From ed411ac9b714ad7f35c80faac1b7aac328580082 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Thu, 9 Apr 2026 12:14:06 +0200 Subject: [PATCH 01/11] updated material data, air-measured permittivity until 120 Celsius and added h_offset column for permeability data --- .../complex_permeability/LEA_MTB/3F46.csv | 5748 ++++++++--------- .../data/complex_permeability/LEA_MTB/N95.csv | 4232 ++++++------ .../data/complex_permittivity/LEA_MTB/N49.csv | 840 +-- .../data/complex_permittivity/LEA_MTB/N95.csv | 1331 +--- 4 files changed, 5154 insertions(+), 6997 deletions(-) diff --git a/materialdatabase/data/complex_permeability/LEA_MTB/3F46.csv b/materialdatabase/data/complex_permeability/LEA_MTB/3F46.csv index 390ea09..7b8947e 100644 --- a/materialdatabase/data/complex_permeability/LEA_MTB/3F46.csv +++ b/materialdatabase/data/complex_permeability/LEA_MTB/3F46.csv @@ -1,2874 +1,2874 @@ -probe,f,T,b,mu_real,mu_imag -YB3,112720.0,25.223,0.0245152386639447,750.2880061448595,7.726960744588255 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-R25x15x10,225950.0,25.2,0.0858393952981536,779.3741530486727,29.31674720257849 -R25x15x10,225050.0,25.2,0.0884929096626156,785.9406726023069,31.105225083113844 -R25x15x10,224050.0,25.2,0.0912914349303126,793.4724810192613,33.74742476077735 -R25x15x10,222950.0,25.2,0.0935683169156611,799.8700404621276,37.26329688600417 -R25x15x10,221850.0,25.2,0.0966838322014992,809.1267660187981,40.93999927742074 -R25x15x10,220850.0,25.2,0.0994193659356107,815.6653811677237,44.48725493843987 -R25x15x10,219650.0,25.2,0.1026751872505686,831.2466740936582,49.546074349197845 -R25x15x10,517480.0,25.326,0.0195402422740781,679.7104093724728,6.107292828827351 -R25x15x10,516980.0,25.326,0.0216347929247856,682.1669613758855,6.382366203193459 -R25x15x10,516380.0,25.326,0.0236862249515821,687.6536710418474,6.728066648980644 -R25x15x10,515780.0,25.326,0.0253253834841123,676.4817204789466,6.98396234274241 -R25x15x10,515180.0,25.326,0.0273631660367128,676.5525339468895,7.319148350773455 -R25x15x10,514480.0,25.326,0.0295394313391612,682.05168567059,7.683639799626548 -R25x15x10,513780.0,25.326,0.0316122621630967,683.8900759731839,8.032246735142326 -R25x15x10,513080.0,25.326,0.0337080366781304,686.0317406078312,8.422556301525296 -R25x15x10,512280.0,25.326,0.0358218476429009,687.7628157261375,8.786780657343131 -R25x15x10,511480.0,25.326,0.0377472065366993,686.0970401202794,9.228371033316416 -R25x15x10,510680.0,25.326,0.039746161059231,688.0362892219819,9.70292559715791 -R25x15x10,509780.0,25.326,0.0417951529386398,688.796348879672,10.169707256970444 -R25x15x10,508880.0,25.326,0.0440294050584667,692.3043695562579,10.637570083552218 -R25x15x10,507980.0,25.326,0.0461677107229479,698.4784766160035,11.124623825528545 -R25x15x10,507180.0,25.326,0.0481954799841577,700.7707008598559,11.68469931634876 -R25x15x10,506080.0,25.326,0.0505353270663343,706.6781078615228,12.36769818332496 -R25x15x10,504680.0,25.326,0.0537394916838118,712.449576980767,13.340965326641628 -R25x15x10,503580.0,25.326,0.0559830367779686,715.1565526713297,14.091339632043075 -R25x15x10,502480.0,25.326,0.0583187016991944,718.5913113162331,14.776950103303408 -R25x15x10,501280.0,25.326,0.0604241991661247,723.1346854739767,15.775869640107134 -R25x15x10,500080.0,25.326,0.0624028987861313,722.4051663701654,16.719830778802244 -R25x15x10,498880.0,25.326,0.0647044218845384,728.6291525359893,17.785565529010775 -R25x15x10,497580.0,25.326,0.066998340716082,730.1046876733287,18.57229587798533 -R25x15x10,496180.0,25.326,0.0694168588955054,737.0668912470528,19.8434359349594 -R25x15x10,494780.0,25.326,0.0718505859966297,742.9707765828402,21.21740595305505 -R25x15x10,493380.0,25.326,0.0743166711376837,746.945047257423,22.49156459444725 -R25x15x10,491980.0,25.326,0.07652322269674,750.3235980984547,23.88530101803117 -R25x15x10,490480.0,25.326,0.0789980978285243,757.5118488155136,25.41158671771636 -R25x15x10,488780.0,25.326,0.081563184078342,768.5996374061085,27.402158891024044 -R25x15x10,487080.0,25.326,0.0841102855659018,774.6318624436111,29.070771708418025 -R25x15x10,485580.0,25.326,0.0864210353468223,778.1580392964314,30.967458016652603 -R25x15x10,483780.0,25.326,0.0892928853994971,789.6890283504919,33.52161293650214 -R25x15x10,481980.0,25.326,0.091439340814046,794.6239513884982,36.29258874287923 -R25x15x10,767600.0,25.188,0.0145302097959243,676.7848086711293,6.561708206434805 -R25x15x10,766800.0,25.188,0.0163968534223984,672.0094827437837,6.856234465696098 -R25x15x10,766100.0,25.188,0.0183528960485784,674.1693565884767,7.092504843657709 -R25x15x10,765500.0,25.188,0.0202893715401158,673.7519005234088,7.331406297977432 -R25x15x10,764700.0,25.188,0.0222817251433663,676.5025254893426,7.642324653493109 -R25x15x10,763900.0,25.188,0.0242500743920876,682.8500685169399,7.982267622816321 -R25x15x10,763000.0,25.188,0.0260482934687261,679.5049316632723,8.31364044419196 -R25x15x10,762000.0,25.188,0.0279267799019015,679.3323864006377,8.70925161194022 -R25x15x10,761100.0,25.188,0.029910569059316,681.1940292696914,9.076028749010009 -R25x15x10,760100.0,25.188,0.0318963372654603,682.4121531223567,9.445582170640035 -R25x15x10,759100.0,25.188,0.0339533903577432,685.7518861336545,9.883576224411833 -R25x15x10,757900.0,25.188,0.03602903848104,689.0851623312959,10.284039325681078 -R25x15x10,756900.0,25.188,0.037977455287633,688.6937916310147,10.732107530698311 -R25x15x10,755700.0,25.188,0.0400097964883942,695.1111472196658,11.342205409429669 -R25x15x10,754500.0,25.188,0.0418801744681394,690.831527517894,11.74977853694731 -R25x15x10,753200.0,25.188,0.0438792057275799,694.9511715026459,12.376886063937352 -R25x15x10,752100.0,25.188,0.0458303458028537,702.2754250198835,13.024665528436149 -R25x15x10,750600.0,25.188,0.0480157696424779,703.9199362644636,13.500424051880453 -R25x15x10,748500.0,25.188,0.0511757957877687,710.5525677709463,14.46991362810668 -R25x15x10,747200.0,25.188,0.0532719220736251,715.4350713520657,15.26850434570437 -R25x15x10,745600.0,25.188,0.0553193659499095,718.3425514097619,15.771254019096949 -R25x15x10,744200.0,25.188,0.0576516648176984,721.3700166140769,16.474715049012453 -R25x15x10,742400.0,25.188,0.0599629893480312,726.7066502593673,17.414363818293246 -R25x15x10,740900.0,25.188,0.0620603883432062,731.3458946642248,18.421344631170538 -R25x15x10,739000.0,25.188,0.0640660575473806,735.5187503028413,19.63110926311101 -R25x15x10,737400.0,25.188,0.0663782138461724,739.1975249799705,20.57328624398788 -R25x15x10,735500.0,25.188,0.0685933913576926,742.4994043238004,21.75836684114905 -R25x15x10,733800.0,25.188,0.0707666308772261,747.3291327715158,22.90401261670233 -R25x15x10,731800.0,25.188,0.0729044043186464,752.1641905473558,24.329643224731 -R25x15x10,729900.0,25.188,0.0749064138828107,752.7800183139791,25.55607240798321 -R25x15x10,727700.0,25.188,0.0770557343542287,756.2314502411004,27.08945370961937 -R25x15x10,725600.0,25.188,0.0792524821806872,764.3097573947845,28.90600383534994 -R25x15x10,723700.0,25.188,0.0813916105781587,770.124287769579,30.412054413337017 -R25x15x10,721400.0,25.188,0.0836071626529613,776.2272613566925,32.52512204622233 -R25x15x10,719300.0,25.188,0.0860526121911748,781.1047915753003,34.41719169195688 -R25x15x10,717500.0,25.188,0.0877344923407722,787.603675832011,36.42543941841081 -R25x15x10,715500.0,25.188,0.089868781522029,791.3028177094271,38.47569560399665 -R25x15x10,713100.0,25.188,0.0920810081626168,797.3168911801679,41.59878324903133 -R25x15x10,934370.0,25.204,0.0095022915708723,677.9050036582619,7.478359792428835 -R25x15x10,933970.0,25.204,0.0113668224876302,675.7669911938699,7.656579996823602 -R25x15x10,933270.0,25.204,0.013244214711542,678.7762567085131,7.963102656544696 -R25x15x10,932470.0,25.204,0.0150910469844907,675.1666464608871,8.219394144351908 -R25x15x10,931670.0,25.204,0.0169506991414562,674.1553592689648,8.488317509236255 -R25x15x10,930870.0,25.204,0.0189133896014314,678.6323358317325,8.758334874359278 -R25x15x10,929770.0,25.204,0.0206505382896043,673.1972027285537,8.95801871088822 -R25x15x10,928970.0,25.204,0.0225727728539198,678.229689396632,9.336376039536562 -R25x15x10,927970.0,25.204,0.0243469948330411,678.6569641768641,9.670305320400477 -R25x15x10,926970.0,25.204,0.0261858931094407,677.8883988840793,9.964092962845584 -R25x15x10,925970.0,25.204,0.0282300601219897,682.5940906104087,10.316282995400664 -R25x15x10,924770.0,25.204,0.0300938655111798,685.3147633238863,10.664736149165297 -R25x15x10,923670.0,25.204,0.0320781518534861,689.876064921406,11.13708614252239 -R25x15x10,922470.0,25.204,0.03383307944264,685.8912686967734,11.47679116518356 -R25x15x10,921370.0,25.204,0.0356835052855948,687.5607706324558,11.983700217261909 -R25x15x10,919970.0,25.204,0.0375775784725743,685.8095803063846,12.362809519162354 -R25x15x10,918670.0,25.204,0.0396066233310219,693.4003843885373,13.03822046978402 -R25x15x10,917170.0,25.204,0.0416027047671692,694.1257793132958,13.461418883993366 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-R25x15x10,1077580.0,25.264,0.0490859482626364,720.4301201285683,17.850601447424246 -R25x15x10,1075780.0,25.264,0.0508879595941916,722.0827084464837,18.620443116048808 -R25x15x10,1073980.0,25.264,0.0527518097919222,722.4333838311362,19.137678058619287 -R25x15x10,1071880.0,25.264,0.0545901552400624,724.3997411375782,19.92411719849365 -R25x15x10,1069980.0,25.264,0.056502851938265,729.2460613167141,20.774111473800808 -R25x15x10,1067980.0,25.264,0.0585897504051734,732.9924239213391,21.64894212841653 -R25x15x10,1065580.0,25.264,0.0604759134150306,732.7676205720356,22.51028172252589 -R25x15x10,1063480.0,25.264,0.0624842046438962,740.0350270592512,23.65828984209591 -R25x15x10,1061480.0,25.264,0.0643022537332327,740.5007283286221,24.61777250075912 -R25x15x10,1058980.0,25.264,0.0662907473719814,746.2675899128166,25.848205704049445 -R25x15x10,1056680.0,25.264,0.0679305276000789,748.0662820718352,26.945097720786123 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-R25x15x10,751360.0,50.035,0.0209392670255342,698.7899921097402,9.840255673716252 -R25x15x10,750560.0,50.033,0.0232947848983791,701.9990992872682,10.175125412267017 -R25x15x10,749260.0,50.011,0.0254844479412658,703.7879803777944,10.682664111652874 -R25x15x10,748660.0,50.027,0.0277871154052562,705.8981667463872,11.133274598400826 -R25x15x10,747560.0,50.029,0.0299747341551485,705.5666878151391,11.617076752948966 -R25x15x10,746460.0,50.011,0.0322383724932988,705.6414196224295,12.1263441816542 -R25x15x10,745360.0,50.029,0.0347576664380533,711.6092265597288,12.639244731186013 -R25x15x10,744160.0,50.029,0.037064870672231,714.7104679039526,13.316357242436958 -R25x15x10,742860.0,50.013,0.0394641312076083,717.1665140846254,13.975117846075348 -R25x15x10,741560.0,50.027,0.0415831209223321,717.1176398754232,14.68524462574536 -R25x15x10,740060.0,50.027,0.0440069539265117,719.4057859694605,15.451775714974085 -R25x15x10,738660.0,50.021,0.0463810570143984,723.7803351306161,16.316513944071495 -R25x15x10,737060.0,50.027,0.0488664227392996,731.3211875029184,17.307125556184168 -R25x15x10,735460.0,50.036,0.051272826258751,737.5109864316345,18.39010474718357 -R25x15x10,733160.0,50.017,0.0548024844404453,744.0428548052853,19.548842373016583 -R25x15x10,731260.0,50.013,0.0572417222227845,746.7561470233433,20.733660946963195 -R25x15x10,729360.0,50.009,0.0599852340013713,753.5375303411914,21.85001046626074 -R25x15x10,727560.0,50.007,0.0625575646003338,757.7571366025863,22.91339816001913 -R25x15x10,725460.0,50.027,0.0650318805976553,763.6655025753755,24.47514824857538 -R25x15x10,723360.0,50.033,0.0675958040311256,769.5295407861496,26.062001383890124 -R25x15x10,720960.0,50.05,0.0701985424927361,776.2208945385719,28.029198833101624 -R25x15x10,718760.0,50.056,0.0727058348118521,781.7052216999282,30.09418691239621 -R25x15x10,716260.0,50.054,0.0752176327746284,785.6891080294524,32.193627395334964 -R25x15x10,713960.0,50.056,0.0774816144598091,790.8587962977598,34.44960392253967 -R25x15x10,711260.0,50.058,0.0800224469457892,795.3150868351878,36.94291550174277 -R25x15x10,708860.0,50.056,0.0825948552915062,800.3707819771128,39.13514017462498 -R25x15x10,705960.0,50.064,0.0852193972765504,812.5609286137229,42.43239733576491 -R25x15x10,703260.0,50.076,0.0876583023648925,819.8917907741973,46.3992542778483 -R25x15x10,700660.0,50.084,0.0901319048162129,828.3027955043019,49.94293336190414 -R25x15x10,915010.0,50.052,0.0095434629173208,706.0230100565242,10.436415825309256 -R25x15x10,915010.0,50.061,0.0116851507146083,656.8244046805481,11.511013181890194 -R25x15x10,913810.0,50.063,0.0139272385205272,705.4978252566957,11.01113678353102 -R25x15x10,913310.0,50.067,0.0160654999353088,702.0870923521821,11.532010421693789 -R25x15x10,912410.0,50.058,0.0182867000521376,704.7839635632773,11.90500166814685 -R25x15x10,911210.0,50.08,0.0204288706803443,699.9674120096122,12.204512548484168 -R25x15x10,910510.0,50.07,0.0226477953493187,703.8995603849762,12.650556990016812 -R25x15x10,909310.0,50.082,0.024817694874428,707.4595351544821,13.115306998240468 -R25x15x10,908110.0,50.076,0.027025066452969,707.002025532815,13.500410030358228 -R25x15x10,907110.0,50.07,0.0293073105430692,712.5981353139058,14.072370243518328 -R25x15x10,905810.0,50.063,0.0315550698640894,715.0886444944716,14.604236747282156 -R25x15x10,904510.0,50.065,0.0338454229052058,717.077207085388,15.183321810369204 -R25x15x10,903010.0,50.068,0.0358975482070301,715.2498192770031,15.849959537178442 -R25x15x10,901710.0,50.068,0.0380974941105223,717.9869184552574,16.46534849792869 -R25x15x10,900210.0,50.065,0.0404791037367788,720.9374019557766,17.11649165352069 -R25x15x10,898510.0,50.053,0.0428558064786898,723.8853445946758,17.92355500886913 -R25x15x10,896910.0,50.061,0.0450846298821126,729.0057240615322,18.55836229906585 -R25x15x10,895310.0,50.065,0.0473081344467497,731.5914875372067,19.42396589598884 -R25x15x10,892710.0,50.057,0.0508788979432448,744.1928717099667,20.906671382954205 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-R25x15x10,1062140.0,50.035,0.0420834862325844,736.4995113504823,20.028925364966184 -R25x15x10,1058940.0,50.031,0.0444396941979987,738.264376925826,20.89156045792009 -R25x15x10,1058940.0,50.043,0.0462917281970823,716.287113250629,18.0772459096905 -R25x15x10,1056140.0,50.031,0.0493855452395025,750.3093186438574,23.17459686752287 -R25x15x10,1054040.0,50.013,0.0516019482066907,755.6816969283532,24.3467327284285 -R25x15x10,1051940.0,50.035,0.0535788643237715,753.9755679879061,25.504848317047145 -R25x15x10,1049440.0,50.039,0.0558455473470927,760.1548935547438,26.87158419242292 -R25x15x10,1047040.0,50.031,0.0580130600150648,759.7605553219759,28.093579540389936 -R25x15x10,1044640.0,50.033,0.0602630078031511,764.2171001431843,29.513191627758 -R25x15x10,1042140.0,50.039,0.0626174623757041,770.3711318781111,30.932923760290507 -R25x15x10,1039640.0,50.051,0.0648347417068585,775.7707500239521,32.61671784390828 -R25x15x10,1036840.0,50.047,0.0672600404145074,783.0003478734694,34.46778480001888 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-R25x15x10,1268830.0,50.036,0.0306525668344005,728.5121384397341,20.6360928484665 -R25x15x10,1267930.0,50.048,0.0325850058626358,731.3165711964764,21.39290414428586 -R25x15x10,1266230.0,50.05,0.0348281956351178,738.2845558834435,22.072391648959897 -R25x15x10,1263830.0,50.04,0.0369250462221488,739.7142031739904,22.782600033107787 -R25x15x10,1262630.0,50.048,0.0390078955132453,743.000758926508,23.52416289425885 -R25x15x10,1259430.0,50.053,0.0411942273622606,743.3960852035441,24.191800062866303 -R25x15x10,1258630.0,50.038,0.0429849751844118,745.8735284104995,25.05382658606085 -R25x15x10,1255830.0,50.048,0.0457214234612718,747.1389918321134,26.41287269957764 -R25x15x10,1253430.0,50.034,0.0478736133522463,751.7174512931891,27.548276991021808 -R25x15x10,1251030.0,50.04,0.0500563708667768,760.2078307702126,28.844210232883555 -R25x15x10,1426045.6,50.067,0.0104039786566229,715.9916700875262,19.57513705964389 -R25x15x10,1426045.6,50.085,0.0123183584868731,662.0967343241653,20.49377707741453 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-R25x15x10,1409645.6,50.085,0.035531980056061,747.5656110986532,26.45295228939856 -R25x15x10,1407945.6,50.095,0.0375193145267751,751.4835476891243,27.19615428146852 -R25x15x10,1406045.6,50.098,0.0392936634999643,748.9232586475035,27.94107616126458 -R25x15x10,1403545.6,50.105,0.0412501157516021,750.1618394380158,28.96303860917361 -R25x15x10,1397945.6,50.092,0.0443651031285893,755.4454467218563,30.552427429205625 -R25x15x10,1397945.6,50.103,0.0460474964888394,738.9163992510647,32.6818673350814 -R25x15x10,1395945.6,50.092,0.0479145581001508,761.5876814354012,32.56402147440127 -R25x15x10,1393545.6,50.086,0.0499797210919532,772.3430388153555,34.01069165259065 -R25x15x10,1391145.6,50.11,0.0518654663978241,771.5763825997187,35.05154617992135 -R25x15x10,1711516.0,50.092,0.0104803037915863,742.2919965449988,27.22242838722325 -R25x15x10,1711516.0,50.106,0.0121065426944196,499.1141692132608,1.958034756654359 -R25x15x10,1709116.0,50.103,0.0140626236322666,739.2983173043745,27.942483299069863 -R25x15x10,1707516.0,50.109,0.0158200133136831,737.3647398335384,28.287109485272417 -R25x15x10,1706316.0,50.099,0.017619299937172,747.2830648858139,29.11990728288519 -R25x15x10,1705016.0,50.103,0.0192331759292495,746.6911921853969,29.388363034439216 -R25x15x10,1703716.0,50.107,0.0210079677479651,749.3376892122512,30.045830032849736 -R25x15x10,1701716.0,50.109,0.0227666718575378,749.5442333590772,30.66352387129036 -R25x15x10,1699616.0,50.099,0.0245724601950181,752.0809388929191,31.40174487308573 -R25x15x10,1698316.0,50.101,0.0264588200443877,764.847228377071,32.21822748217232 -R25x15x10,1696216.0,50.109,0.0282727941992426,765.721456491758,32.748746450359455 -R25x15x10,1694416.0,50.117,0.0299582305536246,762.994338395027,33.464436963141466 -R25x15x10,1692816.0,50.121,0.0317361699117916,765.0504082190122,34.16573821889429 -R25x15x10,1690916.0,50.119,0.0333697983044064,768.8643887905289,35.44853113396439 -R25x15x10,1688916.0,50.123,0.035100913984432,767.7431137484267,36.06232106947511 -R25x15x10,1686916.0,50.123,0.0368420911647543,770.9240242374387,36.8703971530196 -R25x15x10,1685316.0,50.127,0.0386013708462379,774.3702566031524,37.796105988194284 -R25x15x10,1682016.0,50.129,0.041132666345568,777.7058688786633,39.21034802736526 -R25x15x10,1679416.0,50.125,0.0429333231437679,778.9465124207682,40.22953298157196 -R25x15x10,1677316.0,50.122,0.0447430869084264,781.321403478573,41.83620675615805 -R25x15x10,1674516.0,50.129,0.0464580404486275,783.7959247321123,42.95415646068886 -R25x15x10,1672016.0,50.108,0.0483854018354319,789.6213776020238,44.15940036759626 -R25x15x10,1669316.0,50.125,0.0499814428178253,789.9302710232035,45.32391815210714 -R25x15x10,1666516.0,50.134,0.0519414030472259,800.5462282865113,46.49531094329312 -R25x15x10,2031206.4,50.124,0.0104195283012646,760.737154319867,37.57381704313861 -R25x15x10,2027206.4,50.117,0.0123841071093856,769.5249516619167,38.04120846271929 -R25x15x10,2027206.4,50.118,0.0138729763383871,765.063545071382,37.76609167982117 -R25x15x10,2027206.4,50.117,0.0153692378855776,487.181801589493,14.824262740566144 -R25x15x10,2025206.4,50.121,0.0168775019029951,766.884004642299,39.24152796526118 -R25x15x10,2024006.4,50.132,0.0184139071324725,779.3183840588301,40.35222711700944 -R25x15x10,2022606.4,50.13,0.0199654193244071,781.1887999301674,41.02458539230752 -R25x15x10,2020606.4,50.107,0.0216901608840864,786.4566014055459,41.66237350318529 -R25x15x10,2019206.4,50.121,0.0232323984763226,788.5879772669005,42.52363245881384 -R25x15x10,2017506.4,50.117,0.0248013306292075,788.2463882336324,43.186024097301015 -R25x15x10,2015806.4,50.129,0.0263499681796149,789.9734786466004,43.95219644005689 -R25x15x10,2014106.4,50.135,0.0276724107614378,791.6667278277631,44.94817052034103 -R25x15x10,2012106.4,50.14,0.0292885347118675,794.4310861811528,45.79121636690769 -R25x15x10,2010506.4,50.127,0.0308136269694316,795.721516160805,46.81321301182279 -R25x15x10,2008206.4,50.133,0.0324016487010779,796.6504873838305,47.48365787790559 -R25x15x10,2006106.4,50.131,0.0339406879370249,800.8996369077129,49.78130210103633 -R25x15x10,2002606.4,50.126,0.036246457189822,803.7955797030443,51.25437793514269 -R25x15x10,2000106.4,50.137,0.0378991799206704,806.9236947114128,52.32776199819935 -R25x15x10,1998506.4,50.126,0.0393282809507633,805.896341067946,53.16216589417324 -R25x15x10,1996206.4,50.141,0.0407893317615654,809.7505800627227,54.02616626925593 -R25x15x10,1994006.4,50.135,0.0422754776095276,810.2748061996458,55.30656400985594 -R25x15x10,1991806.4,50.125,0.0438062545747587,813.3862836522072,56.45731410901036 -R25x15x10,1988906.4,50.147,0.0452932713748198,814.3734305534639,57.77519618334559 -R25x15x10,1987306.4,50.141,0.0466513760095235,814.2122205029673,58.84141717829271 -R25x15x10,1984606.4,50.141,0.0482029218145324,818.4382301072097,60.26787332928326 -R25x15x10,1981406.4,50.151,0.0497931744648051,818.7748719586898,61.21948087921438 -R25x15x10,1979406.4,50.168,0.0511779050619587,821.9940091491276,62.49000088403173 -R25x15x10,2193088.8,50.138,0.0104552327677612,781.5057405976237,43.92105254351476 -R25x15x10,2191388.8,50.13,0.0119638909069327,782.1640736168447,44.24378801897482 -R25x15x10,2189788.8,50.119,0.0134689229835801,782.5539010826361,44.69655475053366 -R25x15x10,2188788.8,50.127,0.014844012138987,783.2582001442489,45.4468675936977 -R25x15x10,2187388.8,50.13,0.0163283747487191,783.7479504550176,45.87151561524917 -R25x15x10,2185588.8,50.136,0.0178104932816085,795.791098517044,47.15136746442575 -R25x15x10,2184088.8,50.136,0.0192541909143185,795.3888877813438,47.86511956470965 -R25x15x10,2182588.8,50.132,0.0208372128088619,802.2594851496964,48.67326804812072 -R25x15x10,2179788.8,50.13,0.0223828418156171,802.8660546317485,49.42578184446779 -R25x15x10,2179788.8,50.133,0.0237506343671181,376.5683569854338,5.179849858239409 -R25x15x10,2177488.8,50.148,0.0251350373631506,801.2710792324506,50.74136937579083 -R25x15x10,2175688.8,50.153,0.0265137011145175,802.7501822995262,51.7798279497357 -R25x15x10,2173888.8,50.143,0.0279323291299319,807.3373964670046,52.8505610193187 -R25x15x10,2171588.8,50.143,0.0294649761672375,811.4246024270207,54.905916797265824 -R25x15x10,2169888.8,50.145,0.0308926098930015,814.8947051104235,55.92647265576661 -R25x15x10,2167988.8,50.141,0.0323343788928827,814.1929959804933,56.56010502994049 -R25x15x10,2164188.8,50.152,0.0344189436024066,818.934376428175,58.077302014303896 -R25x15x10,2162188.8,50.152,0.0358505817620087,818.0100718726637,58.84777159950847 -R25x15x10,2159688.8,50.145,0.0374032290533536,822.1311291449298,60.02831631068472 -R25x15x10,2157288.8,50.162,0.0387518159276112,822.9532938037487,61.12345271747851 -R25x15x10,2156088.8,50.155,0.0401209197197519,823.1254008621274,61.944673765893285 -R25x15x10,2152588.8,50.158,0.0415921211788688,827.7388174259644,63.534277145741214 -R25x15x10,2149988.8,50.162,0.0429619552365987,826.5589298329427,64.39733890896457 -R25x15x10,2147988.8,50.166,0.0443414113990457,832.4836689604883,65.8843203204685 -R25x15x10,2145088.8,50.153,0.0457547117886267,829.5070407912107,66.3521091998706 -R25x15x10,2142088.8,50.172,0.0471859730300042,833.1482901170331,68.06933171759121 -R25x15x10,2140488.8,50.162,0.0484668233836923,836.828370196835,69.0893489835502 -R25x15x10,2137688.8,50.178,0.0498701896540302,839.6640722592903,70.57594433984023 -R25x15x10,2134988.8,50.176,0.0513338649847187,842.7172100960663,71.86196554412851 -R25x15x10,107450.0,69.94,0.0245641776659373,723.697921187748,6.949261526080307 -R25x15x10,107250.0,69.955,0.0272580752217582,729.9096660658056,7.827464392453104 -R25x15x10,106950.0,69.95,0.0296793418707063,723.5138364176133,8.469845841433436 -R25x15x10,106750.0,69.959,0.0325428347412815,736.6781046266432,9.111499159742788 -R25x15x10,106550.0,69.965,0.0348947847478218,735.2418300329186,9.781302639733482 -R25x15x10,106250.0,69.968,0.0376346207671911,739.454852607607,10.580910602285204 -R25x15x10,106050.0,69.955,0.040775819315034,751.5368984737685,11.314367593212529 -R25x15x10,105750.0,69.966,0.0435511050339633,757.5061989821736,12.312952571777922 -R25x15x10,105450.0,69.979,0.0464377795353319,762.0288021615455,13.332057888485558 -R25x15x10,105450.0,69.973,0.0487240391013397,767.7570753329555,14.511573173535652 -R25x15x10,104850.0,69.993,0.0518581467827339,771.2705509107022,15.64789534566996 -R25x15x10,104550.0,69.99,0.0546281575222767,775.737557970988,17.00454048600973 -R25x15x10,104150.0,69.982,0.0575537420059571,782.1204646281583,18.58114923493061 -R25x15x10,103650.0,69.982,0.0618124478733054,793.0499537775446,20.84218663684993 -R25x15x10,103250.0,69.998,0.0646338202565986,798.9147769748453,22.87842205978584 -R25x15x10,102850.0,69.981,0.0676385916160969,808.3453138421036,25.153825166373867 -R25x15x10,102050.0,69.996,0.0713003688763418,818.3914553261507,28.30954591055367 -R25x15x10,101850.0,69.983,0.0737135645919725,820.6360603539474,30.5078000796932 -R25x15x10,101350.0,69.984,0.0771483589936653,831.3425138735666,34.02294271096566 -R25x15x10,100750.0,69.993,0.0805021616825149,843.9946033990268,38.31830254697216 -R25x15x10,100150.0,70.001,0.0839603047613117,856.0472443859721,43.271729989508245 -R25x15x10,99450.0,70.018,0.0874875589910225,870.2889384090721,48.98976431090845 -R25x15x10,98150.0,70.03,0.0925458122878059,889.1313973725197,58.272488465651726 -R25x15x10,98150.0,70.029,0.0943176064079949,898.763122455187,61.696498180231735 -R25x15x10,96650.0,70.057,0.0999436125824494,925.2153370598952,73.94141230452792 -R25x15x10,96550.0,70.062,0.1019053037708745,934.7207303713074,78.4629995529797 -R25x15x10,234110.0,70.073,0.0245133852111111,724.992708591069,5.751158749656205 -R25x15x10,233710.0,70.078,0.0269778419405567,727.0687280116608,6.594677114395132 -R25x15x10,233310.0,70.084,0.0296427039750494,732.8041593120421,7.187961215939018 -R25x15x10,232810.0,70.085,0.0321144859887479,733.9774146319432,7.530363750802689 -R25x15x10,232410.0,70.079,0.03469891268312,736.2617373201307,8.296494295505468 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-R25x15x10,222510.0,70.099,0.0717124780875432,802.2904913101163,27.65831975786409 -R25x15x10,221310.0,70.132,0.0749044810651493,810.6383839803005,30.85715870487045 -R25x15x10,220110.0,70.131,0.0781442714597432,824.473920482758,35.11386446129096 -R25x15x10,218810.0,70.14,0.0814740123561438,833.7736230408985,38.96756255492289 -R25x15x10,217610.0,70.127,0.0842427664052481,840.1062958637746,43.73386019824244 -R25x15x10,216310.0,70.14,0.0874813990693952,852.0249746112983,49.02254392003742 -R25x15x10,215010.0,70.137,0.090858977742848,867.7947566511725,54.58784681428179 -R25x15x10,213410.0,70.16,0.0943213119741302,881.9639624063794,61.25173227693127 -R25x15x10,212010.0,70.174,0.0976790184631029,897.6201654769472,68.48872117321748 -R25x15x10,210610.0,70.163,0.1006493509460725,904.5868560752484,76.01340182855112 -R25x15x10,503780.0,70.17,0.0104862233976099,716.0762691871507,7.390632802396959 -R25x15x10,502880.0,70.182,0.0129193760015163,718.4383866388536,7.760470793467862 -R25x15x10,502680.0,70.175,0.0153153598478863,716.4856489237028,8.168106584375977 -R25x15x10,502080.0,70.176,0.0179702507312094,725.5812692720276,8.66885067168495 -R25x15x10,501380.0,70.17,0.0203927859993015,723.340403243256,9.088771188831029 -R25x15x10,500680.0,70.183,0.0229212590222077,726.0986291072877,9.510007944837309 -R25x15x10,499880.0,70.165,0.024941526570711,718.0765733720903,10.089083250755632 -R25x15x10,499180.0,70.168,0.0274667252287778,723.2225868715327,10.653834458899434 -R25x15x10,498380.0,70.151,0.0299120528846463,724.6332591779733,11.218359655824347 -R25x15x10,497480.0,70.133,0.0324346000138113,728.5479897619128,11.905671407078572 -R25x15x10,496580.0,70.096,0.034955639822752,732.1806961166403,12.573378914491224 -R25x15x10,495780.0,70.061,0.0372779814248248,732.6481054352038,13.39611388455972 -R25x15x10,494780.0,70.066,0.0397647144200344,735.1259151588413,14.177541247636546 -R25x15x10,493680.0,70.053,0.0420389748986155,734.644759406031,15.060963355261311 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-R25x15x10,473080.0,70.126,0.0783110706447738,817.5122094231594,39.31270727181059 -R25x15x10,471080.0,70.125,0.0810468981813706,825.4717638695322,42.66348388092635 -R25x15x10,468680.0,70.132,0.0841580647008992,834.5006670993517,46.50337478921639 -R25x15x10,466880.0,70.172,0.0867627805805143,839.6768860502627,49.71875911752621 -R25x15x10,464580.0,70.174,0.0894871037702809,852.655383121142,54.81965452704174 -R25x15x10,462180.0,70.174,0.0930170425257252,871.1397802962102,60.89203490889658 -R25x15x10,459880.0,70.191,0.0956152296811577,878.6956888669341,66.5073251335077 -R25x15x10,457780.0,70.222,0.0981077568965183,884.8235674562192,72.44648720099946 -R25x15x10,455780.0,70.236,0.1008499958726973,897.9398637947365,79.02566373700867 -R25x15x10,746440.0,70.237,0.010292428564257,715.8920632661702,10.390289998925551 -R25x15x10,745640.0,70.252,0.0125804608024022,715.6302469519786,10.722261076759253 -R25x15x10,745040.0,70.258,0.0148843953226241,718.5783841040228,10.99512526388066 -R25x15x10,744040.0,70.255,0.0171763142957064,715.724614166575,11.582120544720164 -R25x15x10,743240.0,70.25,0.0194030483200992,712.494306019093,11.84865113963768 -R25x15x10,742340.0,70.255,0.0218043393902347,715.8884958726347,12.277672869164029 -R25x15x10,741440.0,70.254,0.0241885914697746,718.603105392421,12.761559247889076 -R25x15x10,740340.0,70.268,0.0263913397018731,722.22471099758,13.427916767915589 -R25x15x10,739240.0,70.267,0.0285814240388253,720.3770388804562,14.001438354090231 -R25x15x10,738240.0,70.254,0.0309093299405239,723.1707500442518,14.526830296176495 -R25x15x10,736940.0,70.267,0.033267417332824,725.1292076364981,15.187858398314589 -R25x15x10,735640.0,70.268,0.0357745122833948,729.3657551214817,15.911926591285267 -R25x15x10,734440.0,70.269,0.0380796717374581,731.193979642124,16.6611415604435 -R25x15x10,733040.0,70.269,0.0405730123923832,737.3173252012924,17.53808809907202 -R25x15x10,731540.0,70.267,0.0427006374139269,736.6178727727358,18.30479680889896 -R25x15x10,729540.0,70.284,0.0452370403676513,739.4346057452984,19.25413155270333 -R25x15x10,728440.0,70.276,0.0474476703091793,745.6396883458433,20.373967594525848 -R25x15x10,726640.0,70.293,0.050109148513036,754.6171327164478,21.160933621323352 -R25x15x10,724140.0,70.288,0.05352058735407,759.4685284911037,22.913222091598804 -R25x15x10,722140.0,70.29,0.0561054776625238,766.2925578803155,24.44803307922771 -R25x15x10,720140.0,70.31,0.0586354871060327,770.7754769730667,26.02509619669923 -R25x15x10,718140.0,70.316,0.0613357054851379,778.5119074234809,27.63909194425012 -R25x15x10,715840.0,70.316,0.0639458708080779,783.7156705163551,29.33027484050169 -R25x15x10,713640.0,70.321,0.0663989289658363,788.5654767861839,31.39344840092704 -R25x15x10,711140.0,70.328,0.0690589389060371,794.8684914316921,33.547154550707 -R25x15x10,708640.0,70.337,0.0716437608349043,802.4209455462119,35.92673116691067 -R25x15x10,706140.0,70.345,0.0741197872330046,810.1584926503933,38.35580227886814 -R25x15x10,703340.0,70.352,0.0767630518528302,814.9267745647735,41.01303853951928 -R25x15x10,700540.0,70.361,0.0790830165755583,823.4290244231828,44.85777547558919 -R25x15x10,698540.0,70.19,0.0811095224693054,827.7137189917438,47.3889754331136 -R25x15x10,695540.0,69.993,0.083627753413499,834.4004318747417,52.01617234228204 -R25x15x10,692540.0,69.965,0.0862410068021973,840.9831097182728,55.78721006370477 -R25x15x10,689540.0,69.97,0.0887933624550034,850.6996089850435,60.010682916511946 -R25x15x10,686740.0,70.036,0.0912884792822656,862.0590392355213,64.13268027254635 -R25x15x10,904310.0,69.947,0.0099035963004183,721.0934871157295,13.423305043963731 -R25x15x10,903510.0,69.95,0.0121374805783416,722.7841077255994,13.795311678093352 -R25x15x10,902710.0,69.95,0.0140685730053364,709.1619113796187,14.18882855664607 -R25x15x10,901810.0,69.951,0.0165220376198822,718.4532347011525,14.65364320147817 -R25x15x10,901210.0,69.96,0.0187594379533672,723.3881380932543,15.180039750999864 -R25x15x10,899910.0,69.952,0.0209111437233869,718.1681842934386,15.506826809673074 -R25x15x10,898810.0,69.96,0.0231920413095475,720.9070175089337,16.01457919864382 -R25x15x10,897710.0,69.973,0.0252587679234509,722.4889345456094,16.528514974641247 -R25x15x10,896510.0,69.962,0.0275445795512398,728.0322946127794,17.211446324070828 -R25x15x10,895410.0,69.972,0.0298312059257916,728.5155974806524,17.714305170217074 -R25x15x10,894010.0,69.965,0.0321310057628854,731.7096325314537,18.32576909290909 -R25x15x10,892610.0,69.964,0.0343799780000001,734.76837922501,19.018526800530875 -R25x15x10,891210.0,69.96,0.0364734889885687,733.2459985744514,19.707438562053884 -R25x15x10,889610.0,69.967,0.0386939035135335,736.5286114858565,20.56656780873917 -R25x15x10,888010.0,69.965,0.0410296227267447,739.4400829055274,20.97662772917713 -R25x15x10,886310.0,69.948,0.0433446859697257,744.4572863895417,21.942218138643383 -R25x15x10,884710.0,69.958,0.0457118123589997,747.1011639323514,22.7804074080288 -R25x15x10,882910.0,69.962,0.0479472211550719,752.6721258093652,24.11478595430592 -R25x15x10,880010.0,69.964,0.0515567255239089,765.0657172073368,25.75509671043065 -R25x15x10,877710.0,69.974,0.0540774017533774,771.6692316853547,27.25657207323784 -R25x15x10,875610.0,69.971,0.0562179946865622,770.6133863829234,28.4934033388958 -R25x15x10,873010.0,69.978,0.0588070550849123,780.0610654230916,30.37965513098526 -R25x15x10,871010.0,69.996,0.0610273358593634,782.497990787991,31.81031698436464 -R25x15x10,868310.0,69.999,0.0633053866353974,785.1616165055461,33.53807538009173 -R25x15x10,865610.0,69.988,0.065650876733738,791.963742193912,35.40528608107648 -R25x15x10,863110.0,70.009,0.0679543314760294,797.3445473905075,37.68887230671879 -R25x15x10,860510.0,70.023,0.0703385152413717,800.2287747487571,39.68971059623084 -R25x15x10,1068431.4,70.011,0.0104759808065768,727.0866178134329,16.499531199391786 -R25x15x10,1067931.4,70.021,0.0123212609009342,713.5784347021712,16.7694378878396 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-R25x15x10,1049931.4,70.03,0.040270503538242,746.4160174003318,24.55703481587762 -R25x15x10,1047931.4,70.038,0.0427179912487802,753.3033705921517,25.55443320733028 -R25x15x10,1046031.4,70.035,0.0448821902560047,757.8779269406148,26.63401966007088 -R25x15x10,1043931.4,70.051,0.0471060920654996,762.5716819453692,27.8159758894882 -R25x15x10,1040831.4,70.042,0.0502610342113809,771.7604757499515,29.62594098212808 -R25x15x10,1038531.4,70.056,0.0524382634622413,773.1101431967679,30.965884452177747 -R25x15x10,1036231.4,70.054,0.0544168298182274,776.314497784143,32.55763969881239 -R25x15x10,1033631.4,70.049,0.056774427216778,781.87868176908,34.07533934820351 -R25x15x10,1031131.4,70.055,0.0589203602420201,784.223106366097,35.349046980759454 -R25x15x10,1028331.4,70.044,0.0612926588535426,789.4133652228601,37.01801631999714 -R25x15x10,1025931.4,70.056,0.0634433747091123,796.3617006344014,38.95400254526391 -R25x15x10,1022931.4,70.078,0.0657438298927816,799.8365863101159,40.70893671070969 -R25x15x10,1019631.4,70.087,0.0680376228122942,806.9327729727553,43.69786288859172 -R25x15x10,1016831.4,70.085,0.070306156778298,812.0600726198772,45.79169287673735 -R25x15x10,1265518.7,70.075,0.0104611838632749,724.4308654660564,21.492041575096124 -R25x15x10,1264418.7,70.084,0.0124737324635214,728.0180651692631,21.806492712615785 -R25x15x10,1263218.7,70.098,0.0147014276254962,730.3294211056093,22.14284325444372 -R25x15x10,1262218.7,70.088,0.01655301689074,733.3128418490493,22.81701916186209 -R25x15x10,1260918.7,70.105,0.0185549414566487,733.3415269236776,23.301442825051986 -R25x15x10,1259718.7,70.109,0.0206007987415482,735.9717737918451,23.68428628608999 -R25x15x10,1258418.7,70.098,0.0226191710831261,739.3043383378255,24.39469035329608 -R25x15x10,1256818.7,70.096,0.0245104146268037,736.2380780560603,24.864410125340672 -R25x15x10,1255218.7,70.097,0.0265115595055389,744.7416115898225,25.387530697946858 -R25x15x10,1254018.7,70.107,0.0285796069686403,745.7309581157087,25.904690170498807 -R25x15x10,1252318.7,70.115,0.0306222162214504,748.3924913128027,26.67522019832679 -R25x15x10,1250318.7,70.104,0.032643459095024,751.9435042319287,27.683240762514487 -R25x15x10,1248618.7,70.116,0.0348451468082731,757.5815682816668,28.43456176901764 -R25x15x10,1246518.7,70.097,0.0368573046219453,756.2705092276499,29.160281358874386 -R25x15x10,1245018.7,70.113,0.0388883327632322,760.7284178190129,30.013991242389825 -R25x15x10,1243118.7,70.119,0.0408817651156009,764.3367980620944,31.073925857524507 -R25x15x10,1240618.7,70.12,0.0429876646788483,767.045700982813,32.252423878912595 -R25x15x10,1237418.7,70.122,0.0457121239537732,768.2215334213131,33.9116081181468 -R25x15x10,1235218.7,70.109,0.0478555891967407,773.1882217780573,35.09725729184062 -R25x15x10,1232918.7,70.121,0.0498758693483167,777.0498887843372,36.27620366813625 -R25x15x10,1230218.7,70.125,0.0519588305543048,784.4718687332577,37.78386271845137 -R25x15x10,1407505.6,70.107,0.0104955563539949,736.807289592141,26.36068912352507 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materialdatabase/meta/data_enums.py | 6 +- .../processing/complex_permittivity.py | 172 +++++-------- .../processing/utils/empirical.py | 52 ++-- materialdatabase/processing/utils/physic.py | 11 + 5 files changed, 198 insertions(+), 286 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index c814f0d..7f8e5f9 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -9,27 +9,77 @@ logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO) -def plot_permittivity_magnitude_fit(complex_perm): - """ - Plot measured vs fitted permittivity magnitude (ε_abs) for all (f, T) combinations. +def fit_complex_permittivity_example(is_plot: bool = True) -> None: + """Fit the permittivity. - Parameters - ---------- - complex_perm : ComplexPermittivity - Instance containing measurement data and fitted parameters. + :param is_plot: True to show visual outputs + :type is_plot: bool """ - eps_a_measured = np.sqrt(complex_perm.measurement_data["eps_real"] ** 2 + complex_perm.measurement_data["eps_imag"] ** 2) + # Initialize the material database + mdb_data = mdb.Data() - fit_func = complex_perm.eps_a_fit_function.get_function() - eps_a_fitted = fit_func( - (complex_perm.measurement_data["f"], complex_perm.measurement_data["T"]), - *complex_perm.params_eps_a + # Load permittivity data for a specific material and setup + # permittivity = mdb_data.get_complex_permittivity( + # material=mdb.Material.N95, + # data_source=mdb.DataSource.LEA_MTB, + # probe_codes=["LE2"] + # ) + # permittivity = mdb_data.get_complex_permittivity( + # material=mdb.Material.N49, + # data_source=mdb.DataSource.LEA_MTB, + # probe_codes=["U3G"] + # ) + permittivity = mdb_data.get_complex_permittivity( + material=mdb.Material._3F46, + data_source=mdb.DataSource.LEA_MTB, + probe_codes=["L95"] ) + print("Exemplary complex permittivity data:") + print(permittivity.measurement_data, "\n") + + # Fit permittivity magnitude ε_abs + f_min_measurement = 1e5 + f_max_measurement = 1.5e6 + T_min_measurement = 25 + T_max_measurement = 130 + + permittivity.measurement_data = permittivity.filter_fT(permittivity.measurement_data, + f_min_measurement, + f_max_measurement, + T_min_measurement, + T_max_measurement) + + # Fit permittivity magnitude + permittivity.fit_sigma() + + # Frequency and temperatures at which measurement data is available + f = permittivity.measurement_data["f"] + T = permittivity.measurement_data["T"] + + # Compute measured amplitudes and loss angles + eps_r_meas = (permittivity.measurement_data["eps_real"] - 1j * permittivity.measurement_data["eps_imag"]).to_numpy() + delta_meas = np.degrees(np.arctan2(eps_r_meas.imag, eps_r_meas.real)) + + # Fit data + eps_real_fit, eps_imag_fit = permittivity.fit_real_and_imaginary_part_at_f_and_T(f, T) + + # Compute fitted amplitudes and loss angles + eps_r_fit = eps_real_fit + 1j * eps_imag_fit + delta_fit = np.degrees(np.arctan2(eps_r_fit.imag, eps_r_fit.real)) + + def mre(x, x_est): + return np.mean(abs((x - x_est) / x)) + + # --------------- + # Parity Plots + # --------------- + # Amplitude + print(f"MRE (amplitude): {np.round(100 * mre(abs(eps_r_meas), abs(eps_r_fit)), decimals=2)} %") plt.figure(figsize=(8, 6)) - plt.scatter(eps_a_measured, eps_a_fitted, alpha=0.7, label="Fitted vs Measured") - plt.plot([eps_a_measured.min(), eps_a_measured.max()], - [eps_a_measured.min(), eps_a_measured.max()], + plt.scatter(abs(eps_r_meas), abs(eps_r_fit), alpha=0.7, label="Fitted vs Measured") + plt.plot([abs(eps_r_meas).min(), abs(eps_r_meas).max()], + [abs(eps_r_meas).min(), abs(eps_r_meas).max()], 'r--', label="Ideal Fit (y = x)") plt.xlabel("Measured ε_abs") plt.ylabel("Fitted ε_abs") @@ -37,117 +87,60 @@ def plot_permittivity_magnitude_fit(complex_perm): plt.legend() plt.grid(True) plt.tight_layout() - plt.show() - - -def plot_permittivity_vs_frequency(complex_perm): - """ - Plot measured and fitted permittivity magnitude (ε_abs) vs frequency for each temperature. - Each temperature is displayed in its own subplot. - Parameters - ---------- - complex_perm : ComplexPermittivity - Instance containing measurement data and fitted parameters. - """ - unique_temps = np.unique(complex_perm.measurement_data["T"]) - - eps_a_measured = np.sqrt(complex_perm.measurement_data["eps_real"] ** 2 + complex_perm.measurement_data["eps_imag"] ** 2) - - fit_func = complex_perm.eps_a_fit_function.get_function() - eps_a_fitted = fit_func( - (complex_perm.measurement_data["f"], complex_perm.measurement_data["T"]), - *complex_perm.params_eps_a - ) + # Loss angle + print(f"MRE (angle): {np.round(100 * mre(abs(delta_meas), abs(delta_fit)), decimals=2)} %") + plt.figure(figsize=(8, 6)) + plt.scatter(delta_meas, delta_fit, alpha=0.7, label="Fitted vs Measured") + plt.plot([delta_meas.min(), delta_meas.max()], + [delta_meas.min(), delta_meas.max()], + 'r--', label="Ideal Fit (y = x)") + plt.xlabel("Measured Loss Angle δ (°)") + plt.ylabel("Fitted Loss Angle δ (°)") + plt.title("Loss Angle: Measured vs. Fitted") + plt.legend() + plt.grid(True) + plt.tight_layout() + # --------------- + # over frequency (at temperature levels) + # --------------- + unique_temps = np.unique(T) n_temps = len(unique_temps) + + # Amplitude fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) if n_temps == 1: axes = [axes] # ensure iterable for ax, temp in zip(axes, unique_temps, strict=False): - mask = complex_perm.measurement_data["T"] == temp - f_vals = complex_perm.measurement_data["f"][mask] - measured_vals = eps_a_measured[mask] - fitted_vals = eps_a_fitted[mask] + mask = T == temp + f_vals = f[mask] + measured_vals = abs(eps_r_meas)[mask] + fitted_vals = abs(eps_r_fit)[mask] ax.plot(f_vals, measured_vals, 'o', label="Measured") ax.plot(f_vals, fitted_vals, 'x', label="Fitted") ax.set_title(f"Temperature = {temp} °C") - ax.set_ylabel("ε_abs") + ax.set_ylabel("Fitted ε_abs") ax.grid(True) ax.legend() axes[-1].set_xlabel("Frequency (Hz)") plt.tight_layout() - plt.show() - - -def plot_permittivity_loss_angle_fit(complex_perm): - """ - Plot measured vs fitted loss angle (δ) for all (f, T) combinations. - - Parameters - ---------- - complex_perm : ComplexPermittivity - Instance containing measurement data and fitted parameters. - """ - # Compute measured loss angle δ = atan(ε_imag / ε_real), convert to degrees - delta_measured = np.degrees(np.arctan2(complex_perm.measurement_data["eps_imag"], complex_perm.measurement_data["eps_real"])) - - fit_func = complex_perm.eps_pv_fit_function.get_function() - delta_fitted = np.degrees(fit_func( - (complex_perm.measurement_data["f"], complex_perm.measurement_data["T"]), - *complex_perm.params_eps_pv - )) - plt.figure(figsize=(8, 6)) - plt.scatter(delta_measured, delta_fitted, alpha=0.7, label="Fitted vs Measured") - plt.plot([delta_measured.min(), delta_measured.max()], - [delta_measured.min(), delta_measured.max()], - 'r--', label="Ideal Fit (y = x)") - plt.xlabel("Measured Loss Angle δ (°)") - plt.ylabel("Fitted Loss Angle δ (°)") - plt.title("Loss Angle: Measured vs. Fitted") - plt.legend() - plt.grid(True) - plt.tight_layout() - plt.show() - - -def plot_permittivity_loss_angle_vs_frequency(complex_perm): - """ - Plot measured and fitted loss angle (δ) vs frequency for each temperature. - - Each temperature is displayed in its own subplot. - - Parameters - ---------- - complex_perm : ComplexPermittivity - Instance containing measurement data and fitted parameters. - """ - unique_temps = np.unique(complex_perm.measurement_data["T"]) - - delta_measured = np.degrees(np.arctan2(complex_perm.measurement_data["eps_imag"], complex_perm.measurement_data["eps_real"])) - - fit_func = complex_perm.eps_pv_fit_function.get_function() - delta_fitted = np.degrees(fit_func( - (complex_perm.measurement_data["f"], complex_perm.measurement_data["T"]), - *complex_perm.params_eps_pv - )) - - n_temps = len(unique_temps) + # Loss angle fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) if n_temps == 1: axes = [axes] # ensure iterable for ax, temp in zip(axes, unique_temps, strict=False): - mask = complex_perm.measurement_data["T"] == temp - f_vals = complex_perm.measurement_data["f"][mask] - measured_vals = delta_measured[mask] - fitted_vals = delta_fitted[mask] + mask = T == temp + f_vals = f[mask] + measured_vals = delta_meas[mask] + fitted_vals = delta_fit[mask] ax.plot(f_vals, measured_vals, 'o', label="Measured") ax.plot(f_vals, fitted_vals, 'x', label="Fitted") @@ -158,53 +151,9 @@ def