diff --git a/extra_tests/regression_fits/central_16.exportgrid b/extra_tests/regression_fits/central_16.exportgrid index 7174ae7165..2017bd1e08 100644 --- a/extra_tests/regression_fits/central_16.exportgrid +++ b/extra_tests/regression_fits/central_16.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.3123116493225098, 0.09169617295265198, 0.06519101560115814, 0.06523817032575607, - -16.369380950927734, 0.06538866460323334, 0.0653429925441742, 0.09145598858594894, - -2.312312126159668, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.239422559738159, 0.09356347471475601, 0.06599677354097366, 0.06605043262243271, - -16.38014030456543, 0.06621687114238739, 0.06616499274969101, 0.09329996258020401, - -2.239422559738159, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.1688363552093506, 0.09562729299068451, 0.06695534288883209, 0.06701629608869553, - -16.389476776123047, 0.06720040738582611, 0.0671415776014328, 0.09533820301294327, - 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0.5778199490000002, + "replica_fitted": 30.465310533, + "replica_set_to_replica_fitted": 29.887490584000002 } }, "version": { - "tensorflow": "2.15.0, mkl=??", + "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post121.dev0+306ca7c6e", - "validphys": "4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/extra_tests/regression_fits/feature_scaling_81.json b/extra_tests/regression_fits/feature_scaling_81.json index b97434b685..e1d03447dd 100644 --- a/extra_tests/regression_fits/feature_scaling_81.json +++ b/extra_tests/regression_fits/feature_scaling_81.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 18.170032501220703, + "Total": 30.279093980789185, "start": 0.0, - "replica_set": 0.27858686447143555, - "replica_fitted": 18.16972517967224, - "replica_set_to_replica_fitted": 17.891138315200806 + "replica_set": 0.3903789520263672, + "replica_fitted": 30.278961420059204, + 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"replica_fitted": 7.535805837999999, + "replica_set_to_replica_fitted": 6.941743516999999 } }, "version": { - "tensorflow": "2.15.0, mkl=??", + "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post121.dev0+306ca7c6e", - "validphys": "4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/extra_tests/regression_fits/no_csr_613.exportgrid b/extra_tests/regression_fits/no_csr_613.exportgrid index 3d89827329..df82f2f8c5 100644 --- a/extra_tests/regression_fits/no_csr_613.exportgrid +++ b/extra_tests/regression_fits/no_csr_613.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -1.4216703176498413, 1.0145999193191528, 1.0258723497390747, 1.0047584772109985, - -22.811077117919922, 1.004300832748413, 1.0254151821136475, 1.0153990983963013, +- [0.0, 0.0, -1.4216703176498413, 1.0146000385284424, 1.0258724689483643, 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a/extra_tests/regression_fits/no_csr_613.json b/extra_tests/regression_fits/no_csr_613.json index 6ee8a669ef..c3872355dc 100644 --- a/extra_tests/regression_fits/no_csr_613.json +++ b/extra_tests/regression_fits/no_csr_613.json @@ -58,43 +58,43 @@ "stop_epoch": 1100, "best_epoch": 1099, "erf_tr": 52.448638916015625, - "erf_vl": 52.26507568359375, - "chi2": 29.395509719848633, + "erf_vl": 52.265071868896484, + "chi2": 29.3955078125, "pos_state": "POS_VETO", "arc_lengths": [ - 0.9916716067259327, - 1.925851668408791, - 1.3551547467615719, - 1.0247357427688604, - 1.3300023139034007 + 0.9916716082991431, + 1.925850885881192, + 1.3551547641298252, + 1.0247357422266838, + 1.3300020695083878 ], "integrability": [ - 0.0005367033954793154, - 0.002806974371196369, - 1.8677989657378546e-05, - 0.0024828700115913094, - 0.01674120547249991 + 0.0005367029261819367, + 0.002806974167469556, + 1.8677989657267524e-05, + 0.002482869982487257, + 0.016741260187701057 ], "timing": { "walltime": { - "Total": 15.619742631912231, + "Total": 27.68090558052063, "start": 0.0, - "replica_set": 0.2965066432952881, - "replica_fitted": 15.619616270065308, - "replica_set_to_replica_fitted": 15.32310962677002 + "replica_set": 