +0.594232 +0.769935 +0.568286 +0.771025 +0.556103 +0.752194 +0.581445 +0.741651 +0.584777 +0.740502 +0.557053 +0.739765 +0.583722 +0.737542 +0.548171 +0.726555 +0.555331 +0.731077 +0.547680 +0.749436 +0.545954 +0.750240 +0.566078 +0.745603 +0.527011 +0.723738 +0.532336 +0.750531 +0.497826 +0.718755 +0.561787 +0.703916 +0.519058 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.750780 +0.535577 +0.750780 +0.535577 +0.750780 +0.535577 +0.750780 +0.535577 +0.750780 +0.535577 +0.750780 +0.541860 +0.733875 +0.529071 +0.748139 +0.538019 +0.754316 +0.539641 +0.749886 +0.566379 +0.749578 +0.565217 +0.750994 +0.559552 +0.755262 +0.564130 +0.739086 +0.590164 +0.769112 +0.566448 +0.776460 +0.581072 +0.774652 +0.577698 +0.766764 +0.583770 +0.766447 +0.585544 +0.786238 +0.569397 +0.780593 +0.577924 +0.769491 +0.575264 +0.792869 +0.578363 +0.798151 +0.608988 +0.810698 +0.596522 +0.787359 +0.591713 +0.806915 +0.600429 +0.812074 +0.597863 +0.789645 +0.586296 +0.789355 +0.606691 +0.785283 +0.593474 +0.819098 +0.621908 +0.790485 +0.608288 +0.807011 +0.609006 +0.821652 +0.619710 +0.808065 +0.613013 +0.812240 +0.607850 +0.819404 +0.621860 +0.807844 +0.631301 +0.826027 +0.630833 +0.823228 +0.648448 +0.828020 +0.646854 +0.842562 +0.652797 +0.840392 +0.634260 +0.836270 +0.652455 +0.842485 +0.645118 +0.842173 +0.642514 +0.835345 +0.645158 +0.841902 +0.631620 +0.840812 +0.644007 +0.842635 +0.636643 +0.846221 +0.636853 +0.833034 +0.634490 +0.842874 +0.636892 +0.830160 +0.627659 +0.832912 +0.644764 +0.852228 +0.647250 +0.841195 +0.637891 +0.840139 +0.652345 +0.849332 +0.638653 +0.821967 +0.632193 +0.842317 +0.641270 +0.833517 +0.636369 +0.850549 +0.638313 +0.852217 +0.650887 +0.861443 +0.634429 +0.849209 +0.645066 +0.865792 +0.635288 +0.838520 +0.638268 +0.853671 +0.635466 +0.856851 +0.649496 +0.846980 +0.634623 +0.845994 +0.636907 +0.849802 +0.637737 +0.840467 +0.634071 +0.852936 +0.641823 +0.842152 +0.629813 +0.835596 +0.629223 +0.837162 +0.629664 +0.844374 +0.628601 +0.851499 +0.631013 +0.843919 +0.625672 +0.840029 +0.628367 +0.850443 +0.634071 +0.828971 +0.626351 +0.846602 +0.628959 +0.852911 +0.609350 +0.823104 +0.618425 +0.823684 +0.610615 +0.835167 +0.612469 +0.836798 +0.617232 +0.840631 +0.592311 +0.838499 +0.595311 +0.809520 +0.586937 +0.820650 +0.625780 +0.822610 +0.622975 +0.821384 +0.619111 +0.788570 +0.614617 +0.807189 +0.621478 +0.805352 +0.609308 +0.795877 +0.615199 +0.801901 +0.591932 +0.786349 +0.590555 +0.796038 +0.598844 +0.786955 +0.590084 +0.778078 +0.594867 +0.795265 +0.589790 +0.795599 +0.593905 +0.786408 +0.577221 +0.774891 +0.587634 +0.755633 +0.575066 +0.742850 +0.569781 +0.780125 +0.570351 +0.755314 +0.560501 +0.741127 +0.581457 +0.759930 +0.546504 +0.747623 +0.551518 +0.720067 +0.553183 +0.725497 +0.570861 +0.740360 +0.545211 +0.743468 +0.558925 +0.696594 +0.563119 +0.712238 +0.530936 +0.690911 +0.534244 +0.699482 +0.550993 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.756584 +0.521838 +0.756584 +0.521838 +0.756584 +0.521838 +0.756584 +0.521838 +0.756584 +0.495777 +0.752574 +0.532349 +0.773069 +0.511039 +0.766625 +0.522125 +0.730060 +0.548278 +0.750674 +0.514425 +0.778752 +0.545967 +0.753390 +0.534821 +0.785386 +0.566565 +0.730633 +0.579144 +0.770060 +0.589799 +0.791163 +0.553675 +0.778219 +0.545074 +0.792657 +0.571698 +0.790772 +0.576585 +0.768858 +0.583361 +0.777721 +0.589448 +0.765800 +0.588132 +0.788669 +0.569076 +0.776653 +0.580786 +0.805956 +0.620410 +0.792543 +0.601964 +0.797168 +0.606242 +0.805928 +0.617150 +0.811312 +0.596314 +0.793712 +0.595630 +0.778836 +0.584651 +0.807636 +0.607185 +0.808797 +0.601294 +0.809895 +0.636155 +0.806296 +0.616607 +0.812032 +0.604695 +0.796463 +0.614820 +0.805972 +0.624938 +0.824444 +0.635746 +0.832074 +0.629991 +0.823489 +0.645670 +0.832262 +0.640885 +0.826833 +0.651217 +0.834085 +0.623668 +0.842727 +0.643232 +0.837593 +0.641383 +0.834037 +0.614894 +0.833966 +0.634759 +0.841276 +0.635701 +0.840837 +0.626584 +0.843618 +0.634241 +0.837864 +0.621079 +0.830396 +0.627446 +0.830690 +0.634529 +0.835771 +0.643096 +0.834522 +0.626810 +0.848315 +0.651707 +0.839085 +0.629664 +0.845777 +0.645601 +0.837523 +0.657201 +0.845808 +0.627571 +0.830364 +0.637295 +0.842817 +0.637884 +0.846963 +0.641255 +0.856088 +0.637828 +0.844729 +0.626785 +0.831259 +0.629757 +0.834092 +0.625859 +0.843239 +0.629537 +0.839209 +0.622043 +0.850282 +0.646047 +0.839053 +0.634190 +0.842560 +0.625500 +0.825736 +0.626858 +0.850241 +0.636156 +0.835047 +0.626372 +0.836463 +0.631660 +0.837402 +0.627988 +0.845619 +0.629807 +0.842757 +0.626895 +0.854484 +0.622234 +0.826289 +0.624043 +0.842401 +0.621392 +0.850768 +0.624267 +0.830043 +0.630514 +0.843478 +0.633594 +0.831114 +0.609850 +0.822118 +0.605987 +0.828873 +0.610721 +0.824674 +0.617565 +0.849092 +0.624452 +0.821252 +0.592679 +0.832470 +0.592444 +0.832755 +0.597642 +0.817022 +0.622458 +0.803951 +0.623972 +0.798066 +0.613591 +0.816410 +0.607738 +0.801545 +0.623387 +0.798608 +0.621062 +0.819878 +0.621846 +0.798783 +0.597231 +0.776334 +0.592688 +0.778495 +0.587435 +0.771919 +0.599169 +0.764046 +0.589715 +0.779458 +0.582302 +0.773535 +0.610657 +0.792085 +0.578665 +0.775571 +0.561563 +0.764528 +0.583037 +0.758121 +0.586647 +0.751182 +0.588072 +0.737996 +0.570454 +0.764708 +0.560443 +0.749539 +0.572890 +0.729595 +0.566440 +0.724952 +0.559851 +0.725882 +0.571376 +0.773328 +0.556095 +0.735788 +0.537250 +0.752773 +0.535778 +0.723059 +0.504675 +0.743385 +0.522322 +0.729486 +0.525901 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.748161 +0.547953 +0.748161 +0.547953 +0.748161 +0.547953 +0.748161 +0.547953 +0.748161 +0.547953 +0.748161 +0.547953 +0.748161 +0.545749 +0.755923 +0.528684 +0.735465 +0.561451 +0.757804 +0.553724 +0.751213 +0.550412 +0.743356 +0.520450 +0.752266 +0.585332 +0.730208 +0.559511 +0.758844 +0.575117 +0.761226 +0.563433 +0.773243 +0.581048 +0.783532 +0.578264 +0.778287 +0.588024 +0.768274 +0.584589 +0.779582 +0.573974 +0.753139 +0.575015 +0.795519 +0.569493 +0.815533 +0.600199 +0.795616 +0.587433 +0.789723 +0.609152 +0.821701 +0.588940 +0.827406 +0.592227 +0.822399 +0.584028 +0.815969 +0.599542 +0.818695 +0.578561 +0.822163 +0.602760 +0.828360 +0.612605 +0.825559 +0.615395 +0.828378 +0.606402 +0.822487 +0.608481 +0.813335 +0.620540 +0.823671 +0.607700 +0.815735 +0.625813 +0.828620 +0.619176 +0.852036 +0.638294 +0.862772 +0.644925 +0.852544 +0.629142 +0.844281 +0.635459 +0.836404 +0.659422 +0.853149 +0.649211 +0.834314 +0.612028 +0.845220 +0.631964 +0.859360 +0.628167 +0.864303 +0.631813 +0.864854 +0.634592 +0.870787 +0.627503 +0.861352 +0.614515 +0.850572 +0.638484 +0.861150 +0.615789 +0.841877 +0.627664 +0.858793 +0.651527 +0.853195 +0.633306 +0.850978 +0.649689 +0.856653 +0.641725 +0.863979 +0.626816 +0.845018 +0.637395 +0.855152 +0.627000 +0.875971 +0.642979 +0.863199 +0.642963 +0.882773 +0.633437 +0.859100 +0.635897 +0.860395 +0.626037 +0.864571 +0.631926 +0.874093 +0.630408 +0.862337 +0.630208 +0.874736 +0.651757 +0.871000 +0.634412 +0.861741 +0.633820 +0.861724 +0.639341 +0.856731 +0.626068 +0.863300 +0.634684 +0.859997 +0.622538 +0.858575 +0.622096 +0.849502 +0.635456 +0.855829 +0.624396 +0.830213 +0.624509 +0.845002 +0.632197 +0.843722 +0.622698 +0.834223 +0.620520 +0.833300 +0.636408 +0.837012 +0.607312 +0.810133 +0.611019 +0.838534 +0.611178 +0.841476 +0.624604 +0.835080 +0.621080 +0.822685 +0.607243 +0.812889 +0.578532 +0.816263 +0.608771 +0.814660 +0.613721 +0.837731 +0.627879 +0.852620 +0.620396 +0.829956 +0.600655 +0.814278 +0.611299 +0.819862 +0.628254 +0.834901 +0.614213 +0.817094 +0.598814 +0.794717 +0.586614 +0.799932 +0.586062 +0.796794 +0.599814 +0.793683 +0.591400 +0.805859 +0.597254 +0.790373 +0.583733 +0.786518 +0.568551 +0.770523 +0.577424 +0.765839 +0.585384 +0.772905 +0.592300 +0.788876 +0.580422 +0.766155 +0.565086 +0.778335 +0.589747 +0.787713 +0.580698 +0.788272 +0.581919 +0.754472 +0.527313 +0.747943 +0.531782 +0.774761 +0.553762 +0.745940 +0.567135 +0.750744 +0.561388 +0.758420 +0.509090 +0.770026 +0.521660 +0.709672 +0.545871 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.756349 +0.525509 +0.756349 +0.525509 +0.756349 +0.525509 +0.756349 +0.525509 +0.756349 +0.511709 +0.761845 +0.551534 +0.761570 +0.512172 +0.751571 +0.512820 +0.755905 +0.549781 +0.781794 +0.505945 +0.748246 +0.540885 +0.764649 +0.554855 +0.783345 +0.550508 +0.764118 +0.532712 +0.786863 +0.563861 +0.798197 +0.548872 +0.793296 +0.548642 +0.795373 +0.547654 +0.788127 +0.547943 +0.782003 +0.571979 +0.790463 +0.552755 +0.776337 +0.569302 +0.808245 +0.587318 +0.829043 +0.572280 +0.824122 +0.574600 +0.812121 +0.592566 +0.825354 +0.591390 +0.817632 +0.594554 +0.830280 +0.608621 +0.820787 +0.556955 +0.810706 +0.574234 +0.819099 +0.589551 +0.824834 +0.580357 +0.844556 +0.580038 +0.845244 +0.586749 +0.816818 +0.585975 +0.833408 +0.596689 +0.838493 +0.590401 +0.830303 +0.600122 +0.840213 +0.620296 +0.840309 +0.617308 +0.846755 +0.619725 +0.867056 +0.625017 +0.849622 +0.627082 +0.852644 +0.624899 +0.861918 +0.623061 +0.853627 +0.600810 +0.858703 +0.624479 +0.849878 +0.610402 +0.863735 +0.605826 +0.859717 +0.628249 +0.881860 +0.613562 +0.861619 +0.616985 +0.857941 +0.614538 +0.851184 +0.601867 +0.847777 +0.619899 +0.866757 +0.627710 +0.857458 +0.612325 +0.860473 +0.639750 +0.857101 +0.619129 +0.854556 +0.616550 +0.852451 +0.611740 +0.847595 +0.605827 +0.870205 +0.625143 +0.878813 +0.628594 +0.875750 +0.615180 +0.869605 +0.622824 +0.871444 +0.619291 +0.867697 +0.612141 +0.865651 +0.615132 +0.859431 +0.610867 +0.865102 +0.634587 +0.868762 +0.620325 +0.869205 +0.619234 +0.880547 +0.628088 +0.858195 +0.609397 +0.860201 +0.608835 +0.865547 +0.610511 +0.857285 +0.599090 +0.868172 +0.615836 +0.863000 +0.622929 +0.865514 +0.618663 +0.863925 +0.611821 +0.873800 +0.599430 +0.866286 +0.604025 +0.868020 +0.608889 +0.868576 +0.595977 +0.860750 +0.607242 +0.860912 +0.608044 +0.863995 +0.605399 +0.865908 +0.595530 +0.856162 +0.576940 +0.859214 +0.571543 +0.842816 +0.581345 +0.854317 +0.609664 +0.818491 +0.603956 +0.828576 +0.606397 +0.828498 +0.599309 +0.822011 +0.613282 +0.825437 +0.594787 +0.827987 +0.597515 +0.821946 +0.583804 +0.815646 +0.575222 +0.797277 +0.565038 +0.800639 +0.585806 +0.802050 +0.585256 +0.804979 +0.571636 +0.786016 +0.560580 +0.787587 +0.564559 +0.792172 +0.561422 +0.779082 +0.575347 +0.760050 +0.591142 +0.779135 +0.560984 +0.804342 +0.561737 +0.759905 +0.583990 +0.790115 +0.559516 +0.778641 +0.543715 +0.738634 +0.553650 +0.737740 +0.521630 +0.748299 +0.542050 +0.756639 +0.498302 +0.735106 +0.518051 +0.778196 +0.544594 +0.741960 +0.500775 +0.725962 +0.520316 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.759402 +0.510859 +0.759402 +0.510859 +0.759402 +0.510859 +0.759402 +0.510859 +0.759402 +0.484721 +0.767389 +0.525472 +0.761415 +0.475082 +0.753855 +0.532022 +0.762936 +0.528586 +0.769716 +0.546018 +0.783430 +0.535746 +0.793453 +0.543508 +0.769150 +0.539052 +0.787416 +0.524768 +0.760329 +0.546169 +0.776275 +0.536840 +0.770515 +0.531850 +0.785976 +0.551209 +0.792960 +0.565385 +0.795116 +0.542563 +0.809808 +0.564279 +0.772747 +0.554981 +0.783435 +0.569630 +0.826152 +0.560000 +0.786629 +0.573978 +0.833904 +0.589378 +0.828099 +0.576612 +0.818403 +0.563220 +0.833410 +0.577141 +0.816189 +0.578977 +0.825872 +0.584090 +0.819035 +0.598891 +0.823354 +0.572580 +0.834784 +0.582475 +0.831315 +0.587355 +0.821414 +0.589103 +0.840844 +0.602714 +0.840654 +0.595429 +0.812255 +0.609325 +0.836915 +0.606393 +0.830355 +0.618093 +0.856241 +0.622503 +0.840934 +0.609716 +0.835609 +0.618637 +0.830766 +0.617258 +0.864060 +0.615742 +0.849998 +0.607274 +0.843475 +0.599586 +0.841917 +0.599963 +0.854370 +0.607629 +0.871884 +0.616250 +0.866832 +0.615435 +0.876576 +0.616177 +0.865267 +0.613565 +0.864805 +0.612957 +0.844410 +0.619716 +0.859795 +0.600320 +0.851172 +0.622699 +0.861900 +0.624707 +0.885324 +0.621900 +0.858230 +0.615018 +0.848442 +0.608280 +0.840423 +0.609280 +0.870972 +0.617920 +0.869829 +0.611558 +0.868707 +0.613563 +0.884161 +0.616974 +0.868151 +0.626794 +0.873040 +0.605210 +0.869127 +0.603085 +0.857249 +0.609287 +0.874834 +0.615916 +0.871169 +0.639764 +0.862509 +0.612389 +0.870163 +0.624550 +0.867209 +0.621131 +0.869012 +0.618518 +0.863640 +0.622437 +0.867031 +0.600424 +0.872355 +0.613310 +0.860535 +0.606905 +0.869339 +0.618654 +0.864130 +0.620042 +0.866646 +0.603792 +0.860628 +0.606890 +0.857463 +0.593260 +0.864689 +0.605838 +0.857626 +0.612851 +0.873481 +0.621506 +0.876116 +0.610927 +0.845729 +0.609201 +0.858283 +0.582514 +0.841657 +0.583698 +0.845746 +0.591720 +0.846912 +0.601410 +0.835821 +0.606894 +0.834991 +0.608427 +0.846515 +0.594056 +0.816880 +0.606487 +0.827899 +0.602397 +0.827567 +0.618378 +0.833784 +0.587173 +0.825422 +0.590570 +0.809787 +0.558521 +0.815360 +0.582574 +0.790687 +0.572303 +0.808105 +0.574829 +0.801679 +0.583470 +0.799005 +0.571817 +0.798234 +0.537722 +0.768953 +0.594598 +0.769623 +0.558181 +0.767519 +0.573106 +0.766238 +0.568104 +0.787551 +0.590860 +0.755317 +0.556234 +0.766079 +0.558263 +0.749521 +0.534820 +0.730020 +0.525072 +0.723894 +0.500412 +0.739598 +0.528947 +0.729989 +0.511787 +0.752979 +0.525202 +0.748654 +0.530813 +0.766630 +0.521979 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.761857 +0.499572 +0.761857 +0.499572 +0.761857 +0.499572 +0.761857 +0.499572 +0.761857 +0.542425 +0.739488 +0.477697 +0.778043 +0.528715 +0.753658 +0.492303 +0.753871 +0.558827 +0.785694 +0.557890 +0.788751 +0.563619 +0.770228 +0.552394 +0.783569 +0.540739 +0.780626 +0.541535 +0.753665 +0.525681 +0.769756 +0.553370 +0.792162 +0.550955 +0.798000 +0.552932 +0.784144 +0.558380 +0.804063 +0.557327 +0.797873 +0.550459 +0.799367 +0.556505 +0.793981 +0.560535 +0.812065 +0.564158 +0.799510 +0.595212 +0.823061 +0.577135 +0.815468 +0.571331 +0.813397 +0.575666 +0.829220 +0.588614 +0.815872 +0.572904 +0.836136 +0.590400 +0.813104 +0.594937 +0.821238 +0.570841 +0.826399 +0.589212 +0.838600 +0.573978 +0.813902 +0.599647 +0.834507 +0.597092 +0.841009 +0.595181 +0.824437 +0.612125 +0.845182 +0.609764 +0.838790 +0.608398 +0.855053 +0.620269 +0.862399 +0.613579 +0.851313 +0.602085 +0.840340 +0.614791 +0.856439 +0.620255 +0.842791 +0.597172 +0.847988 +0.610672 +0.849024 +0.612494 +0.850452 +0.610865 +0.875778 +0.620038 +0.877248 +0.632003 +0.868305 +0.623869 +0.881225 +0.618760 +0.859396 +0.614579 +0.858360 +0.618001 +0.844970 +0.611305 +0.865028 +0.617760 +0.865058 +0.622054 +0.865991 +0.619916 +0.860970 +0.624908 +0.858482 +0.618765 +0.863020 +0.597813 +0.876973 +0.606634 +0.879107 +0.615097 +0.879050 +0.610452 +0.871719 +0.609155 +0.872243 +0.615361 +0.869484 +0.617392 +0.873421 +0.617387 +0.863049 +0.594748 +0.861790 +0.619540 +0.871742 +0.613458 +0.885092 +0.631452 +0.883901 +0.622357 +0.887129 +0.628523 +0.867733 +0.627144 +0.873226 +0.614997 +0.863916 +0.612069 +0.873675 +0.607750 +0.871785 +0.617727 +0.875576 +0.610059 +0.869514 +0.623409 +0.868582 +0.623134 +0.874508 +0.613845 +0.868351 +0.604673 +0.872045 +0.594382 +0.861987 +0.596606 +0.865775 +0.598570 +0.863580 +0.613023 +0.860595 +0.615898 +0.854802 +0.587483 +0.858795 +0.581020 +0.844153 +0.581770 +0.855020 +0.593341 +0.815819 +0.591144 +0.828763 +0.599617 +0.817608 +0.593931 +0.834555 +0.597665 +0.821118 +0.603188 +0.822581 +0.595313 +0.819109 +0.593557 +0.832920 +0.590378 +0.822006 +0.561816 +0.801592 +0.560610 +0.817361 +0.564569 +0.799166 +0.553744 +0.787276 +0.547305 +0.800871 +0.564843 +0.780103 +0.567632 +0.768989 +0.554882 +0.777811 +0.554157 +0.784711 +0.572528 +0.792021 +0.560242 +0.773311 +0.559755 +0.758425 +0.567236 +0.761923 +0.507521 +0.742274 +0.557580 +0.752241 +0.527230 +0.748094 +0.506937 +0.737720 +0.532836 +0.735195 +0.502545 +0.746126 +0.496364 +0.768788 +0.501840 +0.761435 +0.510582 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.762616 +0.558596 +0.762616 +0.558596 +0.762616 +0.558596 +0.762616 +0.558596 +0.762616 +0.558596 +0.762616 +0.521751 +0.798604 +0.554218 +0.756155 +0.555597 +0.744059 +0.565972 +0.787633 +0.526538 +0.780138 +0.500117 +0.773679 +0.529796 +0.800308 +0.542005 +0.782430 +0.569787 +0.797515 +0.568478 +0.780099 +0.549378 +0.778838 +0.554744 +0.777857 +0.563458 +0.791136 +0.557171 +0.788730 +0.569760 +0.782241 +0.564335 +0.792598 +0.581232 +0.799964 +0.576738 +0.830958 +0.581614 +0.815250 +0.600172 +0.809320 +0.612753 +0.824019 +0.580973 +0.822049 +0.579190 +0.812346 +0.597873 +0.841268 +0.588900 +0.814340 +0.576565 +0.825851 +0.592397 +0.818908 +0.573392 +0.842624 +0.596916 +0.834379 +0.597577 +0.835630 +0.603092 +0.826931 +0.609448 +0.823276 +0.561557 +0.834989 +0.618284 +0.863448 +0.626281 +0.839082 +0.636797 +0.856100 +0.624576 +0.860741 +0.633308 +0.850412 +0.614770 +0.867396 +0.619456 +0.850276 +0.632328 +0.857533 +0.615557 +0.858366 +0.616980 +0.868500 +0.616189 +0.857847 +0.616678 +0.861846 +0.609060 +0.858016 +0.615085 +0.871243 +0.642145 +0.876905 +0.620648 +0.857018 +0.605821 +0.867058 +0.630797 +0.855395 +0.632583 +0.877193 +0.615913 +0.851946 +0.620485 +0.855845 +0.607768 +0.866466 +0.612143 +0.854955 +0.618844 +0.873353 +0.613565 +0.867300 +0.609571 +0.878502 +0.622162 +0.890001 +0.622786 +0.882897 +0.618246 +0.873849 +0.628822 +0.884268 +0.626710 +0.868753 +0.605267 +0.866381 +0.624468 +0.870161 +0.619071 +0.871173 +0.615192 +0.864836 +0.622238 +0.870949 +0.610318 +0.861778 +0.612730 +0.878278 +0.628711 +0.869375 +0.620094 +0.865782 +0.614748 +0.869300 +0.606935 +0.863731 +0.607859 +0.854374 +0.604572 +0.866090 +0.606983 +0.861059 +0.616630 +0.871761 +0.600301 +0.859523 +0.600848 +0.872634 +0.595709 +0.852924 +0.595579 +0.856496 +0.597976 +0.841636 +0.603955 +0.866131 +0.604350 +0.838169 +0.583310 +0.856169 +0.576189 +0.839766 +0.569900 +0.835716 +0.592627 +0.818724 +0.593724 +0.824391 +0.592766 +0.827721 +0.597162 +0.822152 +0.594706 +0.825267 +0.597135 +0.821170 +0.593041 +0.806104 +0.578629 +0.810752 +0.573465 +0.811290 +0.572614 +0.798579 +0.556492 +0.804886 +0.555118 +0.792697 +0.562195 +0.801798 +0.579473 +0.804608 +0.563808 +0.783847 +0.549251 +0.776068 +0.559164 +0.778553 +0.568607 +0.775619 +0.546216 +0.789937 +0.539659 +0.768442 +0.552576 +0.772355 +0.563473 +0.757088 +0.526258 +0.746913 +0.512706 +0.731187 +0.515351 +0.783343 +0.500502 +0.748672 +0.504914 +0.719827 +0.494955 +0.755329 +0.522836 +0.736513 +0.497134 +0.718851 +0.507569 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.762531 +0.533938 +0.762531 +0.533938 +0.762531 +0.533938 +0.762531 +0.533938 +0.762531 +0.500195 +0.761152 +0.524074 +0.771829 +0.531538 +0.755739 +0.546203 +0.760026 +0.523322 +0.767769 +0.540842 +0.750581 +0.519623 +0.758513 +0.525771 +0.757615 +0.560222 +0.781111 +0.548162 +0.782970 +0.573911 +0.782136 +0.524370 +0.774784 +0.537956 +0.765505 +0.560928 +0.852021 +0.563771 +0.825182 +0.575371 +0.823148 +0.539100 +0.772988 +0.580111 +0.803415 +0.572241 +0.807930 +0.584411 +0.826190 +0.577322 +0.811834 +0.578970 +0.817705 +0.563945 +0.826635 +0.587544 +0.842088 +0.578766 +0.813460 +0.594646 +0.819142 +0.561891 +0.806798 +0.594211 +0.827399 +0.573477 +0.848922 +0.597136 +0.841197 +0.577327 +0.840780 +0.603426 +0.829019 +0.594231 +0.849530 +0.588616 +0.827481 +0.625947 +0.869242 +0.615007 +0.863953 +0.611222 +0.840179 +0.605622 +0.852685 +0.626230 +0.836178 +0.606132 +0.846803 +0.618242 +0.880599 +0.617726 +0.867337 +0.610944 +0.865759 +0.619796 +0.873132 +0.620589 +0.864324 +0.617412 +0.876943 +0.603799 +0.865471 +0.607193 +0.877434 +0.620002 +0.868874 +0.622064 +0.866395 +0.603289 +0.851260 +0.609737 +0.869184 +0.621937 +0.883132 +0.621085 +0.856515 +0.637900 +0.870457 +0.624909 +0.851609 +0.614336 +0.862579 +0.613834 +0.875216 +0.609322 +0.869667 +0.610760 +0.860999 +0.605967 +0.886364 +0.609529 +0.872724 +0.621448 +0.875533 +0.613976 +0.885958 +0.617582 +0.858667 +0.608481 +0.872566 +0.615096 +0.858672 +0.620632 +0.866750 +0.598364 +0.867149 +0.610962 +0.861929 +0.605242 +0.852858 +0.606783 +0.855967 +0.618975 +0.870881 +0.627214 +0.864662 +0.604733 +0.861907 +0.601572 +0.863665 +0.601088 +0.867526 +0.607317 +0.878213 +0.602047 +0.865047 +0.600528 +0.869897 +0.607295 +0.883006 +0.609856 +0.857815 +0.589006 +0.845287 +0.600959 +0.852057 +0.593367 +0.845324 +0.599703 +0.865849 +0.591567 +0.856543 +0.571074 +0.853191 +0.595385 +0.845847 +0.574215 +0.839387 +0.585235 +0.813948 +0.604204 +0.823602 +0.593970 +0.824698 +0.589568 +0.840263 +0.596799 +0.817756 +0.596311 +0.808850 +0.582682 +0.808674 +0.576351 +0.789641 +0.578136 +0.792585 +0.558761 +0.803081 +0.552812 +0.816069 +0.560161 +0.801742 +0.563674 +0.797072 +0.559963 +0.795562 +0.549767 +0.790829 +0.548644 +0.783626 +0.560751 +0.784080 +0.553070 +0.787823 +0.550040 +0.747663 +0.545707 +0.774708 +0.569676 +0.768102 +0.533879 +0.776891 +0.556927 +0.754584 +0.542646 +0.762255 +0.535121 +0.747325 +0.520454 +0.752330 +0.530062 +0.759381 +0.497473 +0.749439 +0.521056 +0.699510 +0.482640 +0.738209 +0.516197 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.547024 +0.804435 +0.557354 +0.818232 +0.569918 +0.787576 +0.542791 +0.827924 +0.528362 +0.797804 +0.558079 +0.804689 +0.567877 +0.837573 +0.584236 +0.809155 +0.557890 +0.816969 +0.538342 +0.793334 +0.558065 +0.811025 +0.570460 +0.827128 +0.583071 +0.835529 +0.556712 +0.813029 +0.584815 +0.843808 +0.584606 +0.858740 +0.582685 +0.842780 +0.576674 +0.841997 +0.575394 +0.824144 +0.581218 +0.849858 +0.589528 +0.834210 +0.594329 +0.860536 +0.585216 +0.863548 +0.597468 +0.831237 +0.588663 +0.837136 +0.599723 +0.858265 +0.604418 +0.847881 +0.631492 +0.867301 +0.621258 +0.880448 +0.619506 +0.872054 +0.601630 +0.877571 +0.612533 +0.864733 +0.613939 +0.871560 +0.623294 +0.871402 +0.625217 +0.875109 +0.605663 +0.873099 +0.607849 +0.885496 +0.627705 +0.892216 +0.608778 +0.886463 +0.611719 +0.880588 +0.609803 +0.891380 +0.615103 +0.884122 +0.617258 +0.872630 +0.610555 +0.875848 +0.610544 +0.879587 +0.620194 +0.881647 +0.614473 +0.869155 +0.620640 +0.893579 +0.614861 +0.886607 +0.617139 +0.891849 +0.618513 +0.884295 +0.614843 +0.902464 +0.607575 +0.887586 +0.616136 +0.890423 +0.631543 +0.885930 +0.617381 +0.889171 +0.609876 +0.905302 +0.616406 +0.898603 +0.604818 +0.885973 +0.615252 +0.875881 +0.622769 +0.869322 +0.610443 +0.861875 +0.608683 +0.862891 +0.620059 +0.860948 +0.613156 +0.872377 +0.611245 +0.872725 +0.608479 +0.866779 +0.615762 +0.873680 +0.604098 +0.862202 +0.597903 +0.860752 +0.618299 +0.875714 +0.614964 +0.847883 +0.601867 +0.858698 +0.603449 +0.862325 +0.598404 +0.849796 +0.597735 +0.871203 +0.592429 +0.881424 +0.593142 +0.880289 +0.592490 +0.870538 +0.588911 +0.873009 +0.584282 +0.881263 +0.585691 +0.868493 +0.584637 +0.870204 +0.577033 +0.846556 +0.606171 +0.853270 +0.593908 +0.852339 +0.591034 +0.849337 +0.615693 +0.848253 +0.585764 +0.821419 +0.585559 +0.832422 +0.560192 +0.833392 +0.568846 +0.831950 +0.565666 +0.816280 +0.579665 +0.817794 +0.577122 +0.825570 +0.567111 +0.817497 +0.574189 +0.811434 +0.563476 +0.807789 +0.546986 +0.805297 +0.563214 +0.807930 +0.573283 +0.786199 +0.551032 +0.769274 +0.541752 +0.786165 +0.543574 +0.812362 +0.556799 +0.780567 +0.547106 +0.777261 +0.527005 +0.756786 +0.519996 +0.798812 +0.543155 +0.780155 +0.519145 +0.779063 +0.546266 +0.764499 +0.510149 +0.764212 +0.474630 +0.770585 +0.525187 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.795077 +0.508588 +0.795077 +0.508588 +0.795077 +0.508588 +0.795077 +0.508588 +0.795077 +0.486975 +0.779478 +0.528965 +0.790929 +0.492513 +0.801140 +0.504285 +0.793161 +0.503834 +0.782939 +0.521727 +0.797291 +0.522813 +0.789589 +0.507925 +0.804894 +0.533867 +0.832768 +0.538850 +0.808446 +0.540137 +0.794259 +0.541129 +0.826196 +0.532346 +0.799371 +0.545108 +0.827477 +0.542275 +0.808643 +0.533549 +0.814445 +0.540012 +0.830926 +0.555838 +0.827671 +0.545680 +0.829613 +0.544265 +0.828525 +0.567656 +0.832057 +0.556352 +0.829408 +0.546610 +0.844256 +0.553158 +0.850204 +0.570737 +0.854151 +0.557909 +0.854055 +0.547617 +0.844737 +0.576491 +0.842010 +0.573166 +0.875611 +0.586515 +0.854016 +0.585132 +0.841329 +0.564060 +0.872125 +0.584548 +0.863599 +0.574579 +0.848259 +0.608050 +0.877064 +0.611458 +0.891496 +0.608459 +0.871063 +0.576341 +0.873733 +0.602180 +0.865219 +0.607748 +0.874833 +0.600500 +0.873157 +0.599338 +0.882524 +0.606785 +0.885618 +0.590553 +0.886679 +0.606817 +0.878172 +0.587354 +0.895391 +0.607186 +0.893567 +0.602769 +0.884684 +0.604904 +0.885718 +0.592860 +0.881966 +0.594254 +0.870760 +0.591080 +0.889545 +0.602310 +0.891322 +0.590197 +0.860261 +0.590424 +0.878560 +0.595520 +0.879193 +0.600203 +0.890434 +0.594595 +0.874835 +0.590678 +0.894449 +0.595592 +0.902267 +0.601614 +0.912377 +0.614122 +0.892525 +0.592285 +0.901995 +0.589616 +0.896392 +0.590641 +0.892970 +0.602761 +0.892393 +0.579772 +0.905406 +0.614978 +0.891302 +0.598904 +0.892262 +0.596282 +0.892868 +0.605447 +0.879368 +0.597730 +0.892832 +0.589365 +0.883176 +0.599385 +0.883084 +0.582259 +0.880560 +0.580200 +0.876392 +0.584069 +0.882457 +0.588333 +0.894452 +0.593598 +0.891048 +0.590538 +0.889636 +0.581830 +0.876905 +0.575070 +0.879535 +0.595997 +0.878126 +0.571095 +0.886654 +0.569732 +0.865297 +0.565955 +0.878532 +0.576055 +0.879497 +0.563428 +0.873643 +0.570365 +0.869755 +0.568195 +0.852604 +0.584635 +0.835564 +0.588383 +0.835895 +0.588373 +0.846079 +0.576662 +0.846751 +0.576486 +0.860276 +0.586217 +0.834573 +0.552115 +0.824810 +0.562143 +0.837526 +0.556546 +0.820627 +0.554227 +0.811362 +0.552725 +0.820443 +0.546621 +0.826281 +0.536859 +0.836243 +0.555694 +0.803064 +0.532139 +0.822458 +0.545315 +0.804757 +0.547520 +0.783949 +0.547205 +0.814672 +0.536716 +0.785710 +0.522998 +0.790028 +0.501685 +0.794855 +0.510632 +0.785490 +0.538254 +0.750707 +0.500067 +0.766388 +0.526451 +0.752297 +0.526157 +0.771895 +0.492144 +0.796309 +0.494171 +0.761826 +0.474443 +0.750563 +0.437251 +0.713575 +0.486919 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.783864 +0.488609 +0.783864 +0.488609 +0.783864 +0.488609 +0.783864 +0.488609 +0.783864 +0.484509 +0.775065 +0.477817 +0.816314 +0.478790 +0.768785 +0.509220 +0.766492 +0.477468 +0.812152 +0.502811 +0.802059 +0.537912 +0.801817 +0.507886 +0.781722 +0.517531 +0.818088 +0.526612 +0.813428 +0.522766 +0.808612 +0.536501 +0.807341 +0.524384 +0.817860 +0.540602 +0.843927 +0.535228 +0.802209 +0.526828 +0.814837 +0.540105 +0.842328 +0.551685 +0.851158 +0.542309 +0.828186 +0.559830 +0.847417 +0.563731 +0.854424 +0.550537 +0.828840 +0.562086 +0.833014 +0.582355 +0.855500 +0.568956 +0.859860 +0.580192 +0.852694 +0.561614 +0.850473 +0.563156 +0.845438 +0.579334 +0.859904 +0.579032 +0.835561 +0.562692 +0.859403 +0.563692 +0.846293 +0.572953 +0.853718 +0.579340 +0.838244 +0.599914 +0.872851 +0.607124 +0.864485 +0.598783 +0.877210 +0.587999 +0.857076 +0.591172 +0.869817 +0.605503 +0.890753 +0.598436 +0.881381 +0.607955 +0.884851 +0.591635 +0.889444 +0.592239 +0.874609 +0.600910 +0.875122 +0.575206 +0.888350 +0.607999 +0.902166 +0.607732 +0.885218 +0.603636 +0.881553 +0.590831 +0.886684 +0.587817 +0.877277 +0.585979 +0.885411 +0.597366 +0.880096 +0.592791 +0.866162 +0.593262 +0.882273 +0.584043 +0.881056 +0.606707 +0.893272 +0.600749 +0.872144 +0.595459 +0.898524 +0.594419 +0.887102 +0.601113 +0.902921 +0.609675 +0.902603 +0.595585 +0.889693 +0.596687 +0.899679 +0.588773 +0.895339 +0.594477 +0.909129 +0.599527 +0.882118 +0.596271 +0.881386 +0.593283 +0.879984 +0.593063 +0.893680 +0.601941 +0.885314 +0.589077 +0.884677 +0.581387 +0.880740 +0.592592 +0.896171 +0.571242 +0.879135 +0.579590 +0.875594 +0.592318 +0.882804 +0.582739 +0.892864 +0.599444 +0.901447 +0.589296 +0.889727 +0.595128 +0.890335 +0.578417 +0.884267 +0.581318 +0.886483 +0.584955 +0.896732 +0.588389 +0.867372 +0.587419 +0.896331 +0.571331 +0.877411 +0.578135 +0.876098 +0.584855 +0.883590 +0.563657 +0.868200 +0.580831 +0.853841 +0.587371 +0.865516 +0.605270 +0.842538 +0.585869 +0.849578 +0.578066 +0.842178 +0.578987 +0.850690 +0.572271 +0.836189 +0.555332 +0.841796 +0.566311 +0.820486 +0.543820 +0.828736 +0.547325 +0.828037 +0.547277 +0.832539 +0.555658 +0.820422 +0.552623 +0.805176 +0.555852 +0.837641 +0.536542 +0.803105 +0.536737 +0.792042 +0.559298 +0.798012 +0.537796 +0.804154 +0.533895 +0.807400 +0.521984 +0.801553 +0.516680 +0.776857 +0.530847 +0.795204 +0.514904 +0.804187 +0.536077 +0.780318 +0.537385 +0.800407 +0.493343 +0.817998 +0.491219 +0.768488 +0.483920 +0.723938 +0.494037 +0.732943 +0.489494 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.789875 +0.519156 +0.789875 +0.519156 +0.789875 +0.519156 +0.789875 +0.519156 +0.789875 +0.468069 +0.773861 +0.510546 +0.785106 +0.527330 +0.781666 +0.519592 +0.802854 +0.490103 +0.817994 +0.545699 +0.798162 +0.540784 +0.782119 +0.533611 +0.794617 +0.541347 +0.813055 +0.542944 +0.815675 +0.522775 +0.818939 +0.535610 +0.817625 +0.534646 +0.807127 +0.546386 +0.827833 +0.539688 +0.808260 +0.532346 +0.792928 +0.534826 +0.844945 +0.560188 +0.834863 +0.546569 +0.837476 +0.581546 +0.845690 +0.565980 +0.844762 +0.539450 +0.846641 +0.580531 +0.852030 +0.567020 +0.828249 +0.562524 +0.855286 +0.548970 +0.868790 +0.565241 +0.860007 +0.574576 +0.867380 +0.560040 +0.854440 +0.575965 +0.854448 +0.560404 +0.864677 +0.579099 +0.866877 +0.563330 +0.844119 +0.570391 +0.844274 +0.586067 +0.869757 +0.601348 +0.871373 +0.597043 +0.868844 +0.602564 +0.864710 +0.603474 +0.858974 +0.614334 +0.881054 +0.600574 +0.892957 +0.598388 +0.863842 +0.597114 +0.893352 +0.608286 +0.882389 +0.584495 +0.890783 +0.580854 +0.888844 +0.599293 +0.903675 +0.607308 +0.903019 +0.599971 +0.879928 +0.586946 +0.886436 +0.590542 +0.867905 +0.586369 +0.880801 +0.611809 +0.885086 +0.601830 +0.873848 +0.601428 +0.894945 +0.595756 +0.893652 +0.599637 +0.904196 +0.614958 +0.884671 +0.595806 +0.911625 +0.588885 +0.901186 +0.615978 +0.914856 +0.606731 +0.902780 +0.600641 +0.889293 +0.583498 +0.908317 +0.602299 +0.912409 +0.597554 +0.901724 +0.597097 +0.879371 +0.590275 +0.884770 +0.602913 +0.902564 +0.610515 +0.897680 +0.609966 +0.885356 +0.592797 +0.885563 +0.596349 +0.890461 +0.598707 +0.901801 +0.584744 +0.889281 +0.587626 +0.898160 +0.592944 +0.887426 +0.608255 +0.901821 +0.601934 +0.895878 +0.593665 +0.882445 +0.597772 +0.890360 +0.590415 +0.879069 +0.584420 +0.871440 +0.592053 +0.883530 +0.591878 +0.891469 +0.583861 +0.872556 +0.586939 +0.884033 +0.567371 +0.870717 +0.558812 +0.870506 +0.570196 +0.887275 +0.587410 +0.841395 +0.598433 +0.861775 +0.588700 +0.851381 +0.578564 +0.866070 +0.577437 +0.853161 +0.560808 +0.854735 +0.558668 +0.839609 +0.568962 +0.831840 +0.545249 +0.839210 +0.567647 +0.818818 +0.548310 +0.829575 +0.535487 +0.823736 +0.558235 +0.825072 +0.561661 +0.815216 +0.552398 +0.826073 +0.537719 +0.813611 +0.551286 +0.820223 +0.531353 +0.804506 +0.527194 +0.817348 +0.537280 +0.781280 +0.527858 +0.767677 +0.520599 +0.768204 +0.530775 +0.775042 +0.508088 +0.767773 +0.526630 +0.787791 +0.523796 +0.763577 +0.495047 +0.764925 +0.491426 +0.754624 +0.487638 +0.779498 +0.493309 +0.738405 +0.491370 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.788646 +0.499950 +0.788646 +0.499950 +0.788646 +0.499950 +0.788646 +0.499950 +0.788646 +0.455579 +0.798606 +0.498151 +0.754278 +0.516748 +0.816545 +0.484949 +0.795116 +0.512816 +0.779913 +0.526049 +0.828635 +0.525992 +0.791696 +0.534076 +0.802517 +0.546660 +0.773681 +0.555295 +0.817734 +0.533613 +0.812316 +0.509489 +0.828485 +0.551150 +0.837855 +0.546311 +0.836482 +0.552378 +0.837082 +0.556808 +0.818134 +0.540581 +0.848801 +0.562882 +0.810790 +0.545756 +0.831442 +0.562199 +0.860336 +0.559279 +0.854912 +0.554709 +0.857009 +0.568847 +0.877373 +0.578299 +0.825712 +0.566018 +0.871556 +0.564454 +0.855599 +0.563205 +0.863161 +0.573118 +0.868960 +0.579840 +0.861340 +0.566934 +0.868621 +0.595847 +0.850644 +0.593288 +0.858741 +0.591439 +0.878681 +0.575029 +0.875862 +0.587233 +0.877478 +0.590552 +0.878192 +0.612182 +0.884510 +0.597271 +0.871994 +0.584313 +0.889250 +0.613348 +0.896781 +0.616780 +0.872738 +0.604208 +0.886942 +0.602222 +0.908955 +0.613109 +0.907557 +0.593596 +0.876903 +0.604981 +0.908690 +0.603420 +0.905002 +0.613749 +0.908153 +0.616341 +0.907630 +0.620224 +0.921773 +0.610975 +0.882499 +0.603638 +0.895723 +0.599678 +0.912358 +0.616238 +0.887268 +0.603110 +0.894890 +0.599794 +0.891843 +0.595543 +0.885647 +0.604038 +0.884688 +0.604336 +0.904477 +0.608058 +0.926427 +0.613116 +0.900399 +0.604458 +0.920539 +0.592291 +0.900041 +0.600822 +0.911335 +0.594115 +0.906282 +0.610597 +0.909715 +0.607655 +0.908026 +0.612589 +0.914156 +0.593988 +0.901339 +0.614306 +0.900533 +0.600397 +0.890947 +0.581547 +0.880329 +0.593176 +0.892626 +0.595915 +0.890833 +0.607529 +0.881853 +0.590753 +0.889787 +0.594067 +0.879396 +0.583954 +0.904577 +0.595259 +0.889483 +0.589508 +0.881973 +0.581986 +0.907962 +0.581112 +0.893028 +0.589402 +0.893548 +0.582021 +0.883251 +0.575327 +0.890194 +0.585448 +0.892578 +0.588170 +0.890269 +0.574733 +0.889053 +0.568132 +0.881872 +0.559792 +0.878554 +0.580164 +0.846069 +0.591213 +0.847698 +0.571833 +0.845756 +0.584416 +0.865842 +0.570948 +0.851881 +0.586045 +0.847687 +0.567280 +0.830930 +0.552820 +0.842849 +0.560219 +0.851587 +0.559721 +0.828390 +0.550692 +0.814457 +0.540180 +0.819584 +0.552634 +0.843879 +0.555866 +0.813111 +0.540505 +0.808215 +0.531577 +0.783204 +0.539590 +0.807557 +0.545994 +0.808684 +0.539058 +0.791109 +0.524206 +0.794744 +0.539267 +0.777753 +0.541667 +0.761995 +0.532214 +0.791595 +0.516554 +0.788004 +0.524887 +0.787861 +0.509768 +0.785356 +0.516251 +0.780854 +0.469402 +0.758001 +0.482622 +0.772711 +0.495935 +0.732024 +0.489425 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.809967 +0.499185 +0.809967 +0.499185 +0.809967 +0.499185 +0.809967 +0.499185 +0.809967 +0.499185 +0.809967 +0.499185 +0.809967 +0.540532 +0.837878 +0.495998 +0.805582 +0.504933 +0.834236 +0.496623 +0.790081 +0.516700 +0.778280 +0.519990 +0.825295 +0.527148 +0.803575 +0.545031 +0.797900 +0.514446 +0.792508 +0.548377 +0.831971 +0.530056 +0.829575 +0.541442 +0.833880 +0.556845 +0.841098 +0.547756 +0.839444 +0.530149 +0.830302 +0.540750 +0.826016 +0.556177 +0.803039 +0.556300 +0.837230 +0.551444 +0.832649 +0.548622 +0.852904 +0.558700 +0.868069 +0.567611 +0.865658 +0.553210 +0.829543 +0.565078 +0.861136 +0.558129 +0.867926 +0.569455 +0.845157 +0.602430 +0.875180 +0.574980 +0.856680 +0.575799 +0.866769 +0.587704 +0.870916 +0.598399 +0.880554 +0.583723 +0.858545 +0.591073 +0.866792 +0.597239 +0.863077 +0.600939 +0.876552 +0.589934 +0.879587 +0.605825 +0.885281 +0.612178 +0.882799 +0.610111 +0.881345 +0.613913 +0.892448 +0.609592 +0.909209 +0.619161 +0.903138 +0.580835 +0.886553 +0.609325 +0.904470 +0.610716 +0.903606 +0.604648 +0.892901 +0.606070 +0.912615 +0.618309 +0.894717 +0.601980 +0.878154 +0.588588 +0.883087 +0.600301 +0.893146 +0.608583 +0.908208 +0.599169 +0.892150 +0.601276 +0.896224 +0.603420 +0.875381 +0.619211 +0.895559 +0.610634 +0.901124 +0.611995 +0.912282 +0.613482 +0.914477 +0.610520 +0.901582 +0.599747 +0.911044 +0.608623 +0.907926 +0.598575 +0.903127 +0.594056 +0.908509 +0.610553 +0.883022 +0.600882 +0.894285 +0.582263 +0.886976 +0.614431 +0.910498 +0.593657 +0.891338 +0.597104 +0.889727 +0.579238 +0.888796 +0.590141 +0.889933 +0.581102 +0.886972 +0.579462 +0.908939 +0.594537 +0.893477 +0.586061 +0.892169 +0.589698 +0.887608 +0.590963 +0.891918 +0.569064 +0.885039 +0.589741 +0.874450 +0.573035 +0.891829 +0.577074 +0.875052 +0.583559 +0.884456 +0.593758 +0.884524 +0.573134 +0.879144 +0.560853 +0.879039 +0.568214 +0.886399 +0.556716 +0.871430 +0.565383 +0.835093 +0.587181 +0.853119 +0.583984 +0.842523 +0.588743 +0.851632 +0.591943 +0.875541 +0.579486 +0.852989 +0.550784 +0.818078 +0.555686 +0.849780 +0.558524 +0.841553 +0.561924 +0.841470 +0.539252 +0.825954 +0.553194 +0.815794 +0.517875 +0.852380 +0.551239 +0.821265 +0.559452 +0.835823 +0.517859 +0.805610 +0.527457 +0.807368 +0.545918 +0.820657 +0.530920 +0.825609 +0.529895 +0.798307 +0.540658 +0.782647 +0.515158 +0.776487 +0.523097 +0.781516 +0.518900 +0.769751 +0.506927 +0.777283 +0.532072 +0.785003 +0.515984 +0.760798 +0.479964 +0.780633 +0.504369 +0.755013 +0.508844 +0.804004 +0.500106 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.826867 +0.487750 +0.826867 +0.487750 +0.826867 +0.487750 +0.826867 +0.487750 +0.826867 +0.501768 +0.797303 +0.473942 +0.806839 +0.506804 +0.837547 +0.482505 +0.836215 +0.523780 +0.832717 +0.534443 +0.842568 +0.526501 +0.821301 +0.517007 +0.838451 +0.502058 +0.821949 +0.528329 +0.819859 +0.529168 +0.808357 +0.525248 +0.833532 +0.520444 +0.823326 +0.546681 +0.856816 +0.531614 +0.811071 +0.546867 +0.838003 +0.561198 +0.833330 +0.545267 +0.843309 +0.544697 +0.861942 +0.562118 +0.875385 +0.553119 +0.859016 +0.573181 +0.868543 +0.560503 +0.856317 +0.568230 +0.861953 +0.564397 +0.873455 +0.566526 +0.871523 +0.555483 +0.890511 +0.573293 +0.875710 +0.566586 +0.876331 +0.555743 +0.889255 +0.573252 +0.881224 +0.580302 +0.880972 +0.580030 +0.896896 +0.569053 +0.882286 +0.597217 +0.895202 +0.610430 +0.899533 +0.608684 +0.902200 +0.600543 +0.914899 +0.591954 +0.897391 +0.605684 +0.893933 +0.603615 +0.903086 +0.613816 +0.913500 +0.605176 +0.915029 +0.605808 +0.910435 +0.600052 +0.912271 +0.601407 +0.920943 +0.613382 +0.908386 +0.603680 +0.902220 +0.599648 +0.929313 +0.611044 +0.919688 +0.596673 +0.899934 +0.611259 +0.892788 +0.594194 +0.914133 +0.610570 +0.908129 +0.602271 +0.910443 +0.614309 +0.913606 +0.591199 +0.911304 +0.603756 +0.898611 +0.603393 +0.913666 +0.610627 +0.908409 +0.606387 +0.915927 +0.600014 +0.899696 +0.603815 +0.912792 +0.604231 +0.907983 +0.609821 +0.917214 +0.592205 +0.904238 +0.599720 +0.885866 +0.606661 +0.910149 +0.602461 +0.895387 +0.618130 +0.915515 +0.600361 +0.890547 +0.591660 +0.887020 +0.584565 +0.878273 +0.594645 +0.896089 +0.587991 +0.892624 +0.584917 +0.889535 +0.585883 +0.895244 +0.573668 +0.894192 +0.596446 +0.888932 +0.574480 +0.895297 +0.583472 +0.897526 +0.581700 +0.874553 +0.578587 +0.899516 +0.578533 +0.915747 +0.579708 +0.910316 +0.575670 +0.903241 +0.581100 +0.908506 +0.579445 +0.896365 +0.570859 +0.895142 +0.565229 +0.885234 +0.587530 +0.872048 +0.593987 +0.885323 +0.602181 +0.883200 +0.584723 +0.861729 +0.580499 +0.865329 +0.574927 +0.866485 +0.556049 +0.857129 +0.556295 +0.844142 +0.557953 +0.862958 +0.581215 +0.866413 +0.561638 +0.854447 +0.545688 +0.854236 +0.536390 +0.831761 +0.538607 +0.844526 +0.549577 +0.839089 +0.526428 +0.831646 +0.540394 +0.818342 +0.532660 +0.833091 +0.543145 +0.812690 +0.535291 +0.821448 +0.522931 +0.797132 +0.515781 +0.801344 +0.482990 +0.788765 +0.527874 +0.788483 +0.511420 +0.789551 +0.508942 +0.786826 +0.491594 +0.784299 +0.488334 +0.791535 +0.467776 +0.801133 +0.495325 +0.768497 +0.482568 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.789401 +0.469107 +0.789401 +0.469107 +0.789401 +0.469107 +0.789401 +0.469107 +0.789401 +0.454592 +0.792615 +0.457300 +0.788072 +0.444361 +0.785977 +0.505660 +0.794155 +0.498931 +0.845274 +0.494809 +0.820411 +0.497156 +0.844299 +0.513851 +0.815223 +0.500039 +0.801896 +0.518587 +0.808209 +0.501630 +0.820541 +0.492148 +0.866840 +0.519921 +0.839152 +0.537457 +0.847326 +0.520726 +0.823195 +0.506567 +0.857644 +0.537211 +0.837582 +0.530807 +0.838853 +0.526973 +0.874789 +0.560744 +0.875862 +0.540777 +0.873711 +0.527549 +0.873640 +0.556107 +0.876583 +0.573244 +0.871271 +0.552369 +0.879723 +0.532960 +0.863307 +0.551166 +0.884000 +0.553003 +0.866972 +0.549749 +0.886603 +0.549880 +0.894001 +0.565072 +0.879204 +0.559835 +0.873985 +0.553172 +0.885565 +0.578503 +0.883740 +0.588218 +0.904333 +0.584832 +0.900873 +0.575339 +0.899631 +0.579841 +0.909895 +0.585772 +0.914453 +0.585393 +0.891197 +0.568171 +0.902028 +0.593420 +0.920717 +0.577289 +0.923703 +0.564476 +0.914347 +0.581669 +0.908713 +0.580476 +0.909722 +0.587143 +0.909245 +0.578319 +0.914742 +0.587602 +0.921255 +0.583916 +0.922901 +0.580864 +0.903214 +0.574772 +0.911158 +0.589545 +0.923764 +0.588054 +0.915612 +0.596573 +0.906867 +0.572809 +0.898843 +0.584724 +0.908373 +0.590820 +0.914191 +0.579925 +0.931397 +0.586188 +0.931836 +0.576748 +0.920814 +0.580255 +0.920524 +0.581717 +0.927638 +0.580717 +0.923259 +0.577943 +0.921993 +0.573949 +0.915782 +0.593395 +0.917006 +0.580677 +0.912493 +0.591397 +0.926253 +0.577408 +0.934754 +0.578866 +0.884279 +0.576743 +0.899182 +0.577471 +0.911237 +0.581406 +0.919485 +0.575548 +0.917387 +0.565839 +0.916146 +0.577535 +0.907766 +0.567489 +0.910433 +0.571578 +0.923662 +0.574075 +0.908736 +0.553459 +0.907470 +0.567535 +0.907469 +0.567782 +0.913331 +0.562704 +0.906760 +0.553428 +0.914118 +0.559961 +0.901789 +0.569726 +0.890525 +0.546504 +0.903168 +0.558700 +0.912332 +0.543401 +0.889004 +0.570650 +0.865567 +0.574158 +0.867843 +0.560083 +0.878458 +0.568240 +0.900316 +0.569313 +0.883461 +0.564212 +0.871073 +0.546253 +0.863811 +0.556198 +0.862462 +0.540527 +0.870833 +0.545720 +0.864912 +0.538954 +0.857022 +0.544594 +0.837886 +0.515104 +0.834817 +0.536255 +0.842538 +0.530126 +0.845578 +0.529455 +0.838959 +0.535594 +0.814795 +0.498494 +0.820438 +0.539043 +0.812726 +0.528089 +0.797078 +0.503194 +0.817358 +0.505461 +0.797433 +0.503444 +0.814099 +0.527454 +0.816695 +0.494747 +0.807180 +0.499076 +0.759067 +0.499745 +0.772510 +0.460384 +0.834841 +0.471003 +0.761452 +0.450040 +0.775285 +0.477044 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.837854 +0.491932 +0.837854 +0.491932 +0.837854 +0.491932 +0.837854 +0.491932 +0.837854 +0.471829 +0.810752 +0.499572 +0.815193 +0.487340 +0.854678 +0.488884 +0.843690 +0.471527 +0.827992 +0.496734 +0.815382 +0.495046 +0.838389 +0.522086 +0.842610 +0.547886 +0.825202 +0.516111 +0.810652 +0.509638 +0.840625 +0.495273 +0.832305 +0.522240 +0.837724 +0.535251 +0.849698 +0.540416 +0.856254 +0.511810 +0.845781 +0.519198 +0.834829 +0.534699 +0.843212 +0.557896 +0.860525 +0.537344 +0.861653 +0.546199 +0.878202 +0.525049 +0.876008 +0.547286 +0.887661 +0.548435 +0.909811 +0.536081 +0.867246 +0.549443 +0.867476 +0.556850 +0.889589 +0.553955 +0.883721 +0.562088 +0.874287 +0.554887 +0.879186 +0.545094 +0.879585 +0.572030 +0.897960 +0.545095 +0.878537 +0.570376 +0.869451 +0.561329 +0.915439 +0.569207 +0.907608 +0.592865 +0.912639 +0.575224 +0.897888 +0.583941 +0.906444 +0.566241 +0.887966 +0.573030 +0.910035 +0.592427 +0.915795 +0.583099 +0.912583 +0.572321 +0.907374 +0.589471 +0.916909 +0.588215 +0.915708 +0.593231 +0.914051 +0.578039 +0.905684 +0.585852 +0.913844 +0.575062 +0.905802 +0.575328 +0.885984 +0.580375 +0.907283 +0.581053 +0.906755 +0.589862 +0.911882 +0.601944 +0.924823 +0.581147 +0.900722 +0.595046 +0.913794 +0.585021 +0.905635 +0.579994 +0.920266 +0.577830 +0.922325 +0.566817 +0.909878 +0.581383 +0.934938 +0.592351 +0.917555 +0.587804 +0.907430 +0.571791 +0.915056 +0.568171 +0.927355 +0.572484 +0.912857 +0.573120 +0.913624 +0.592458 +0.901731 +0.562297 +0.925284 +0.580679 +0.896245 +0.564964 +0.908593 +0.569627 +0.920679 +0.579900 +0.921546 +0.578205 +0.931761 +0.575592 +0.913575 +0.577791 +0.921946 +0.576230 +0.913766 +0.587554 +0.908733 +0.580194 +0.917001 +0.571524 +0.920695 +0.554486 +0.902367 +0.573429 +0.907874 +0.554164 +0.914417 +0.573839 +0.913786 +0.561949 +0.919572 +0.557455 +0.933435 +0.557792 +0.907437 +0.555302 +0.886920 +0.556856 +0.896245 +0.562490 +0.894006 +0.561498 +0.881516 +0.559340 +0.892155 +0.561551 +0.878368 +0.563346 +0.883397 +0.571676 +0.866277 +0.549605 +0.870081 +0.562689 +0.856793 +0.558118 +0.877767 +0.535349 +0.849015 +0.528405 +0.849469 +0.542012 +0.857961 +0.538404 +0.846105 +0.535787 +0.855637 +0.533732 +0.846908 +0.522940 +0.843254 +0.525413 +0.824623 +0.524899 +0.808988 +0.531346 +0.825902 +0.504844 +0.803521 +0.508652 +0.810184 +0.510961 +0.821634 +0.510275 +0.817851 +0.493488 +0.811519 +0.501138 +0.805927 +0.484308 +0.786987 +0.493244 +0.817584 +0.476832 +0.836778 +0.482342 +0.795908 +0.489661 +0.760565 +0.484139 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.831189 +0.471837 +0.831189 +0.471837 +0.831189 +0.471837 +0.831189 +0.471837 +0.831189 +0.471837 +0.831189 +0.451293 +0.806128 +0.464157 +0.821950 +0.440840 +0.839443 +0.510515 +0.832175 +0.512385 +0.811818 +0.473625 +0.827809 +0.500387 +0.841326 +0.515285 +0.820179 +0.531504 +0.830688 +0.529304 +0.838035 +0.528897 +0.856370 +0.516174 +0.852222 +0.508282 +0.854452 +0.536442 +0.865767 +0.514436 +0.827190 +0.529338 +0.855887 +0.518600 +0.851165 +0.531144 +0.872690 +0.525525 +0.866059 +0.566326 +0.880586 +0.549115 +0.877530 +0.532460 +0.878101 +0.526178 +0.866253 +0.547175 +0.867983 +0.542763 +0.871985 +0.559272 +0.887074 +0.554299 +0.879190 +0.544793 +0.857063 +0.550205 +0.883930 +0.557497 +0.885901 +0.567335 +0.893152 +0.538340 +0.864930 +0.544751 +0.875370 +0.573512 +0.898730 +0.583584 +0.904793 +0.595733 +0.915939 +0.587218 +0.915603 +0.586292 +0.895033 +0.566772 +0.887807 +0.573702 +0.920484 +0.589430 +0.921702 +0.569577 +0.897856 +0.578069 +0.919715 +0.589848 +0.926961 +0.568611 +0.919617 +0.580306 +0.905609 +0.583872 +0.905255 +0.588062 +0.915699 +0.584437 +0.906486 +0.577566 +0.909015 +0.604261 +0.925413 +0.596351 +0.915899 +0.584712 +0.906749 +0.594360 +0.919554 +0.585757 +0.916047 +0.593047 +0.919376 +0.583045 +0.906004 +0.581993 +0.914253 +0.564538 +0.919832 +0.574690 +0.919574 +0.580894 +0.918626 +0.598357 +0.942777 +0.595325 +0.938016 +0.586215 +0.930570 +0.578475 +0.922173 +0.582475 +0.910036 +0.583410 +0.921406 +0.579735 +0.921533 +0.583297 +0.938107 +0.591482 +0.937516 +0.572725 +0.916870 +0.583320 +0.928136 +0.585628 +0.932804 +0.576429 +0.909354 +0.563952 +0.921057 +0.570266 +0.926511 +0.563597 +0.923738 +0.570586 +0.931281 +0.584724 +0.926292 +0.568791 +0.915053 +0.570720 +0.916104 +0.564579 +0.882770 +0.554701 +0.907900 +0.569817 +0.924783 +0.569024 +0.909019 +0.567117 +0.906991 +0.531585 +0.899733 +0.526768 +0.902158 +0.542077 +0.892087 +0.573806 +0.884533 +0.574002 +0.882369 +0.563846 +0.885313 +0.578791 +0.878129 +0.568791 +0.877593 +0.557158 +0.876702 +0.551711 +0.863875 +0.551151 +0.882693 +0.542353 +0.876167 +0.549495 +0.854790 +0.535760 +0.847103 +0.540046 +0.864049 +0.542227 +0.843755 +0.533715 +0.861329 +0.524748 +0.830636 +0.520997 +0.835013 +0.509392 +0.830892 +0.516914 +0.837547 +0.510595 +0.830611 +0.517909 +0.843397 +0.511396 +0.805229 +0.506238 +0.785086 +0.500652 +0.821838 +0.491133 +0.777739 +0.486227 +0.811186 +0.481116 +0.806696 +0.491777 +0.794722 +0.490379 +0.801702 +0.457816 +0.797065 +0.456932 +0.742300 +0.479991 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.827950 +0.497671 +0.827950 +0.497671 +0.827950 +0.497671 +0.827950 +0.497671 +0.827950 +0.485751 +0.790299 +0.498260 +0.817838 +0.491550 +0.842398 +0.503204 +0.823614 +0.500407 +0.796916 +0.517958 +0.859182 +0.481567 +0.782609 +0.528715 +0.817794 +0.499565 +0.812181 +0.539755 +0.817532 +0.530078 +0.839216 +0.535572 +0.826093 +0.509196 +0.831005 +0.524213 +0.840312 +0.506567 +0.862074 +0.539911 +0.874330 +0.527744 +0.879484 +0.531657 +0.862999 +0.527197 +0.863941 +0.538162 +0.852172 +0.524588 +0.873118 +0.554031 +0.870227 +0.542651 +0.855286 +0.548032 +0.856465 +0.542645 +0.890740 +0.543188 +0.876559 +0.533780 +0.875929 +0.554213 +0.860771 +0.538818 +0.856093 +0.551661 +0.882012 +0.543928 +0.907371 +0.556898 +0.881230 +0.544322 +0.879177 +0.545974 +0.883671 +0.569548 +0.884489 +0.566697 +0.906430 +0.598339 +0.919586 +0.587094 +0.910476 +0.569760 +0.906816 +0.584116 +0.931928 +0.589018 +0.901328 +0.588244 +0.925797 +0.568801 +0.899449 +0.572792 +0.915350 +0.591431 +0.914657 +0.573282 +0.919872 +0.575505 +0.919316 +0.589508 +0.916505 +0.580806 +0.919058 +0.588475 +0.918696 +0.576601 +0.907139 +0.567619 +0.900916 +0.586488 +0.920380 +0.592175 +0.909258 +0.595801 +0.917796 +0.592825 +0.918558 +0.582399 +0.917169 +0.583257 +0.912996 +0.582170 +0.923209 +0.583423 +0.928897 +0.574840 +0.937001 +0.584282 +0.939053 +0.590585 +0.928306 +0.572605 +0.928127 +0.585406 +0.932900 +0.589766 +0.927075 +0.576200 +0.913569 +0.576713 +0.940293 +0.577456 +0.924698 +0.575306 +0.923028 +0.590456 +0.932835 +0.598106 +0.938470 +0.579968 +0.912506 +0.587335 +0.924826 +0.579023 +0.916301 +0.572224 +0.916448 +0.580845 +0.926446 +0.573713 +0.929293 +0.567602 +0.919733 +0.564772 +0.918051 +0.566747 +0.908549 +0.576117 +0.910197 +0.558705 +0.903683 +0.559137 +0.903649 +0.569503 +0.917068 +0.558096 +0.911178 +0.567018 +0.895003 +0.544392 +0.898470 +0.549326 +0.890137 +0.552371 +0.906174 +0.571080 +0.897155 +0.564862 +0.880687 +0.576678 +0.878374 +0.592285 +0.880667 +0.573745 +0.872244 +0.572387 +0.890863 +0.547157 +0.879314 +0.539978 +0.885200 +0.518525 +0.862408 +0.521368 +0.865215 +0.534851 +0.859369 +0.540700 +0.869188 +0.538684 +0.852026 +0.532905 +0.840616 +0.530807 +0.849031 +0.534836 +0.851785 +0.527925 +0.811362 +0.524902 +0.831710 +0.526610 +0.831120 +0.531849 +0.832506 +0.516939 +0.834950 +0.501152 +0.808447 +0.499694 +0.823950 +0.511654 +0.771028 +0.471212 +0.803868 +0.493101 +0.814513 +0.510095 +0.779445 +0.460305 +0.841443 +0.484703 +0.795954 +0.476336 +0.780228 +0.485034 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.805808 +0.484114 +0.805808 +0.484114 +0.805808 +0.484114 +0.805808 +0.484114 +0.805808 +0.484114 +0.805808 +0.476688 +0.771653 +0.480752 +0.828383 +0.462600 +0.773295 +0.508990 +0.815747 +0.535213 +0.822036 +0.493320 +0.838995 +0.482422 +0.844927 +0.502081 +0.837200 +0.509670 +0.817482 +0.493455 +0.857671 +0.516201 +0.845497 +0.507973 +0.829474 +0.527174 +0.844302 +0.530499 +0.842560 +0.525158 +0.843825 +0.539655 +0.857288 +0.539278 +0.853671 +0.541518 +0.860741 +0.532674 +0.834873 +0.502631 +0.872028 +0.537283 +0.865291 +0.535953 +0.864606 +0.548400 +0.857177 +0.529089 +0.892540 +0.553180 +0.869323 +0.539075 +0.861708 +0.544815 +0.872061 +0.546928 +0.872411 +0.547454 +0.876226 +0.554816 +0.901799 +0.551454 +0.884012 +0.567902 +0.895109 +0.552504 +0.887604 +0.560803 +0.892120 +0.578965 +0.883424 +0.595687 +0.911368 +0.580125 +0.905857 +0.581562 +0.892760 +0.580020 +0.906967 +0.582675 +0.901161 +0.579565 +0.922007 +0.577559 +0.917200 +0.577551 +0.914757 +0.573223 +0.897884 +0.579680 +0.923878 +0.572130 +0.919160 +0.586079 +0.902289 +0.578796 +0.905460 +0.579411 +0.908493 +0.581544 +0.907050 +0.582920 +0.889406 +0.580315 +0.915047 +0.581316 +0.914167 +0.584206 +0.923429 +0.587803 +0.922181 +0.572201 +0.905487 +0.569048 +0.905974 +0.561341 +0.916579 +0.572397 +0.921809 +0.582489 +0.941075 +0.578812 +0.914226 +0.581129 +0.928995 +0.584186 +0.926350 +0.595488 +0.935808 +0.577874 +0.934183 +0.589449 +0.904621 +0.589675 +0.922591 +0.591001 +0.915759 +0.582250 +0.933178 +0.573874 +0.927685 +0.600627 +0.937278 +0.600244 +0.916630 +0.569902 +0.926364 +0.567979 +0.924007 +0.558029 +0.915541 +0.576175 +0.919753 +0.571251 +0.922586 +0.565893 +0.916661 +0.571103 +0.914246 +0.571086 +0.919372 +0.564117 +0.915786 +0.571830 +0.909740 +0.551858 +0.911922 +0.568861 +0.909903 +0.576079 +0.893003 +0.562060 +0.903660 +0.544062 +0.905970 +0.549419 +0.906838 +0.550161 +0.908262 +0.559827 +0.872921 +0.562067 +0.872812 +0.563008 +0.892435 +0.563092 +0.874957 +0.559805 +0.873810 +0.546958 +0.871053 +0.550829 +0.890517 +0.564887 +0.863490 +0.532142 +0.829540 +0.531129 +0.847054 +0.527727 +0.862721 +0.534513 +0.860247 +0.526647 +0.835178 +0.527528 +0.841292 +0.547479 +0.857563 +0.514071 +0.830592 +0.506262 +0.832667 +0.518511 +0.823499 +0.504867 +0.808312 +0.506294 +0.825729 +0.525825 +0.819809 +0.492800 +0.804603 +0.491464 +0.799109 +0.498336 +0.790992 +0.509473 +0.824963 +0.480650 +0.828020 +0.514937 +0.808676 +0.480296 +0.824681 +0.463593 +0.773041 +0.516810 +0.749507 +0.486276 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.828152 +0.485216 +0.828152 +0.485216 +0.828152 +0.485216 +0.828152 +0.485216 +0.828152 +0.475973 +0.808654 +0.508833 +0.823715 +0.485738 +0.834067 +0.461075 +0.826674 +0.504833 +0.883629 +0.486327 +0.841088 +0.489659 +0.817659 +0.500888 +0.867086 +0.513702 +0.841913 +0.508548 +0.847619 +0.522760 +0.861204 +0.491676 +0.863846 +0.517002 +0.854770 +0.527973 +0.872275 +0.510924 +0.872394 +0.527595 +0.834117 +0.522057 +0.890375 +0.532056 +0.889333 +0.518865 +0.890772 +0.526621 +0.861660 +0.525277 +0.878666 +0.552329 +0.905278 +0.551059 +0.882727 +0.539023 +0.865612 +0.538346 +0.889641 +0.531910 +0.882256 +0.538991 +0.892654 +0.526237 +0.879359 +0.555859 +0.900171 +0.556776 +0.895872 +0.536550 +0.889897 +0.536405 +0.900445 +0.561943 +0.911709 +0.560071 +0.896790 +0.550945 +0.887458 +0.557985 +0.923120 +0.573782 +0.903327 +0.579070 +0.924216 +0.569065 +0.904255 +0.585651 +0.920107 +0.573573 +0.908259 +0.581790 +0.928252 +0.576717 +0.938534 +0.571895 +0.927256 +0.558522 +0.926125 +0.572258 +0.941931 +0.571298 +0.941407 +0.580642 +0.928172 +0.566636 +0.932072 +0.567192 +0.930275 +0.577138 +0.928693 +0.585558 +0.931318 +0.591828 +0.930536 +0.575860 +0.928546 +0.592512 +0.923197 +0.581801 +0.923874 +0.578223 +0.922874 +0.573784 +0.927726 +0.572229 +0.916274 +0.566441 +0.911983 +0.578224 +0.944150 +0.579522 +0.931693 +0.564351 +0.918325 +0.574769 +0.930590 +0.584992 +0.934276 +0.576462 +0.929560 +0.587504 +0.902428 +0.580622 +0.902837 +0.580594 +0.922747 +0.577304 +0.928123 +0.588080 +0.914057 +0.589991 +0.937498 +0.598120 +0.934932 +0.579105 +0.910495 +0.558378 +0.925519 +0.566897 +0.938226 +0.585845 +0.949428 +0.566739 +0.941684 +0.574299 +0.947516 +0.586677 +0.941657 +0.564343 +0.938808 +0.580385 +0.938384 +0.568022 +0.930850 +0.549392 +0.938372 +0.575577 +0.939052 +0.558872 +0.935263 +0.570593 +0.927585 +0.556667 +0.938305 +0.551095 +0.937486 +0.547148 +0.922938 +0.571522 +0.897025 +0.554501 +0.906885 +0.566898 +0.904285 +0.549000 +0.906749 +0.557827 +0.892274 +0.566928 +0.893550 +0.561307 +0.867338 +0.522371 +0.863906 +0.540485 +0.887637 +0.541319 +0.880783 +0.542836 +0.886888 +0.510345 +0.883157 +0.501552 +0.858338 +0.520595 +0.863023 +0.523817 +0.872294 +0.532354 +0.852473 +0.517753 +0.853222 +0.530640 +0.844939 +0.519189 +0.843251 +0.520541 +0.837538 +0.507550 +0.821547 +0.517825 +0.841565 +0.513979 +0.812836 +0.502120 +0.832074 +0.497038 +0.810463 +0.470995 +0.815936 +0.470321 +0.768157 +0.516514 +0.826495 +0.455770 +0.741863 +0.453403 +0.792878 +0.480868 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.826442 +0.441014 +0.826442 +0.441014 +0.826442 +0.441014 +0.826442 +0.441014 +0.826442 +0.428849 +0.841987 +0.426268 +0.786434 +0.449352 +0.829515 +0.447423 +0.863376 +0.472654 +0.832337 +0.472468 +0.808556 +0.473627 +0.882794 +0.500581 +0.860451 +0.499077 +0.860580 +0.477638 +0.823900 +0.497195 +0.853503 +0.513709 +0.855311 +0.499901 +0.870963 +0.489289 +0.872785 +0.501486 +0.851540 +0.500105 +0.881086 +0.516259 +0.867734 +0.505476 +0.877104 +0.522309 +0.887492 +0.521893 +0.885026 +0.525649 +0.883241 +0.530876 +0.901358 +0.522757 +0.883227 +0.524979 +0.908779 +0.503670 +0.892454 +0.493998 +0.879357 +0.534148 +0.892641 +0.535730 +0.914562 +0.535253 +0.914532 +0.540711 +0.897808 +0.539685 +0.891497 +0.542362 +0.916247 +0.552893 +0.917676 +0.540575 +0.884797 +0.546539 +0.899283 +0.547265 +0.933628 +0.566433 +0.925494 +0.561539 +0.921912 +0.568341 +0.922996 +0.556436 +0.925576 +0.551151 +0.925142 +0.565335 +0.924010 +0.557725 +0.932468 +0.559623 +0.911207 +0.558771 +0.940262 +0.555904 +0.935662 +0.566395 +0.945410 +0.555949 +0.947943 +0.561236 +0.927738 +0.563908 +0.933979 +0.563381 +0.930603 +0.556232 +0.934630 +0.561007 +0.932107 +0.547824 +0.914459 +0.559140 +0.928471 +0.562064 +0.933208 +0.560500 +0.928271 +0.556618 +0.938631 +0.550428 +0.945501 +0.558297 +0.954645 +0.570190 +0.951283 +0.568546 +0.943957 +0.535673 +0.925835 +0.556044 +0.954618 +0.564017 +0.957508 +0.556008 +0.947260 +0.555295 +0.929929 +0.554466 +0.927287 +0.550283 +0.950029 +0.562058 +0.946139 +0.548175 +0.924594 +0.571012 +0.942343 +0.566018 +0.953330 +0.573764 +0.937338 +0.556638 +0.940295 +0.564311 +0.939473 +0.560806 +0.950125 +0.553057 +0.945938 +0.558748 +0.943326 +0.555073 +0.935425 +0.549840 +0.942645 +0.569236 +0.947913 +0.551997 +0.942666 +0.550416 +0.943354 +0.557987 +0.933092 +0.533973 +0.924477 +0.536799 +0.945556 +0.552278 +0.939419 +0.547194 +0.921090 +0.544779 +0.941147 +0.562733 +0.913832 +0.559357 +0.911755 +0.535487 +0.904267 +0.541844 +0.895565 +0.538806 +0.902707 +0.547494 +0.899655 +0.543409 +0.899990 +0.529026 +0.886938 +0.523554 +0.891494 +0.518206 +0.880715 +0.522651 +0.882001 +0.529045 +0.881804 +0.514976 +0.866873 +0.531499 +0.874570 +0.497338 +0.879701 +0.496489 +0.859571 +0.496510 +0.861336 +0.506113 +0.859915 +0.521994 +0.820151 +0.514187 +0.821833 +0.518531 +0.849169 +0.522374 +0.850377 +0.479608 +0.791541 +0.465865 +0.833583 +0.463637 +0.818202 +0.488147 +0.819263 +0.455210 +0.819423 +0.475549 +0.797516 +0.473178 +0.822880 +0.479216 +0.792701 +0.467979 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.827863 +0.461795 +0.827863 +0.461795 +0.827863 +0.461795 +0.827863 +0.445353 +0.808173 +0.448388 +0.784729 +0.450777 +0.832271 +0.486221 +0.866590 +0.456597 +0.835350 +0.455938 +0.834507 +0.418660 +0.843419 +0.477342 +0.850800 +0.492263 +0.851769 +0.484489 +0.874240 +0.466746 +0.855172 +0.491815 +0.863530 +0.475743 +0.828990 +0.488372 +0.863576 +0.514601 +0.863807 +0.509782 +0.865442 +0.527251 +0.875979 +0.510043 +0.851898 +0.512971 +0.883477 +0.501127 +0.876455 +0.513806 +0.886093 +0.521253 +0.884832 +0.533490 +0.900679 +0.509089 +0.883874 +0.523640 +0.882763 +0.508995 +0.899815 +0.542979 +0.896770 +0.512451 +0.877035 +0.512859 +0.889216 +0.532260 +0.901535 +0.559055 +0.892313 +0.535319 +0.900159 +0.539769 +0.895494 +0.543108 +0.890694 +0.537755 +0.897105 +0.534701 +0.907126 +0.558580 +0.944009 +0.568540 +0.930874 +0.564539 +0.919659 +0.561126 +0.940549 +0.560240 +0.928075 +0.565587 +0.932890 +0.559486 +0.935233 +0.554210 +0.927874 +0.547670 +0.917191 +0.550055 +0.951437 +0.556542 +0.934231 +0.563722 +0.937380 +0.567417 +0.947821 +0.572200 +0.931620 +0.549348 +0.941157 +0.563206 +0.921882 +0.557249 +0.948825 +0.564657 +0.919706 +0.551142 +0.917036 +0.560422 +0.929211 +0.568078 +0.923777 +0.557086 +0.940110 +0.553530 +0.936030 +0.557028 +0.929928 +0.552612 +0.953932 +0.557604 +0.945114 +0.570042 +0.952164 +0.544526 +0.930749 +0.553530 +0.938342 +0.563842 +0.954879 +0.556867 +0.938144 +0.551076 +0.929684 +0.554254 +0.931013 +0.573457 +0.946559 +0.551646 +0.941725 +0.555069 +0.924057 +0.564494 +0.942635 +0.554212 +0.956720 +0.569899 +0.941792 +0.556783 +0.949354 +0.563523 +0.964640 +0.565614 +0.941043 +0.566602 +0.934891 +0.568714 +0.945052 +0.552643 +0.948052 +0.569274 +0.948254 +0.553811 +0.951598 +0.569085 +0.941651 +0.554797 +0.963473 +0.562861 +0.935634 +0.545842 +0.940741 +0.539211 +0.934537 +0.551666 +0.943088 +0.546838 +0.936348 +0.534886 +0.930159 +0.564440 +0.920441 +0.566699 +0.916243 +0.550086 +0.911353 +0.555525 +0.910406 +0.545899 +0.891645 +0.546263 +0.903910 +0.541720 +0.878265 +0.533080 +0.895111 +0.526962 +0.896370 +0.515013 +0.893188 +0.519302 +0.869160 +0.528081 +0.892710 +0.498551 +0.881919 +0.513574 +0.870851 +0.521566 +0.870022 +0.519796 +0.857726 +0.506009 +0.857228 +0.511812 +0.858303 +0.492937 +0.855703 +0.485326 +0.829011 +0.536789 +0.869337 +0.512071 +0.849153 +0.485677 +0.855293 +0.498156 +0.824010 +0.461474 +0.833317 +0.469113 +0.850683 +0.458112 +0.808852 +0.462783 +0.832772 +0.473250 +0.845007 +0.377794 +0.791016 +0.464715 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.830739 +0.451418 +0.830739 +0.451418 +0.830739 +0.451418 +0.830739 +0.451418 +0.830739 +0.451418 +0.830739 +0.458111 +0.800322 +0.453169 +0.861724 +0.444627 +0.813063 +0.456459 +0.817430 +0.440846 +0.840944 +0.457672 +0.839360 +0.450769 +0.847455 +0.500954 +0.865114 +0.488967 +0.839538 +0.496016 +0.830551 +0.489957 +0.867323 +0.496625 +0.884421 +0.507552 +0.844485 +0.503979 +0.871908 +0.476393 +0.863232 +0.489222 +0.890388 +0.498454 +0.859006 +0.505183 +0.896339 +0.510053 +0.863488 +0.508784 +0.888698 +0.521163 +0.894592 +0.533456 +0.914435 +0.520948 +0.896113 +0.514542 +0.905575 +0.533801 +0.910251 +0.509989 +0.904404 +0.503569 +0.905377 +0.523342 +0.905185 +0.536965 +0.910878 +0.542550 +0.909909 +0.535311 +0.895302 +0.532216 +0.888920 +0.525324 +0.888723 +0.536204 +0.910270 +0.543078 +0.905682 +0.560769 +0.934571 +0.548779 +0.906126 +0.563453 +0.929663 +0.555983 +0.914659 +0.561973 +0.935473 +0.540027 +0.916288 +0.547439 +0.932790 +0.554049 +0.943271 +0.555836 +0.949659 +0.555422 +0.945904 +0.562747 +0.944341 +0.563621 +0.938407 +0.549015 +0.933149 +0.556486 +0.948960 +0.568482 +0.931548 +0.578669 +0.935672 +0.564831 +0.935092 +0.564126 +0.914390 +0.569007 +0.945037 +0.560884 +0.943525 +0.567229 +0.943574 +0.567601 +0.923568 +0.546212 +0.950032 +0.555691 +0.950509 +0.567947 +0.933264 +0.565067 +0.950178 +0.551835 +0.935990 +0.553772 +0.943021 +0.571654 +0.959508 +0.567506 +0.931076 +0.560535 +0.939340 +0.551658 +0.932556 +0.559484 +0.933872 +0.555034 +0.924379 +0.549136 +0.910523 +0.551341 +0.935095 +0.545775 +0.913252 +0.556490 +0.926801 +0.540132 +0.923974 +0.520719 +0.913237 +0.519512 +0.922969 +0.532233 +0.933898 +0.534643 +0.919528 +0.514937 +0.932059 +0.532364 +0.922569 +0.538243 +0.921729 +0.514145 +0.898395 +0.506442 +0.907768 +0.518422 +0.915458 +0.517118 +0.912648 +0.524488 +0.899441 +0.503686 +0.903249 +0.507928 +0.897106 +0.512613 +0.910377 +0.539049 +0.870584 +0.514183 +0.865574 +0.517853 +0.887431 +0.518363 +0.878567 +0.529846 +0.878639 +0.508362 +0.883953 +0.520145 +0.865951 +0.505992 +0.860875 +0.495055 +0.865029 +0.503935 +0.859321 +0.474067 +0.858184 +0.480535 +0.857626 +0.470529 +0.855409 +0.481015 +0.864472 +0.468124 +0.837902 +0.479476 +0.822352 +0.466749 +0.836996 +0.475582 +0.840098 +0.478537 +0.803258 +0.481339 +0.825318 +0.455678 +0.786554 +0.457273 +0.790986 +0.457902 +0.765272 +0.464324 +0.792822 +0.456684 +0.806673 +0.445051 +0.802747 +0.428644 +0.762818 +0.432211 +0.790570 +0.420719 +0.768311 +0.440449 +0.814147 +0.427191 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.838235 +0.455791 +0.838235 +0.455791 +0.838235 +0.455791 +0.838235 +0.455791 +0.838235 +0.455791 +0.838235 +0.455791 +0.838235 +0.488457 +0.822931 +0.434689 +0.841157 +0.474967 +0.812616 +0.457717 +0.860931 +0.509019 +0.836247 +0.478098 +0.822705 +0.474678 +0.852711 +0.476128 +0.833146 +0.516643 +0.838615 +0.461210 +0.873408 +0.453825 +0.823217 +0.484893 +0.863265 +0.500386 +0.857277 +0.477401 +0.887919 +0.494093 +0.852941 +0.504390 +0.866596 +0.507270 +0.872594 +0.513251 +0.883735 +0.473133 +0.869054 +0.491565 +0.866298 +0.474037 +0.874007 +0.518970 +0.883910 +0.491509 +0.880006 +0.512148 +0.873145 +0.519646 +0.893286 +0.515275 +0.898175 +0.532864 +0.890456 +0.509369 +0.891004 +0.520661 +0.879792 +0.512437 +0.895133 +0.528706 +0.890734 +0.514296 +0.879909 +0.545545 +0.902303 +0.529643 +0.914435 +0.544377 +0.894823 +0.560796 +0.910026 +0.560210 +0.912153 +0.536118 +0.909342 +0.552626 +0.931630 +0.566442 +0.931622 +0.548130 +0.912709 +0.552401 +0.929021 +0.535074 +0.925900 +0.559907 +0.922085 +0.529887 +0.918798 +0.557363 +0.928906 +0.560214 +0.920979 +0.555760 +0.913155 +0.531377 +0.902627 +0.542782 +0.925493 +0.558615 +0.917132 +0.532662 +0.911587 +0.550942 +0.920854 +0.544425 +0.930814 +0.541539 +0.927652 +0.538428 +0.915631 +0.533959 +0.937688 +0.550553 +0.940893 +0.555579 +0.931226 +0.552015 +0.939582 +0.530074 +0.931677 +0.544311 +0.926228 +0.527802 +0.933250 +0.531402 +0.929346 +0.543895 +0.933806 +0.555564 +0.918814 +0.551886 +0.924841 +0.546898 +0.926502 +0.540916 +0.916667 +0.532112 +0.929561 +0.533517 +0.919600 +0.539216 +0.930146 +0.536909 +0.921187 +0.537255 +0.932879 +0.533330 +0.919870 +0.551994 +0.944608 +0.531185 +0.924447 +0.528853 +0.920812 +0.532168 +0.903520 +0.519436 +0.897492 +0.517166 +0.913162 +0.516682 +0.911699 +0.521614 +0.916779 +0.519345 +0.920735 +0.522259 +0.909054 +0.512832 +0.898780 +0.504876 +0.909966 +0.520767 +0.910491 +0.531559 +0.873752 +0.506235 +0.882862 +0.524110 +0.864457 +0.515809 +0.893999 +0.523262 +0.872486 +0.528757 +0.884512 +0.518854 +0.870747 +0.502882 +0.871334 +0.507428 +0.887500 +0.509212 +0.865767 +0.489336 +0.852823 +0.487532 +0.839696 +0.496822 +0.857624 +0.488744 +0.856046 +0.481591 +0.833184 +0.487144 +0.843983 +0.476112 +0.815392 +0.485274 +0.831143 +0.479865 +0.819705 +0.473346 +0.813490 +0.448087 +0.812866 +0.488127 +0.824700 +0.449585 +0.806116 +0.447621 +0.803509 +0.445889 +0.799846 +0.459572 +0.797454 +0.431403 +0.788416 +0.433657 +0.797889 +0.396943 +0.765042 +0.441544 +0.794586 +0.441544 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.829068 +0.452510 +0.829068 +0.452510 +0.829068 +0.452510 +0.829068 +0.452510 +0.829068 +0.452510 +0.829068 +0.412433 +0.770418 +0.435257 +0.838916 +0.480764 +0.824735 +0.441507 +0.823553 +0.464514 +0.821667 +0.440445 +0.831854 +0.428506 +0.845567 +0.435988 +0.860559 +0.481700 +0.822336 +0.463636 +0.818260 +0.481595 +0.845250 +0.486570 +0.886539 +0.515096 +0.847377 +0.476122 +0.825160 +0.497681 +0.878883 +0.489449 +0.857877 +0.482719 +0.851286 +0.472324 +0.877502 +0.503927 +0.893631 +0.502966 +0.890686 +0.520346 +0.857090 +0.465323 +0.870882 +0.502707 +0.879279 +0.492837 +0.872145 +0.513519 +0.879835 +0.513362 +0.895424 +0.518169 +0.881658 +0.527724 +0.902586 +0.533189 +0.900809 +0.521493 +0.884836 +0.521973 +0.888549 +0.530705 +0.906536 +0.520474 +0.888439 +0.535939 +0.920419 +0.549994 +0.907309 +0.534877 +0.897918 +0.536123 +0.917598 +0.548897 +0.910563 +0.548743 +0.910562 +0.548567 +0.902827 +0.542795 +0.935821 +0.539991 +0.913670 +0.542459 +0.908824 +0.531171 +0.917911 +0.539439 +0.906712 +0.524715 +0.930523 +0.557142 +0.945521 +0.545052 +0.915124 +0.540860 +0.917657 +0.537134 +0.920841 +0.529444 +0.920793 +0.554313 +0.928765 +0.556671 +0.916541 +0.539563 +0.913246 +0.544614 +0.916022 +0.543660 +0.910287 +0.542850 +0.913867 +0.538734 +0.933390 +0.554811 +0.939251 +0.548090 +0.935715 +0.546727 +0.939689 +0.536691 +0.935245 +0.539038 +0.936276 +0.528199 +0.914756 +0.532602 +0.938477 +0.547440 +0.932315 +0.542454 +0.914609 +0.539178 +0.933212 +0.537129 +0.928112 +0.531884 +0.913336 +0.528551 +0.925035 +0.541699 +0.911362 +0.541920 +0.929833 +0.527537 +0.919230 +0.536720 +0.930320 +0.528746 +0.912785 +0.544955 +0.925412 +0.533893 +0.919172 +0.525315 +0.932266 +0.535988 +0.912905 +0.519409 +0.918063 +0.510457 +0.909722 +0.519235 +0.910000 +0.525165 +0.927208 +0.524114 +0.912273 +0.518446 +0.897337 +0.507167 +0.904710 +0.505760 +0.909996 +0.508847 +0.904589 +0.524261 +0.881460 +0.518590 +0.871966 +0.527127 +0.890148 +0.524769 +0.904999 +0.516665 +0.883232 +0.534716 +0.876548 +0.503530 +0.863213 +0.508281 +0.864490 +0.495821 +0.866398 +0.497840 +0.852029 +0.493574 +0.847750 +0.489327 +0.865676 +0.458551 +0.858615 +0.479235 +0.851069 +0.470769 +0.854236 +0.467651 +0.837798 +0.477991 +0.818553 +0.465582 +0.828460 +0.467873 +0.798082 +0.477463 +0.815851 +0.456677 +0.810973 +0.467946 +0.805942 +0.440455 +0.792298 +0.438159 +0.779314 +0.414945 +0.781417 +0.442934 +0.815701 +0.450741 +0.798114 +0.428781 +0.781840 +0.409557 +0.761889 +0.417217 +0.798334 +0.429693 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.834125 +0.454413 +0.834125 +0.454413 +0.834125 +0.454413 +0.834125 +0.454413 +0.834125 +0.421867 +0.869798 +0.446098 +0.829554 +0.433012 +0.815769 +0.484128 +0.857052 +0.460105 +0.850122 +0.441828 +0.865223 +0.436510 +0.841348 +0.447417 +0.847724 +0.459961 +0.853659 +0.487978 +0.854916 +0.482553 +0.857311 +0.462713 +0.874152 +0.483768 +0.849916 +0.472950 +0.881071 +0.462114 +0.838735 +0.471766 +0.857420 +0.505213 +0.853664 +0.482919 +0.875283 +0.485237 +0.861372 +0.523228 +0.897513 +0.515047 +0.899293 +0.506585 +0.916117 +0.485741 +0.896823 +0.490539 +0.896912 +0.505324 +0.879750 +0.495848 +0.883195 +0.482455 +0.907983 +0.496018 +0.908302 +0.516862 +0.900012 +0.516070 +0.907040 +0.521725 +0.899669 +0.510859 +0.915298 +0.521165 +0.899200 +0.519925 +0.910365 +0.547581 +0.910237 +0.538836 +0.928365 +0.528113 +0.916583 +0.529406 +0.947770 +0.557746 +0.924101 +0.536696 +0.913011 +0.551185 +0.927637 +0.538223 +0.940185 +0.539358 +0.951273 +0.539706 +0.953420 +0.541417 +0.938533 +0.533249 +0.932642 +0.539761 +0.946644 +0.543161 +0.935477 +0.550317 +0.929897 +0.533848 +0.930826 +0.552929 +0.918804 +0.538121 +0.910502 +0.546476 +0.911258 +0.547280 +0.913088 +0.542860 +0.911990 +0.549525 +0.918132 +0.537334 +0.922000 +0.542932 +0.916781 +0.536556 +0.923087 +0.545361 +0.943957 +0.535857 +0.943160 +0.541486 +0.949031 +0.549668 +0.926710 +0.540682 +0.937210 +0.544034 +0.927616 +0.525927 +0.931140 +0.538626 +0.953997 +0.529677 +0.931983 +0.537702 +0.942017 +0.549188 +0.950629 +0.543115 +0.944461 +0.538601 +0.943282 +0.530250 +0.939996 +0.535051 +0.948387 +0.529104 +0.945538 +0.534089 +0.933344 +0.540252 +0.930498 +0.512971 +0.938417 +0.550018 +0.950951 +0.537441 +0.965405 +0.535259 +0.938637 +0.515553 +0.934816 +0.520558 +0.917202 +0.520278 +0.950742 +0.524338 +0.952136 +0.520054 +0.929490 +0.503809 +0.916821 +0.503924 +0.917918 +0.523585 +0.934142 +0.508988 +0.913635 +0.540765 +0.905322 +0.540868 +0.897715 +0.520360 +0.901879 +0.526701 +0.916402 +0.513916 +0.906783 +0.525524 +0.900560 +0.513183 +0.901487 +0.509926 +0.895103 +0.500553 +0.881334 +0.498672 +0.874190 +0.487839 +0.870414 +0.485504 +0.878637 +0.512803 +0.869166 +0.485670 +0.853372 +0.454984 +0.849361 +0.447019 +0.865739 +0.486271 +0.826893 +0.479260 +0.825631 +0.475160 +0.831746 +0.490314 +0.807529 +0.467664 +0.823941 +0.510594 +0.817136 +0.472121 +0.802625 +0.437957 +0.836273 +0.447290 +0.809450 +0.409939 +0.806751 +0.438688 +0.797666 +0.410469 +0.778702 +0.433178 +0.816978 +0.408332 +0.780171 +0.427445 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.853908 +0.408904 +0.853908 +0.408904 +0.853908 +0.408904 +0.853908 +0.408904 +0.853908 +0.408904 +0.853908 +0.386865 +0.785780 +0.390432 +0.835460 +0.424289 +0.864551 +0.411990 +0.861714 +0.458249 +0.842608 +0.422793 +0.853367 +0.445169 +0.843529 +0.424136 +0.880277 +0.454660 +0.848374 +0.451711 +0.883476 +0.482121 +0.901360 +0.462755 +0.854462 +0.429878 +0.837531 +0.471543 +0.851563 +0.461869 +0.890302 +0.478643 +0.870748 +0.466968 +0.876190 +0.481039 +0.875210 +0.483012 +0.923653 +0.492267 +0.889284 +0.493956 +0.910910 +0.483435 +0.897066 +0.486701 +0.926188 +0.489042 +0.879928 +0.471047 +0.914517 +0.490054 +0.921175 +0.509520 +0.900198 +0.513911 +0.901177 +0.488152 +0.913577 +0.503839 +0.909677 +0.496394 +0.908981 +0.498219 +0.906841 +0.514474 +0.930602 +0.518532 +0.929451 +0.513856 +0.939176 +0.503495 +0.921213 +0.526001 +0.934834 +0.528992 +0.933252 +0.508790 +0.914724 +0.529246 +0.926928 +0.519980 +0.933669 +0.517126 +0.941139 +0.540021 +0.935726 +0.522202 +0.942576 +0.530464 +0.933368 +0.534414 +0.942784 +0.537436 +0.945058 +0.515715 +0.932073 +0.529566 +0.951018 +0.525971 +0.931191 +0.513157 +0.934129 +0.528370 +0.947957 +0.536896 +0.949791 +0.519302 +0.938588 +0.524141 +0.932294 +0.520719 +0.941345 +0.528395 +0.936613 +0.522470 +0.937367 +0.522069 +0.944650 +0.514390 +0.944366 +0.524661 +0.968852 +0.515511 +0.949478 +0.514696 +0.947850 +0.521375 +0.959368 +0.518542 +0.945359 +0.523247 +0.946250 +0.511378 +0.928259 +0.528237 +0.953469 +0.513597 +0.948130 +0.530457 +0.943726 +0.513143 +0.948223 +0.524832 +0.956878 +0.528651 +0.945017 +0.502481 +0.946551 +0.511648 +0.955039 +0.530482 +0.936462 +0.507595 +0.956174 +0.523293 +0.963302 +0.529644 +0.956439 +0.502505 +0.937479 +0.518228 +0.935647 +0.500186 +0.938057 +0.512531 +0.959457 +0.511821 +0.939881 +0.513029 +0.930645 +0.508471 +0.928227 +0.492716 +0.938998 +0.505737 +0.934034 +0.500772 +0.930032 +0.515008 +0.915781 +0.522885 +0.911805 +0.528883 +0.931681 +0.516664 +0.927579 +0.512206 +0.897917 +0.516010 +0.891281 +0.501626 +0.897276 +0.490675 +0.903887 +0.482189 +0.893692 +0.488722 +0.885127 +0.477747 +0.869806 +0.461393 +0.876669 +0.484676 +0.881829 +0.448054 +0.862780 +0.445682 +0.880449 +0.459297 +0.865237 +0.460361 +0.832062 +0.451693 +0.849148 +0.434440 +0.836431 +0.449034 +0.825054 +0.463927 +0.836353 +0.458298 +0.837026 +0.418728 +0.844634 +0.461316 +0.812056 +0.406138 +0.822193 +0.439579 +0.819221 +0.460123 +0.862484 +0.408305 +0.804290 +0.389700 +0.806734 +0.381423 +0.819373 +0.419376 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.847275 +0.416565 +0.847275 +0.416565 +0.847275 +0.416565 +0.847275 +0.416565 +0.847275 +0.425499 +0.816024 +0.397595 +0.843841 +0.433943 +0.840726 +0.418159 +0.857259 +0.431007 +0.855033 +0.470812 +0.867163 +0.455714 +0.837051 +0.441884 +0.837278 +0.432599 +0.848511 +0.471931 +0.856261 +0.458128 +0.854855 +0.432464 +0.853188 +0.449395 +0.846220 +0.454681 +0.845610 +0.444031 +0.858604 +0.466829 +0.893515 +0.455335 +0.884202 +0.455244 +0.870987 +0.469630 +0.877241 +0.486369 +0.896678 +0.482322 +0.886033 +0.475426 +0.895364 +0.489414 +0.901210 +0.471801 +0.884014 +0.475688 +0.876365 +0.486906 +0.930578 +0.485608 +0.892075 +0.508786 +0.917209 +0.506253 +0.916001 +0.500470 +0.902023 +0.476583 +0.908595 +0.494721 +0.913057 +0.496708 +0.904303 +0.509651 +0.909495 +0.516992 +0.921435 +0.509194 +0.924545 +0.512683 +0.921961 +0.525279 +0.939407 +0.528546 +0.947514 +0.538473 +0.938241 +0.528606 +0.935271 +0.519743 +0.940772 +0.525961 +0.941731 +0.516720 +0.933764 +0.516057 +0.936084 +0.526308 +0.945224 +0.524267 +0.950766 +0.514320 +0.942727 +0.521972 +0.936592 +0.523140 +0.948833 +0.521692 +0.945415 +0.528524 +0.929337 +0.506452 +0.930000 +0.518464 +0.945417 +0.528562 +0.934315 +0.511326 +0.935026 +0.532768 +0.929701 +0.515342 +0.943922 +0.514288 +0.945180 +0.514132 +0.946150 +0.515400 +0.948729 +0.533685 +0.967818 +0.504914 +0.947232 +0.541999 +0.953285 +0.510686 +0.946639 +0.518679 +0.944835 +0.514338 +0.948833 +0.509915 +0.951901 +0.530621 +0.955254 +0.529870 +0.948353 +0.538043 +0.958063 +0.519404 +0.939548 +0.535080 +0.947993 +0.527426 +0.949839 +0.520252 +0.950648 +0.535118 +0.969198 +0.516044 +0.951017 +0.523930 +0.960589 +0.535415 +0.961609 +0.529745 +0.937343 +0.522581 +0.947546 +0.511199 +0.958280 +0.502523 +0.929157 +0.530737 +0.963247 +0.519595 +0.947102 +0.512238 +0.936227 +0.522966 +0.949699 +0.495342 +0.940838 +0.504920 +0.939794 +0.494087 +0.935094 +0.503128 +0.917826 +0.527027 +0.916960 +0.490234 +0.907730 +0.511983 +0.909623 +0.502262 +0.903160 +0.495675 +0.903083 +0.501286 +0.881102 +0.507018 +0.906205 +0.517142 +0.902544 +0.465103 +0.900362 +0.474077 +0.850238 +0.458425 +0.909262 +0.479799 +0.885969 +0.495747 +0.882196 +0.459833 +0.884775 +0.478183 +0.875936 +0.462528 +0.843250 +0.477149 +0.841303 +0.482578 +0.851407 +0.461732 +0.835239 +0.463762 +0.878128 +0.465458 +0.826888 +0.453452 +0.825792 +0.413729 +0.861558 +0.424728 +0.818050 +0.416045 +0.813513 +0.417247 +0.826419 +0.440106 +0.831615 +0.453959 +0.814613 +0.403107 +0.809565 +0.437104 +1000.000000 +1000.000000 +1000.000000 +0.000000 +0.000000 +1000.000000 +0.853067 +0.419863 +0.853067 +0.419863 +0.853067 +0.419863 +0.853067 +0.419863 +0.853067 +0.419863 +0.853067 +0.402915 +0.793254 +0.401114 +0.860195 +0.431367 +0.849521 +0.427108 +0.849484 +0.434539 +0.861235 +0.457094 +0.841248 +0.436294 +0.855935 +0.435415 +0.864925 +0.453062 +0.838695 +0.466914 +0.875008 +0.425572 +0.861667 +0.459294 +0.875234 +0.466695 +0.856167 +0.450254 +0.901660 +0.473232 +0.874111 +0.450698 +0.872552 +0.461721 +0.862908 +0.479475 +0.906089 +0.483994 +0.874337 +0.466756 +0.874271 +0.495014 +0.887287 +0.476888 +0.902319 +0.486341 +0.897008 +0.486287 +0.921357 +0.471550 +0.914682 +0.482769 +0.905668 +0.507863 +0.918434 +0.492956 +0.923946 +0.493771 +0.913567 +0.496804 +0.897150 +0.485640 +0.930573 +0.505469 +0.916824 +0.498081 +0.902986 +0.509430 +0.926675 +0.521069 +0.925077 +0.531528 +0.930627 +0.512579 +0.913101 +0.545713 +0.931310 +0.514262 +0.946204 +0.528925 +0.918437 +0.525173 +0.944450 +0.518928 +0.924358 +0.528404 +0.943020 +0.514215 +0.945605 +0.524166 +0.953381 +0.527769 +0.959415 +0.535418 +0.939049 +0.510917 +0.944337 +0.538551 +0.950737 +0.527859 +0.944405 +0.519406 +0.945166 +0.523329 +0.941306 +0.535354 +0.938851 +0.523698 +0.930890 +0.523242 +0.948421 +0.525879 +0.942586 +0.519621 +0.960462 +0.522898 +0.953416 +0.522173 +0.950463 +0.511195 +0.950500 +0.522174 +0.969259 +0.529579 +0.956174 +0.530428 +0.970424 +0.530875 +0.948004 +0.526985 +0.948395 +0.540562 +0.964508 +0.544944 +0.971832 +0.539955 +0.981577 +0.542033 +0.955472 +0.529098 +0.963316 +0.567078 +0.992313 +0.549740 +0.961959 +0.523852 +0.948625 +0.529477 +0.958790 +0.538518 +0.947622 +0.517340 +0.960166 +0.535626 +0.971900 +0.534499 +0.961374 +0.529422 +0.962372 +0.539929 +0.959123 +0.537913 +0.958316 +0.523910 +0.948784 +0.512949 +0.956327 +0.534630 +0.957437 +0.524725 +0.942749 +0.516332 +0.936945 +0.511500 +0.948610 +0.515905 +0.936541 +0.495592 +0.931120 +0.517622 +0.921030 +0.528816 +0.917032 +0.519652 +0.911382 +0.515912 +0.929622 +0.522611 +0.932055 +0.505437 +0.900972 +0.515823 +0.906514 +0.512728 +0.899533 +0.493945 +0.889641 +0.492605 +0.887001 +0.473112 +0.885440 +0.458192 +0.885995 +0.483469 +0.888628 +0.468417 +0.877763 +0.494994 +0.889215 +0.489316 +0.883769 +0.474352 +0.861623 +0.446771 +0.846864 +0.474710 +0.859008 +0.481492 +0.871163 +0.450983 +0.853424 +0.448924 +0.858118 +0.488679 +0.856069 +0.431332 +0.837745 +0.440481 +0.810319 +0.458670 +0.809547 +0.414286 +0.808748 +0.443494 +0.819204 +0.443494 +0.819204 +0.443494 +0.819204 +0.443494 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.875614 +0.453575 +0.875614 +0.453575 +0.875614 +0.453575 +0.875614 +0.453575 +0.875614 +0.453575 +0.875614 +0.453575 +0.875614 +0.399350 +0.833814 +0.425136 +0.874408 +0.462651 +0.892699 +0.472938 +0.859733 +0.450958 +0.847085 +0.455480 +0.846365 +0.447879 +0.873000 +0.456142 +0.881746 +0.493225 +0.895744 +0.453319 +0.877236 +0.497852 +0.898186 +0.484706 +0.912129 +0.468146 +0.871842 +0.474623 +0.900151 +0.480410 +0.901775 +0.465258 +0.891493 +0.473681 +0.898559 +0.490590 +0.920017 +0.512013 +0.920474 +0.507548 +0.929788 +0.487593 +0.933071 +0.498012 +0.921688 +0.521712 +0.899595 +0.509082 +0.908579 +0.498489 +0.909651 +0.499988 +0.919832 +0.497003 +0.938725 +0.518739 +0.916682 +0.508648 +0.921618 +0.489082 +0.914800 +0.510412 +0.921672 +0.518851 +0.938014 +0.515178 +0.934462 +0.534202 +0.929706 +0.533040 +0.930634 +0.535672 +0.932828 +0.536044 +0.948816 +0.531255 +0.950372 +0.517495 +0.942091 +0.519386 +0.943643 +0.525949 +0.951761 +0.518502 +0.948866 +0.538545 +0.965254 +0.528971 +0.942787 +0.520247 +0.960712 +0.531094 +0.956284 +0.519409 +0.962094 +0.547959 +0.964221 +0.519429 +0.955192 +0.529029 +0.946249 +0.529030 +0.945166 +0.508344 +0.937213 +0.540090 +0.949699 +0.536019 +0.949805 +0.527957 +0.938243 +0.513516 +0.922834 +0.523439 +0.963160 +0.536798 +0.954980 +0.527414 +0.962163 +0.538198 +0.970616 +0.525256 +0.963956 +0.541764 +0.962702 +0.523054 +0.960844 +0.514278 +0.966316 +0.512781 +0.958775 +0.538444 +0.964399 +0.532447 +0.967911 +0.523628 +0.971634 +0.528087 +0.974241 +0.551438 +0.975463 +0.548136 +0.949273 +0.514303 +0.966994 +0.533822 +0.976586 +0.518823 +0.965964 +0.531492 +0.949795 +0.529317 +0.968946 +0.523324 +0.956067 +0.528009 +0.956538 +0.515773 +0.964165 +0.526146 +0.961923 +0.522012 +0.949204 +0.521197 +0.947822 +0.521938 +0.957807 +0.509172 +0.953229 +0.497910 +0.949074 +0.499525 +0.938794 +0.514602 +0.939972 +0.494811 +0.934073 +0.525795 +0.908350 +0.523576 +0.936661 +0.522369 +0.897039 +0.528183 +0.920016 +0.516083 +0.914161 +0.523395 +0.921601 +0.500062 +0.912090 +0.512445 +0.913047 +0.493444 +0.898307 +0.483918 +0.898041 +0.498046 +0.882914 +0.473969 +0.878466 +0.467943 +0.912337 +0.481858 +0.895313 +0.492631 +0.889603 +0.472181 +0.870980 +0.457662 +0.881983 +0.474859 +0.853884 +0.483736 +0.846758 +0.448199 +0.854507 +0.441016 +0.848982 +0.465308 +0.843567 +0.471157 +0.867780 +0.458388 +0.848761 +0.419042 +0.846489 +0.473430 +0.842712 +0.469739 +0.798800 +0.396156 +0.807837 +0.398557 +0.822126 +0.409784 +0.807042 +0.421484 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.844569 +0.428585 +0.844569 +0.428585 +0.844569 +0.428585 +0.844569 +0.428585 +0.844569 +0.448354 +0.848044 +0.426892 +0.817926 +0.410930 +0.836395 +0.447934 +0.879386 +0.449415 +0.879361 +0.464338 +0.868649 +0.447804 +0.866046 +0.460711 +0.856220 +0.461269 +0.860281 +0.464622 +0.864287 +0.497982 +0.872997 +0.421606 +0.860986 +0.454990 +0.876414 +0.466053 +0.881787 +0.479931 +0.894983 +0.465430 +0.884019 +0.471864 +0.873341 +0.496486 +0.895953 +0.460679 +0.907991 +0.495110 +0.889228 +0.462388 +0.897852 +0.490134 +0.918402 +0.453393 +0.920729 +0.508183 +0.921734 +0.489149 +0.910344 +0.490763 +0.892102 +0.477907 +0.928266 +0.494806 +0.897741 +0.521370 +0.913522 +0.502886 +0.920934 +0.514839 +0.936142 +0.502364 +0.926652 +0.519856 +0.923912 +0.506236 +0.925430 +0.538341 +0.928196 +0.518598 +0.945153 +0.523030 +0.915779 +0.506851 +0.926242 +0.533238 +0.936697 +0.533982 +0.938418 +0.513241 +0.933590 +0.525684 +0.944592 +0.513445 +0.954254 +0.546181 +0.952785 +0.526965 +0.960166 +0.546832 +0.955006 +0.523402 +0.941529 +0.534686 +0.963171 +0.535334 +0.959980 +0.548789 +0.952495 +0.537903 +0.951647 +0.526784 +0.942618 +0.528536 +0.955485 +0.526376 +0.945548 +0.539030 +0.967749 +0.534085 +0.942494 +0.531619 +0.937428 +0.519729 +0.930915 +0.519599 +0.962182 +0.519145 +0.957590 +0.523692 +0.962326 +0.527833 +0.975157 +0.522204 +0.965009 +0.528991 +0.964810 +0.521328 +0.963004 +0.525074 +0.954186 +0.538412 +0.955530 +0.533743 +0.959035 +0.532105 +0.970275 +0.534676 +0.955714 +0.528007 +0.969888 +0.566462 +0.965174 +0.550481 +0.952860 +0.526677 +0.961110 +0.533986 +0.948985 +0.517499 +0.955126 +0.531810 +0.958888 +0.524197 +0.957330 +0.516820 +0.967553 +0.519784 +0.953464 +0.513210 +0.969995 +0.521790 +0.946845 +0.515089 +0.941489 +0.512188 +0.952775 +0.507181 +0.942092 +0.517214 +0.961180 +0.513817 +0.936983 +0.505802 +0.934044 +0.502067 +0.954341 +0.507308 +0.940849 +0.514554 +0.946899 +0.509381 +0.932673 +0.506921 +0.907897 +0.529004 +0.927002 +0.524445 +0.909677 +0.517378 +0.911183 +0.485927 +0.900975 +0.516144 +0.907600 +0.488530 +0.912778 +0.503423 +0.896604 +0.479864 +0.907125 +0.489275 +0.891272 +0.488678 +0.880688 +0.509516 +0.871673 +0.469998 +0.859310 +0.471449 +0.861085 +0.466655 +0.864125 +0.465058 +0.863953 +0.480158 +0.851718 +0.499086 +0.867339 +0.447194 +0.874118 +0.449685 +0.856437 +0.469519 +0.833735 +0.455222 +0.825501 +0.411149 +0.849558 +0.409186 +0.819470 +0.452754 +0.822603 +0.445295 +0.849007 +0.417461 +0.826765 +0.451946 +0.839205 +0.438503 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.878901 +0.433540 +0.878901 +0.433540 +0.878901 +0.433540 +0.878901 +0.433540 +0.878901 +0.433540 +0.878901 +0.433540 +0.878901 +0.391523 +0.860508 +0.399167 +0.875598 +0.449985 +0.889164 +0.451467 +0.871942 +0.434116 +0.892628 +0.407642 +0.873476 +0.471981 +0.861458 +0.427281 +0.883299 +0.476445 +0.924414 +0.446379 +0.880942 +0.478771 +0.915294 +0.457121 +0.896357 +0.469380 +0.878028 +0.491627 +0.905319 +0.450639 +0.884338 +0.468914 +0.912076 +0.478716 +0.897843 +0.472114 +0.939808 +0.475816 +0.906929 +0.486859 +0.927430 +0.481228 +0.943837 +0.484525 +0.916093 +0.474804 +0.922024 +0.477870 +0.926601 +0.478094 +0.944245 +0.477683 +0.931781 +0.509851 +0.927355 +0.495205 +0.927117 +0.506535 +0.940472 +0.506725 +0.931443 +0.515247 +0.929035 +0.502819 +0.928655 +0.520630 +0.950391 +0.522967 +0.940934 +0.530840 +0.946567 +0.504638 +0.951561 +0.536337 +0.948268 +0.527043 +0.953075 +0.533585 +0.947868 +0.540051 +0.975053 +0.546174 +0.967064 +0.528373 +0.958856 +0.527225 +0.976848 +0.534500 +0.959425 +0.523230 +0.973710 +0.521763 +0.983020 +0.519270 +0.959286 +0.543238 +0.977482 +0.535135 +0.932824 +0.530886 +0.944575 +0.525850 +0.937020 +0.516491 +0.942772 +0.540738 +0.950590 +0.534392 +0.960324 +0.515478 +0.944372 +0.513979 +0.940392 +0.520162 +0.961698 +0.522149 +0.955426 +0.528049 +0.957637 +0.532352 +0.973265 +0.535151 +0.970849 +0.531285 +0.954744 +0.526782 +0.953321 +0.527246 +0.960841 +0.537500 +0.990092 +0.540528 +0.983826 +0.539998 +0.986303 +0.536352 +0.990113 +0.521012 +0.967593 +0.543766 +0.998982 +0.533776 +0.983819 +0.526560 +0.977794 +0.531368 +0.977254 +0.542163 +0.978289 +0.550225 +0.987984 +0.527745 +0.978898 +0.536964 +0.986334 +0.518743 +0.970868 +0.504378 +0.976124 +0.540904 +0.992588 +0.509104 +0.973916 +0.518960 +0.996403 +0.503945 +0.969037 +0.511558 +0.954046 +0.488800 +0.982244 +0.501781 +0.969323 +0.505110 +0.950928 +0.507504 +0.955927 +0.508342 +0.962798 +0.523262 +0.943679 +0.521164 +0.944787 +0.532751 +0.934633 +0.515333 +0.946660 +0.516580 +0.933387 +0.493743 +0.922716 +0.497369 +0.927862 +0.491571 +0.929739 +0.500519 +0.882206 +0.481620 +0.899959 +0.492067 +0.874642 +0.480383 +0.904978 +0.481974 +0.895760 +0.459322 +0.902341 +0.463881 +0.882261 +0.462740 +0.865590 +0.482658 +0.871345 +0.475824 +0.864414 +0.457265 +0.851019 +0.471485 +0.868045 +0.467821 +0.854873 +0.445940 +0.800716 +0.434624 +0.819907 +0.430103 +0.851539 +0.437429 +0.852179 +0.408320 +0.831174 +0.412030 +0.815851 +0.423448 +0.804583 +0.439849 +0.849946 +0.414599 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.863572 +0.389842 +0.863572 +0.389842 +0.863572 +0.389842 +0.863572 +0.389842 +0.863572 +0.352849 +0.830925 +0.376501 +0.854765 +0.372045 +0.886940 +0.420979 +0.849011 +0.397412 +0.883651 +0.415852 +0.906235 +0.411158 +0.858318 +0.445596 +0.876589 +0.409904 +0.874902 +0.421935 +0.875848 +0.454448 +0.909656 +0.437502 +0.877136 +0.433418 +0.890639 +0.407195 +0.889925 +0.449546 +0.894417 +0.440240 +0.884791 +0.435567 +0.872569 +0.451518 +0.911197 +0.433460 +0.907657 +0.472644 +0.929172 +0.445343 +0.920636 +0.463764 +0.944412 +0.455445 +0.925706 +0.469917 +0.927413 +0.470781 +0.935246 +0.473484 +0.935180 +0.477036 +0.937228 +0.475420 +0.934750 +0.499606 +0.943409 +0.469516 +0.942718 +0.491732 +0.941707 +0.481977 +0.933792 +0.496339 +0.935378 +0.475805 +0.940149 +0.503112 +0.947463 +0.511956 +0.948080 +0.502249 +0.945338 +0.504999 +0.960797 +0.508737 +0.960949 +0.501758 +0.952518 +0.501908 +0.947598 +0.508925 +0.965513 +0.512358 +0.973194 +0.502236 +0.978149 +0.498846 +0.967919 +0.515408 +0.968710 +0.494537 +0.958260 +0.510172 +0.956556 +0.509205 +0.965698 +0.518804 +0.973919 +0.506474 +0.961831 +0.507026 +0.962061 +0.504754 +0.962854 +0.497582 +0.972988 +0.522542 +0.981779 +0.510861 +0.973417 +0.511054 +0.955182 +0.503040 +0.961144 +0.496548 +0.976849 +0.504259 +0.986726 +0.511818 +0.969267 +0.521778 +0.991153 +0.526047 +0.987753 +0.506625 +0.969553 +0.501158 +0.972122 +0.507013 +0.984456 +0.530689 +0.982499 +0.517479 +0.985155 +0.516765 +0.996543 +0.517637 +0.986789 +0.523173 +0.977692 +0.536734 +0.994698 +0.511692 +0.966550 +0.507877 +0.982967 +0.495216 +0.976717 +0.519273 +0.991652 +0.524876 +0.984017 +0.517260 +0.993654 +0.516868 +0.984660 +0.497866 +0.982761 +0.494646 +0.980022 +0.514728 +0.989831 +0.504706 +0.979795 +0.507270 +1.005512 +0.508322 +0.972543 +0.492912 +0.957845 +0.486413 +0.957422 +0.478928 +0.961980 +0.495928 +0.986023 +0.493828 +0.971118 +0.507470 +0.956321 +0.507298 +0.941507 +0.498512 +0.951124 +0.511781 +0.948888 +0.495854 +0.941883 +0.512885 +0.917829 +0.490016 +0.941120 +0.486775 +0.924082 +0.485873 +0.921374 +0.473942 +0.922644 +0.476898 +0.919849 +0.473513 +0.902858 +0.482327 +0.948389 +0.481585 +0.919037 +0.459152 +0.921098 +0.455386 +0.889329 +0.464432 +0.899104 +0.456886 +0.897151 +0.466447 +0.892583 +0.422378 +0.895068 +0.434473 +0.894940 +0.405033 +0.894542 +0.424436 +0.851188 +0.423634 +0.847866 +0.427312 +0.883427 +0.417911 +0.875000 +0.419088 +0.889048 +0.398998 +0.848013 +0.439778 +0.825675 +0.368428 +0.832134 +0.419288 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.847653 +0.406220 +0.847653 +0.406220 +0.847653 +0.406220 +0.847653 +0.406220 +0.847653 +0.313296 +0.826752 +0.378731 +0.829472 +0.440990 +0.856223 +0.398939 +0.857266 +0.414531 +0.871442 +0.437928 +0.866618 +0.417322 +0.879462 +0.406094 +0.872117 +0.443056 +0.871502 +0.415818 +0.884090 +0.458003 +0.885075 +0.421748 +0.873490 +0.434728 +0.902000 +0.435557 +0.876596 +0.460527 +0.913657 +0.436530 +0.895201 +0.447031 +0.887859 +0.452821 +0.922705 +0.458117 +0.919268 +0.429312 +0.923836 +0.479746 +0.937349 +0.468282 +0.939936 +0.461284 +0.911986 +0.481006 +0.946262 +0.467003 +0.927139 +0.475181 +0.928757 +0.451423 +0.917846 +0.485173 +0.932974 +0.494919 +0.934635 +0.483950 +0.933636 +0.468343 +0.918219 +0.475815 +0.941774 +0.481111 +0.938328 +0.494477 +0.930186 +0.478410 +0.951288 +0.518987 +0.946141 +0.494258 +0.957443 +0.525451 +0.957470 +0.515987 +0.949358 +0.504227 +0.960127 +0.492653 +0.966638 +0.512396 +0.951833 +0.487431 +0.964609 +0.510897 +0.957292 +0.500760 +0.979346 +0.510509 +0.971009 +0.492299 +0.962340 +0.503618 +0.975303 +0.505460 +0.972342 +0.496197 +0.975464 +0.504695 +0.954537 +0.500964 +0.957527 +0.497550 +0.945757 +0.500724 +0.981671 +0.509841 +0.972751 +0.511551 +0.955676 +0.500146 +0.965066 +0.497590 +0.960277 +0.512179 +0.976634 +0.510756 +0.987349 +0.510075 +0.975161 +0.516750 +0.986192 +0.506044 +1.000898 +0.523947 +0.973680 +0.499965 +0.975894 +0.504860 +0.980314 +0.542143 +0.985412 +0.531163 +0.996495 +0.522089 +0.997579 +0.534732 +0.990084 +0.531251 +0.980395 +0.515025 +0.990754 +0.534912 +0.980586 +0.510093 +0.993786 +0.528186 +0.989006 +0.519831 +1.015912 +0.530625 +1.010600 +0.519733 +0.994223 +0.519887 +0.974596 +0.516877 +0.978897 +0.512671 +0.989997 +0.513531 +1.001572 +0.512428 +0.986012 +0.504373 +0.979942 +0.496219 +0.973051 +0.495504 +0.976355 +0.484801 +0.977617 +0.476177 +0.992342 +0.506292 +0.974954 +0.485187 +0.957100 +0.501055 +0.956106 +0.511999 +0.969230 +0.507274 +0.944700 +0.525264 +0.959634 +0.505238 +0.956379 +0.501291 +0.939046 +0.491613 +0.937407 +0.495034 +0.947734 +0.488179 +0.922900 +0.454637 +0.908716 +0.476249 +0.906415 +0.477747 +0.938374 +0.444498 +0.939063 +0.475126 +0.914683 +0.479370 +0.901053 +0.442360 +0.920091 +0.470204 +0.907155 +0.465885 +0.913858 +0.457094 +0.882876 +0.449851 +0.888467 +0.462889 +0.877265 +0.475745 +0.881261 +0.428218 +0.841257 +0.413815 +0.882904 +0.455761 +0.883456 +0.401785 +0.859294 +0.395912 +0.834146 +0.399796 +0.877087 +0.417819 +0.858965 +0.417819 +0.858965 +0.417819 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.865494 +0.416059 +0.865494 +0.416059 +0.865494 +0.416059 +0.865494 +0.416059 +0.865494 +0.416059 +0.865494 +0.365322 +0.815709 +0.389036 +0.867111 +0.426567 +0.880352 +0.432574 +0.849017 +0.424436 +0.880836 +0.406147 +0.858862 +0.406706 +0.888836 +0.417331 +0.872980 +0.420924 +0.884098 +0.443852 +0.917247 +0.447712 +0.879130 +0.437191 +0.894240 +0.405394 +0.870335 +0.442233 +0.913151 +0.444187 +0.914392 +0.433610 +0.886692 +0.449318 +0.898346 +0.481526 +0.924300 +0.459814 +0.915959 +0.457379 +0.911848 +0.455729 +0.937013 +0.457973 +0.931200 +0.463438 +0.921054 +0.483362 +0.939268 +0.475699 +0.907298 +0.478118 +0.933127 +0.490300 +0.926063 +0.479293 +0.943740 +0.462250 +0.931852 +0.461894 +0.935981 +0.483512 +0.934523 +0.456972 +0.948230 +0.483224 +0.941366 +0.488091 +0.947501 +0.516888 +0.969875 +0.489359 +0.957025 +0.492761 +0.957594 +0.508488 +0.967188 +0.498685 +0.942265 +0.502368 +0.974710 +0.493809 +0.958126 +0.502547 +0.954348 +0.508155 +0.969367 +0.498243 +0.956785 +0.482010 +0.976088 +0.514695 +0.968086 +0.499349 +0.945573 +0.502475 +0.961890 +0.504479 +0.969675 +0.497669 +0.959445 +0.501824 +0.973340 +0.499663 +0.955948 +0.496219 +0.967469 +0.527387 +0.979588 +0.513979 +0.973077 +0.519788 +0.957579 +0.515070 +0.954726 +0.512298 +0.996462 +0.511592 +0.969168 +0.520390 +0.964870 +0.491600 +0.990099 +0.509169 +1.002154 +0.521953 +0.995918 +0.515083 +0.972575 +0.514619 +0.999752 +0.505159 +0.973307 +0.495422 +0.992278 +0.508321 +0.983258 +0.512209 +0.986872 +0.517264 +0.962597 +0.520941 +0.966755 +0.513712 +0.964768 +0.502059 +0.967642 +0.500924 +0.978454 +0.517883 +0.984626 +0.501710 +0.983548 +0.504556 +0.980545 +0.507079 +0.970673 +0.476970 +0.973656 +0.497441 +0.974465 +0.515310 +0.970811 +0.485077 +0.961216 +0.478453 +0.972091 +0.488539 +0.964187 +0.499239 +0.956044 +0.481293 +0.962045 +0.474657 +0.958474 +0.486132 +0.952141 +0.471834 +0.951799 +0.491523 +0.936426 +0.506010 +0.959071 +0.508578 +0.942619 +0.494746 +0.924269 +0.508961 +0.925282 +0.471250 +0.932421 +0.469506 +0.928660 +0.481102 +0.917600 +0.459606 +0.905215 +0.461081 +0.917837 +0.465505 +0.909148 +0.455978 +0.904482 +0.468864 +0.911901 +0.479260 +0.908732 +0.441710 +0.895987 +0.427925 +0.881884 +0.428692 +0.879888 +0.452695 +0.893773 +0.441269 +0.893298 +0.417713 +0.874581 +0.419338 +0.860950 +0.437018 +0.884974 +0.451760 +0.850959 +0.416364 +0.846610 +0.412346 +0.880187 +0.397706 +0.866386 +0.372284 +0.805081 +0.362206 +0.858889 +0.368231 +0.858343 +0.377398 +0.843452 +0.377398 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.859552 +0.410770 +0.859552 +0.410770 +0.859552 +0.410770 +0.859552 +0.410770 +0.859552 +0.410770 +0.859552 +0.410770 +0.859552 +0.384267 +0.815378 +0.362067 +0.862448 +0.432819 +0.836122 +0.437424 +0.880085 +0.411041 +0.879349 +0.356641 +0.832902 +0.396917 +0.844547 +0.426743 +0.877345 +0.430575 +0.873593 +0.415078 +0.910097 +0.393215 +0.867781 +0.440957 +0.910694 +0.420747 +0.889431 +0.432795 +0.905477 +0.428228 +0.866001 +0.427797 +0.893398 +0.415370 +0.922460 +0.462079 +0.935273 +0.474123 +0.936283 +0.458378 +0.899032 +0.436776 +0.912784 +0.468546 +0.912079 +0.453243 +0.953947 +0.452036 +0.910704 +0.450275 +0.954674 +0.468880 +0.925204 +0.473830 +0.939948 +0.473595 +0.935567 +0.459146 +0.936591 +0.480339 +0.917936 +0.476230 +0.932780 +0.475229 +0.940417 +0.495977 +0.952353 +0.484591 +0.952704 +0.491543 +0.950201 +0.503374 +0.941778 +0.482811 +0.958537 +0.480821 +0.939598 +0.493364 +0.944129 +0.491192 +0.954224 +0.498917 +0.949322 +0.497153 +0.953977 +0.479813 +0.963313 +0.485603 +0.951129 +0.494967 +0.952916 +0.489452 +0.951960 +0.493741 +0.972163 +0.502128 +0.971035 +0.477730 +0.949353 +0.502010 +0.944686 +0.489681 +0.944526 +0.497945 +0.967264 +0.511207 +0.962866 +0.502693 +0.963296 +0.493968 +0.945013 +0.508467 +0.962968 +0.504633 +0.970082 +0.479820 +0.955486 +0.486149 +0.971485 +0.494102 +0.966892 +0.482496 +0.968522 +0.492620 +0.971081 +0.492624 +0.967559 +0.506413 +0.979096 +0.495811 +0.964930 +0.496814 +0.967887 +0.502923 +0.973013 +0.504471 +0.979398 +0.506596 +0.978772 +0.485241 +0.977868 +0.511669 +0.985967 +0.496439 +0.987634 +0.490383 +0.980266 +0.509484 +0.985871 +0.502582 +0.981164 +0.488026 +0.977760 +0.496304 +0.958502 +0.513377 +0.956881 +0.485733 +0.970925 +0.469816 +0.965925 +0.494868 +0.951284 +0.483944 +0.961119 +0.487226 +0.971482 +0.495245 +0.958818 +0.481522 +0.971937 +0.490465 +0.971144 +0.467360 +0.942156 +0.464044 +0.946356 +0.512219 +0.941362 +0.497627 +0.941014 +0.500729 +0.937474 +0.498617 +0.938978 +0.489465 +0.918606 +0.485354 +0.925154 +0.470794 +0.919952 +0.470283 +0.921058 +0.452482 +0.922861 +0.469315 +0.913969 +0.452872 +0.900218 +0.459531 +0.904288 +0.448765 +0.901653 +0.443536 +0.911597 +0.468934 +0.905676 +0.468013 +0.855528 +0.440791 +0.879912 +0.462380 +0.898686 +0.439690 +0.868788 +0.433359 +0.862941 +0.438558 +0.871173 +0.442721 +0.855560 +0.441711 +0.843695 +0.420656 +0.860931 +0.404570 +0.826859 +0.398691 +0.859540 +0.388898 +0.826859 +0.411135 +0.839293 +0.388007 +0.823926 +0.364656 +0.813357 +0.396013 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.887907 +0.420903 +0.887907 +0.420903 +0.887907 +0.420903 +0.887907 +0.420903 +0.887907 +0.420903 +0.887907 +0.420903 +0.887907 +0.417598 +0.850872 +0.397910 +0.900982 +0.443766 +0.865963 +0.421032 +0.896778 +0.370354 +0.881587 +0.384866 +0.886752 +0.435136 +0.865517 +0.396549 +0.851473 +0.438174 +0.851021 +0.387915 +0.872662 +0.421532 +0.880715 +0.422395 +0.860235 +0.421720 +0.871930 +0.443164 +0.896062 +0.438606 +0.875632 +0.418382 +0.854213 +0.410272 +0.913124 +0.467399 +0.919975 +0.450642 +0.923608 +0.448492 +0.908588 +0.478825 +0.906219 +0.463226 +0.901807 +0.442338 +0.908289 +0.464791 +0.913318 +0.459438 +0.921448 +0.479225 +0.915515 +0.482414 +0.929326 +0.469084 +0.925025 +0.473491 +0.935129 +0.469487 +0.934717 +0.486609 +0.937395 +0.467301 +0.950553 +0.493989 +0.941373 +0.479456 +0.941606 +0.498652 +0.936350 +0.497930 +0.957143 +0.496818 +0.950593 +0.502465 +0.957684 +0.499049 +0.936922 +0.490506 +0.963537 +0.483208 +0.945388 +0.491638 +0.959468 +0.503836 +0.961594 +0.487486 +0.975742 +0.488006 +0.966327 +0.484084 +0.961109 +0.490665 +0.955674 +0.503186 +0.937422 +0.473332 +0.944045 +0.505434 +0.939567 +0.505096 +0.969942 +0.491867 +0.964000 +0.507539 +0.964474 +0.495697 +0.963765 +0.492031 +0.948102 +0.487388 +0.952353 +0.504421 +0.967542 +0.484022 +0.952665 +0.496204 +0.971712 +0.492165 +0.982030 +0.500034 +0.971163 +0.498904 +0.980115 +0.499341 +0.970766 +0.503377 +0.969579 +0.501492 +0.973661 +0.514376 +0.981776 +0.498525 +0.985165 +0.515495 +0.987456 +0.512530 +0.987880 +0.520550 +0.972856 +0.505991 +0.965193 +0.516368 +0.979994 +0.502912 +0.994994 +0.501968 +0.969122 +0.482828 +0.964845 +0.485363 +0.970295 +0.504917 +0.965952 +0.500999 +0.974767 +0.473865 +0.949377 +0.493957 +0.989990 +0.481251 +0.954461 +0.498332 +0.968134 +0.482868 +0.968100 +0.485290 +0.967562 +0.480606 +0.938232 +0.474688 +0.942711 +0.480844 +0.957353 +0.457980 +0.935510 +0.498482 +0.922779 +0.493276 +0.928404 +0.478421 +0.928714 +0.482861 +0.943430 +0.493690 +0.923621 +0.497096 +0.926420 +0.466433 +0.906612 +0.463878 +0.898131 +0.482666 +0.916682 +0.470557 +0.892574 +0.442007 +0.902263 +0.449519 +0.906966 +0.445258 +0.915196 +0.447361 +0.904435 +0.460285 +0.897325 +0.430257 +0.878349 +0.436198 +0.880545 +0.420008 +0.881497 +0.468954 +0.847228 +0.441497 +0.862238 +0.474722 +0.856609 +0.443931 +0.858206 +0.434520 +0.880326 +0.416472 +0.848753 +0.398580 +0.876556 +0.407305 +0.862599 +0.355166 +0.862147 +0.420171 +0.846440 +0.379336 +0.778327 +0.394214 +0.857062 +0.394214 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.860251 +0.399320 +0.860251 +0.399320 +0.860251 +0.399320 +0.860251 +0.399320 +0.860251 +0.399320 +0.860251 +0.374743 +0.832152 +0.360546 +0.840150 +0.395797 +0.854536 +0.441619 +0.886067 +0.375883 +0.914138 +0.385350 +0.856850 +0.403891 +0.857317 +0.389861 +0.871639 +0.414431 +0.872808 +0.438986 +0.864021 +0.426719 +0.899137 +0.423400 +0.878867 +0.415572 +0.902497 +0.435673 +0.892852 +0.438936 +0.901446 +0.433868 +0.917117 +0.432501 +0.898130 +0.418417 +0.913389 +0.451771 +0.929825 +0.430454 +0.944121 +0.469075 +0.933958 +0.455832 +0.925501 +0.455526 +0.934448 +0.450670 +0.943263 +0.470786 +0.919478 +0.445991 +0.953467 +0.444856 +0.928456 +0.464605 +0.951001 +0.481718 +0.934649 +0.459511 +0.941739 +0.473107 +0.956008 +0.485943 +0.929754 +0.457063 +0.941445 +0.481191 +0.947598 +0.476182 +0.954990 +0.492693 +0.959772 +0.500437 +0.971198 +0.496693 +0.961702 +0.506675 +0.956971 +0.482187 +0.965516 +0.497636 +0.957874 +0.498080 +0.972515 +0.492366 +0.980558 +0.504953 +0.970192 +0.498601 +0.972866 +0.504176 +0.965665 +0.506592 +0.972165 +0.500730 +0.966305 +0.483384 +0.963018 +0.488215 +0.947598 +0.497591 +0.946075 +0.486548 +0.951632 +0.499604 +0.948252 +0.497515 +0.961396 +0.495477 +0.963814 +0.485150 +0.957845 +0.485127 +0.950117 +0.489781 +0.962291 +0.478061 +0.955415 +0.498364 +0.994166 +0.499425 +0.969478 +0.493813 +0.984768 +0.495780 +0.956675 +0.485019 +0.956848 +0.503808 +0.969057 +0.499766 +1.002195 +0.513046 +1.014533 +0.514784 +0.999696 +0.521679 +1.005168 +0.513950 +0.999529 +0.518587 +1.000628 +0.518437 +0.999722 +0.516594 +0.997092 +0.506004 +0.989659 +0.504048 +0.997647 +0.503904 +0.994885 +0.510037 +1.006169 +0.510890 +1.002695 +0.515865 +0.989421 +0.482111 +0.983468 +0.487053 +1.003135 +0.490794 +0.996536 +0.499504 +0.981503 +0.471943 +0.984818 +0.483858 +0.989593 +0.473217 +0.973990 +0.468443 +1.005158 +0.484046 +0.988977 +0.480320 +0.979246 +0.494752 +0.936919 +0.479985 +0.928007 +0.501681 +0.924264 +0.476214 +0.925709 +0.505756 +0.951969 +0.492449 +0.913877 +0.458335 +0.922688 +0.458418 +0.921906 +0.464648 +0.913628 +0.453997 +0.897859 +0.447600 +0.894665 +0.434444 +0.905614 +0.434656 +0.901326 +0.443932 +0.896518 +0.462831 +0.923850 +0.416275 +0.895905 +0.430608 +0.871933 +0.433425 +0.875953 +0.431968 +0.872335 +0.422885 +0.879683 +0.441795 +0.875968 +0.447772 +0.851462 +0.413755 +0.871466 +0.407405 +0.839189 +0.387560 +0.836207 +0.408903 +0.871522 +0.401325 +0.850121 +0.395077 +0.906623 +0.438049 +0.823041 +0.401768 +0.876089 +0.401768 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.877592 +0.382724 +0.877592 +0.382724 +0.877592 +0.382724 +0.877592 +0.382724 +0.877592 +0.382724 +0.877592 +0.377579 +0.817586 +0.380300 +0.858688 +0.377054 +0.883997 +0.390820 +0.890090 +0.339994 +0.906310 +0.381857 +0.879431 +0.368897 +0.849265 +0.376547 +0.873963 +0.408193 +0.882428 +0.406864 +0.874602 +0.402391 +0.907727 +0.413195 +0.920076 +0.390719 +0.888145 +0.390977 +0.898643 +0.417485 +0.911389 +0.420940 +0.914666 +0.410366 +0.922150 +0.403350 +0.936270 +0.446717 +0.915807 +0.438543 +0.944822 +0.445547 +0.939882 +0.437992 +0.926981 +0.456515 +0.930049 +0.424320 +0.947088 +0.423534 +0.925589 +0.422435 +0.946704 +0.446772 +0.938043 +0.444957 +0.956198 +0.457713 +0.947888 +0.434184 +0.942093 +0.461894 +0.953676 +0.474484 +0.934125 +0.431857 +0.921979 +0.450798 +0.931941 +0.477992 +0.984161 +0.482860 +0.951217 +0.473758 +0.965945 +0.462677 +0.964791 +0.473629 +0.968710 +0.474040 +0.956846 +0.465063 +0.969868 +0.473289 +0.979834 +0.466881 +0.985960 +0.480240 +0.978014 +0.469245 +0.992465 +0.469910 +0.981711 +0.481947 +0.997007 +0.484194 +0.982697 +0.463798 +0.972336 +0.485357 +0.973003 +0.490279 +0.971180 +0.473816 +0.978421 +0.488355 +0.986680 +0.482050 +0.975972 +0.468009 +0.982242 +0.467031 +0.977002 +0.476253 +0.980385 +0.460322 +0.989864 +0.477052 +0.990104 +0.487526 +1.006798 +0.476864 +0.985824 +0.477901 +0.978303 +0.470450 +0.993040 +0.461008 +0.995554 +0.477459 +0.981157 +0.498122 +0.987873 +0.477513 +1.002310 +0.483028 +1.000483 +0.487316 +1.012030 +0.480328 +1.005646 +0.519862 +0.998340 +0.482672 +1.013724 +0.496981 +1.012134 +0.476054 +0.987990 +0.485763 +1.001021 +0.475122 +0.991478 +0.499394 +0.998701 +0.483573 +0.993621 +0.504203 +0.999182 +0.471385 +1.003770 +0.481839 +1.002358 +0.497144 +1.003653 +0.481307 +0.999593 +0.468192 +0.977394 +0.470307 +0.976029 +0.464720 +0.970669 +0.445858 +0.962859 +0.450419 +1.012069 +0.465638 +0.982477 +0.482670 +0.956310 +0.483628 +0.963371 +0.467079 +0.925268 +0.469006 +0.961565 +0.477155 +0.962519 +0.463736 +0.959473 +0.481419 +0.948813 +0.438306 +0.942671 +0.442651 +0.920099 +0.436441 +0.935352 +0.424604 +0.929950 +0.418962 +0.935851 +0.433735 +0.915670 +0.429740 +0.920753 +0.423542 +0.918523 +0.447542 +0.924831 +0.416782 +0.894862 +0.421575 +0.903217 +0.419503 +0.885212 +0.423926 +0.885361 +0.437898 +0.902626 +0.440326 +0.869804 +0.453687 +0.878050 +0.399439 +0.882539 +0.409701 +0.903369 +0.393580 +0.911111 +0.353698 +0.815928 +0.415164 +0.821177 +0.385660 +0.876803 +0.385660 +0.876803 +0.385660 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.878042 +0.354682 +0.878042 +0.354682 +0.878042 +0.354682 +0.878042 +0.354682 +0.878042 +0.354682 +0.878042 +0.342034 +0.846666 +0.365313 +0.885361 +0.342409 +0.856052 +0.356322 +0.892712 +0.389417 +0.900882 +0.398293 +0.925388 +0.381852 +0.880881 +0.373503 +0.908506 +0.422197 +0.902701 +0.427036 +0.928239 +0.384033 +0.894063 +0.411073 +0.934000 +0.365786 +0.869163 +0.400269 +0.935201 +0.405781 +0.936978 +0.415241 +0.928137 +0.387947 +0.908554 +0.394074 +0.904043 +0.424564 +0.914720 +0.411701 +0.920745 +0.414422 +0.937764 +0.453286 +0.954897 +0.446865 +0.937162 +0.430201 +0.930844 +0.441089 +0.927007 +0.429521 +0.936257 +0.436333 +0.949798 +0.456221 +0.940843 +0.442312 +0.943148 +0.437123 +0.940305 +0.455518 +0.945524 +0.446320 +0.943026 +0.434335 +0.928535 +0.454027 +0.971167 +0.491007 +0.973459 +0.456535 +0.955989 +0.464436 +0.974084 +0.491547 +0.974971 +0.472492 +0.968625 +0.477609 +0.976819 +0.486225 +0.966206 +0.463091 +0.960449 +0.474053 +0.980587 +0.475617 +0.973335 +0.457268 +0.979446 +0.467498 +0.974562 +0.474743 +1.001660 +0.469281 +0.978777 +0.462479 +0.972027 +0.482897 +0.982778 +0.472601 +0.961040 +0.466108 +0.969726 +0.484666 +0.982579 +0.473885 +0.967015 +0.473743 +0.971287 +0.466866 +0.980484 +0.480887 +0.973022 +0.470026 +0.999281 +0.474191 +1.000899 +0.479245 +0.995749 +0.481703 +0.994322 +0.453954 +0.987331 +0.476683 +0.975746 +0.475372 +1.006143 +0.474285 +0.990847 +0.497463 +1.007125 +0.494089 +1.002844 +0.504662 +1.016829 +0.502486 +1.011813 +0.505841 +1.016164 +0.508391 +0.998072 +0.496981 +1.005189 +0.487645 +0.993747 +0.460373 +0.987488 +0.496688 +1.007335 +0.474771 +0.988110 +0.490390 +1.000477 +0.488016 +1.016448 +0.484274 +0.997760 +0.489249 +0.998581 +0.476467 +1.007984 +0.501509 +1.001345 +0.476642 +1.020030 +0.480598 +0.985663 +0.459727 +0.993092 +0.463376 +0.995860 +0.453327 +0.992760 +0.470182 +0.991393 +0.471192 +1.006678 +0.489109 +0.966340 +0.490186 +0.984019 +0.491875 +0.936362 +0.449913 +0.947405 +0.460962 +0.952274 +0.460813 +0.958265 +0.470731 +0.958693 +0.431255 +0.960553 +0.451917 +0.926437 +0.446846 +0.920505 +0.415682 +0.916615 +0.435426 +0.945213 +0.467972 +0.925970 +0.437260 +0.937840 +0.419227 +0.931139 +0.434579 +0.927608 +0.380990 +0.868318 +0.395241 +0.871412 +0.403362 +0.863356 +0.386943 +0.874299 +0.404436 +0.828081 +0.352153 +0.857369 +0.356654 +0.843788 +0.407740 +0.861083 +0.371536 +0.851663 +0.358634 +0.833198 +0.371365 +0.845105 +0.379303 +0.848648 +0.379303 +0.848648 +0.379303 +0.848648 +0.379303 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.879605 +0.351107 +0.879605 +0.351107 +0.879605 +0.351107 +0.879605 +0.351107 +0.879605 +0.351107 +0.879605 +0.372356 +0.865991 +0.325502 +0.874633 +0.360808 +0.862031 +0.367011 +0.902151 +0.347131 +0.884779 +0.335226 +0.862482 +0.383975 +0.889045 +0.359008 +0.878675 +0.434597 +0.909725 +0.388354 +0.879710 +0.372807 +0.895712 +0.406133 +0.890317 +0.406416 +0.918996 +0.390862 +0.925099 +0.413911 +0.893130 +0.409587 +0.912683 +0.390103 +0.921609 +0.420036 +0.910570 +0.436951 +0.911648 +0.415628 +0.890471 +0.423798 +0.941754 +0.424829 +0.956310 +0.446275 +0.946559 +0.431696 +0.956045 +0.438047 +0.939219 +0.404823 +0.926225 +0.438099 +0.943318 +0.444757 +0.945773 +0.440611 +0.953932 +0.460046 +0.951484 +0.460193 +0.924243 +0.449750 +0.928847 +0.435098 +0.916744 +0.451488 +0.963399 +0.457032 +0.955326 +0.470633 +0.961116 +0.457350 +0.968117 +0.497096 +0.959998 +0.476135 +0.966420 +0.475279 +0.975741 +0.462611 +0.983090 +0.462164 +0.967573 +0.476310 +0.985047 +0.467503 +0.976879 +0.478625 +0.981098 +0.494845 +0.957063 +0.473650 +0.970185 +0.475757 +0.978846 +0.481657 +0.971565 +0.465962 +0.963136 +0.462028 +0.970105 +0.490147 +0.994956 +0.474054 +0.979467 +0.474059 +0.987180 +0.486778 +0.972867 +0.471563 +0.973705 +0.479944 +0.964359 +0.464557 +0.999510 +0.480841 +0.995405 +0.476603 +1.000850 +0.468935 +1.008103 +0.489908 +1.012100 +0.464265 +0.981563 +0.474956 +0.990306 +0.468871 +0.990198 +0.465192 +0.975209 +0.473537 +0.990453 +0.461088 +0.990939 +0.463028 +0.973543 +0.470125 +0.998199 +0.477831 +0.996341 +0.464891 +0.974744 +0.460167 +0.990983 +0.443918 +0.968571 +0.454220 +0.990983 +0.468346 +0.979188 +0.466420 +0.993254 +0.486472 +0.982162 +0.453023 +0.961443 +0.443596 +0.973084 +0.450322 +0.975429 +0.447565 +0.976386 +0.440122 +0.970005 +0.441852 +0.953520 +0.442988 +0.961179 +0.451560 +0.960099 +0.420824 +0.952758 +0.418873 +0.939365 +0.411614 +0.953343 +0.450880 +0.937774 +0.446691 +0.934833 +0.430516 +0.937758 +0.430470 +0.923666 +0.443852 +0.943208 +0.453061 +0.926072 +0.434097 +0.920834 +0.428956 +0.902237 +0.415723 +0.912146 +0.411573 +0.909982 +0.378951 +0.909117 +0.393695 +0.881668 +0.407794 +0.909933 +0.399171 +0.883353 +0.385425 +0.875029 +0.361239 +0.866700 +0.385266 +0.856573 +0.408877 +0.857881 +0.416335 +0.897572 +0.411729 +0.854305 +0.384417 +0.855218 +0.381940 +0.837790 +0.372970 +0.851142 +0.379559 +0.851951 +0.374486 +0.836496 +0.371185 +0.829910 +0.305132 +0.830212 +0.347163 +0.834822 +0.350268 +0.832206 +0.350268 +0.832206 +0.350268 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.874743 +0.356163 +0.874743 +0.356163 +0.874743 +0.356163 +0.874743 +0.356163 +0.874743 +0.356163 +0.874743 +0.356163 +0.874743 +0.370521 +0.856112 +0.336244 +0.898774 +0.335516 +0.852251 +0.396729 +0.873205 +0.381827 +0.893116 +0.401198 +0.888992 +0.346539 +0.849289 +0.404757 +0.876002 +0.399245 +0.891954 +0.393679 +0.867967 +0.393107 +0.895313 +0.399684 +0.923927 +0.418311 +0.904367 +0.399929 +0.931433 +0.391803 +0.954250 +0.424254 +0.900356 +0.427491 +0.967286 +0.434182 +0.922926 +0.441561 +0.955672 +0.453570 +0.955127 +0.432192 +0.937785 +0.444851 +0.947114 +0.419277 +0.924862 +0.415672 +0.944885 +0.461296 +0.926034 +0.438138 +0.955461 +0.475847 +0.973618 +0.452091 +0.962478 +0.464457 +0.949034 +0.447873 +0.952081 +0.462573 +0.941554 +0.451639 +0.975474 +0.464764 +0.957831 +0.458006 +0.977009 +0.481760 +0.959302 +0.479821 +0.969882 +0.471521 +0.945784 +0.478516 +0.996729 +0.463145 +0.969087 +0.481636 +0.971607 +0.469349 +0.977880 +0.483115 +0.994076 +0.479740 +0.987768 +0.475598 +0.973607 +0.474291 +0.991645 +0.486675 +0.991902 +0.479084 +0.997019 +0.474993 +0.983015 +0.474865 +0.979984 +0.472321 +0.970915 +0.484474 +0.982238 +0.466870 +0.986061 +0.468400 +0.975967 +0.483873 +1.009249 +0.480834 +0.972272 +0.476869 +0.966847 +0.471200 +0.989882 +0.476036 +0.993063 +0.481298 +0.995087 +0.470257 +1.007631 +0.482322 +0.998497 +0.475337 +0.995037 +0.458100 +0.992539 +0.451995 +0.976972 +0.475340 +0.979695 +0.454131 +0.986346 +0.468865 +0.988584 +0.469300 +0.966477 +0.464071 +0.996818 +0.460689 +0.973432 +0.477065 +0.974070 +0.460326 +0.974647 +0.461270 +0.953567 +0.462926 +0.991157 +0.465986 +0.958214 +0.467757 +0.988536 +0.457057 +0.971534 +0.441314 +0.982953 +0.455292 +0.983918 +0.440004 +0.967425 +0.448895 +0.951533 +0.446353 +0.975044 +0.439742 +0.944929 +0.443552 +0.962261 +0.448200 +0.974332 +0.436179 +0.961279 +0.423941 +0.962488 +0.426285 +0.932979 +0.432796 +0.927385 +0.439828 +0.946698 +0.453950 +0.924143 +0.434573 +0.919226 +0.437252 +0.925293 +0.442246 +0.934201 +0.411666 +0.916399 +0.435475 +0.901775 +0.436158 +0.887775 +0.413553 +0.915704 +0.409283 +0.904294 +0.439325 +0.906855 +0.405827 +0.883007 +0.403248 +0.888137 +0.414901 +0.861283 +0.404320 +0.884311 +0.357209 +0.882077 +0.407367 +0.878399 +0.410298 +0.859337 +0.372605 +0.858977 +0.411540 +0.835622 +0.402186 +0.863492 +0.337613 +0.851047 +0.354892 +0.878232 +0.363210 +0.809931 +0.308425 +0.832999 +0.339198 +0.842036 +0.342176 +0.840388 +0.342176 +0.840388 +0.342176 +0.840388 +0.342176 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.861784 +0.338760 +0.861784 +0.338760 +0.861784 +0.338760 +0.861784 +0.338760 +0.861784 +0.338760 +0.861784 +0.338760 +0.861784 +0.352483 +0.850666 +0.302767 +0.879230 +0.342115 +0.872518 +0.371397 +0.833604 +0.392425 +0.914823 +0.389245 +0.905515 +0.369036 +0.866348 +0.357751 +0.835158 +0.404796 +0.884822 +0.372035 +0.889112 +0.375825 +0.912375 +0.412949 +0.892783 +0.384160 +0.912104 +0.421590 +0.908558 +0.381607 +0.908818 +0.409576 +0.906946 +0.427148 +0.929792 +0.426730 +0.925209 +0.403755 +0.931302 +0.431544 +0.916528 +0.404490 +0.944264 +0.442612 +0.931962 +0.442564 +0.931640 +0.418189 +0.934253 +0.444245 +0.947069 +0.436425 +0.942367 +0.446729 +0.966488 +0.449993 +0.948368 +0.441751 +0.961669 +0.438223 +0.945744 +0.444440 +0.947080 +0.433792 +0.949046 +0.479355 +0.951109 +0.466538 +0.973818 +0.470892 +0.977941 +0.465141 +0.973868 +0.459617 +0.954669 +0.477800 +0.979626 +0.482677 +0.971193 +0.484000 +0.976215 +0.470789 +0.977839 +0.477030 +0.999710 +0.477382 +0.993799 +0.462204 +0.984700 +0.477644 +0.996312 +0.476270 +0.995517 +0.480064 +0.993061 +0.477874 +0.989335 +0.465460 +0.975744 +0.480889 +0.980519 +0.493049 +0.985549 +0.454835 +0.971868 +0.457689 +0.977462 +0.477472 +0.995438 +0.486133 +0.977069 +0.460800 +0.968869 +0.470638 +1.001756 +0.465550 +0.991260 +0.481130 +0.986138 +0.464432 +0.995486 +0.488725 +0.986273 +0.475222 +0.995672 +0.466331 +0.990718 +0.471822 +0.972226 +0.463094 +0.986239 +0.443702 +0.978572 +0.470500 +0.997612 +0.473601 +0.975933 +0.463728 +0.977949 +0.472633 +0.978832 +0.466191 +0.983987 +0.458872 +0.971679 +0.460961 +0.968094 +0.448557 +0.983100 +0.444975 +0.979126 +0.456425 +0.975383 +0.459832 +0.991281 +0.456055 +0.965514 +0.461891 +0.975661 +0.442996 +0.969915 +0.448632 +0.969541 +0.430928 +0.964498 +0.449395 +0.958424 +0.448964 +0.962758 +0.437439 +0.942007 +0.427184 +0.962935 +0.409853 +0.943738 +0.425495 +0.944625 +0.430940 +0.935993 +0.437650 +0.915193 +0.435394 +0.930037 +0.432006 +0.938063 +0.434060 +0.937540 +0.424149 +0.914742 +0.432374 +0.925213 +0.432930 +0.923368 +0.407073 +0.929508 +0.401080 +0.910258 +0.423263 +0.893212 +0.427200 +0.892199 +0.385682 +0.906422 +0.392101 +0.875274 +0.384014 +0.893336 +0.412278 +0.884803 +0.394006 +0.870610 +0.410218 +0.856210 +0.360436 +0.895573 +0.378874 +0.859091 +0.390259 +0.841461 +0.370485 +0.865388 +0.340779 +0.849770 +0.353240 +0.860216 +0.327766 +0.815350 +0.384408 +0.851369 +0.340595 +0.841779 +0.340595 +0.841779 +0.340595 +0.841779 +0.340595 +0.841779 +0.340595 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.894661 +0.360158 +0.894661 +0.360158 +0.894661 +0.360158 +0.894661 +0.360158 +0.894661 +0.360158 +0.894661 +0.360158 +0.894661 +0.374376 +0.891364 +0.313864 +0.850220 +0.398954 +0.892296 +0.367655 +0.941466 +0.396237 +0.892045 +0.402791 +0.892908 +0.395119 +0.891396 +0.385765 +0.889334 +0.430191 +0.928189 +0.402087 +0.915559 +0.374099 +0.942089 +0.408739 +0.926663 +0.405247 +0.919846 +0.369889 +0.941620 +0.401806 +0.932248 +0.442810 +0.923416 +0.397115 +0.937831 +0.425648 +0.929350 +0.413356 +0.956661 +0.439766 +0.970536 +0.432043 +0.968574 +0.443098 +0.959445 +0.422705 +0.975046 +0.438383 +0.951539 +0.427072 +0.962961 +0.425412 +0.953002 +0.439922 +0.975598 +0.443721 +0.963722 +0.468454 +0.961091 +0.435269 +0.963969 +0.457914 +0.961827 +0.453826 +0.950646 +0.470111 +0.976478 +0.466730 +0.999418 +0.460331 +0.973394 +0.469026 +0.989670 +0.458132 +0.992221 +0.484250 +1.001117 +0.488894 +0.976846 +0.477648 +0.998499 +0.474134 +0.994682 +0.476787 +1.018686 +0.469655 +1.003901 +0.467425 +0.994728 +0.474629 +1.003134 +0.470984 +1.005920 +0.479317 +1.012515 +0.468149 +0.996655 +0.478653 +0.984100 +0.475062 +0.987465 +0.462676 +0.988832 +0.469638 +0.974408 +0.465842 +0.984700 +0.479297 +0.987745 +0.484929 +0.999746 +0.488923 +0.988214 +0.456388 +1.013735 +0.464761 +1.030329 +0.470395 +1.022923 +0.475033 +1.025059 +0.479708 +1.022845 +0.489124 +1.015858 +0.486467 +1.015114 +0.460707 +1.002088 +0.456643 +1.010076 +0.444562 +1.003307 +0.479078 +1.016572 +0.463271 +0.998058 +0.464288 +0.984731 +0.467586 +1.009335 +0.450891 +0.986358 +0.467226 +0.984130 +0.464205 +0.989486 +0.474183 +0.996923 +0.438855 +1.005648 +0.440757 +0.986646 +0.461706 +1.000965 +0.457722 +0.990899 +0.442480 +0.971877 +0.448269 +0.997629 +0.440454 +0.988961 +0.433383 +0.982372 +0.455007 +0.984229 +0.442267 +0.991844 +0.448312 +0.983256 +0.434301 +0.978568 +0.434398 +0.987782 +0.424382 +0.982677 +0.462644 +0.928208 +0.430380 +0.938068 +0.444383 +0.928338 +0.439973 +0.931848 +0.439139 +0.937055 +0.436862 +0.898926 +0.402609 +0.916056 +0.427044 +0.923431 +0.430654 +0.909064 +0.393840 +0.904427 +0.402883 +0.906682 +0.389637 +0.892340 +0.411751 +0.894459 +0.397200 +0.898104 +0.401864 +0.910988 +0.421284 +0.862140 +0.379030 +0.881797 +0.428606 +0.856615 +0.411894 +0.927058 +0.397682 +0.846699 +0.405146 +0.874225 +0.343336 +0.890284 +0.395764 +0.866629 +0.350268 +0.860146 +0.319413 +0.900776 +0.343176 +0.864192 +0.329663 +0.887005 +0.337619 +0.875038 +0.337619 +0.875038 +0.337619 +0.875038 +0.337619 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.899564 +0.330104 +0.899564 +0.330104 +0.899564 +0.330104 +0.899564 +0.330104 +0.899564 +0.330104 +0.899564 +0.330104 +0.899564 +0.353123 +0.916691 +0.315963 +0.880949 +0.333465 +0.910707 +0.340884 +0.907037 +0.376556 +0.864928 +0.330262 +0.903129 +0.384432 +0.862284 +0.400176 +0.891847 +0.376239 +0.910815 +0.416838 +0.923894 +0.381349 +0.915373 +0.433581 +0.944318 +0.414897 +0.915136 +0.387779 +0.935794 +0.376043 +0.955141 +0.401340 +0.912670 +0.393453 +0.951314 +0.404064 +0.947156 +0.406168 +0.966472 +0.417705 +0.943968 +0.407875 +0.963856 +0.442559 +0.950532 +0.405483 +0.939550 +0.402909 +0.946110 +0.427773 +0.965661 +0.438902 +0.951487 +0.439692 +0.976736 +0.432860 +0.979162 +0.424602 +0.963274 +0.422709 +0.977767 +0.424701 +0.974556 +0.427768 +0.968448 +0.454980 +0.989952 +0.445955 +0.982430 +0.446839 +0.972256 +0.454667 +0.981490 +0.450693 +0.997031 +0.454236 +1.007858 +0.453147 +1.015813 +0.456974 +0.992045 +0.466366 +1.000422 +0.446065 +1.005414 +0.452295 +0.993262 +0.446399 +0.994841 +0.452961 +1.003973 +0.453338 +1.004286 +0.468155 +1.012400 +0.456746 +1.000098 +0.460798 +0.982155 +0.454637 +0.987805 +0.469394 +1.000008 +0.440125 +0.980080 +0.441351 +1.010249 +0.479230 +1.011826 +0.442265 +1.006900 +0.473863 +0.994492 +0.463383 +1.008194 +0.444382 +1.014181 +0.452558 +1.009101 +0.454638 +1.043692 +0.466903 +1.020755 +0.445951 +1.011304 +0.438663 +1.027713 +0.448207 +1.012617 +0.464139 +1.007213 +0.435384 +1.005260 +0.448339 +1.005006 +0.450178 +1.001524 +0.442461 +0.999523 +0.458120 +0.993631 +0.448478 +1.005154 +0.468869 +0.981945 +0.441576 +1.001078 +0.454989 +1.013040 +0.430010 +0.995750 +0.430247 +1.001131 +0.435931 +1.003626 +0.446258 +0.991807 +0.432906 +0.992968 +0.430738 +0.992737 +0.430738 +0.992640 +0.440391 +0.997188 +0.437726 +0.978850 +0.440908 +0.984839 +0.432658 +0.990261 +0.403973 +0.963179 +0.402884 +0.968651 +0.391475 +0.950482 +0.427439 +0.956732 +0.414816 +0.962728 +0.404929 +0.959952 +0.434205 +0.939428 +0.427446 +0.970711 +0.410005 +0.957067 +0.402350 +0.914609 +0.403169 +0.955593 +0.389426 +0.914580 +0.402929 +0.927311 +0.387644 +0.935640 +0.395649 +0.906565 +0.379316 +0.922292 +0.376065 +0.942028 +0.372008 +0.896846 +0.375067 +0.905861 +0.375329 +0.934900 +0.364024 +0.884225 +0.366842 +0.903658 +0.379428 +0.848370 +0.370436 +0.877333 +0.361965 +0.886989 +0.382186 +0.886401 +0.354287 +0.868194 +0.351640 +0.835127 +0.366146 +0.880528 +0.366146 +0.880528 +0.366146 +0.880528 +0.366146 +0.880528 +0.366146 +0.880528 +0.366146 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.881863 +0.346739 +0.881863 +0.346739 +0.881863 +0.346739 +0.881863 +0.346739 +0.881863 +0.346739 +0.881863 +0.346739 +0.881863 +0.323985 +0.875576 +0.324030 +0.872785 +0.364904 +0.891600 +0.351284 +0.881205 +0.346111 +0.914667 +0.373786 +0.897645 +0.348915 +0.903592 +0.379193 +0.924423 +0.393615 +0.940437 +0.345089 +0.888885 +0.398007 +0.890514 +0.379356 +0.911234 +0.400729 +0.916800 +0.395205 +0.893457 +0.364464 +0.925066 +0.400915 +0.931448 +0.408002 +0.953143 +0.404546 +0.961962 +0.409063 +0.929996 +0.410529 +0.939548 +0.408843 +0.961415 +0.433076 +0.966990 +0.391218 +0.953312 +0.403658 +0.949613 +0.399924 +0.955668 +0.417891 +0.979797 +0.427401 +0.977735 +0.426593 +0.977971 +0.397562 +0.965435 +0.418423 +0.945501 +0.422755 +0.970871 +0.422714 +0.967742 +0.457322 +0.974729 +0.450755 +0.969055 +0.441240 +0.994712 +0.462858 +0.987819 +0.440577 +0.966805 +0.448697 +1.008429 +0.464556 +0.983930 +0.439400 +1.016468 +0.433771 +0.980953 +0.434742 +1.007777 +0.448573 +0.992024 +0.445482 +0.988133 +0.468376 +1.013376 +0.444154 +1.010098 +0.439535 +0.992402 +0.452807 +0.990639 +0.449276 +0.992638 +0.466498 +1.003341 +0.462952 +1.011086 +0.468770 +1.001063 +0.446370 +1.014408 +0.458141 +0.998934 +0.462981 +0.999828 +0.449858 +1.005139 +0.455871 +1.015208 +0.458543 +1.023288 +0.449022 +1.012152 +0.457908 +1.023415 +0.461692 +1.029420 +0.439890 +1.021481 +0.448298 +1.008800 +0.445471 +1.011498 +0.462438 +1.012545 +0.441344 +0.991114 +0.452645 +1.012910 +0.438332 +1.012816 +0.444399 +1.005701 +0.448264 +0.989636 +0.457905 +1.009241 +0.439803 +1.016962 +0.443926 +1.009285 +0.454948 +1.011269 +0.450435 +0.997652 +0.436370 +0.995863 +0.439563 +1.009075 +0.453336 +1.009906 +0.454021 +0.990700 +0.451265 +0.998215 +0.431135 +0.995645 +0.439926 +1.024641 +0.434581 +0.970295 +0.419528 +0.993063 +0.417823 +1.008431 +0.429353 +0.971172 +0.396359 +0.994825 +0.398360 +0.985297 +0.450258 +0.942838 +0.428389 +0.972042 +0.416415 +0.954289 +0.425225 +0.959452 +0.431986 +0.946501 +0.426587 +0.958865 +0.401492 +0.922746 +0.400342 +0.942503 +0.402082 +0.928445 +0.396051 +0.940335 +0.376720 +0.908892 +0.380971 +0.920116 +0.376328 +0.913005 +0.391271 +0.894347 +0.368444 +0.891685 +0.364422 +0.886366 +0.346389 +0.899901 +0.385519 +0.900544 +0.368067 +0.920703 +0.358132 +0.854238 +0.367431 +0.884188 +0.337202 +0.869847 +0.341725 +0.882043 +0.363771 +0.868903 +0.341710 +0.849630 +0.300116 +0.837209 +0.327469 +0.837063 +0.335199 +0.851914 +0.335199 +0.851914 +0.335199 +0.851914 +0.335199 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.344631 +0.918298 +0.308201 +0.921494 +0.329645 +0.904072 +0.346321 +0.935318 +0.357926 +0.915505 +0.355486 +0.888545 +0.416826 +0.913667 +0.383900 +0.938527 +0.391059 +0.879839 +0.356152 +0.929139 +0.397923 +0.938099 +0.369148 +0.928707 +0.377350 +0.945094 +0.340868 +0.917033 +0.383020 +0.937215 +0.382183 +0.940185 +0.388609 +0.927127 +0.392453 +0.935853 +0.407548 +0.966848 +0.383061 +0.937760 +0.374253 +0.940358 +0.409458 +0.973780 +0.419787 +0.949923 +0.412887 +0.966545 +0.379553 +0.932570 +0.415449 +0.975482 +0.434916 +0.965464 +0.424504 +0.983243 +0.419505 +0.969386 +0.418802 +0.950796 +0.430876 +0.981467 +0.428988 +0.957157 +0.439615 +0.990888 +0.450286 +0.955625 +0.434340 +0.995158 +0.448003 +0.976267 +0.455211 +1.003190 +0.455339 +1.008379 +0.450774 +0.977826 +0.452479 +1.008190 +0.432835 +0.990389 +0.438300 +1.003817 +0.444669 +0.983912 +0.445785 +1.000347 +0.438218 +0.999512 +0.456369 +0.998588 +0.459972 +1.029569 +0.454090 +0.990764 +0.435613 +1.001480 +0.459639 +1.008432 +0.457951 +0.993701 +0.457642 +0.988004 +0.444963 +1.012846 +0.447562 +0.995915 +0.470963 +1.001433 +0.463892 +1.005043 +0.431552 +0.991644 +0.438261 +0.991967 +0.416695 +1.013162 +0.440714 +1.014108 +0.423287 +0.999875 +0.437198 +0.998069 +0.456477 +1.000376 +0.450720 +1.012396 +0.439870 +1.006524 +0.435419 +1.020835 +0.435785 +1.002219 +0.443071 +1.002709 +0.449170 +0.986909 +0.455576 +0.993061 +0.447503 +0.984940 +0.444193 +1.012772 +0.440304 +0.989055 +0.422791 +1.002765 +0.442890 +1.007482 +0.427165 +1.005478 +0.446550 +0.980501 +0.436397 +0.990641 +0.438408 +0.991173 +0.421008 +0.995846 +0.422924 +0.980699 +0.422630 +0.979908 +0.405465 +0.970737 +0.429377 +0.990635 +0.423717 +0.973751 +0.381627 +0.962220 +0.420686 +0.975406 +0.382905 +0.961219 +0.425468 +0.932979 +0.419345 +0.955328 +0.413553 +0.950799 +0.410266 +0.951729 +0.401678 +0.947465 +0.410679 +0.928150 +0.403163 +0.923726 +0.386085 +0.918520 +0.391821 +0.931431 +0.396261 +0.925221 +0.395340 +0.932088 +0.378843 +0.926539 +0.390520 +0.939212 +0.395193 +0.918989 +0.369448 +0.894406 +0.379228 +0.894899 +0.374705 +0.884424 +0.383961 +0.897022 +0.363976 +0.870548 +0.361355 +0.873899 +0.361412 +0.890154 +0.377392 +0.893155 +0.338647 +0.876405 +0.347014 +0.844890 +0.314050 +0.825816 +0.343680 +0.854334 +0.297239 +0.881150 +0.334915 +0.841680 +0.334915 +0.841680 +0.334915 +0.841680 +0.334915 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.381841 +0.941191 +0.353829 +0.938401 +0.390400 +0.953957 +0.360780 +0.951030 +0.394343 +0.918587 +0.431576 +0.955167 +0.425331 +0.944808 +0.395528 +0.949259 +0.434309 +0.978770 +0.399148 +0.982255 +0.408753 +0.948038 +0.428286 +0.978697 +0.419224 +0.963278 +0.422105 +0.948944 +0.433655 +0.974323 +0.415240 +0.988747 +0.437963 +0.973762 +0.429345 +0.971392 +0.437070 +0.981953 +0.446538 +0.981441 +0.442102 +0.982504 +0.468398 +0.985258 +0.460245 +0.985084 +0.451232 +0.995380 +0.443291 +0.979340 +0.430870 +0.986449 +0.447415 +1.005727 +0.447571 +1.012946 +0.441048 +0.989253 +0.431472 +0.996147 +0.470990 +1.004836 +0.455172 +0.985940 +0.427299 +0.984824 +0.447170 +1.004836 +0.467649 +0.996587 +0.435723 +0.987826 +0.444300 +0.979743 +0.438874 +0.999609 +0.434536 +1.000727 +0.462602 +1.009979 +0.457772 +1.029423 +0.441592 +0.999914 +0.442597 +1.005844 +0.445365 +1.021550 +0.445410 +1.003386 +0.444870 +1.005722 +0.441638 +0.997732 +0.444839 +1.010339 +0.432148 +0.995126 +0.433111 +0.983313 +0.425449 +1.000031 +0.450530 +1.009933 +0.462979 +1.006128 +0.445717 +0.997607 +0.452790 +0.995463 +0.440953 +0.987868 +0.432884 +0.980293 +0.428807 +0.997843 +0.443277 +0.988633 +0.422140 +0.979644 +0.423709 +1.003350 +0.435559 +0.992032 +0.428690 +0.978933 +0.408198 +0.987203 +0.424538 +0.994789 +0.425321 +0.997377 +0.409626 +0.982587 +0.419113 +0.978610 +0.417602 +0.969399 +0.399595 +0.970225 +0.396363 +0.979441 +0.408645 +0.982256 +0.422433 +0.957644 +0.420599 +0.947898 +0.404213 +0.923067 +0.400756 +0.951015 +0.401657 +0.938609 +0.410233 +0.948777 +0.400734 +0.931447 +0.391718 +0.944281 +0.384957 +0.930326 +0.389129 +0.904527 +0.395329 +0.910957 +0.383449 +0.912204 +0.375762 +0.904492 +0.408854 +0.923146 +0.383963 +0.913831 +0.379013 +0.909233 +0.381153 +0.935935 +0.369914 +0.891194 +0.374000 +0.893920 +0.342360 +0.841684 +0.345248 +0.886326 +0.388056 +0.881295 +0.340678 +0.875888 +0.341102 +0.884001 +0.318348 +0.858721 +0.318866 +0.842331 +0.337376 +0.861853 +0.326105 +0.861684 +0.326105 +0.861684 +0.326105 +0.861684 +0.326105 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.347330 +0.878412 +0.328069 +0.871350 +0.321435 +0.900314 +0.363265 +0.879182 +0.357289 +0.855740 +0.354160 +0.917222 +0.353743 +0.878729 +0.325921 +0.867934 +0.379849 +0.890888 +0.371310 +0.894861 +0.397612 +0.869230 +0.390609 +0.909721 +0.370049 +0.932931 +0.406223 +0.897229 +0.393970 +0.925847 +0.381010 +0.905966 +0.369466 +0.933551 +0.402631 +0.923691 +0.383028 +0.963981 +0.359320 +0.927519 +0.376692 +0.944130 +0.404327 +0.936294 +0.366726 +0.952679 +0.409984 +0.942550 +0.391427 +0.941237 +0.398627 +0.968472 +0.413197 +0.964489 +0.412354 +0.970657 +0.406655 +0.968194 +0.400291 +0.967886 +0.449172 +0.969746 +0.408932 +0.989090 +0.445303 +0.972640 +0.415879 +0.974869 +0.430531 +0.976534 +0.411098 +0.973829 +0.437297 +0.964572 +0.461475 +0.970476 +0.448769 +0.985524 +0.457854 +1.004674 +0.440074 +0.997019 +0.423747 +0.985352 +0.435679 +0.996511 +0.452091 +1.022813 +0.442650 +1.001626 +0.424848 +0.988978 +0.462825 +0.995940 +0.440112 +1.006546 +0.447900 +0.992849 +0.437798 +0.998406 +0.445688 +0.994821 +0.444912 +0.991414 +0.436386 +0.990273 +0.443497 +0.979308 +0.432383 +1.000032 +0.441922 +0.993912 +0.450028 +1.011970 +0.434590 +0.997948 +0.435995 +0.994681 +0.452858 +1.011697 +0.441206 +1.011859 +0.441897 +1.004402 +0.455204 +1.011000 +0.444748 +1.008057 +0.437552 +0.990032 +0.433718 +0.988489 +0.438779 +1.002070 +0.450395 +1.008852 +0.440323 +1.000002 +0.446313 +0.997347 +0.429113 +1.004111 +0.423933 +1.004201 +0.413623 +0.999733 +0.440210 +0.988933 +0.441284 +0.988140 +0.452471 +1.014060 +0.424396 +0.998663 +0.422989 +0.981990 +0.420643 +1.000294 +0.425841 +0.998029 +0.410563 +0.984557 +0.416832 +0.997515 +0.413473 +0.984009 +0.414638 +0.970811 +0.405070 +0.960453 +0.413849 +0.966262 +0.410363 +0.973714 +0.398755 +0.978522 +0.413195 +0.958274 +0.415426 +0.949958 +0.429189 +0.930117 +0.406790 +0.928563 +0.404810 +0.926395 +0.419839 +0.922587 +0.382857 +0.941837 +0.396098 +0.923529 +0.393104 +0.922444 +0.407214 +0.908973 +0.378082 +0.917986 +0.365043 +0.898943 +0.367133 +0.914758 +0.370131 +0.945939 +0.383509 +0.915179 +0.360644 +0.894808 +0.368873 +0.879515 +0.379966 +0.881286 +0.359849 +0.874704 +0.354740 +0.862963 +0.334884 +0.900342 +0.352251 +0.854654 +0.327649 +0.891919 +0.339811 +0.826762 +0.370311 +0.852443 +0.316454 +0.865800 +0.345924 +0.857041 +0.345924 +0.857041 +0.345924 +0.857041 +0.345924 +0.857041 +0.345924 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.368844 +0.908105 +0.328198 +0.934359 +0.400343 +0.915240 +0.358000 +0.878617 +0.348190 +0.930459 +0.366710 +0.940475 +0.358656 +0.912428 +0.333541 +0.890709 +0.361571 +0.913340 +0.310193 +0.908194 +0.343976 +0.924657 +0.385941 +0.934893 +0.377005 +0.945438 +0.396430 +0.950169 +0.377561 +0.920337 +0.350552 +0.932602 +0.368549 +0.943623 +0.401589 +0.930262 +0.401659 +0.976513 +0.396824 +0.991013 +0.390107 +0.989131 +0.406487 +0.970407 +0.413774 +0.987365 +0.409223 +0.955668 +0.403363 +0.966251 +0.382973 +0.945900 +0.431652 +0.966325 +0.411781 +0.962928 +0.415372 +0.985894 +0.407906 +0.991154 +0.425746 +0.966771 +0.413183 +0.965261 +0.451323 +1.014231 +0.440247 +0.998683 +0.436498 +0.993508 +0.426540 +1.015121 +0.450505 +1.013355 +0.445153 +0.992521 +0.453981 +0.996938 +0.443363 +0.993835 +0.428571 +0.968920 +0.413632 +0.994972 +0.448637 +1.008364 +0.442719 +0.998420 +0.446274 +0.989382 +0.427814 +0.997051 +0.443039 +0.975830 +0.455078 +0.994802 +0.431376 +0.977823 +0.432547 +1.007843 +0.440498 +0.995135 +0.429978 +0.991702 +0.445485 +0.973812 +0.437434 +0.986664 +0.437199 +1.010362 +0.460199 +1.009274 +0.443437 +1.023037 +0.450016 +1.016352 +0.451757 +1.030433 +0.458817 +1.010152 +0.428119 +1.030174 +0.442876 +1.021611 +0.442896 +1.038136 +0.434763 +1.034926 +0.439916 +1.030637 +0.431984 +1.008552 +0.426620 +1.024524 +0.432864 +1.042353 +0.444419 +1.030885 +0.443527 +1.011291 +0.424272 +1.013651 +0.423669 +1.012527 +0.442676 +1.013580 +0.414833 +1.013598 +0.432262 +1.018910 +0.436643 +1.020568 +0.434746 +1.032156 +0.428315 +1.014642 +0.420409 +1.013271 +0.440582 +1.013416 +0.418033 +0.997312 +0.436535 +0.992928 +0.410771 +0.992335 +0.414116 +0.975531 +0.400837 +0.983234 +0.412630 +0.974393 +0.394225 +0.977929 +0.413218 +0.976474 +0.436909 +0.952292 +0.405872 +0.956993 +0.419805 +0.954836 +0.394128 +0.953306 +0.409679 +0.942373 +0.408659 +0.911489 +0.393562 +0.923482 +0.407808 +0.923587 +0.400780 +0.932081 +0.375855 +0.935884 +0.379124 +0.930249 +0.397363 +0.915632 +0.393920 +0.939007 +0.395061 +0.946583 +0.349309 +0.955532 +0.392006 +0.893179 +0.383769 +0.901556 +0.392503 +0.899403 +0.382601 +0.937415 +0.373061 +0.894517 +0.355460 +0.898938 +0.349809 +0.882570 +0.375355 +0.875716 +0.343198 +0.892345 +0.334806 +0.889853 +0.337803 +0.900276 +0.351120 +0.885971 +0.351120 +0.885971 +0.351120 +0.885971 +0.351120 +0.885971 +0.351120 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.320157 +0.904804 +0.303777 +0.908407 +0.304821 +0.880892 +0.337601 +0.907142 +0.318049 +0.926377 +0.330374 +0.964914 +0.352580 +0.895871 +0.362337 +0.889295 +0.322959 +0.891411 +0.342277 +0.940426 +0.342232 +0.931663 +0.335311 +0.944614 +0.338339 +0.896086 +0.361699 +0.946943 +0.371188 +0.948199 +0.373978 +0.950302 +0.352494 +0.957505 +0.391797 +0.966157 +0.384192 +0.969812 +0.368541 +0.999341 +0.367393 +0.962471 +0.376122 +0.957350 +0.392364 +0.953738 +0.356831 +0.964865 +0.374108 +0.981410 +0.385898 +0.975920 +0.392056 +0.971397 +0.393134 +0.979772 +0.413644 +0.982476 +0.392916 +0.984868 +0.411626 +0.982481 +0.382803 +0.968185 +0.412371 +0.992235 +0.410526 +0.984202 +0.425901 +1.001278 +0.407748 +0.999566 +0.408771 +1.014735 +0.432916 +0.988515 +0.438641 +1.021145 +0.435433 +1.005669 +0.411326 +1.008015 +0.404694 +1.005023 +0.424265 +1.007674 +0.409214 +1.043000 +0.424030 +1.019484 +0.421606 +0.996581 +0.433254 +1.016777 +0.426152 +1.019573 +0.419436 +0.997886 +0.412525 +0.993410 +0.427912 +1.014412 +0.424907 +1.002067 +0.417037 +1.003752 +0.420373 +0.997699 +0.420107 +1.004467 +0.422696 +1.018935 +0.397236 +1.024619 +0.415945 +1.024170 +0.430297 +1.035129 +0.418458 +1.008985 +0.401695 +1.022845 +0.415812 +1.038331 +0.422173 +1.027030 +0.402489 +1.024468 +0.418853 +1.019076 +0.414718 +1.023782 +0.413533 +0.998018 +0.418600 +1.034072 +0.455362 +1.034798 +0.431399 +1.009257 +0.432389 +0.999671 +0.429168 +1.008773 +0.422035 +1.024783 +0.411715 +1.021057 +0.423410 +1.030336 +0.425709 +1.034438 +0.421124 +1.009595 +0.418598 +1.018695 +0.404321 +1.009941 +0.415083 +1.012285 +0.412819 +1.013483 +0.407274 +1.017052 +0.401007 +1.000645 +0.407218 +1.008266 +0.412600 +1.008325 +0.391386 +0.990044 +0.388696 +0.991015 +0.371806 +0.984703 +0.402523 +0.934303 +0.410707 +0.955606 +0.408533 +0.997994 +0.383679 +0.956594 +0.399160 +0.970948 +0.412504 +0.972609 +0.378523 +0.978578 +0.407020 +0.965450 +0.374810 +0.968428 +0.363477 +0.961862 +0.371567 +0.935991 +0.383762 +0.950911 +0.366665 +0.928179 +0.351590 +0.918042 +0.340688 +0.917825 +0.356114 +0.901502 +0.319491 +0.944111 +0.379457 +0.930539 +0.337836 +0.913514 +0.362057 +0.918950 +0.317517 +0.888868 +0.365360 +0.876433 +0.337715 +0.901504 +0.305496 +0.890943 +0.327406 +0.860971 +0.336191 +0.889627 +0.336191 +0.889627 +0.336191 +0.889627 +0.336191 +0.889627 +0.336191 +0.889627 +0.336191 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.327774 +0.924659 +0.361733 +0.926363 +0.323887 +0.930349 +0.328995 +0.908273 +0.330441 +0.935354 +0.335487 +0.910831 +0.346866 +0.905928 +0.336127 +0.943504 +0.353758 +0.951663 +0.354961 +0.926226 +0.330605 +0.926239 +0.357062 +0.927997 +0.367073 +0.930404 +0.377060 +0.940195 +0.359900 +0.934121 +0.375065 +0.963043 +0.375636 +0.965527 +0.380668 +0.960825 +0.376134 +0.950316 +0.388622 +0.954588 +0.381459 +0.962918 +0.379988 +0.960820 +0.378622 +0.980471 +0.386287 +0.975388 +0.380781 +0.981245 +0.361440 +0.978331 +0.390544 +0.967432 +0.390246 +0.963690 +0.373241 +0.982788 +0.403190 +0.994587 +0.403701 +0.991351 +0.422849 +1.024354 +0.420830 +0.994492 +0.419165 +0.996405 +0.424186 +0.985450 +0.430336 +1.013863 +0.433518 +1.006667 +0.414994 +0.988815 +0.390146 +1.008314 +0.420897 +1.013296 +0.426823 +1.016838 +0.408824 +1.011811 +0.419950 +1.011416 +0.420393 +1.015182 +0.421174 +1.018163 +0.404839 +1.007016 +0.407977 +0.992659 +0.421277 +0.999166 +0.422881 +0.997492 +0.413497 +1.001362 +0.418409 +0.994698 +0.422703 +1.006231 +0.407922 +0.993991 +0.407006 +1.030034 +0.415146 +1.040848 +0.424597 +1.031311 +0.408743 +1.025176 +0.424682 +1.029896 +0.438090 +1.035743 +0.426970 +1.015631 +0.428688 +1.014348 +0.430716 +1.009992 +0.422576 +1.034425 +0.418124 +1.021630 +0.425151 +1.012850 +0.436954 +1.052558 +0.443526 +1.029643 +0.437412 +1.005008 +0.405711 +1.022372 +0.419172 +1.029247 +0.404075 +1.006591 +0.426314 +1.023271 +0.438241 +1.028319 +0.424480 +1.036010 +0.416458 +1.007708 +0.413218 +1.027580 +0.417344 +1.015055 +0.391124 +1.016775 +0.400490 +0.994679 +0.418056 +1.033789 +0.394859 +0.998104 +0.393313 +1.008167 +0.368584 +1.015005 +0.386370 +0.990389 +0.417232 +1.014726 +0.405074 +0.972520 +0.390805 +0.986581 +0.391400 +0.986340 +0.405256 +0.982854 +0.399686 +0.978286 +0.390664 +0.955493 +0.377623 +0.977171 +0.399220 +0.957735 +0.337838 +0.910923 +0.350462 +0.901051 +0.335979 +0.916306 +0.329782 +0.914096 +0.326162 +0.905810 +0.332646 +0.884382 +0.327293 +0.903335 +0.330967 +0.903640 +0.337130 +0.885649 +0.317060 +0.909502 +0.343534 +0.877457 +0.312784 +0.905311 +0.312968 +0.880099 +0.318060 +0.870327 +0.300096 +0.881632 +0.277052 +0.889131 +0.294125 +0.878020 +0.282155 +0.829366 +0.290424 +0.882927 +0.290424 +0.882927 +0.290424 +0.882927 +0.290424 +0.882927 +0.290424 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.320323 +0.934084 +0.279751 +0.921901 +0.305699 +0.938924 +0.348123 +0.945923 +0.307148 +0.917403 +0.306732 +0.898840 +0.307296 +0.903018 +0.306355 +0.901333 +0.400869 +0.944857 +0.331948 +0.927768 +0.351332 +0.965944 +0.338499 +0.931626 +0.344961 +0.935936 +0.352741 +0.931057 +0.350207 +0.960309 +0.353199 +0.942628 +0.373513 +0.953149 +0.367522 +0.969858 +0.394176 +0.964970 +0.353319 +0.944615 +0.374664 +0.955592 +0.377669 +0.972901 +0.376870 +0.942306 +0.391210 +0.970151 +0.360469 +0.962516 +0.387450 +0.975778 +0.400172 +0.993898 +0.384552 +0.961791 +0.389312 +0.991836 +0.393170 +0.976857 +0.410949 +0.969209 +0.393055 +0.972273 +0.419585 +0.986819 +0.407864 +0.995467 +0.422118 +0.997956 +0.390266 +1.023938 +0.417858 +0.978722 +0.404811 +0.979096 +0.414743 +0.999096 +0.418785 +1.014956 +0.414019 +0.982986 +0.387004 +1.004937 +0.406024 +1.005298 +0.416222 +1.014581 +0.416094 +1.000349 +0.435273 +1.020097 +0.439604 +1.019371 +0.418838 +1.033132 +0.403216 +0.982546 +0.409435 +1.011087 +0.430302 +1.014111 +0.428451 +0.999396 +0.405180 +0.997785 +0.426401 +1.017817 +0.402810 +1.016476 +0.398046 +1.013486 +0.403370 +1.009956 +0.420400 +1.039070 +0.420426 +1.023209 +0.406337 +1.036867 +0.424607 +1.042478 +0.405525 +1.012313 +0.414850 +1.012532 +0.415042 +0.989639 +0.408594 +1.008205 +0.405207 +1.006461 +0.398912 +1.004427 +0.391670 +1.006212 +0.383841 +0.989341 +0.388425 +0.995997 +0.392027 +0.990625 +0.386814 +0.989822 +0.375430 +0.990739 +0.380577 +0.999667 +0.386108 +0.971995 +0.389499 +0.990219 +0.370908 +1.001644 +0.399718 +1.001753 +0.391026 +0.993710 +0.372035 +0.981190 +0.346713 +0.977796 +0.384978 +0.982921 +0.370104 +0.972125 +0.361150 +0.981010 +0.379871 +0.957234 +0.354021 +0.978506 +0.369945 +0.967658 +0.343357 +0.959022 +0.389843 +0.943125 +0.352956 +0.948228 +0.373480 +0.926453 +0.364844 +0.930153 +0.368961 +0.948845 +0.358964 +0.936201 +0.360195 +0.936084 +0.333198 +0.923371 +0.351728 +0.931664 +0.345389 +0.925312 +0.324816 +0.912108 +0.334051 +0.908169 +0.340643 +0.911678 +0.323820 +0.896702 +0.336877 +0.903676 +0.336726 +0.901263 +0.304761 +0.898972 +0.323164 +0.906249 +0.310611 +0.869889 +0.314556 +0.891707 +0.314960 +0.875770 +0.330943 +0.898299 +0.312775 +0.876825 +0.307057 +0.844053 +0.272873 +0.851182 +0.289510 +0.839382 +0.292719 +0.856182 +0.289814 +0.844872 +0.289814 +0.844872 +0.289814 +0.844872 +0.289814 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.319315 +0.908706 +0.311645 +0.859770 +0.310012 +0.923208 +0.298845 +0.939014 +0.349089 +0.863897 +0.310414 +0.907957 +0.383683 +0.928130 +0.306466 +0.947079 +0.370628 +0.909508 +0.324259 +0.907635 +0.323845 +0.912046 +0.333849 +0.932101 +0.356080 +0.958802 +0.365174 +0.950453 +0.339067 +0.939965 +0.373365 +0.936543 +0.333310 +0.963196 +0.383674 +0.972874 +0.362646 +0.981931 +0.383494 +0.962429 +0.361015 +0.977049 +0.356289 +0.971742 +0.373115 +0.955742 +0.354499 +0.967758 +0.391477 +0.956116 +0.376942 +0.965036 +0.365739 +0.959774 +0.371438 +0.977105 +0.378853 +0.970685 +0.369901 +0.949907 +0.390243 +1.001231 +0.390302 +0.992460 +0.406495 +0.994414 +0.436730 +0.986600 +0.391881 +1.000287 +0.411477 +1.014764 +0.431777 +1.025788 +0.421378 +1.018625 +0.428079 +0.997357 +0.401104 +1.001014 +0.429641 +1.010329 +0.406688 +1.009249 +0.423209 +1.022209 +0.392768 +1.010368 +0.412732 +1.010708 +0.425433 +1.009205 +0.401538 +0.999174 +0.414645 +0.987846 +0.405697 +0.998893 +0.419288 +1.025900 +0.420952 +1.004316 +0.422809 +1.015163 +0.418364 +1.003104 +0.411491 +1.005089 +0.421468 +1.001293 +0.416704 +1.019424 +0.408885 +1.024468 +0.432195 +1.030894 +0.412085 +1.020229 +0.412131 +1.013625 +0.407274 +1.024881 +0.389967 +0.985119 +0.397883 +1.009677 +0.385125 +0.995721 +0.388218 +0.988138 +0.382717 +1.017416 +0.402154 +0.977524 +0.388491 +0.977479 +0.403705 +0.998137 +0.395999 +0.977653 +0.380176 +0.999245 +0.389015 +0.999546 +0.381381 +1.002433 +0.397187 +0.997601 +0.385113 +1.003242 +0.389516 +0.979424 +0.385693 +0.990147 +0.389492 +1.002196 +0.383222 +0.995199 +0.367520 +0.988462 +0.380621 +0.973068 +0.359852 +0.994251 +0.370485 +0.984601 +0.370124 +0.981626 +0.331421 +0.954207 +0.358066 +0.963111 +0.348867 +0.977808 +0.372285 +0.930867 +0.356410 +0.931725 +0.376019 +0.964188 +0.364630 +0.941990 +0.382542 +0.938851 +0.351861 +0.935259 +0.360303 +0.924175 +0.342112 +0.918993 +0.323694 +0.924330 +0.343492 +0.927140 +0.322055 +0.914129 +0.327387 +0.917443 +0.345784 +0.901800 +0.327403 +0.922651 +0.329705 +0.886147 +0.315643 +0.882125 +0.322408 +0.886780 +0.343902 +0.882880 +0.335228 +0.867161 +0.333195 +0.882257 +0.294203 +0.878242 +0.286825 +0.889197 +0.297320 +0.842560 +0.318189 +0.858595 +0.266729 +0.888059 +0.264263 +0.834452 +0.294079 +0.863071 +0.294079 +0.863071 +0.294079 +0.863071 +0.294079 +0.863071 +0.294079 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.330076 +0.905332 +0.354537 +0.888790 +0.293563 +0.883018 +0.348583 +0.907979 +0.348082 +0.924998 +0.324994 +0.906135 +0.339741 +0.949875 +0.336448 +0.934126 +0.358878 +0.948231 +0.374803 +0.952456 +0.371675 +0.942621 +0.363350 +0.970992 +0.380253 +0.951426 +0.393524 +0.949945 +0.359300 +0.975795 +0.365900 +0.987975 +0.391853 +0.977290 +0.385545 +0.958313 +0.386661 +0.976633 +0.369749 +0.967095 +0.392660 +0.983200 +0.391865 +0.961700 +0.374820 +0.967833 +0.381537 +0.979527 +0.412892 +0.994560 +0.416134 +0.988539 +0.410508 +0.979003 +0.415780 +0.959019 +0.390674 +0.990736 +0.421973 +0.982117 +0.425903 +1.011361 +0.407495 +0.993760 +0.403106 +0.998581 +0.412396 +1.008445 +0.412543 +1.020896 +0.418928 +1.008225 +0.428573 +1.006123 +0.420374 +1.021843 +0.416905 +0.997099 +0.399767 +0.990909 +0.409562 +1.006025 +0.411589 +1.001683 +0.424934 +1.008723 +0.407849 +0.998299 +0.406363 +1.006295 +0.420027 +1.003395 +0.420067 +1.001362 +0.407977 +1.017521 +0.408905 +1.010951 +0.420007 +1.024281 +0.398201 +1.010002 +0.409573 +1.031403 +0.405092 +1.013992 +0.385820 +1.007829 +0.402661 +1.014972 +0.390642 +1.000039 +0.390103 +0.992606 +0.394569 +0.982000 +0.390026 +0.997600 +0.395369 +0.992522 +0.400809 +0.989885 +0.391694 +0.985197 +0.379551 +0.998852 +0.393949 +0.987779 +0.393618 +1.005794 +0.390506 +0.989796 +0.378460 +0.976859 +0.370565 +1.012605 +0.380740 +0.967468 +0.388716 +0.988061 +0.377728 +0.976807 +0.377778 +0.974400 +0.377001 +0.966786 +0.367302 +0.988964 +0.370604 +0.983318 +0.378752 +0.950043 +0.345049 +0.960036 +0.368511 +0.968118 +0.349254 +0.945168 +0.368478 +0.931292 +0.371437 +0.935365 +0.385866 +0.937963 +0.364635 +0.938083 +0.360248 +0.932151 +0.350123 +0.932343 +0.336935 +0.937224 +0.339730 +0.936175 +0.347269 +0.925138 +0.325897 +0.948151 +0.339945 +0.937074 +0.327385 +0.907842 +0.352900 +0.902547 +0.341273 +0.920860 +0.339448 +0.935712 +0.317480 +0.904855 +0.338460 +0.891403 +0.331897 +0.916238 +0.320823 +0.865788 +0.330006 +0.864812 +0.312720 +0.865003 +0.294293 +0.880353 +0.302382 +0.858876 +0.319083 +0.910216 +0.341962 +0.917334 +0.305253 +0.883148 +0.305253 +0.883148 +0.305253 +0.883148 +0.305253 +0.883148 +0.305253 +0.883148 +0.305253 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.332404 +0.931073 +0.334685 +0.951108 +0.341214 +0.968271 +0.313662 +0.885791 +0.342336 +0.939158 +0.319750 +0.940131 +0.350364 +0.932464 +0.303236 +0.897009 +0.306285 +0.944272 +0.308798 +0.922159 +0.341380 +0.940498 +0.288347 +0.932446 +0.331081 +0.977846 +0.335913 +0.959339 +0.326753 +0.952100 +0.360349 +0.939353 +0.362862 +0.945638 +0.344016 +0.986819 +0.339905 +0.974042 +0.399926 +0.965755 +0.375417 +0.966189 +0.360798 +0.967567 +0.357672 +0.986336 +0.361888 +0.988921 +0.358509 +0.991389 +0.386058 +0.964858 +0.380431 +0.967350 +0.375991 +0.993491 +0.377078 +0.989202 +0.396218 +0.986647 +0.371330 +0.982869 +0.399330 +0.978051 +0.416739 +0.994329 +0.402970 +0.973346 +0.403853 +0.991788 +0.389666 +0.998596 +0.412748 +1.007925 +0.412326 +0.982768 +0.407663 +0.993230 +0.411311 +1.004292 +0.421965 +1.009381 +0.413151 +0.994787 +0.415819 +1.007766 +0.427571 +0.996652 +0.417883 +1.022158 +0.435040 +1.019812 +0.419355 +0.999161 +0.409805 +1.028627 +0.404488 +1.019770 +0.402609 +1.022440 +0.416662 +1.025905 +0.410253 +1.025577 +0.406631 +1.005040 +0.394434 +1.035611 +0.404437 +1.027956 +0.412325 +1.029682 +0.420425 +1.044217 +0.410824 +1.032879 +0.405240 +1.029805 +0.414517 +1.051902 +0.412709 +1.028733 +0.401302 +1.039041 +0.404517 +1.019984 +0.373977 +1.026146 +0.401356 +1.032137 +0.385195 +1.032291 +0.398345 +1.010936 +0.397898 +1.010056 +0.404970 +1.014433 +0.377491 +0.988015 +0.390108 +1.021542 +0.396476 +1.012117 +0.381876 +1.014536 +0.405241 +1.006227 +0.388004 +1.003806 +0.374209 +1.021297 +0.386357 +0.996556 +0.380328 +1.006524 +0.372423 +1.027765 +0.359810 +0.971612 +0.371318 +0.996706 +0.387297 +0.970018 +0.367136 +0.983720 +0.349802 +0.984016 +0.346676 +0.976878 +0.354647 +0.995884 +0.366990 +0.955829 +0.369848 +0.924081 +0.372811 +0.943019 +0.371287 +0.936193 +0.367920 +0.942239 +0.359788 +0.969831 +0.350793 +0.931852 +0.353853 +0.938091 +0.343007 +0.920437 +0.329844 +0.941675 +0.338028 +0.954235 +0.344241 +0.953962 +0.333524 +0.921329 +0.326927 +0.946558 +0.345728 +0.948498 +0.317351 +0.905548 +0.312047 +0.928023 +0.323841 +0.948892 +0.313573 +0.923369 +0.316711 +0.919902 +0.309220 +0.890495 +0.297682 +0.872105 +0.282843 +0.917213 +0.298998 +0.882390 +0.333599 +0.889726 +0.294171 +0.901937 +0.305147 +0.896443 +0.305147 +0.896443 +0.305147 +0.896443 +0.305147 +0.896443 +0.305147 +0.896443 +0.305147 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.288775 +0.941236 +0.218094 +0.916279 +0.303842 +0.934902 +0.287575 +0.949858 +0.274909 +0.938948 +0.261526 +0.883209 +0.315275 +0.905524 +0.319755 +0.951512 +0.284201 +0.944739 +0.315076 +0.940374 +0.280844 +0.946701 +0.309065 +0.941996 +0.337824 +0.946394 +0.340220 +0.965627 +0.313233 +0.955580 +0.324160 +0.942822 +0.353057 +0.941810 +0.325580 +0.982817 +0.337019 +0.977994 +0.354733 +0.952817 +0.348205 +0.985893 +0.322113 +0.971027 +0.344767 +0.977589 +0.357283 +0.998326 +0.337871 +0.987656 +0.350842 +1.002580 +0.367569 +0.990455 +0.360932 +0.983213 +0.358147 +0.976987 +0.366044 +0.978410 +0.389500 +1.014818 +0.386390 +0.996696 +0.376111 +1.006105 +0.382555 +0.990282 +0.391624 +1.003874 +0.395746 +1.014027 +0.391504 +1.012037 +0.385063 +1.020835 +0.384494 +1.020135 +0.390913 +1.016484 +0.402451 +1.015779 +0.387542 +1.024863 +0.385953 +1.039767 +0.382862 +1.006755 +0.396449 +1.027728 +0.395557 +1.031993 +0.418558 +1.011413 +0.382879 +1.023613 +0.397219 +1.019962 +0.392522 +1.017049 +0.386943 +1.032786 +0.394055 +1.025254 +0.393623 +1.018875 +0.374715 +1.025687 +0.396985 +1.026419 +0.379023 +1.034651 +0.414260 +1.036525 +0.389292 +1.028526 +0.394497 +1.033173 +0.370527 +1.043464 +0.377040 +1.032099 +0.392798 +1.039061 +0.385149 +1.023535 +0.377861 +0.997664 +0.380694 +1.032657 +0.371450 +1.008992 +0.351387 +1.020351 +0.352847 +1.031012 +0.375524 +1.030892 +0.376669 +1.031793 +0.377294 +1.038542 +0.358499 +1.019030 +0.393435 +1.049865 +0.369501 +1.035611 +0.377791 +1.003399 +0.362328 +1.003708 +0.366368 +1.002110 +0.364192 +1.007956 +0.354553 +1.003597 +0.360922 +1.002028 +0.355943 +1.015200 +0.368262 +0.980193 +0.319709 +0.989667 +0.352941 +0.979138 +0.348013 +0.978440 +0.327710 +0.986818 +0.337659 +0.976739 +0.350470 +0.946022 +0.364650 +0.957103 +0.362536 +0.959635 +0.359203 +0.962955 +0.335561 +0.948471 +0.349430 +0.977110 +0.351005 +0.953532 +0.330051 +0.936988 +0.345019 +0.937623 +0.344085 +0.943843 +0.329243 +0.948816 +0.301102 +0.949952 +0.301335 +0.928884 +0.311423 +0.939772 +0.305427 +0.931410 +0.295601 +0.944502 +0.302224 +0.902439 +0.300474 +0.901817 +0.293965 +0.891281 +0.276525 +0.919295 +0.263051 +0.898687 +0.272204 +0.916268 +0.267229 +0.904786 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +0.911416 +0.270593 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.240652 +0.929507 +0.280729 +0.925885 +0.248355 +0.955199 +0.245778 +0.917888 +0.227824 +0.915434 +0.271972 +0.894098 +0.274317 +0.922470 +0.330644 +0.949516 +0.338571 +0.953025 +0.324239 +0.919236 +0.317249 +0.982300 +0.320458 +0.961811 +0.339838 +0.930772 +0.312403 +0.950503 +0.313840 +0.967219 +0.353556 +0.937624 +0.345564 +0.947063 +0.339959 +0.981683 +0.321806 +0.963515 +0.315358 +0.963972 +0.360708 +0.962016 +0.351684 +0.973877 +0.371981 +0.992533 +0.360037 +1.015907 +0.336260 +0.970045 +0.340972 +0.974709 +0.371620 +0.980883 +0.360358 +0.986893 +0.349338 +1.002626 +0.354094 +0.983904 +0.374065 +1.011175 +0.360273 +1.010241 +0.397186 +1.011649 +0.382164 +1.008868 +0.398999 +1.006355 +0.380177 +1.008604 +0.376781 +1.019483 +0.390456 +1.009771 +0.383657 +1.025820 +0.379103 +1.027356 +0.380443 +1.016263 +0.385601 +1.034106 +0.387633 +1.032949 +0.391429 +1.023891 +0.407937 +1.022704 +0.401035 +1.020602 +0.384133 +1.016912 +0.387236 +1.028910 +0.394011 +1.012948 +0.390489 +1.025494 +0.385498 +1.009496 +0.385787 +1.025568 +0.397749 +1.029929 +0.398891 +1.026426 +0.387491 +1.067658 +0.389408 +1.050833 +0.393053 +1.051694 +0.386107 +1.039912 +0.395582 +1.051624 +0.378429 +1.050596 +0.385317 +1.032138 +0.385978 +1.020384 +0.385274 +1.032552 +0.391571 +1.035309 +0.367185 +1.039388 +0.375672 +1.017828 +0.388400 +1.023889 +0.378604 +1.031844 +0.353864 +1.001093 +0.394491 +1.036381 +0.362662 +1.030201 +0.388513 +1.019153 +0.389426 +1.037891 +0.386524 +0.994136 +0.360761 +1.030373 +0.379286 +1.031600 +0.366705 +1.002181 +0.364208 +1.024880 +0.338190 +1.007205 +0.364232 +1.025617 +0.367365 +1.014213 +0.346501 +1.018685 +0.361979 +1.007972 +0.347873 +0.986833 +0.350638 +1.008764 +0.340880 +1.003400 +0.344498 +0.997886 +0.365075 +0.980036 +0.360045 +0.938505 +0.336838 +0.955163 +0.339871 +0.980856 +0.348478 +0.957635 +0.330601 +0.963894 +0.325128 +0.950016 +0.303602 +0.964010 +0.340630 +0.957706 +0.309593 +0.937611 +0.306119 +0.914512 +0.339626 +0.907711 +0.304143 +0.936617 +0.298967 +0.930501 +0.308140 +0.939225 +0.323659 +0.914531 +0.278461 +0.914392 +0.282085 +0.884010 +0.311546 +0.897487 +0.307921 +0.889035 +0.266332 +0.887168 +0.273909 +0.885228 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +0.891230 +0.295266 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.283015 +0.895961 +0.306473 +0.919599 +0.261420 +0.915385 +0.305997 +0.927464 +0.281629 +0.845032 +0.336499 +0.923958 +0.315490 +0.966218 +0.311941 +0.948032 +0.288923 +0.923970 +0.308042 +0.940037 +0.320398 +0.920715 +0.320447 +0.959309 +0.348588 +0.957189 +0.286020 +0.924777 +0.333386 +0.966538 +0.340631 +0.939259 +0.337280 +0.991401 +0.336238 +0.942429 +0.350465 +0.971126 +0.312432 +0.987262 +0.340953 +0.976733 +0.343888 +0.959096 +0.318945 +0.991928 +0.356226 +1.010658 +0.341557 +0.984527 +0.352591 +1.002557 +0.366552 +0.973734 +0.365670 +0.983337 +0.338581 +0.987188 +0.364284 +0.967604 +0.383638 +1.010262 +0.380850 +1.014972 +0.383418 +1.008445 +0.381062 +1.022308 +0.381594 +1.011678 +0.379071 +1.018180 +0.379042 +0.991023 +0.382369 +1.024038 +0.374684 +1.015906 +0.367584 +1.016901 +0.381083 +0.999417 +0.390425 +1.014363 +0.373886 +1.023244 +0.387203 +1.021551 +0.387062 +1.015267 +0.382520 +1.022420 +0.404873 +1.045445 +0.392838 +1.025754 +0.380947 +1.027118 +0.392346 +1.021147 +0.393244 +1.013603 +0.403995 +1.037695 +0.405693 +1.046124 +0.413178 +1.045574 +0.398883 +1.064228 +0.404268 +1.044173 +0.390822 +1.060888 +0.408476 +1.057374 +0.396004 +1.043202 +0.400641 +1.053503 +0.399451 +1.039654 +0.376563 +1.055120 +0.381506 +1.020192 +0.383188 +1.037472 +0.368595 +1.028123 +0.402608 +1.043182 +0.380394 +1.014449 +0.380738 +1.029709 +0.385006 +1.016813 +0.376388 +1.015082 +0.375539 +1.019544 +0.378774 +1.033649 +0.369347 +1.020178 +0.362636 +1.016868 +0.364414 +1.021458 +0.360526 +1.007929 +0.356873 +1.020633 +0.370505 +1.033879 +0.338551 +0.998319 +0.358134 +1.018271 +0.356824 +1.005016 +0.358172 +0.998464 +0.335009 +0.994980 +0.341249 +0.992833 +0.341726 +0.976752 +0.344062 +0.981138 +0.341952 +0.958651 +0.362487 +0.976859 +0.338995 +0.957247 +0.365104 +0.950147 +0.352304 +0.971650 +0.336994 +0.970143 +0.337826 +0.950935 +0.325765 +0.945266 +0.335048 +0.963173 +0.344524 +0.963795 +0.306088 +0.944501 +0.317208 +0.943071 +0.322991 +0.932803 +0.312721 +0.938012 +0.323303 +0.940756 +0.282447 +0.910667 +0.313835 +0.931655 +0.311525 +0.915786 +0.308813 +0.923744 +0.305584 +0.915088 +0.295579 +0.913361 +0.270682 +0.880362 +0.321002 +0.861386 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +0.902937 +0.290615 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.331358 +0.945720 +0.319331 +0.960378 +0.330606 +0.983388 +0.343923 +0.918375 +0.319545 +0.935395 +0.313891 +0.989113 +0.360408 +0.968376 +0.356786 +0.978059 +0.372409 +0.964448 +0.389022 +0.962004 +0.369276 +1.000142 +0.343231 +0.965190 +0.337832 +0.975629 +0.335503 +0.973083 +0.304717 +0.986632 +0.364776 +0.983963 +0.354541 +1.000475 +0.372941 +0.983703 +0.378171 +0.987302 +0.331331 +0.991135 +0.363518 +1.051216 +0.358577 +0.987094 +0.361613 +1.018711 +0.390487 +1.019212 +0.401726 +1.009661 +0.382840 +0.999089 +0.407787 +1.021170 +0.408015 +1.019582 +0.402990 +1.031774 +0.393854 +1.013762 +0.381181 +1.025531 +0.363120 +1.032358 +0.384486 +1.016390 +0.408424 +1.040839 +0.368452 +1.026238 +0.396248 +1.027381 +0.382202 +1.030915 +0.388547 +1.041355 +0.375023 +1.023733 +0.397697 +1.024225 +0.403168 +1.035564 +0.395182 +1.038877 +0.404805 +1.025085 +0.395409 +1.033423 +0.399556 +1.021542 +0.378263 +1.029138 +0.393825 +1.044236 +0.389550 +1.031925 +0.382289 +1.036633 +0.399663 +1.039189 +0.396427 +1.055975 +0.394060 +1.054471 +0.393944 +1.037575 +0.382820 +1.044447 +0.366647 +1.045920 +0.393996 +1.041767 +0.367289 +1.017460 +0.374242 +1.023943 +0.380338 +1.006640 +0.373436 +1.046526 +0.380747 +1.035557 +0.387275 +1.009950 +0.381608 +1.040647 +0.369596 +1.017441 +0.366997 +1.017658 +0.364681 +1.008229 +0.381598 +1.034478 +0.374308 +1.030536 +0.347859 +1.009010 +0.373679 +1.024347 +0.370333 +1.007048 +0.346620 +1.009086 +0.359572 +1.011844 +0.344330 +1.003932 +0.347403 +1.004952 +0.341371 +1.014845 +0.344997 +1.009470 +0.334512 +0.990576 +0.340056 +0.995387 +0.363849 +0.961470 +0.377826 +0.965734 +0.374280 +0.945121 +0.365289 +0.971948 +0.354845 +0.975920 +0.331502 +0.966283 +0.328410 +0.954302 +0.336135 +0.953181 +0.329666 +0.964134 +0.335746 +0.939328 +0.324907 +0.944102 +0.293590 +0.960688 +0.274055 +0.941838 +0.317725 +0.927440 +0.328277 +0.939692 +0.294588 +0.891960 +0.306583 +0.911790 +0.310943 +0.921865 +0.307604 +0.925613 +0.302135 +0.893784 +0.293753 +0.875929 +0.308193 +0.911332 +0.297263 +0.917451 +0.315815 +0.835516 +0.299736 +0.901571 +0.299736 +0.901571 +0.299736 +0.901571 +0.299736 +0.901571 +0.299736 +0.901571 +0.299736 +0.901571 +0.299736 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.259015 +0.901456 +0.287845 +0.902926 +0.258886 +0.857060 +0.271611 +0.911865 +0.246548 +0.935442 +0.336342 +0.962770 +0.343146 +0.886835 +0.304309 +0.950335 +0.315526 +0.931028 +0.271590 +0.913789 +0.312126 +0.944779 +0.310127 +0.915293 +0.336375 +0.930124 +0.336563 +0.980952 +0.328395 +0.944149 +0.380643 +0.976532 +0.343233 +0.969044 +0.345052 +0.971642 +0.358817 +0.976850 +0.334605 +0.992074 +0.321652 +0.967790 +0.343609 +0.982878 +0.345645 +1.004060 +0.360029 +0.968081 +0.321039 +0.989972 +0.387349 +0.967919 +0.343619 +1.025341 +0.355717 +0.992480 +0.358753 +1.005109 +0.387541 +1.019843 +0.396556 +1.015531 +0.391809 +1.000942 +0.384496 +1.013905 +0.374746 +1.005312 +0.405583 +1.019439 +0.400338 +1.017475 +0.367533 +1.028129 +0.383757 +1.039200 +0.395268 +1.008418 +0.392631 +1.035820 +0.383777 +1.030711 +0.376713 +1.025603 +0.372752 +1.030798 +0.388361 +1.036840 +0.392088 +1.027604 +0.395342 +1.024591 +0.396128 +1.031069 +0.394441 +1.005239 +0.393593 +1.013490 +0.400180 +1.020981 +0.376493 +1.035293 +0.405920 +1.024680 +0.416021 +1.032262 +0.394133 +1.023595 +0.389103 +1.044862 +0.391678 +1.039830 +0.385686 +1.057253 +0.377152 +1.042369 +0.385189 +1.029205 +0.395297 +1.029970 +0.389298 +1.031401 +0.378320 +1.027452 +0.404483 +1.025311 +0.387422 +1.021478 +0.368488 +1.043222 +0.377513 +1.021078 +0.373831 +1.007574 +0.392548 +1.018423 +0.379716 +1.035746 +0.386936 +1.019570 +0.366568 +1.018150 +0.362742 +1.006615 +0.371841 +1.011026 +0.358428 +0.999595 +0.354195 +1.009731 +0.381827 +1.022266 +0.377539 +1.004421 +0.340575 +0.984456 +0.340705 +1.013055 +0.332246 +0.996048 +0.345413 +1.009957 +0.366689 +1.010383 +0.353929 +0.988846 +0.346860 +0.989636 +0.325397 +0.956936 +0.350283 +0.974710 +0.348745 +0.970887 +0.345396 +0.944677 +0.354543 +0.980158 +0.359334 +0.952261 +0.340425 +0.952003 +0.322375 +0.957780 +0.330480 +0.959540 +0.333371 +0.949886 +0.309903 +0.928795 +0.314585 +0.931841 +0.330892 +0.966918 +0.303966 +0.947630 +0.312865 +0.954253 +0.320004 +0.933201 +0.301022 +0.914091 +0.289219 +0.913701 +0.273696 +0.896489 +0.300281 +0.921935 +0.259489 +0.897207 +0.309210 +0.916141 +0.295349 +0.918497 +0.251486 +0.872201 +0.291908 +0.828927 +0.285348 +0.902280 +0.285348 +0.902280 +0.285348 +0.902280 +0.285348 +0.902280 +0.285348 +0.902280 +0.285348 +0.902280 +0.285348 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.258763 +0.954622 +0.254002 +0.910468 +0.266806 +0.966774 +0.253992 +0.956802 +0.255491 +0.940289 +0.276032 +0.947688 +0.314322 +0.946116 +0.316940 +0.947364 +0.328691 +0.913182 +0.306786 +0.974702 +0.318295 +0.954202 +0.343546 +0.989454 +0.298404 +0.929584 +0.334060 +0.981421 +0.323217 +0.967358 +0.328493 +0.975587 +0.332046 +0.957484 +0.370100 +1.012156 +0.328213 +0.951326 +0.352751 +0.990682 +0.328661 +0.981789 +0.335077 +0.988589 +0.318221 +0.979847 +0.367296 +0.973488 +0.335636 +0.987767 +0.355739 +0.973918 +0.361393 +0.975843 +0.354237 +0.994123 +0.368682 +0.997946 +0.361780 +1.003581 +0.402877 +1.005348 +0.368610 +1.008307 +0.391783 +1.014264 +0.391207 +1.019881 +0.408765 +1.013609 +0.385780 +1.003901 +0.362706 +0.996871 +0.377647 +1.006756 +0.378488 +1.022042 +0.375180 +1.015293 +0.378385 +1.010618 +0.388117 +1.017383 +0.397367 +1.046037 +0.389954 +1.021641 +0.389095 +1.037763 +0.385359 +1.055234 +0.377777 +1.054911 +0.396047 +1.071716 +0.394744 +1.057553 +0.388014 +1.037398 +0.404403 +1.046283 +0.407878 +1.044194 +0.395051 +1.049430 +0.367071 +1.062702 +0.397639 +1.054519 +0.391851 +1.057305 +0.380075 +1.058339 +0.372909 +1.076521 +0.372312 +1.061582 +0.396009 +1.035620 +0.364780 +1.056372 +0.376022 +1.053911 +0.385539 +1.034703 +0.401672 +1.053554 +0.380998 +1.040759 +0.377019 +1.060631 +0.374806 +1.038076 +0.364959 +1.043515 +0.378383 +1.039619 +0.377556 +1.019209 +0.374299 +1.004619 +0.352267 +1.013159 +0.369656 +1.019982 +0.375162 +1.044560 +0.354641 +1.033609 +0.343463 +1.027018 +0.369218 +0.997980 +0.338582 +0.998150 +0.357155 +0.989204 +0.332969 +1.003477 +0.365168 +1.005228 +0.334392 +1.008588 +0.337160 +0.995819 +0.341615 +1.003123 +0.336336 +0.978165 +0.335877 +1.006587 +0.378628 +0.984611 +0.349852 +0.978518 +0.350227 +0.982593 +0.359485 +1.003349 +0.348583 +0.980611 +0.327022 +0.997211 +0.337259 +0.949851 +0.312707 +0.947969 +0.306818 +0.991546 +0.315217 +0.980223 +0.330208 +0.973418 +0.339020 +0.964644 +0.312136 +0.960462 +0.333151 +0.965944 +0.305980 +0.972376 +0.336163 +0.955790 +0.281354 +0.948810 +0.300260 +0.946052 +0.282269 +0.905629 +0.324503 +0.941266 +0.288425 +0.940379 +0.275155 +0.928392 +0.252568 +0.968755 +0.296028 +0.936679 +0.296028 +0.936679 +0.296028 +0.936679 +0.296028 +0.936679 +0.296028 +0.936679 +0.296028 +0.936679 +0.296028 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.299571 +0.948701 +0.253554 +0.996031 +0.292092 +0.962099 +0.284722 +0.951633 +0.321899 +0.932371 +0.267698 +0.937398 +0.275619 +0.942741 +0.288964 +0.979750 +0.298717 +0.972887 +0.273306 +0.952107 +0.292154 +0.934259 +0.284179 +0.958746 +0.301087 +0.963082 +0.307456 +0.979031 +0.318741 +0.981678 +0.293739 +0.993546 +0.320591 +0.981662 +0.300364 +1.013156 +0.328104 +0.980583 +0.310418 +1.006181 +0.321884 +1.001884 +0.315880 +1.000827 +0.357202 +0.996392 +0.335356 +1.013061 +0.310066 +0.999645 +0.355800 +1.018089 +0.356498 +1.017308 +0.349715 +0.999234 +0.317120 +1.021241 +0.350199 +1.031790 +0.367563 +1.011818 +0.347060 +1.032313 +0.366857 +1.049350 +0.375300 +1.038226 +0.360365 +1.031613 +0.384166 +1.024150 +0.345770 +1.039388 +0.382135 +1.059797 +0.363525 +1.033779 +0.335951 +1.035001 +0.381056 +1.042205 +0.371881 +1.035480 +0.370068 +1.032258 +0.370325 +1.053985 +0.390605 +1.057174 +0.381490 +1.038529 +0.380762 +1.011356 +0.381175 +1.044934 +0.392442 +1.045757 +0.362750 +1.035393 +0.364676 +1.048300 +0.374422 +1.051082 +0.394485 +1.042554 +0.362554 +1.065229 +0.364498 +1.043417 +0.372826 +1.069146 +0.373355 +1.042690 +0.366538 +1.070283 +0.387021 +1.051918 +0.363914 +1.047800 +0.363947 +1.066264 +0.368809 +1.047171 +0.382477 +1.056060 +0.362904 +1.056041 +0.355289 +1.050441 +0.360110 +1.046042 +0.360937 +1.039732 +0.336810 +1.048302 +0.359913 +1.066903 +0.363525 +1.053886 +0.339574 +1.022186 +0.347981 +1.011669 +0.343451 +1.042129 +0.354896 +1.056966 +0.350581 +1.041893 +0.361107 +1.036847 +0.346599 +1.030193 +0.356720 +1.029001 +0.339809 +1.040848 +0.344225 +1.034587 +0.342436 +1.031389 +0.343564 +1.021139 +0.305074 +1.013798 +0.313315 +1.000994 +0.328460 +1.010673 +0.327623 +0.974244 +0.338606 +0.987883 +0.323927 +1.007089 +0.334514 +0.991473 +0.341588 +1.005114 +0.336066 +1.003158 +0.308198 +0.983606 +0.320214 +0.980266 +0.316520 +0.974320 +0.306662 +0.978226 +0.303447 +0.960269 +0.317500 +0.987522 +0.281002 +0.943128 +0.279827 +0.961055 +0.272750 +0.944039 +0.279997 +0.950917 +0.298618 +0.917608 +0.274211 +0.921160 +0.269457 +0.965497 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +0.929895 +0.284275 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.269455 +0.906678 +0.259757 +0.946880 +0.285004 +0.881181 +0.245904 +0.923098 +0.277457 +0.915754 +0.261650 +0.987033 +0.308241 +0.953251 +0.304694 +0.945267 +0.300328 +0.923084 +0.243197 +0.963248 +0.272318 +0.971994 +0.291706 +0.951385 +0.294788 +0.987530 +0.301464 +0.991580 +0.310098 +1.019128 +0.307608 +0.994209 +0.313369 +1.000988 +0.310396 +0.988595 +0.296080 +0.975759 +0.327559 +0.978537 +0.330219 +1.006074 +0.300192 +0.975704 +0.360410 +0.970612 +0.340101 +1.013524 +0.342861 +0.991616 +0.333795 +1.016999 +0.367840 +1.014083 +0.340383 +1.014871 +0.323027 +0.978216 +0.347679 +1.042364 +0.332771 +1.036697 +0.344401 +1.035740 +0.367064 +1.046423 +0.357933 +1.017763 +0.346343 +1.040842 +0.381773 +1.036477 +0.375950 +1.056387 +0.361832 +1.065933 +0.382084 +1.061587 +0.349871 +1.038067 +0.365258 +1.055276 +0.378302 +1.035954 +0.385636 +1.038999 +0.356336 +1.056680 +0.379426 +1.057429 +0.352897 +1.036352 +0.350973 +1.022562 +0.372970 +1.054645 +0.364998 +1.044144 +0.360117 +1.034857 +0.372288 +1.052746 +0.374842 +1.052099 +0.375253 +1.068648 +0.354465 +1.052932 +0.382038 +1.071574 +0.365128 +1.076867 +0.377146 +1.066105 +0.384860 +1.072730 +0.375169 +1.063639 +0.362374 +1.057516 +0.347267 +1.052444 +0.367959 +1.054772 +0.366835 +1.067581 +0.372758 +1.066062 +0.375035 +1.057355 +0.366311 +1.073112 +0.358094 +1.053720 +0.387788 +1.038424 +0.370733 +1.056163 +0.364399 +1.040446 +0.367360 +1.060406 +0.327124 +1.020134 +0.358298 +1.052870 +0.349472 +1.037757 +0.378431 +1.045690 +0.346240 +1.015785 +0.354134 +1.021576 +0.322632 +1.035844 +0.342583 +1.036064 +0.323641 +1.019391 +0.342474 +1.064090 +0.327880 +1.034313 +0.316709 +0.991102 +0.333693 +1.032285 +0.349542 +1.015454 +0.354002 +0.969693 +0.343888 +0.999276 +0.360032 +1.001465 +0.319782 +0.958371 +0.337764 +1.003073 +0.330439 +0.998720 +0.315562 +0.983328 +0.318504 +0.960053 +0.313030 +0.963637 +0.294817 +0.987609 +0.299590 +0.986042 +0.292403 +0.980319 +0.309046 +0.973751 +0.284834 +0.941427 +0.335258 +0.983523 +0.308503 +0.967037 +0.316039 +0.915750 +0.307903 +0.959142 +0.291967 +0.962323 +0.297488 +0.928023 +0.279448 +0.931249 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +0.949829 +0.299119 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.291789 +0.913930 +0.266313 +0.911175 +0.324297 +0.938960 +0.289093 +0.908323 +0.261978 +0.894507 +0.269945 +0.929880 +0.234048 +0.963208 +0.270197 +0.979891 +0.251794 +0.972175 +0.226003 +0.954065 +0.250711 +0.997143 +0.278988 +0.961884 +0.316609 +0.967988 +0.300120 +1.018417 +0.308589 +0.986511 +0.301752 +0.999955 +0.290824 +1.030247 +0.272727 +0.991900 +0.328075 +1.015542 +0.337928 +0.982667 +0.331995 +0.993471 +0.309927 +1.009564 +0.336556 +1.024767 +0.324414 +1.003540 +0.311947 +0.994047 +0.337028 +1.021352 +0.322489 +1.029844 +0.340820 +0.981760 +0.345359 +1.014788 +0.351035 +1.023423 +0.367334 +1.026565 +0.350272 +1.038846 +0.345372 +1.014058 +0.374699 +1.010955 +0.353470 +1.053289 +0.347448 +1.026735 +0.364831 +1.032509 +0.366580 +1.044049 +0.372124 +1.051757 +0.357229 +1.043952 +0.349395 +1.068420 +0.369945 +1.048318 +0.370157 +1.050786 +0.361011 +1.043402 +0.373242 +1.052031 +0.365057 +1.048250 +0.404764 +1.049095 +0.385983 +1.032538 +0.368545 +1.080820 +0.395869 +1.066506 +0.388925 +1.060609 +0.375856 +1.048831 +0.384909 +1.060278 +0.357928 +1.074415 +0.386975 +1.066426 +0.365191 +1.078857 +0.375449 +1.091560 +0.391893 +1.098872 +0.370298 +1.078701 +0.382105 +1.075020 +0.375484 +1.069495 +0.376747 +1.049643 +0.385728 +1.092293 +0.369918 +1.062323 +0.380245 +1.075586 +0.374867 +1.081700 +0.382811 +1.067075 +0.378497 +1.048722 +0.370157 +1.060252 +0.358555 +1.055758 +0.358177 +1.066617 +0.378639 +1.040349 +0.362174 +1.060135 +0.367825 +1.066949 +0.358720 +1.057546 +0.332001 +1.052396 +0.349501 +1.023564 +0.357055 +1.052012 +0.345258 +1.057407 +0.343141 +1.050113 +0.329482 +1.049946 +0.349978 +1.031315 +0.323629 +1.044112 +0.346791 +1.017084 +0.318708 +1.052697 +0.349741 +1.008559 +0.367229 +1.001461 +0.367172 +1.006815 +0.352273 +1.007506 +0.352311 +1.011708 +0.331287 +1.007919 +0.322504 +0.995717 +0.340604 +0.980326 +0.292039 +0.962965 +0.324428 +0.972073 +0.327286 +0.994777 +0.326894 +0.970400 +0.313024 +0.929050 +0.295196 +0.965136 +0.308007 +0.950161 +0.267853 +0.977228 +0.296260 +0.965376 +0.305905 +0.941450 +0.278146 +0.927904 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +0.961351 +0.290006 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.310912 +0.949575 +0.301560 +0.908065 +0.284955 +0.980604 +0.372199 +0.926887 +0.275581 +0.941235 +0.303382 +1.003145 +0.292483 +0.943941 +0.256958 +1.038927 +0.318916 +0.996378 +0.326939 +0.999065 +0.279812 +1.015793 +0.328323 +1.006063 +0.350937 +0.997894 +0.326662 +0.984135 +0.336596 +1.003477 +0.339433 +1.015824 +0.320763 +1.008542 +0.342630 +1.043309 +0.346889 +1.029262 +0.314452 +1.024337 +0.354480 +1.021532 +0.349940 +1.028836 +0.356067 +0.997942 +0.354367 +1.021987 +0.359074 +1.028425 +0.348900 +1.011601 +0.383513 +1.063275 +0.392416 +1.036661 +0.396487 +1.047049 +0.410080 +1.061420 +0.404034 +1.028232 +0.364122 +1.047812 +0.384772 +1.048487 +0.372454 +1.059247 +0.363931 +1.060357 +0.376988 +1.083716 +0.380525 +1.094893 +0.388103 +1.075828 +0.360006 +1.075634 +0.381441 +1.058027 +0.383100 +1.085052 +0.391648 +1.056590 +0.383088 +1.052964 +0.388035 +1.050838 +0.387284 +1.069444 +0.381961 +1.063698 +0.382826 +1.070900 +0.375085 +1.060521 +0.382981 +1.065512 +0.378215 +1.068793 +0.385552 +1.070725 +0.394204 +1.080570 +0.375176 +1.066343 +0.373291 +1.071939 +0.384449 +1.080015 +0.376150 +1.073892 +0.363852 +1.057353 +0.377538 +1.052828 +0.373799 +1.066380 +0.379751 +1.060588 +0.360725 +1.070061 +0.374171 +1.085401 +0.367374 +1.075736 +0.363460 +1.061770 +0.369929 +1.063792 +0.390263 +1.052391 +0.371375 +1.054664 +0.372545 +1.055390 +0.363446 +1.049483 +0.342377 +1.064299 +0.372814 +1.052950 +0.363170 +1.062536 +0.356677 +1.053385 +0.362963 +1.060804 +0.362571 +1.051125 +0.375266 +1.040686 +0.354006 +1.061367 +0.376650 +1.036875 +0.328218 +1.040443 +0.317286 +1.017482 +0.333452 +1.042508 +0.364261 +1.025944 +0.361507 +1.011866 +0.354521 +0.998248 +0.332780 +1.007962 +0.335616 +0.973580 +0.354106 +1.002023 +0.355613 +1.035772 +0.339803 +1.004121 +0.301885 +0.972415 +0.324237 +0.977263 +0.305967 +0.988980 +0.315532 +0.967684 +0.319681 +0.954348 +0.348515 +0.978249 +0.300351 +0.967519 +0.312589 +0.937395 +0.291972 +0.974249 +0.275816 +0.942633 +0.278470 +0.938538 +0.268044 +0.910726 +0.304496 +0.951672 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +0.930632 +0.274110 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.251346 +0.977513 +0.304493 +0.951740 +0.283511 +0.952743 +0.213834 +0.996923 +0.256692 +0.982873 +0.325410 +0.981265 +0.308591 +0.981715 +0.300079 +1.032907 +0.324641 +0.966451 +0.281967 +0.970497 +0.307617 +0.952824 +0.307332 +1.012782 +0.324235 +0.999265 +0.287022 +0.990807 +0.317134 +1.028541 +0.349369 +1.000070 +0.319557 +0.982441 +0.327084 +1.012285 +0.338901 +1.018010 +0.343902 +1.033717 +0.317404 +0.999450 +0.348753 +1.043336 +0.342246 +1.041360 +0.339715 +1.032908 +0.342586 +1.024176 +0.344297 +1.009094 +0.331546 +1.024628 +0.399596 +1.039350 +0.365855 +1.050179 +0.387821 +1.062958 +0.374802 +1.063260 +0.368947 +1.053072 +0.401738 +1.025776 +0.367087 +1.061662 +0.376045 +1.073549 +0.351934 +1.065028 +0.356335 +1.044232 +0.363426 +1.071565 +0.375799 +1.053825 +0.360506 +1.031028 +0.374451 +1.075056 +0.367650 +1.042471 +0.390566 +1.060131 +0.378309 +1.055500 +0.386880 +1.040502 +0.393257 +1.060869 +0.391040 +1.073736 +0.372972 +1.050876 +0.380145 +1.061229 +0.365203 +1.039177 +0.374974 +1.050636 +0.373713 +1.072607 +0.356654 +1.087546 +0.368479 +1.087335 +0.377396 +1.072351 +0.373847 +1.060291 +0.376786 +1.089190 +0.370286 +1.087306 +0.388295 +1.062502 +0.367507 +1.052541 +0.388432 +1.071295 +0.386474 +1.054263 +0.371733 +1.056561 +0.367224 +1.072315 +0.393960 +1.071390 +0.373104 +1.043013 +0.365864 +1.062033 +0.358873 +1.057372 +0.367706 +1.073179 +0.390766 +1.044556 +0.374549 +1.065861 +0.371767 +1.059464 +0.353505 +1.071499 +0.355870 +1.036808 +0.334004 +1.053497 +0.358275 +1.059104 +0.340819 +1.063474 +0.359286 +1.043233 +0.339585 +1.014144 +0.327714 +1.039052 +0.365930 +1.028948 +0.313478 +1.049082 +0.333818 +1.033339 +0.370898 +0.999214 +0.340052 +1.022724 +0.371090 +0.983802 +0.337606 +0.992596 +0.350453 +0.982481 +0.340631 +0.999449 +0.349630 +1.021401 +0.299389 +1.000483 +0.329627 +0.992087 +0.300399 +0.986041 +0.274653 +0.963512 +0.316174 +0.990012 +0.338998 +0.963599 +0.307305 +0.936921 +0.287261 +0.961589 +0.299119 +1.014379 +0.262196 +0.965967 +0.285103 +0.948324 +0.304199 +0.991277 +0.276725 +0.938331 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +0.968523 +0.283833 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 +1000.000000 diff --git a/fig/Depth Estimation.png b/fig/Depth Estimation.png new file mode 100644 index 0000000000000000000000000000000000000000..53d381a9ad8dea21f1dffae4a6f321bf0fb488ba --- /dev/null +++ b/fig/Depth Estimation.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c2bd10562bf2362ad065c933e15c0c4b518791a7709d4d787362782b673ad8b +size 3492057 diff --git a/fig/Light Intensity Distribution.png b/fig/Light Intensity Distribution.png new file mode 100644 index 0000000000000000000000000000000000000000..331096e1b6bd86023bd0e60f2bf65c5d1b121941 --- /dev/null +++ b/fig/Light Intensity Distribution.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:539d67e583c0ad0f844043c61a6326d78b4759ae956db6e36633de10dfa31d8b +size 70203 diff --git a/fig/Multi-view reconstruction.png b/fig/Multi-view reconstruction.png new file mode 100644 index 0000000000000000000000000000000000000000..a0281df09f35fbfbda9851a59933ea19eacdd004 --- /dev/null +++ b/fig/Multi-view reconstruction.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3f056963113317c62d45849d4254ca9f1da64e164c01e607c4d0d33cfe2c193 +size 2543285 diff --git a/fig/Semantic Segmentation.png b/fig/Semantic Segmentation.png new file mode 100644 index 0000000000000000000000000000000000000000..fd9a16d281649c42aa0c79f4302a12b205c007f2 --- /dev/null +++ b/fig/Semantic Segmentation.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8ef2877f112ecae83d9428b4261150ecf1d93195d349565fc3e5186aabd3034 +size 9502849 diff --git a/fig/annotation_pipeline.png b/fig/annotation_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..11e2eb2ad50acd686617e1461d82eed086ebd99e --- /dev/null +++ b/fig/annotation_pipeline.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:33ec5936577915d6fd604aa1e6ccbd92429bae0dd8304c1eba49acd258cb7d15 +size 425579 diff --git a/fig/overview.png b/fig/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..4c93c05c7d89c95232f2144e7269c3194c8dd9f6 --- /dev/null +++ b/fig/overview.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a4a2d5d377b39da2ae1e29970067a8f86acef5a10393021f1433ff786f2775e +size 12029026 diff --git a/fig/semantic_category_distribution.png b/fig/semantic_category_distribution.png new file mode 100644 index 0000000000000000000000000000000000000000..1e4c1792b3f9bbf2ec9867eab2798c306b48a75b --- /dev/null +++ b/fig/semantic_category_distribution.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb295bbfb78251271b1907651e556e211d7c5cb9fd7305fb3a759ea6d01d8ac9 +size 207145