plot_permittivity_loss_angle_vs_frequency(complex_perm): axes[-1].set_xlabel("Frequency (Hz)") plt.tight_layout() - plt.show() - -def fit_complex_permittivity_example(is_plot: bool = True) -> None: - """Fit the permittivity. - - :param is_plot: True to show visual outputs - :type is_plot: bool - """ - # Initialize the material database - mdb_data = mdb.Data() - - # Load permittivity data for a specific material and setup - permittivity = mdb_data.get_complex_permittivity( - material=mdb.Material._3F46, - data_source=mdb.DataSource.LEA_MTB - ) - - print("Exemplary complex permittivity data:") - print(permittivity.measurement_data, "\n") - - # Fit permittivity magnitude ε_abs - f_min_measurement = 3e5 - f_max_measurement = 1.5e6 - T_min_measurement = 25 - T_max_measurement = 120 - - permittivity.measurement_data = permittivity.filter_fT(permittivity.measurement_data, - f_min_measurement, - f_max_measurement, - T_min_measurement, - T_max_measurement) - - # Fit permittivity magnitude - permittivity.fit_permittivity_magnitude() - - # Fit dielectric loss angle - permittivity.fit_loss_angle() - - if is_plot: - # Plot measured vs fitted magnitude - plot_permittivity_magnitude_fit(permittivity) - plot_permittivity_loss_angle_fit(permittivity) - - # Plot measured and fitted ε_abs vs frequency for each temperature - plot_permittivity_vs_frequency(permittivity) - plot_permittivity_loss_angle_vs_frequency(permittivity) + # show all plots + plt.show() if __name__ == "__main__": diff --git a/materialdatabase/meta/data_enums.py b/materialdatabase/meta/data_enums.py index 75dd556..dcb862b 100644 --- a/materialdatabase/meta/data_enums.py +++ b/materialdatabase/meta/data_enums.py @@ -8,7 +8,7 @@ from materialdatabase.processing.utils.empirical import steinmetz_qT, enhanced_steinmetz_qT, \ log_enhanced_steinmetz_qT, log_steinmetz_qT, \ fit_mu_abs_TDK_MDT, fit_mu_abs_LEA_MTB_MagNet, \ - fit_eps_qT + fit_sigma_fT class FitFunction(str, Enum): @@ -19,7 +19,7 @@ class FitFunction(str, Enum): mu_abs_TDK_MDT = "mu_abs_TDK_MDT" mu_abs_LEA_MTB = "mu_abs_LEA_MTB_MagNet" mu_abs_MagNet = "mu_abs_LEA_MTB_MagNet" - eps_abs = "fit_eps_qT" + sigma = "fit_eps_qT" def get_log_function(self) -> Any: """ @@ -44,7 +44,7 @@ def get_function(self) -> Any: FitFunction.mu_abs_TDK_MDT: fit_mu_abs_TDK_MDT, FitFunction.mu_abs_LEA_MTB: fit_mu_abs_LEA_MTB_MagNet, FitFunction.mu_abs_MagNet: fit_mu_abs_LEA_MTB_MagNet, - FitFunction.eps_abs: fit_eps_qT + FitFunction.sigma: fit_sigma_fT }[self] diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 297083d..464d7ac 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -12,9 +12,11 @@ from matplotlib.ticker import MaxNLocator # own libraries +from materialdatabase.processing.utils.physic import eps_r_from_sigma from materialdatabase.meta.data_enums import * from materialdatabase.meta.config import * from materialdatabase.processing.utils.empirical import * +from materialdatabase.processing.utils.constants import epsilon_0 logger = logging.getLogger(__name__) @@ -33,10 +35,10 @@ def __init__(self, df_complex_permittivity: pd.DataFrame, material: Material, da self.measurement_data = df_complex_permittivity self.material = material self.data_source = data_source - self.params_eps_a = None - self.eps_a_fit_function = FitFunction.eps_abs - self.params_eps_pv = None - self.eps_pv_fit_function = FitFunction.eps_abs + self.params_sigma_real = None + self.sigma_real_fit_function = FitFunction.sigma + self.params_sigma_imag = None + self.sigma_imag_fit_function = FitFunction.sigma @staticmethod def filter_fT(df: pd.DataFrame, @@ -62,38 +64,47 @@ def filter_fT(df: pd.DataFrame, df = df[df['T'] <= T_max] return df - def fit_permittivity_magnitude(self, - f_min: float | None = None, f_max: float | None = None, - T_min: float | None = None, T_max: float | None = None - ) -> Any: + def fit_sigma(self, + f_min: float | None = None, f_max: float | None = None, + T_min: float | None = None, T_max: float | None = None + ) -> Any: """ - Fit the permittivity magnitude ε_abs as a function of frequency and temperature. + Fit the complex conductivity sigma as a function of frequency and temperature. This method: - 1. Computes ε_abs = sqrt(ε_real² + ε_imag²). - 2. For each probe: Interpolates the magnitude to a uniform frequency grid at each temperature. - 3. Fits the assembled interpolated data. + 1. Computes conductivity sigma from permittivity eps. + 2. For each probe: Interpolates the real/imaginary parts to a uniform frequency grid at each temperature. + 3. Fits the assembled interpolated data for real/imaginary parts. :param f_min: measurements for lower frequencies will be excluded from fitting :param f_max: measurements for higher frequencies will be excluded from fitting :param T_min: measurements for lower temperatures will be excluded from fitting :param T_max: measurements for higher temperatures will be excluded from fitting - :return: Fitted parameters (popt_eps_a) of the ε_abs model. + :return: Fitted parameters of the conductivity real and imaginary parts. :rtype: np.ndarray """ fit_data = self.filter_fT(self.measurement_data, f_min=f_min, f_max=f_max, T_min=T_min, T_max=T_max) - # Step 1: Compute magnitude - eps_a = np.sqrt(fit_data["eps_real"] ** 2 + fit_data["eps_imag"] ** 2) + # Step 1: Compute conductivity sigma + f = fit_data["f"].to_numpy() + + # assemble complex permittivity from the measurement data using the negative sign convention + eps_complex = fit_data["eps_real"].to_numpy() - 1j * fit_data["eps_imag"].to_numpy() + + # compute the complex conductivity sigma, representing the measurement data + sigma_complex = 1j * 2 * np.pi * f * eps_complex * epsilon_0 - fit_data["eps_a"] = eps_a + # split real and imaginary parts for simplified fitting + fit_data["sigma_real"] = np.real(sigma_complex) + fit_data["sigma_imag"] = np.imag(sigma_complex) # Step 2: Interpolate to uniform frequency grid for each T interpolated_f: list[float] = [] interpolated_T: list[float] = [] - interpolated_eps_a: list[float] = [] + interpolated_sigma_real: list[float] = [] + interpolated_sigma_imag: list[float] = [] unique_probes = np.unique(fit_data["probe"]) for probe in unique_probes: @@ -106,87 +117,38 @@ def fit_permittivity_magnitude(self, # Create evenly spaced frequency grid (e.g., same number as original points or a fixed number like 20) f_uniform = np.linspace(df_T["f"].min(), df_T["f"].max(), 20) - # Interpolate magnitude over uniform grid - interp_func = interp1d(df_T["f"], df_T["eps_a"], kind="linear", fill_value="extrapolate") - eps_a_uniform = interp_func(f_uniform) + # Interpolate sigma over uniform grid + interp_sigma_real_func = interp1d(df_T["f"], df_T["sigma_real"], kind="linear", fill_value="extrapolate") + interp_sigma_imag_func = interp1d(df_T["f"], df_T["sigma_imag"], kind="linear", fill_value="extrapolate") + sigma_real_uniform = interp_sigma_real_func(f_uniform) + sigma_imag_uniform = interp_sigma_imag_func(f_uniform) interpolated_f.extend(f_uniform) interpolated_T.extend([T] * len(f_uniform)) - interpolated_eps_a.extend(eps_a_uniform) + interpolated_sigma_real.extend(sigma_real_uniform) + interpolated_sigma_imag.extend(sigma_imag_uniform) # Step 3: Fit to interpolated dataset - params_eps_a, pcov_eps_a = curve_fit( - self.eps_a_fit_function.get_function(), + params_sigma_real, pcov_sigma_real = curve_fit( + self.sigma_real_fit_function.get_function(), (np.array(interpolated_f), np.array(interpolated_T)), - np.array(interpolated_eps_a), + np.array(interpolated_sigma_real), maxfev=int(1e6) ) - self.params_eps_a = params_eps_a - return params_eps_a - - def fit_loss_angle(self, - f_min: float | None = None, f_max: float | None = None, - T_min: float | None = None, T_max: float | None = None - ) -> Any: - """ - Fit the dielectric losses as a function of frequency and temperature. - - This method: - 1. Computes loss density p_el. - 2. For each probe: Interpolates p_el to a uniform frequency grid at each temperature. - 3. Fits the assembled interpolated data. - - :param f_min: measurements for lower frequencies will be excluded from fitting - :param f_max: measurements for higher frequencies will be excluded from fitting - :param T_min: measurements for lower temperatures will be excluded from fitting - :param T_max: measurements for higher temperatures will be excluded from fitting - :return: Fitted parameters (popt_eps_pv) of the ε_abs model. - :rtype: np.ndarray - """ - fit_data = self.filter_fT(self.measurement_data, - f_min=f_min, f_max=f_max, - T_min=T_min, T_max=T_max) - - # Step 1: Compute magnitude - eps_angle = np.arctan(fit_data["eps_imag"] / fit_data["eps_real"]) - - fit_data["eps_angle"] = eps_angle - - # Step 2: Interpolate to uniform frequency grid for each T - interpolated_f: list[float] = [] - interpolated_T: list[float] = [] - interpolated_eps_angle: list[float] = [] - - unique_probes = np.unique(fit_data["probe"]) - for probe in unique_probes: - df_probe = fit_data[fit_data["probe"] == probe] - - unique_Ts = np.unique(df_probe["T"]) - for T in unique_Ts: - df_T = df_probe[df_probe["T"] == T].sort_values("f") - - # Create evenly spaced frequency grid (e.g., same number as original points or a fixed number like 20) - f_uniform = np.linspace(df_T["f"].min(), df_T["f"].max(), 20) - - # Interpolate magnitude over uniform grid - interp_func = interp1d(df_T["f"], df_T["eps_angle"], kind="linear", fill_value="extrapolate") - eps_angle_uniform = interp_func(f_uniform) - - interpolated_f.extend(f_uniform) - interpolated_T.extend([T] * len(f_uniform)) - interpolated_eps_angle.extend(eps_angle_uniform) - - # Step 3: Fit to interpolated dataset - params_eps_pv, pcov_eps_pv = curve_fit( - self.eps_pv_fit_function.get_function(), + params_sigma_imag, pcov_sigma_imag = curve_fit( + self.sigma_imag_fit_function.get_function(), (np.array(interpolated_f), np.array(interpolated_T)), - np.array(interpolated_eps_angle), + np.array(interpolated_sigma_imag), maxfev=int(1e6) ) - self.params_eps_pv = params_eps_pv - return params_eps_pv + # store fit parameters internally + self.params_sigma_real = params_sigma_real + self.params_sigma_imag = params_sigma_imag + + return params_sigma_real, params_sigma_imag + def fit_real_and_imaginary_part_at_f_and_T(self, f: float, T: float) -> tuple[float, float]: """ @@ -199,28 +161,25 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float, T: float) -> tuple[fl :return: (eps_real, eps_imag) """ # Auto-generate magnitude fit if missing - if self.params_eps_a is None: + if self.params_sigma_real is None or self.params_sigma_imag is None: logger.info("params_eps_a missing, running fit_permittivity_magnitude()...") - self.fit_permittivity_magnitude() + self.fit_sigma() - # Auto-generate loss angle fit if missing - if self.params_eps_pv is None: - logger.info("params_eps_pv missing, running fit_loss_angle()...") - self.fit_loss_angle() + # Predict conductivity real part the fitted model + sigma_real_model = self.sigma_real_fit_function.get_function() + sigma_real = sigma_real_model((np.array([f]), np.array([T])), *self.params_sigma_real)[0] - # Predict magnitude ε_abs from the fitted model - eps_a_model = self.eps_a_fit_function.get_function() - eps_a = eps_a_model((np.array([f]), np.array([T])), *self.params_eps_a)[0] + # Predict conductivity imaginary part from the fitted model + sigma_imag_model = self.sigma_imag_fit_function.get_function() + sigma_imag = sigma_imag_model((np.array([f]), np.array([T])), *self.params_sigma_imag)[0] - # Predict loss angle φ from the fitted model - eps_pv_model = self.eps_pv_fit_function.get_function() - phi = eps_pv_model((np.array([f]), np.array([T])), *self.params_eps_pv)[0] + # assemble complex conductivity + sigma_complex = sigma_real + 1j * sigma_imag - # Convert magnitude + angle → real & imaginary - eps_real = eps_a * np.cos(phi) - eps_imag = eps_a * np.sin(phi) + # Convert complex conductivity to permittivity + eps_complex = eps_r_from_sigma(f, sigma_complex).to_numpy() - return eps_real, eps_imag + return eps_complex.real, eps_complex.imag @staticmethod def grid2txt(df: pd.DataFrame, path: os.PathLike | str) -> None: @@ -265,12 +224,9 @@ def to_grid(self, :param grid_frequency: frequencies for the interpolation grid :param grid_temperature: temperatures for the interpolation grid """ - if self.params_eps_a is None: - self.fit_permittivity_magnitude(f_min_measurement, f_max_measurement, - T_min_measurement, T_max_measurement) - if self.params_eps_pv is None: - self.fit_loss_angle(f_min_measurement, f_max_measurement, - T_min_measurement, T_max_measurement) + if self.params_sigma_real is None or self.params_sigma_imag is None: + self.fit_sigma(f_min_measurement, f_max_measurement, + T_min_measurement, T_max_measurement) records: list[list[float]] = [] for T in grid_temperature: @@ -288,7 +244,7 @@ def plot_grid(df: pd.DataFrame, f_min: float | None = None, f_max: float | None = None, T_min: float | None = None, T_max: float | None = None) -> None: """ - Plot |ε| and phase(ε) as contour maps vs. f (kHz) and T (°C) with shared color scales for magnitude and phase. + Plot |eps| and phase(eps) as contour maps vs. f (kHz) and T (°C) with shared color scales for magnitude and phase. :param df: complex permittivity data as pandas DataFrame :param save_path: path where the plot should be saved diff --git a/materialdatabase/processing/utils/empirical.py b/materialdatabase/processing/utils/empirical.py index 30db99d..3824ea8 100644 --- a/materialdatabase/processing/utils/empirical.py +++ b/materialdatabase/processing/utils/empirical.py @@ -2,6 +2,7 @@ import numpy as np import numpy.typing as npt + # ---------------- # Temperature Fits # ---------------- @@ -18,6 +19,7 @@ def quadratic_temperature(T: float | np.ndarray, c_0: float, c_1: float, c_2: fl """ return c_0 - c_1 * T + c_2 * T ** 2 + # ---------------- # Permeability Fits # ---------------- @@ -205,40 +207,34 @@ def fit_mu_abs_LEA_MTB_MagNet( return (mur_0 * k_0 + k_1 * (mur_1 * B + mur_2 * B ** 2)) * k_f + # ---------------- -# Permittivity Fits +# Permittivity (Conductivity) Fits # ---------------- - -def fit_eps(f: float | np.ndarray, - e_0: float, e_1: float, e_2: float, e_3: float) -> float | np.ndarray: - """ - Polynomial fit suitable for permittivity fit. - - :param f: - :param e_0: constant frequency coefficient - :param e_1: linear frequency coefficient - :param e_2: quadratic frequency coefficient - :param e_3: cubic frequency coefficient - :return: +def fit_sigma_fT(fT: tuple[float | np.ndarray, float | np.ndarray], + c_0: float, + c_T1: float, c_T2: float, + c_f1: float, c_f2: float, c_f3: float, + c_mix11: float, c_mix21: float, c_mix12: float, c_mix31: float, + ) -> float | np.ndarray: """ - return e_0 + e_1 * f + e_2 * f ** 2 + e_3 * f ** 3 - - -def fit_eps_qT(fT: tuple[float | np.ndarray, float | np.ndarray], - e_1: float, e_2: float, e_3: float, - c_0: float, c_1: float, c_2: float) -> float | np.ndarray: - """ - Temperature-dependent polynomial fit suitable for permittivity fit. + Temperature-dependent polynomial fit suitable for conductivity fit. (-> used as permittivity fit) :param fT: tuple of frequency and temperature - :param e_1: linear frequency coefficient - :param e_2: quadratic frequency coefficient - :param e_3: cubic frequency coefficient :param c_0: constant coefficient - :param c_1: linear temperature coefficient - :param c_2: quadratic temperature coefficient + :param c_T1: linear temperature coefficient + :param c_T2: quadratic temperature coefficient + :param c_f1: linear frequency coefficient + :param c_f2: quadratic frequency coefficient + :param c_f3: cubic frequency coefficient + :param c_mix11: deg(f)=1, deg(T)=1 + :param c_mix21: deg(f)=2, deg(T)=1 + :param c_mix12: deg(f)=1, deg(T)=2 + :param c_mix31: deg(f)=3, deg(T)=1 :return: fitted permittivity (real, imaginary, amplitude or loss angle) """ f, T = fT - k = quadratic_temperature(T, c_0, c_1, c_2) - return k / (1.0 + e_1 * f**(e_2+e_3/T)) + return (c_0 + # bulk conductivity + c_T1 * T + c_T2 * T ** 2 + # temperature coefficients -> modify bulk conductivity + c_f1 * f + c_f2 * f ** 2 + c_f3 * f ** 3 + # frequency coefficients -> modify ac conductivity + c_mix11 * f * T + c_mix21 * f ** 2 * T + c_mix12 * f * T ** 2 + c_mix31 * f ** 3 * T) # mix terms diff --git a/materialdatabase/processing/utils/physic.py b/materialdatabase/processing/utils/physic.py index c5316c7..4994006 100644 --- a/materialdatabase/processing/utils/physic.py +++ b/materialdatabase/processing/utils/physic.py @@ -1,5 +1,6 @@ """Physical functions.""" import numpy as np +from materialdatabase.processing.utils.constants import epsilon_0 def pv_mag(f: float | np.ndarray, mu_imag: float | np.ndarray, h_abs: float | np.ndarray) -> float | np.ndarray: """ @@ -35,3 +36,13 @@ def mu_imag_from_pv(f: float | np.ndarray, h_abs: float | np.ndarray, p_hyst: fl :return: imaginary part of the permeability """ return p_hyst / (-0.5 * 2 * np.pi * f * h_abs ** 2) + +def eps_r_from_sigma(f, sigma): + """ + Calculate the (complex) relative permittivity form a (complex) conductivity. + + :param f: frequency + :param sigma: (complex) conductivity + :return: (complex) relative permittivity + """ + return sigma / (1j * 2 * np.pi * f * epsilon_0) From bbf3010925cf9282912626b45d3cc7cf9497cf06 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Thu, 9 Apr 2026 17:40:08 +0200 Subject: [PATCH 03/11] add function sigma_from_eps_r --- materialdatabase/processing/complex_permittivity.py | 5 ++--- materialdatabase/processing/utils/physic.py | 12 +++++++++++- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 464d7ac..9ce421e 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -12,11 +12,10 @@ from matplotlib.ticker import MaxNLocator # own libraries -from materialdatabase.processing.utils.physic import eps_r_from_sigma +from materialdatabase.processing.utils.physic import eps_r_from_sigma, sigma_from_eps_r from materialdatabase.meta.data_enums import * from materialdatabase.meta.config import * from materialdatabase.processing.utils.empirical import * -from materialdatabase.processing.utils.constants import epsilon_0 logger = logging.getLogger(__name__) @@ -94,7 +93,7 @@ def fit_sigma(self, eps_complex = fit_data["eps_real"].to_numpy() - 1j * fit_data["eps_imag"].to_numpy() # compute the complex conductivity sigma, representing the measurement data - sigma_complex = 1j * 2 * np.pi * f * eps_complex * epsilon_0 + sigma_complex = sigma_from_eps_r(f, eps_complex) # split real and imaginary parts for simplified fitting fit_data["sigma_real"] = np.real(sigma_complex) diff --git a/materialdatabase/processing/utils/physic.py b/materialdatabase/processing/utils/physic.py index 4994006..e8aae3c 100644 --- a/materialdatabase/processing/utils/physic.py +++ b/materialdatabase/processing/utils/physic.py @@ -39,10 +39,20 @@ def mu_imag_from_pv(f: float | np.ndarray, h_abs: float | np.ndarray, p_hyst: fl def eps_r_from_sigma(f, sigma): """ - Calculate the (complex) relative permittivity form a (complex) conductivity. + Calculate the (complex) relative permittivity from a (complex) conductivity. :param f: frequency :param sigma: (complex) conductivity :return: (complex) relative permittivity """ return sigma / (1j * 2 * np.pi * f * epsilon_0) + +def sigma_from_eps_r(f, eps_r): + """ + Calculate the (complex) conductivity from a (complex) relative permittivity. + + :param f: frequency + :param eps_r: (complex) relative permittivity + :return: (complex) relative conductivity + """ + return 1j * 2 * np.pi * f * eps_r * epsilon_0 From e097d22a78e4713c413946af7bc8fa515631e99b Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Thu, 9 Apr 2026 18:11:49 +0200 Subject: [PATCH 04/11] linting --- examples/fit_complex_permittivity.py | 171 +++++++++--------- .../processing/complex_permittivity.py | 23 ++- .../processing/utils/empirical.py | 17 +- materialdatabase/processing/utils/physic.py | 4 +- 4 files changed, 113 insertions(+), 102 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index 7f8e5f9..eb3dfc8 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -54,8 +54,8 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: permittivity.fit_sigma() # Frequency and temperatures at which measurement data is available - f = permittivity.measurement_data["f"] - T = permittivity.measurement_data["T"] + f = permittivity.measurement_data["f"].to_numpy() + T = permittivity.measurement_data["T"].to_numpy() # Compute measured amplitudes and loss angles eps_r_meas = (permittivity.measurement_data["eps_real"] - 1j * permittivity.measurement_data["eps_imag"]).to_numpy() @@ -71,89 +71,90 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: def mre(x, x_est): return np.mean(abs((x - x_est) / x)) - # --------------- - # Parity Plots - # --------------- - # Amplitude - print(f"MRE (amplitude): {np.round(100 * mre(abs(eps_r_meas), abs(eps_r_fit)), decimals=2)} %") - plt.figure(figsize=(8, 6)) - plt.scatter(abs(eps_r_meas), abs(eps_r_fit), alpha=0.7, label="Fitted vs Measured") - plt.plot([abs(eps_r_meas).min(), abs(eps_r_meas).max()], - [abs(eps_r_meas).min(), abs(eps_r_meas).max()], - 'r--', label="Ideal Fit (y = x)") - plt.xlabel("Measured ε_abs") - plt.ylabel("Fitted ε_abs") - plt.title("Permittivity Magnitude: Measured vs. Fitted") - plt.legend() - plt.grid(True) - plt.tight_layout() - - # Loss angle - print(f"MRE (angle): {np.round(100 * mre(abs(delta_meas), abs(delta_fit)), decimals=2)} %") - plt.figure(figsize=(8, 6)) - plt.scatter(delta_meas, delta_fit, alpha=0.7, label="Fitted vs Measured") - plt.plot([delta_meas.min(), delta_meas.max()], - [delta_meas.min(), delta_meas.max()], - 'r--', label="Ideal Fit (y = x)") - plt.xlabel("Measured Loss Angle δ (°)") - plt.ylabel("Fitted Loss Angle δ (°)") - plt.title("Loss Angle: Measured vs. Fitted") - plt.legend() - plt.grid(True) - plt.tight_layout() - - # --------------- - # over frequency (at temperature levels) - # --------------- - unique_temps = np.unique(T) - n_temps = len(unique_temps) - - # Amplitude - fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) - - if n_temps == 1: - axes = [axes] # ensure iterable - - for ax, temp in zip(axes, unique_temps, strict=False): - mask = T == temp - f_vals = f[mask] - measured_vals = abs(eps_r_meas)[mask] - fitted_vals = abs(eps_r_fit)[mask] - - ax.plot(f_vals, measured_vals, 'o', label="Measured") - ax.plot(f_vals, fitted_vals, 'x', label="Fitted") - ax.set_title(f"Temperature = {temp} °C") - ax.set_ylabel("Fitted ε_abs") - ax.grid(True) - ax.legend() - - axes[-1].set_xlabel("Frequency (Hz)") - plt.tight_layout() - - # Loss angle - fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) - - if n_temps == 1: - axes = [axes] # ensure iterable - - for ax, temp in