0.26375412940979004, + "replica_fitted": 27.68082618713379, + "replica_set_to_replica_fitted": 27.417072057724 }, "cputime": { - "Total": 16.903149643, + "Total": 28.59983451, "start": 0.0, - "replica_set": 0.6058529689999999, - "replica_fitted": 16.903021783999996, - "replica_set_to_replica_fitted": 16.297168815 + "replica_set": 0.5861168649999993, + "replica_fitted": 28.599753618999998, + "replica_set_to_replica_fitted": 28.013636754 } }, "version": { - "tensorflow": "2.15.0, mkl=??", + "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post121.dev0+306ca7c6e", - "validphys": "4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/extra_tests/regression_fits/no_lagrange_27.exportgrid b/extra_tests/regression_fits/no_lagrange_27.exportgrid index f9780f1ddc..0b2a127488 100644 --- a/extra_tests/regression_fits/no_lagrange_27.exportgrid +++ b/extra_tests/regression_fits/no_lagrange_27.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.2585036754608154, 0.03153830021619797, 0.004340456798672676, 0.004388904664665461, - -16.76668930053711, 0.004543384071439505, 0.004496464040130377, 0.0312916561961174, - -2.2585036754608154, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.187335968017578, 0.03491383418440819, 0.00662692915648222, 0.0066820178180933, - -16.778575897216797, 0.0068528298288583755, 0.006799580994993448, 0.034643277525901794, - -2.187335968017578, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.1184136867523193, 0.03843121975660324, 0.00901030283421278, 0.009072955697774887, - -16.78905487060547, 0.009261862374842167, 0.00920142699033022, 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2.06439208984375, + "best_epoch": 1098, + "erf_tr": 2.800025701522827, + "erf_vl": 2.4886326789855957, + "chi2": 2.0644373893737793, "pos_state": "POS_PASS", "arc_lengths": [ - 1.3221136275673204, - 1.48705955805385, - 1.1735341670498514, - 1.0005936752028055, - 1.2220410951760075 + 1.3221016863483277, + 1.4870383514048613, + 1.1735301400919398, + 1.0005947280900265, + 1.2219902851224111 ], "integrability": [ - 0.0009427258773963554, - 0.0009427258773945235, - 5.0014148199939434e-05, - 0.4746312499046317, - 0.0071308605838549255 + 0.0009427078985058679, + 0.0009427078985030368, + 5.000923419984371e-05, + 0.4746222198009489, + 0.007130517973563011 ], "timing": { "walltime": { - "Total": 22.71401810646057, + "Total": 34.43342328071594, "start": 0.0, - "replica_set": 0.2537527084350586, - "replica_fitted": 22.713902711868286, - "replica_set_to_replica_fitted": 22.460150003433228 + "replica_set": 0.25362443923950195, + "replica_fitted": 34.433255195617676, + 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92 diff --git a/extra_tests/regression_fits/no_msr_92.json b/extra_tests/regression_fits/no_msr_92.json index 77236b6bb5..09ee1a9ca5 100644 --- a/extra_tests/regression_fits/no_msr_92.json +++ b/extra_tests/regression_fits/no_msr_92.json @@ -51,44 +51,44 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 6.576589107513428, - "erf_vl": 5.858458518981934, - "chi2": 3.330002784729004, + "erf_tr": 6.576594829559326, + "erf_vl": 5.858449935913086, + "chi2": 3.3300018310546875, "pos_state": "POS_PASS", "arc_lengths": [ - 1.3370216730458881, - 0.9939697390249507, - 1.179148639665055, - 1.001338529292656, - 1.1751096639490837 + 1.3370216319814459, + 0.9939697402767997, + 1.179148683250488, + 1.0013385209813972, + 1.175109752177304 ], "integrability": [ - 0.0009919883814295052, - 0.0009919883814291375, - 4.665317715074613e-05, - 0.4762776866555213, - 0.007486371498089027 + 0.0009919884832926065, + 0.0009919884832925857, + 4.6653166691515424e-05, + 0.4762776866555214, + 0.007486372429411588 ], "timing": { "walltime": { - "Total": 31.41426372528076, + "Total": 42.30840301513672, "start": 0.0, - "replica_set": 0.25605225563049316, - "replica_fitted": 31.41413378715515, - "replica_set_to_replica_fitted": 31.158081531524658 + "replica_set": 0.28534770011901855, + "replica_fitted": 42.30820417404175, + "replica_set_to_replica_fitted": 42.02285647392273 }, "cputime": { - "Total": 