zip(axes, unique_temps, strict=False): - mask = T == temp - f_vals = f[mask] - measured_vals = delta_meas[mask] - fitted_vals = delta_fit[mask] - - ax.plot(f_vals, measured_vals, 'o', label="Measured") - ax.plot(f_vals, fitted_vals, 'x', label="Fitted") - ax.set_title(f"Temperature = {temp} °C") - ax.set_ylabel("Loss Angle δ (°)") - ax.grid(True) - ax.legend() - - axes[-1].set_xlabel("Frequency (Hz)") - plt.tight_layout() - - # show all plots - plt.show() + if is_plot: + # --------------- + # Parity Plots + # --------------- + # Amplitude + print(f"MRE (amplitude): {np.round(100 * mre(abs(eps_r_meas), abs(eps_r_fit)), decimals=2)} %") + plt.figure(figsize=(8, 6)) + plt.scatter(abs(eps_r_meas), np.abs(eps_r_fit), alpha=0.7, label="Fitted vs Measured") + plt.plot([abs(eps_r_meas).min(), abs(eps_r_meas).max()], + [abs(eps_r_meas).min(), abs(eps_r_meas).max()], + 'r--', label="Ideal Fit (y = x)") + plt.xlabel("Measured ε_abs") + plt.ylabel("Fitted ε_abs") + plt.title("Permittivity Magnitude: Measured vs. Fitted") + plt.legend() + plt.grid(True) + plt.tight_layout() + + # Loss angle + print(f"MRE (angle): {np.round(100 * mre(abs(delta_meas), abs(delta_fit)), decimals=2)} %") + plt.figure(figsize=(8, 6)) + plt.scatter(delta_meas, delta_fit, alpha=0.7, label="Fitted vs Measured") + plt.plot([delta_meas.min(), delta_meas.max()], + [delta_meas.min(), delta_meas.max()], + 'r--', label="Ideal Fit (y = x)") + plt.xlabel("Measured Loss Angle δ (°)") + plt.ylabel("Fitted Loss Angle δ (°)") + plt.title("Loss Angle: Measured vs. Fitted") + plt.legend() + plt.grid(True) + plt.tight_layout() + + # --------------- + # over frequency (at temperature levels) + # --------------- + unique_temps = np.unique(T) + n_temps = len(unique_temps) + + # Amplitude + fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) + + if n_temps == 1: + axes = [axes] # ensure iterable + + for ax, temp in zip(axes, unique_temps, strict=False): + mask = T == temp + f_vals = f[mask] + measured_vals = abs(eps_r_meas)[mask] + fitted_vals = np.abs(eps_r_fit)[mask] + + ax.plot(f_vals, measured_vals, 'o', label="Measured") + ax.plot(f_vals, fitted_vals, 'x', label="Fitted") + ax.set_title(f"Temperature = {temp} °C") + ax.set_ylabel("Fitted ε_abs") + ax.grid(True) + ax.legend() + + axes[-1].set_xlabel("Frequency (Hz)") + plt.tight_layout() + + # Loss angle + fig, axes = plt.subplots(n_temps, 1, figsize=(8, 3 * n_temps), sharex=True) + + if n_temps == 1: + axes = [axes] # ensure iterable + + for ax, temp in zip(axes, unique_temps, strict=False): + mask = T == temp + f_vals = f[mask] + measured_vals = delta_meas[mask] + fitted_vals = delta_fit[mask] + + ax.plot(f_vals, measured_vals, 'o', label="Measured") + ax.plot(f_vals, fitted_vals, 'x', label="Fitted") + ax.set_title(f"Temperature = {temp} °C") + ax.set_ylabel("Loss Angle δ (°)") + ax.grid(True) + ax.legend() + + axes[-1].set_xlabel("Frequency (Hz)") + plt.tight_layout() + + # show all plots + plt.show() if __name__ == "__main__": diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 9ce421e..0cb710f 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -90,7 +90,7 @@ def fit_sigma(self, f = fit_data["f"].to_numpy() # assemble complex permittivity from the measurement data using the negative sign convention - eps_complex = fit_data["eps_real"].to_numpy() - 1j * fit_data["eps_imag"].to_numpy() + eps_complex = fit_data["eps_real"].to_numpy(dtype=float) - 1j * fit_data["eps_imag"].to_numpy(dtype=float) # compute the complex conductivity sigma, representing the measurement data sigma_complex = sigma_from_eps_r(f, eps_complex) @@ -148,8 +148,7 @@ def fit_sigma(self, return params_sigma_real, params_sigma_imag - - def fit_real_and_imaginary_part_at_f_and_T(self, f: float, T: float) -> tuple[float, float]: + def fit_real_and_imaginary_part_at_f_and_T(self, f: float | np.ndarray, T: float | np.ndarray) -> tuple[float | np.ndarray, float | np.ndarray]: """ Compute fitted real and imaginary parts of permittivity at given frequency and temperature. @@ -176,7 +175,7 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float, T: float) -> tuple[fl sigma_complex = sigma_real + 1j * sigma_imag # Convert complex conductivity to permittivity - eps_complex = eps_r_from_sigma(f, sigma_complex).to_numpy() + eps_complex = eps_r_from_sigma(f, sigma_complex) return eps_complex.real, eps_complex.imag @@ -227,13 +226,17 @@ def to_grid(self, self.fit_sigma(f_min_measurement, f_max_measurement, T_min_measurement, T_max_measurement) - records: list[list[float]] = [] - for T in grid_temperature: - for f in grid_frequency: - eps_real, eps_imag = self.fit_real_and_imaginary_part_at_f_and_T(f, T) - records.append([f, T, eps_real, eps_imag]) + f_grid, T_grid = np.meshgrid(grid_frequency, grid_temperature) + eps_real, eps_imag = self.fit_real_and_imaginary_part_at_f_and_T(f_grid, T_grid) - df_grid: pd.DataFrame = pd.DataFrame(records, columns=["f", "T", "eps_real", "eps_imag"]) + df_grid = pd.DataFrame( + { + "f": f_grid.ravel(), + "T": T_grid.ravel(), + "eps_real": np.ravel(eps_real), + "eps_imag": np.ravel(eps_imag), + } + ) return df_grid @staticmethod diff --git a/materialdatabase/processing/utils/empirical.py b/materialdatabase/processing/utils/empirical.py index 3824ea8..b7cc1ec 100644 --- a/materialdatabase/processing/utils/empirical.py +++ b/materialdatabase/processing/utils/empirical.py @@ -218,7 +218,7 @@ def fit_sigma_fT(fT: tuple[float | np.ndarray, float | np.ndarray], c_mix11: float, c_mix21: float, c_mix12: float, c_mix31: float, ) -> float | np.ndarray: """ - Temperature-dependent polynomial fit suitable for conductivity fit. (-> used as permittivity fit) + Temperature-dependent polynomial fit suitable for conductivity fit (-> used as permittivity fit). :param fT: tuple of frequency and temperature :param c_0: constant coefficient @@ -234,7 +234,14 @@ def fit_sigma_fT(fT: tuple[float | np.ndarray, float | np.ndarray], :return: fitted permittivity (real, imaginary, amplitude or loss angle) """ f, T = fT - return (c_0 + # bulk conductivity - c_T1 * T + c_T2 * T ** 2 + # temperature coefficients -> modify bulk conductivity - c_f1 * f + c_f2 * f ** 2 + c_f3 * f ** 3 + # frequency coefficients -> modify ac conductivity - c_mix11 * f * T + c_mix21 * f ** 2 * T + c_mix12 * f * T ** 2 + c_mix31 * f ** 3 * T) # mix terms + + # temperature coefficients -> modify bulk conductivity + c_T = c_T1 * T + c_T2 * T ** 2 + + # frequency coefficients -> modify ac conductivity + c_f = c_f1 * f + c_f2 * f ** 2 + c_f3 * f ** 3 + + # mix terms + c_mix = c_mix11 * f * T + c_mix21 * f ** 2 * T + c_mix12 * f * T ** 2 + c_mix31 * f ** 3 * T + + return c_0 + c_T + c_f + c_mix diff --git a/materialdatabase/processing/utils/physic.py b/materialdatabase/processing/utils/physic.py index e8aae3c..41d0307 100644 --- a/materialdatabase/processing/utils/physic.py +++ b/materialdatabase/processing/utils/physic.py @@ -37,7 +37,7 @@ def mu_imag_from_pv(f: float | np.ndarray, h_abs: float | np.ndarray, p_hyst: fl """ return p_hyst / (-0.5 * 2 * np.pi * f * h_abs ** 2) -def eps_r_from_sigma(f, sigma): +def eps_r_from_sigma(f: float | np.ndarray, sigma: float | np.ndarray) -> complex | np.ndarray: """ Calculate the (complex) relative permittivity from a (complex) conductivity. @@ -47,7 +47,7 @@ def eps_r_from_sigma(f, sigma): """ return sigma / (1j * 2 * np.pi * f * epsilon_0) -def sigma_from_eps_r(f, eps_r): +def sigma_from_eps_r(f: float | np.ndarray, eps_r: float | np.ndarray) -> complex | np.ndarray: """ Calculate the (complex) conductivity from a (complex) relative permittivity. From 8a87cf71f64bd55c213c1358caa41423a5f688af Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Thu, 9 Apr 2026 19:13:25 +0200 Subject: [PATCH 05/11] negative sign convention --- examples/fit_complex_permittivity.py | 22 +++++++++---------- .../processing/complex_permittivity.py | 4 ++-- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index eb3dfc8..82543c6 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -19,21 +19,21 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: mdb_data = mdb.Data() # Load permittivity data for a specific material and setup + permittivity = mdb_data.get_complex_permittivity( + material=mdb.Material.N95, + data_source=mdb.DataSource.LEA_MTB, + probe_codes=["LE2"] + ) # permittivity = mdb_data.get_complex_permittivity( - # material=mdb.Material.N95, + # material=mdb.Material.N49, # data_source=mdb.DataSource.LEA_MTB, - # probe_codes=["LE2"] + # probe_codes=["U3G"] # ) # permittivity = mdb_data.get_complex_permittivity( - # material=mdb.Material.N49, + # material=mdb.Material._3F46, # data_source=mdb.DataSource.LEA_MTB, - # probe_codes=["U3G"] + # probe_codes=["L95"] # ) - permittivity = mdb_data.get_complex_permittivity( - material=mdb.Material._3F46, - data_source=mdb.DataSource.LEA_MTB, - probe_codes=["L95"] - ) print("Exemplary complex permittivity data:") print(permittivity.measurement_data, "\n") @@ -65,7 +65,7 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: eps_real_fit, eps_imag_fit = permittivity.fit_real_and_imaginary_part_at_f_and_T(f, T) # Compute fitted amplitudes and loss angles - eps_r_fit = eps_real_fit + 1j * eps_imag_fit + eps_r_fit = eps_real_fit - 1j * eps_imag_fit delta_fit = np.degrees(np.arctan2(eps_r_fit.imag, eps_r_fit.real)) def mre(x, x_est): @@ -90,7 +90,7 @@ def mre(x, x_est): plt.tight_layout() # Loss angle - print(f"MRE (angle): {np.round(100 * mre(abs(delta_meas), abs(delta_fit)), decimals=2)} %") + print(f"MRE (angle): {np.round(100 * mre(delta_meas, delta_fit), decimals=2)} %") plt.figure(figsize=(8, 6)) plt.scatter(delta_meas, delta_fit, alpha=0.7, label="Fitted vs Measured") plt.plot([delta_meas.min(), delta_meas.max()], diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 0cb710f..d1293af 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -156,7 +156,7 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float | np.ndarray, T: float If parameters are missing, they will be generated by running the corresponding fit methods. :param f: Frequency in Hz :param T: Temperature in °C - :return: (eps_real, eps_imag) + :return: (eps_real, -eps_imag) (negative sign convention of the imaginary part) """ # Auto-generate magnitude fit if missing if self.params_sigma_real is None or self.params_sigma_imag is None: @@ -177,7 +177,7 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float | np.ndarray, T: float # Convert complex conductivity to permittivity eps_complex = eps_r_from_sigma(f, sigma_complex) - return eps_complex.real, eps_complex.imag + return eps_complex.real, -eps_complex.imag @staticmethod def grid2txt(df: pd.DataFrame, path: os.PathLike | str) -> None: From 01f4ac4e9eff458bada93353cc999da6cd807d20 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Wed, 15 Apr 2026 12:51:17 +0200 Subject: [PATCH 06/11] moved mre function into math.py --- examples/fit_complex_permittivity.py | 3 +-- materialdatabase/processing/utils/math.py | 4 ++++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index 82543c6..c649d7f 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -4,6 +4,7 @@ import numpy as np from matplotlib import pyplot as plt import materialdatabase as mdb +from materialdatabase.processing.utils.math import mre # Configure logging to show info level messages logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO) @@ -68,8 +69,6 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: eps_r_fit = eps_real_fit - 1j * eps_imag_fit delta_fit = np.degrees(np.arctan2(eps_r_fit.imag, eps_r_fit.real)) - def mre(x, x_est): - return np.mean(abs((x - x_est) / x)) if is_plot: # --------------- diff --git a/materialdatabase/processing/utils/math.py b/materialdatabase/processing/utils/math.py index 9d00892..e9ddd0a 100644 --- a/materialdatabase/processing/utils/math.py +++ b/materialdatabase/processing/utils/math.py @@ -1 +1,5 @@ """Mathmatical functions.""" +import numpy as np + +def mre(x, x_est): + return np.mean(abs((x - x_est) / x)) From a381084589d257df6e371d1975c0c7c73be1fd4e Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Fri, 17 Apr 2026 17:25:19 +0200 Subject: [PATCH 07/11] add loggings --- materialdatabase/processing/complex_permeability.