33.432995651, + "Total": 43.76437747, "start": 0.0, - "replica_set": 0.5719856840000004, - "replica_fitted": 33.432864246, - "replica_set_to_replica_fitted": 32.860878562 + "replica_set": 0.6081680240000003, + "replica_fitted": 43.764176815, + "replica_set_to_replica_fitted": 43.156008791000005 } }, "version": { - "tensorflow": "2.15.0, mkl=??", + "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post121.dev0+306ca7c6e", - "validphys": "4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/extra_tests/regression_fits/no_sumrules_18.exportgrid b/extra_tests/regression_fits/no_sumrules_18.exportgrid index 7659d2573d..5ec50d519c 100644 --- a/extra_tests/regression_fits/no_sumrules_18.exportgrid +++ b/extra_tests/regression_fits/no_sumrules_18.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.4447741508483887, 0.4820626676082611, 0.45488423109054565, 0.45492836833000183, - -0.5363594889640808, 0.45496734976768494, 0.4549235999584198, 0.48199957609176636, - -2.4447741508483887, 0.0, 0.0, 0.0] +- [0.0, 0.0, -2.4447736740112305, 0.48206278681755066, 0.4548843801021576, 0.4549284875392914, + -0.536359429359436, 0.4549674689769745, 0.45492371916770935, 0.4819997251033783, + -2.4447736740112305, 0.0, 0.0, 0.0] - [0.0, 0.0, -2.3672444820404053, 0.47239094972610474, 0.44413238763809204, 0.44418248534202576, - -0.5363331437110901, 0.4442256689071655, 0.44417592883110046, 0.4723217785358429, + 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] q20: 2.7224999999999997 replica: 18 diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json index 587b3bd8bb..7fa7176fda 100644 --- a/extra_tests/regression_fits/no_sumrules_18.json +++ b/extra_tests/regression_fits/no_sumrules_18.json @@ -51,44 +51,44 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 5.3037943840026855, - "erf_vl": 5.399441719055176, - "chi2": 3.2604076862335205, + "erf_tr": 5.30379056930542, + "erf_vl": 5.399439811706543, + "chi2": 3.260406017303467, "pos_state": "POS_PASS", "arc_lengths": [ - 1.3348380751855529, - 0.9938354368091443, - 1.0016686465602795, - 0.990576007415076, - 1.0071507492820002 + 1.334838142106021, + 0.9938354360870493, + 1.0016686497444203, + 0.9905760062030213, + 1.0071507527276173 ], "integrability": [ - 0.00022732435354577696, - 0.0002273243535456243, - 1.0915415430093334e-05, - 0.47721660882234573, - 0.0018179940379919046 + 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"4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/extra_tests/regression_fits/no_vsr_54.exportgrid b/extra_tests/regression_fits/no_vsr_54.exportgrid index f31dc73533..1a70c0d7de 100644 --- a/extra_tests/regression_fits/no_vsr_54.exportgrid +++ b/extra_tests/regression_fits/no_vsr_54.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.3827316761016846, 0.17211776971817017, 0.14562013745307922, 0.1456664502620697, - -16.241943359375, 0.14570321142673492, 0.14565721154212952, 0.17205867171287537, - -2.3827316761016846, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.307499408721924, 0.1717132031917572, 0.14416269958019257, 0.14421528577804565, - -16.2504825592041, 0.14425590634346008, 0.14420370757579803, 0.17164835333824158, - -2.3074989318847656, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.2346487045288086, 0.17157715559005737, 0.14293113350868225, 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-0.03479951247572899, 0.02461320348083973, 0.02178252674639225, - -0.024013955146074295, -0.0059059057384729385, -0.0021668588742613792, 0.021354395896196365, - -0.010812405496835709, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.00973674189299345, -0.03182876855134964, 0.02229207009077072, 0.01993042230606079, - -0.020882152020931244, -0.005453288089483976, -0.002346264896914363, 0.01960928551852703, +- [0.0, 0.0, -0.010812408290803432, -0.034799519926309586, 0.024613207206130028, 0.02178253047168255, + -0.024013957008719444, -0.00590590899810195, -0.0021668628323823214, 0.021354403346776962, + -0.010812406428158283, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.00973674189299345, -0.03182877227663994, 0.02229207195341587, 0.01993042416870594, + -0.020882152020931244, -0.0054532908834517, -0.0023462676908820868, 0.019609292969107628, -0.00973674189299345, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.008716661483049393, -0.028928091749548912, 0.020068911835551262, 0.018121352419257164, - -0.017974326387047768, -0.005009481683373451, -0.002457613358274102, 0.017907438799738884, - -0.008716663345694542, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.00775183504447341, -0.02610602416098118, 0.01794559694826603, 0.016360441222786903, - -0.015295428223907948, -0.004575174767524004, -0.002506694756448269, 0.016251513734459877, - -0.00775183504447341, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.006841971538960934, -0.023371240124106407, 0.01592409610748291, 0.014652987010776997, - -0.012848636135458946, -0.004151064436882734, -0.0024993238039314747, 0.014644377864897251, - -0.006841971538960934, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.008716662414371967, -0.02892809733748436, 0.02006891556084156, 0.018121354281902313, + -0.01797432452440262, -0.005009484477341175, -0.002457616152241826, 0.01790744438767433, + -0.008716664277017117, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.007751834113150835, -0.026106029748916626, 0.017945600673556328, 0.016360444948077202, + -0.015295431017875671, -0.004575178027153015, -0.0025066977832466364, 0.016251521185040474, + -0.007751834113150835, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.006841972004622221, -0.023371243849396706, 0.01592409797012806, 0.014652990736067295, + -0.012848636135458946, -0.0041510662995278835, -0.0024993254337459803, 0.014644382521510124, + -0.006841972004622221, 0.0, 0.0, 0.0] - [0.0, 0.0, -0.0059868404641747475, -0.020732462406158447, 0.014006377197802067, - 0.01300437655299902, -0.010635213926434517, -0.0037378263659775257, -0.002441328950226307, + 0.01300437655299902, -0.010635212995111942, -0.0037378263659775257, -0.002441328950226307, 0.013089039362967014, -0.005986839532852173, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.005186258815228939, -0.018198544159531593, 0.012194512411952019, 0.011420142836868763, - -0.008654520846903324, -0.0033361674286425114, -0.0023385779932141304, 0.01158872526139021, +- [0.0, 0.0, -0.005186258815228939, -0.018198546022176743, 0.012194513343274593, 0.011420143768191338, + -0.00865451991558075, -0.003336169058457017, -0.0023385800886899233, 0.011588728055357933, -0.0051862578839063644, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.004440127406269312, -0.015778573229908943, 0.010490721091628075, 0.009906020946800709, - -0.0069040278904139996, -0.0029468643479049206, -0.0021969990339130163, 0.010146992281079292, +- [0.0, 0.0, -0.004440127406269312, -0.015778575092554092, 0.01049072202295065, 0.009906021878123283, + -0.006904028356075287, -0.0029468650463968515, -0.0021970001980662346, 0.010146995075047016, -0.004440127406269312, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.003748424118384719, -0.013481857255101204, 0.008897372521460056, 0.008467928506433964, - -0.005379220470786095, -0.002570767654106021, -0.002022572560235858, 0.008767696097493172, +- [0.0, 0.0, -0.003748424118384719, -0.013481859117746353, 0.008897373452782631, 0.008467930369079113, + -0.00537921953946352, -0.002570768352597952, -0.002022573258727789, 0.008767698891460896, -0.003748424118384719, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0031112446449697018, -0.011318105272948742, 0.007417107932269573, - 0.007112081628292799, -0.004073586780577898, -0.0022088871337473392, -0.0018213908188045025, - 0.007455187849700451, -0.0031112446449697018, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0025288208853453398, -0.009297501295804977, 0.006052890792489052, - 0.005845013074576855, -0.0029785633087158203, -0.0018624237272888422, -0.0015996764414012432, - 0.00621438305824995, -0.0025288208853453398, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.002001565182581544, -0.007430891506373882, 