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/materialdatabase/processing/complex_permeability.py b/materialdatabase/processing/complex_permeability.py index 077e4e8..f9e5296 100644 --- a/materialdatabase/processing/complex_permeability.py +++ b/materialdatabase/processing/complex_permeability.py @@ -1,4 +1,5 @@ """Class to represent the data structure and load material data.""" +import logging # python libraries import os @@ -198,6 +199,16 @@ def fit_losses(self, fit_data["b"]), np.log(pv), maxfev=100000) self.params_pv = popt_pv + + # print optimal parameters + logging.info(popt_pv) + + # Check fit quality + fit_function = self.pv_fit_function.get_function() + pv_pred = fit_function((fit_data["f"].to_numpy(), fit_data["T"].to_numpy(), fit_data["b"].to_numpy()), *popt_pv) + rel_error = abs(pv_pred - pv) / pv + logging.info(f"MRE = {np.mean(rel_error)}") + return popt_pv def fit_real_and_imaginary_part_at_f_and_T( From 1b58d9892d5c00a26791aada7b41f094fce9d120 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Tue, 21 Apr 2026 15:50:05 +0200 Subject: [PATCH 08/11] linting --- examples/fit_complex_permittivity.py | 2 +- materialdatabase/processing/utils/math.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index 50660f7..646431d 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -74,7 +74,7 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: # Parity Plots # --------------- # Amplitude - print(f"MRE (amplitude): {np.round(100 * mre(abs(eps_r_meas), abs(eps_r_fit)), decimals=2)} %") + print(f"MRE (amplitude): {np.round(100 * mre(np.abs(eps_r_meas), np.abs(eps_r_fit)), decimals=2)} %") plt.figure(figsize=(8, 6)) plt.scatter(abs(eps_r_meas), np.abs(eps_r_fit), alpha=0.7, label="Fitted vs Measured") plt.plot([abs(eps_r_meas).min(), abs(eps_r_meas).max()], diff --git a/materialdatabase/processing/utils/math.py b/materialdatabase/processing/utils/math.py index 5a22f2e..7481057 100644 --- a/materialdatabase/processing/utils/math.py +++ b/materialdatabase/processing/utils/math.py @@ -1,9 +1,8 @@ """Mathmatical functions.""" import numpy as np -from typing import Any -def mre(x: float | np.ndarray, x_est: float | np.ndarray) -> Any: +def mre(x: float | np.ndarray, x_est: float | np.ndarray) -> float: """ Calculate mean relative absolute error. @@ -11,4 +10,4 @@ def mre(x: float | np.ndarray, x_est: float | np.ndarray) -> Any: :param x_est: estimated value :return: mean relative absolute error """ - return np.mean(abs((x - x_est) / x)) + return float(np.mean(abs((x - x_est) / x))) From 407966fd445e93d9744cdf7e60852003e8d12e89 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Tue, 21 Apr 2026 15:59:57 +0200 Subject: [PATCH 09/11] linting --- .../processing/complex_permeability.py | 18 ++++++++---------- .../processing/complex_permittivity.py | 4 ++-- materialdatabase/processing/data_structure.py | 2 +- 3 files changed, 11 insertions(+), 13 deletions(-) diff --git a/materialdatabase/processing/complex_permeability.py b/materialdatabase/processing/complex_permeability.py index 5b738b8..34603d8 100644 --- a/materialdatabase/processing/complex_permeability.py +++ b/materialdatabase/processing/complex_permeability.py @@ -191,12 +191,10 @@ def fit_losses(self, mu_abs = np.sqrt(fit_data["mu_real"] ** 2 + fit_data["mu_imag"] ** 2) pv = pv_mag(fit_data["f"].to_numpy(), - - (fit_data["mu_imag"].to_numpy() * mu_0), + - (fit_data["mu_imag"].to_numpy() * mu_0), # type: ignore fit_data["b"].to_numpy() / mu_abs / mu_0) popt_pv, pcov_pv = curve_fit(log_pv_fit_function, - (fit_data["f"], - fit_data["T"], - fit_data["b"]), + (fit_data["f"], fit_data["T"], fit_data["b"]), np.log(pv), maxfev=100000) self.params_pv = popt_pv @@ -392,10 +390,10 @@ def get_abs_and_phi_of_mu_grid_at_temperature(dft: pd.DataFrame) -> tuple[np.nda :param dft: permeability data at certain temperature :return: magnitude and phase as grid """ - mu_abs = np.sqrt(dft['mu_real']**2 + dft['mu_imag']**2) + mu_abs = np.sqrt(dft['mu_real'] ** 2 + dft['mu_imag'] ** 2) mu_phi = np.rad2deg(np.arctan2(dft['mu_imag'], dft['mu_real'])) - grid_abs = griddata((dft['b'], dft['f']*1e-3), mu_abs, (grid_b, grid_f_kHz), method='cubic') - grid_phi = griddata((dft['b'], dft['f']*1e-3), mu_phi, (grid_b, grid_f_kHz), method='cubic') + grid_abs = griddata((dft['b'], dft['f'] * 1e-3), mu_abs, (grid_b, grid_f_kHz), method='cubic') + grid_phi = griddata((dft['b'], dft['f'] * 1e-3), mu_phi, (grid_b, grid_f_kHz), method='cubic') return grid_abs, grid_phi # --- Interpolate all temperatures --- @@ -418,7 +416,7 @@ def get_abs_and_phi_of_mu_grid_at_temperature(dft: pd.DataFrame) -> tuple[np.nda n_cols = len(temps) cm = 1 / 2.54 fig = plt.figure(figsize=(9 * cm, 9 * cm)) - gs = gridspec.GridSpec(2, n_cols + 1, width_ratios=[1]*n_cols + [0.05], + gs = gridspec.GridSpec(2, n_cols + 1, width_ratios=[1] * n_cols + [0.05], wspace=0.15, hspace=0.15) # Create axes array @@ -428,10 +426,10 @@ def get_abs_and_phi_of_mu_grid_at_temperature(dft: pd.DataFrame) -> tuple[np.nda # --- Plot contours --- for i, T in enumerate(temps): - cont_abs = ax[0, i].contourf(grid_b*1000, grid_f_kHz, grid_mu_abs_all[i], + cont_abs = ax[0, i].contourf(grid_b * 1000, grid_f_kHz, grid_mu_abs_all[i], levels=levels_abs, cmap='plasma', vmin=abs_min, vmax=abs_max) - cont_phi = ax[1, i].contourf(grid_b*1000, grid_f_kHz, grid_mu_phi_all[i], + cont_phi = ax[1, i].contourf(grid_b * 1000, grid_f_kHz, grid_mu_phi_all[i], levels=levels_phi, cmap='plasma', vmin=phi_min, vmax=phi_max) ax[0, i].set_title(f"{int(T)} °C") diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 070fddf..237263a 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -165,11 +165,11 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float | np.ndarray, T: float # Predict conductivity real part the fitted model sigma_real_model = self.sigma_real_fit_function.get_function() - sigma_real = sigma_real_model((np.array([f]), np.array([T])), *self.params_sigma_real)[0] + sigma_real = sigma_real_model((np.array([f]), np.array([T])), *self.params_sigma_real)[0] # type: ignore # Predict conductivity imaginary part from the fitted model sigma_imag_model = self.sigma_imag_fit_function.get_function() - sigma_imag = sigma_imag_model((np.array([f]), np.array([T])), *self.params_sigma_imag)[0] + sigma_imag = sigma_imag_model((np.array([f]), np.array([T])), *self.params_sigma_imag)[0] # type: ignore # assemble complex conductivity sigma_complex = sigma_real + 1j * sigma_imag diff --git a/materialdatabase/processing/data_structure.py b/materialdatabase/processing/data_structure.py index 2d59c77..b23d172 100644 --- a/materialdatabase/processing/data_structure.py +++ b/materialdatabase/processing/data_structure.py @@ -743,7 +743,7 @@ def residuals(params, h, b_measured): params0 = [1.0, 1.0, 1.0, 0.1, 0.01] # Optimization - result = optimize.least_squares(residuals, params0, args=(h, b)) + result = optimize.least_squares(residuals, params0, args=(h, b)) # type: ignore # Optimal parameter set params_opt = result.x From ce4ef6aa03c8e364d99d1b152a23eda7a13fcdd4 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Tue, 21 Apr 2026 16:41:19 +0200 Subject: [PATCH 10/11] add warning for extrapolation --- materialdatabase/processing/complex_permittivity.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/materialdatabase/processing/complex_permittivity.py b/materialdatabase/processing/complex_permittivity.py index 237263a..4cecd6a 100644 --- a/materialdatabase/processing/complex_permittivity.py +++ b/materialdatabase/processing/complex_permittivity.py @@ -158,6 +158,12 @@ def fit_real_and_imaginary_part_at_f_and_T(self, f: float | np.ndarray, T: float :param T: Temperature in °C :return: (eps_real, -eps_imag) (negative sign convention of the imaginary part) """ + # Check for extrapolation of frequency + if np.any((f < self.measurement_data["f"].min()) | (f > self.measurement_data["f"].max())): + logger.warning(f"To fit frequency {f} extrapolation is required. \n" + f"f_min: {self.measurement_data['f'].min()}\n" + f"f_max: {self.measurement_data['f'].max()}\n") + # Auto-generate magnitude fit if missing if self.params_sigma_real is None or self.params_sigma_imag is None: logger.info("params_eps_a missing, running fit_permittivity_magnitude()...") From 14264d5b6444920653aed6865971d2c53d4327b8 Mon Sep 17 00:00:00 2001 From: Till Piepenbrock Date: Wed, 22 Apr 2026 09:42:29 +0200 Subject: [PATCH 11/11] renamed mre function --- examples/fit_complex_permittivity.py | 6 +++--- materialdatabase/processing/utils/math.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/fit_complex_permittivity.py b/examples/fit_complex_permittivity.py index 646431d..8f9d344 100644 --- a/examples/fit_complex_permittivity.py +++ b/examples/fit_complex_permittivity.py @@ -4,7 +4,7 @@ import numpy as np from matplotlib import pyplot as plt import materialdatabase as mdb -from materialdatabase.processing.utils.math import mre +from materialdatabase.processing.utils.math import mean_relative_absolute_error # Configure logging to show info level messages logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO) @@ -74,7 +74,7 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: # Parity Plots # --------------- # Amplitude - print(f"MRE (amplitude): {np.round(100 * mre(np.abs(eps_r_meas), np.abs(eps_r_fit)), decimals=2)} %") + print(f"MRE (amplitude): {np.round(100 * mean_relative_absolute_error(np.abs(eps_r_meas), np.abs(eps_r_fit)), decimals=2)} %") plt.figure(figsize=(8, 6)) plt.scatter(abs(eps_r_meas), np.abs(eps_r_fit), alpha=0.7, label="Fitted vs Measured") plt.plot([abs(eps_r_meas).min(), abs(eps_r_meas).max()], @@ -88,7 +88,7 @@ def fit_complex_permittivity_example(is_plot: bool = True) -> None: plt.tight_layout() # Loss angle - print(f"MRE (angle): {np.round(100 * mre(delta_meas, delta_fit), decimals=2)} %") + print(f"MRE (angle): {np.round(100 * mean_relative_absolute_error(delta_meas, delta_fit), decimals=2)} %") plt.figure(figsize=(8, 6)) plt.scatter(delta_meas, delta_fit, alpha=0.7, label="Fitted vs Measured") plt.plot([delta_meas.min(), delta_meas.max()], diff --git a/materialdatabase/processing/utils/math.py b/materialdatabase/processing/utils/math.py index 7481057..cb325fb 100644 --- a/materialdatabase/processing/utils/math.py +++ b/materialdatabase/processing/utils/math.py @@ -2,7 +2,7 @@ import numpy as np -def mre(x: float | np.ndarray, x_est: float | np.ndarray) -> float: +def mean_relative_absolute_error(x: float | np.ndarray, x_est: float | np.ndarray) -> float: """ Calculate mean relative absolute error.