0.0048081353306770325, - 0.0046736979857087135, -0.002083447529003024, -0.001532861264422536, -0.0013638482196256518, - 0.00505096185952425, -0.002001565182581544, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0015301386592909694, -0.005730058066546917, 0.0036868893075734377, - 0.0036057098768651485, -0.001375332591123879, -0.0012220809003338218, -0.0011206089984625578, - 0.003971632104367018, -0.0015301386592909694, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.001115531544201076, -0.0042080446146428585, 0.0026940437965095043, - 0.0026494076009839773, -0.0008389418944716454, -0.0009325184510089457, -0.0008770744898356497, - 0.0029845053795725107, -0.001115531544201076, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.000759243150241673, -0.002879831939935684, 0.0018357852241024375, - 0.0018143566558137536, -0.0004564860137179494, -0.0006674370961263776, -0.0006410076166503131, - 0.0020997701212763786, -0.0007592432666569948, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.000463553995359689, -0.0017633828101679683, 0.0011202655732631683, - 0.001111964462324977, -0.0002073734940495342, -0.0004313824756536633, -0.0004212286148685962, - 0.0013308260822668672, -0.0004635539080481976, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0002321321371709928, -0.0008818599744699895, 0.0005590427317656577, - 0.0005568686756305397, -6.779083923902363e-05, -0.00023111050541047007, -0.0002284789370605722, - 0.0006966660148464143, -0.0002321321371709928, 0.0, 0.0, 0.0] -- [0.0, 0.0, -7.16399954399094e-05, -0.0002694059512577951, 0.00017080880934372544, - 0.00017058996309060603, -9.925639460561797e-06, -7.798223668942228e-05, -7.772141543682665e-05, - 0.00022814115800429136, -7.16399954399094e-05, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.003111244412139058, -0.011318106204271317, 0.0074171083979308605, + 0.007112082559615374, -0.004073586780577898, -0.002208887366577983, -0.0018213912844657898, + 0.0074551897123456, -0.003111244412139058, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0025288208853453398, -0.009297503158450127, 0.0060528917238116264, + 0.005845014471560717, -0.0029785633087158203, -0.0018624247750267386, -0.0015996773727238178, + 0.006214385852217674, -0.0025288208853453398, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.002001565182581544, -0.007430892903357744, 0.00480813579633832, 0.004673698451370001, + -0.002083447529003024, -0.0015328617300838232, -0.001363848801702261, 0.005050962790846825, + -0.002001565182581544, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.001530138892121613, -0.005730059929192066, 0.0036868900060653687, + 0.0036057105753570795, -0.0013753327075392008, -0.0012220815988257527, -0.0011206098133698106, + 0.003971633967012167, -0.001530138892121613, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0011155317770317197, -0.004208045545965433, 0.0026940444950014353, + 0.0026494082994759083, -0.0008389418944716454, -0.0009325190330855548, -0.000877075013704598, + 0.002984506543725729, -0.0011155317770317197, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.000759243150241673, -0.0028798324055969715, 0.0018357854569330812, + 0.0018143568886443973, -0.0004564860137179494, -0.0006674373289570212, -0.0006410078494809568, + 0.002099770586937666, -0.0007592432666569948, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0004635540535673499, -0.0017633828101679683, 0.0011202655732631683, + 0.001111964462324977, -0.0002073734940495342, -0.0004313824465498328, -0.0004212285275571048, + 0.001330826198682189, -0.000463553995359689, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0002321321371709928, -0.0008818600326776505, 0.0005590427899733186, + 0.0005568687338382006, -6.779083923902363e-05, -0.00023111059272196144, -0.00022847899526823312, + 0.000696666247677058, -0.0002321321371709928, 0.0, 0.0, 0.0] +- [0.0, 0.0, -7.16399954399094e-05, -0.00026940598036162555, 0.00017080882389564067, + 0.00017058996309060603, -9.925640370056499e-06, -7.798225124133751e-05, -7.772142998874187e-05, + 0.0002281411871081218, -7.16399954399094e-05, 0.0, 0.0, 0.0] - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] q20: 2.7224999999999997 replica: 61 diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json index c0a68b1ca9..a4556512dd 100644 --- a/extra_tests/regression_fits/trainable_prepro_61.json +++ b/extra_tests/regression_fits/trainable_prepro_61.json @@ -52,43 +52,43 @@ "stop_epoch": 1100, "best_epoch": 1099, "erf_tr": 83.885498046875, - "erf_vl": 88.89354705810547, - "chi2": 59.152374267578125, + "erf_vl": 88.89352416992188, + "chi2": 59.15235900878906, "pos_state": "POS_VETO", "arc_lengths": [ - 1.0440971735274283, - 1.528026393222395, - 1.0033084132655448, - 0.9979762352558132, - 1.4192809861706044 + 1.0440971698549606, + 1.5280265070865116, + 1.0033084194026651, + 0.9979762376018926, + 1.4192810248738073 ], "integrability": [ - 0.1109853889793174, - 0.11098538897931604, - 0.00020276384293477223, - 0.00843445386271846, - 0.0018140711035802282 + 0.11098539270460843, + 0.11098539270460639, + 0.00020276380928341275, + 0.008434453979133893, + 0.0018140714964819393 ], "timing": { "walltime": { - "Total": 19.938401699066162, + "Total": 31.93546223640442, "start": 0.0, - "replica_set": 0.2674376964569092, - "replica_fitted": 19.938326358795166, - "replica_set_to_replica_fitted": 19.670888662338257 + "replica_set": 0.2526426315307617, + "replica_fitted": 31.935267210006714, + "replica_set_to_replica_fitted": 31.682624578475952 }, "cputime": { - "Total": 21.699957106, + "Total": 33.434644273, "start": 0.0, - "replica_set": 0.5827538259999994, - "replica_fitted": 21.699880133, - "replica_set_to_replica_fitted": 21.117126307 + "replica_set": 0.5755823529999997, + "replica_fitted": 33.434447626, + "replica_set_to_replica_fitted": 32.858865273 } }, "version": { - "tensorflow": "2.15.0, mkl=??", + "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post121.dev0+306ca7c6e", - "validphys": "4.0.9.post121.dev0+306ca7c6e" + "nnpdf": "4.0.9.post199.dev0+1fe20fa4d", + "validphys": "4.0.9.post199.dev0+1fe20fa4d" } } diff --git a/validphys2/src/validphys/commondataparser.py b/validphys2/src/validphys/commondataparser.py index 5380ae18c6..c6e2c821b1 100644 --- a/validphys2/src/validphys/commondataparser.py +++ b/validphys2/src/validphys/commondataparser.py @@ -35,6 +35,7 @@ by modifying the CommonMetaData using one of the loaded Variants one can change the resulting :py:class:`validphys.coredata.CommonData` object. """ + import dataclasses from functools import cached_property, lru_cache import logging @@ -588,7 +589,6 @@ def load_kinematics(self, fill_to_three=True, drop_minmax=True): d["mid"] = 0.5 * (d["max"] + d["min"]) if drop_minmax: - # TODO: for now we are dropping min/max information since it didn't exist in the past d["min"] = None d["max"] = None else: @@ -786,6 +786,11 @@ def cm_energy(self): return None return float(energy_string[:-3].replace("P", ".")) * factor + @cached_property + def allowed_datasets(self): + """Return the implemented datasets as a list _""" + return [f"{self.setname}_{i.observable_name}" for i in self.implemented_observables] + @cached_property def allowed_observables(self): """ @@ -809,7 +814,7 @@ def select_observable(self, obs_name_raw): @lru_cache -def _parse_entire_set_metadata(metadata_file): +def parse_set_metadata(metadata_file): """Read the metadata file""" return parse_yaml_inp(metadata_file, SetMetaData) @@ -822,7 +827,7 @@ def parse_new_metadata(metadata_file, observable_name, variant=None): The triplet (metadata_file, observable_name, variant) define unequivocally the information to be parsed from the commondata library """ - set_metadata = _parse_entire_set_metadata(metadata_file) + set_metadata = parse_set_metadata(metadata_file) # Select one observable from the entire metadata metadata = set_metadata.select_observable(observable_name) diff --git a/validphys2/src/validphys/loader.py b/validphys2/src/validphys/loader.py index c1093febe0..e08d8bacb9 100644 --- a/validphys2/src/validphys/loader.py +++ b/validphys2/src/validphys/loader.py @@ -2,6 +2,7 @@ Resolve paths to useful objects, and query the existence of different resources within the specified paths. """ + import functools from functools import cached_property import logging @@ -22,7 +23,7 @@ from reportengine import filefinder from reportengine.compat import yaml from validphys import lhaindex -from validphys.commondataparser import load_commondata_old, parse_new_metadata +from validphys.commondataparser import load_commondata_old, parse_new_metadata, parse_set_metadata from validphys.core import ( PDF, CommonDataSpec, @@ -326,6 +327,18 @@ def available_datasets(self): old_datasets = [i for i in legacy_to_new_mapping.keys() if not i.startswith(skip)] return set(old_datasets) + @property + @functools.lru_cache() + def implemented_datasets(self): + """Provide all implemented datasets that can be found in the datafiles folder + regardless of whether they can be used for fits (i.e., whether they include a theory), + are "fake" (integrability/positivity) or are missing some information. + """ + datasets = [] + for metadata_file in self.commondata_folder.glob("*/metadata.yaml"): + datasets += parse_set_metadata(metadata_file).allowed_datasets + return datasets + @property @functools.lru_cache() def available_pdfs(self): diff --git a/validphys2/src/validphys/plotoptions/kintransforms.py b/validphys2/src/validphys/plotoptions/kintransforms.py index a37ae6f6ec..6473b54f9e 100644 --- a/validphys2/src/validphys/plotoptions/kintransforms.py +++ b/validphys2/src/validphys/plotoptions/kintransforms.py @@ -98,8 +98,9 @@ def new_labels(self, k1, k2, k3): return k1, k2, k3 def xq2map(self, k1, k2, k3, **extra_labels): - # This is going to be a problem - return k1, k2 + raise NotImplementedError( + "xq2map is not implemented for this dataset (kin_override set to identity and process_options not implemented" + ) class Kintransform(metaclass=abc.ABCMeta): diff --git a/validphys2/src/validphys/process_options.py b/validphys2/src/validphys/process_options.py index dfba77a6cf..fea8aee6da 100644 --- a/validphys2/src/validphys/process_options.py +++ b/validphys2/src/validphys/process_options.py @@ -3,6 +3,7 @@ Only variables included in the `_Vars` enum and processes included in the ``Processes`` dictionary are allowed. """ + import dataclasses from typing import Callable, Optional, Tuple, Union @@ -66,7 +67,7 @@ class _Process: def __hash__(self): return hash(self.name) - def same_kin_variables(self, kin_cov): + def are_accepted_variables(self, kin_cov): """Check if the kinematic variables from the kinematic coverage are the same of the accepted variables.""" # Accepting in any case the legacy variables @@ -88,7 +89,7 @@ def xq2map(self, kin_df, metadata): # Check if the kinematic variables defined in metadata corresponds to the # accepted variables - if not self.same_kin_variables(metadata.kinematic_coverage): + if not self.are_accepted_variables(metadata.kinematic_coverage): raise NotImplementedError( f"kinematic variables are not supported for process {self.name}. You are using {metadata.kinematic_coverage}, please use {self.accepted_variables} ({metadata.name})" ) @@ -159,7 +160,8 @@ def _hqp_yq_xq2map(kin_dict): def _hqp_yqq_xq2map(kin_dict): # Compute x, Q2 - ratio = np.sqrt(kin_dict[_Vars.m_t2]) / kin_dict[_Vars.sqrts] + mass2 = _get_or_fail(kin_dict, [_Vars.m_t2, _Vars.m_ttBar]) + ratio = np.sqrt(mass2) / kin_dict[_Vars.sqrts] x1 = ratio * np.exp(kin_dict[_Vars.y_ttBar]) x2 = ratio * np.exp(-kin_dict[_Vars.y_ttBar]) q2 = kin_dict[_Vars.m_t2] @@ -218,14 +220,14 @@ def _displusjet_xq2map(kin_dict): HQP_YQQ = _Process( "HQP_YQQ", "Differential cross section w.r.t. absolute rapidity of ttBar", - accepted_variables=(_Vars.y_ttBar, _Vars.m_t2, _Vars.sqrts), + accepted_variables=(_Vars.y_ttBar, _Vars.m_t2, _Vars.sqrts, _Vars.m_ttBar), xq2map_function=_hqp_yqq_xq2map, ) HQP_PTQ = _Process( "HQP_PTQ", "Normalized double differential cross section w.r.t. absolute rapidity and transverse momentum of t", - accepted_variables=(_Vars.pT_t, _Vars.y_t, _Vars.sqrts), + accepted_variables=(_Vars.pT_t, _Vars.y_t, _Vars.sqrts, _Vars.m_t2), xq2map_function=_hqp_ptq_xq2map, ) diff --git a/validphys2/src/validphys/tests/test_datafiles.py b/validphys2/src/validphys/tests/test_datafiles.py new file mode 100644 index 0000000000..05b3246a89 --- /dev/null +++ b/validphys2/src/validphys/tests/test_datafiles.py @@ -0,0 +1,95 @@ +""" + Test all datafiles + + The checks in ``test_all_datasets`` are run for each dataset independently so that one gets one + failure per dataset in case of problems +""" + +import pytest + +from validphys.covmats import INTRA_DATASET_SYS_NAME +from validphys.loader import FallbackLoader +from validphys.plotoptions.kintransforms import identity as kintransform_identity + +l = FallbackLoader() +all_datasets = sorted(l.implemented_datasets) + + +def _load_main_and_variants(dataset_name): + """Given a dataset name, returns a list with the default data and all variants""" + cds = [l.check_commondata(dataset_name)] + for variant_name in cds[0].metadata.variants: + cds.append(l.check_commondata(dataset_name, variant=variant_name)) + return cds + + +@pytest.mark.parametrize("dataset_name", all_datasets) +def test_all_datasets(dataset_name): + """checks that a dataset can be loaded (together with its variants), + that the kinematics, uncertainties and data can be read + """ + # Load the data and all its variants + cds = _load_main_and_variants(dataset_name) + main_cd = cds[0] + + # Check ndata + ndata = main_cd.ndata + + # kinematics check + _ = main_cd.metadata.load_kinematics(drop_minmax=False) + kin_df = main_cd.metadata.load_kinematics() + + # Check that the kinematic coverage is contained in the kinematics dataframe + kin_cov = main_cd.metadata.kinematic_coverage + assert set(kin_cov) <= set(kin_df.columns.get_level_values(0)) + + process_type = main_cd.metadata.process_type + + # check whether the kin override is set to the identity + # and if so, check that the process_type is not simply a string + kin_override = main_cd.metadata.plotting.kinematics_override + if isinstance(kin_override, kintransform_identity) and isinstance(process_type, str): + # Skip for the time being the processes for which there is no implementation but have been + # merged to master: issue #1991 + if process_type not in ("HQP_MQQ", "INC"): + raise NotImplementedError(f"The {process_type=} is not implemented in process_options") + + elif not isinstance(process_type, str): + if not process_type.are_accepted_variables(kin_cov): + raise ValueError( + f"The dataset {dataset_name} uses {kin_cov} while accepted variables for {process_type} are {process_type.accepted_variables}" + ) + + # load the central data for every variant + all_dc = [cd.metadata.load_data_central() for cd in cds] + # and check they have the same lenght (it should've been tested internally already) + assert all(len(i) == ndata for i in all_dc) + + # check the uncertainties can be loaded + # note that due to legacy data there might be datasets without data_uncertainties + # but that would only happen for non-variant (or member 0 of the list) + all_unc = [cd.metadata.load_uncertainties() for cd in cds[1:]] + if main_cd.metadata.data_uncertainties: + all_unc.insert(0, main_cd.metadata.load_uncertainties()) + + for unc in all_unc: + # Check that, if present, the special `stat` key is ADD and UNCORR + if "stat" in unc: + stat = unc["stat"] + assert stat.columns[0][0] == "ADD" + assert stat.columns[0][1] == "UNCORR" + + intra_dataset = unc.columns.get_level_values("type").isin( + list(INTRA_DATASET_SYS_NAME) + ["SKIP", "SCALEVAR"] + ) + + # Check that inter dataset correlations are unique + inter_dataset_corrs = unc.loc[:, ~intra_dataset] + inter_types = inter_dataset_corrs.columns.get_level_values("type") + if not inter_types.is_unique: + raise ValueError( + f"The inter-dataset uncertainties for {dataset_name} are not unique: {inter_types.value_counts()}" + ) + + # Check that all treatments are either MULT or ADD + assert set(unc.columns.get_level_values("treatment").unique()) <= {"MULT", "ADD"}