updating figures
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__pycache__/model_and_data.cpython-39.pyc
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data/model_full-transfer_model_amplitude0.csv
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data/model_full-transfer_model_amplitude02012-07-03-ak-invivo-1.csv
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data/model_full-transfer_model_amplitude02013-01-08-aa-invivo-1.csv
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data/models_d_right.csv
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,cell,EODf,a_zero,delta_a,dend_tau,input_scaling,mem_tau,noise_strength,ref_period,deltat,tau_a,threshold,v_base,v_offset,v_zero
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0,2010-11-08-al-invivo-1,744.66,9.450855200303527,0.0604984400793618,0.0007742334994649,31.363843698084207,0.0017257848281706,7.699289148855061e-05,0.0010273077926126,5e-05,0.1022386553157565,1,0,-0.390625,0
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1,2011-10-25-aa-invivo-1,724.94,17.324840755350014,0.0591913568259293,0.0041765955988219,126.0907181735006,0.0011845579843807,0.0002904021591991,0.0006959832098553,5e-05,0.1089142677011385,1,0,-23.046875,0
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2,2011-10-25-ad-invivo-1,760.5,42.322806986231576,0.1128743560865006,0.0049804702817868,275.52394339936325,0.0023119383270376,0.0006602840501078,0.0009167762282024,5e-05,0.0641670185687361,1,0,-43.359375,0
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3,2012-04-20-ad-invivo-1,811.57,61.15542998217432,0.1809911409228079,0.0065499545871112,317.4201379309943,0.0014460502988963,0.0006154752637016,0.0010070482769896,5e-05,0.1718068006049748,1,0,-39.84375,0
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4,2012-04-20-af-invivo-1,800.03,8.666272965511395,0.0224254352605947,0.0020948092014818,90.3326105895584,0.0011619511953103,0.0002692291234707,0.0004258027499815,5e-05,0.0962203612055438,1,0,-20.21484375,0
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5,2012-04-20-ak-invivo-1,826.07,121.42077896009448,0.2988122723355702,0.0076752433311956,351.9448656755738,0.0011845244136958,0.0004816469185333,0.0012197526571388,5e-05,0.2031230589577027,1,0,10.9375,0
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6,2012-05-10-ad-invivo-1,891.62,27.619562499220827,0.1345056224178351,0.0012767974341065,142.840493368597,0.0044191056484668,0.0008633092166519,0.0005977574869045,5e-05,0.0967205016994888,1,0,-18.359375,0
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7,2012-06-27-ah-invivo-1,752.07,5.2388344597197705,0.03659696633407,0.0037989519965752,128.3173271059041,0.0016438953019065,0.0001808735038323,0.0012129185117702,5e-05,0.0792718029100008,1,0,-35.3515625,0
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8,2012-06-27-an-invivo-1,786.29,4.068827736501855,0.0430511192677837,0.0041551502152155,57.95001821363863,0.0015554768512448,2.764259961706088e-05,0.0009668453379819,5e-05,0.0913176474972983,1,0,-13.671875,0
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9,2012-07-03-ak-invivo-1,928.45,1.8853753804955329,0.0125302977789627,0.0010172326122311,11.282392495019522,0.0002496247649189,4.646383539997057e-06,2.781559315405718e-05,5e-05,0.0216226842883503,1,0,-1.904296875,0
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10,2012-07-12-ag-invivo-1,744.95,16.239434635393348,0.1513550832994938,0.0037504710528958,50.2874125565955,0.0001730827178691,5.723487349821114e-05,0.0003381486858513,5e-05,0.0734227017767456,1,0,-1.5625,0
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11,2012-07-12-ap-invivo-1,772.92,53.79680098764822,0.3098624628785055,0.006491044613537,274.7676513500772,0.0011314183472882,0.0003901229594757,0.0013282603439871,5e-05,0.0991670999413088,1,0,-32.8125,0
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12,2012-12-13-af-invivo-1,673.56,7.535514342013602,0.0422372510373371,0.0065539636971129,204.3615261447709,0.0018080213635166,0.0001348017868766,0.0012332624623859,5e-05,0.0583762732248613,1,0,-57.03125,0
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13,2012-12-13-ag-invivo-1,667.87,7.061602153402829,0.0546674984677376,0.0041487403958944,102.45822047850346,0.001412243047861,3.526703241249884e-05,0.000162275520082,5e-05,0.0393528925991773,1,0,-25.0,0
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14,2012-12-13-ah-invivo-1,664.7,6.823985334369345,0.0368958234780879,0.0066161666396143,102.86922103180392,0.0010940273699727,3.810537599772437e-05,0.0012246243965823,5e-05,0.0779443868884807,1,0,-25.5859375,0
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15,2012-12-13-an-invivo-1,657.91,4.458558193012667,0.0305497815969178,0.0013786872283781,35.834438055915825,0.0029913612464463,1.7125985827976543e-05,0.0012567714114379,5e-05,0.0248706231740213,1,0,-6.25,0
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16,2012-12-13-ao-invivo-1,657.82,5.435481358638207,0.0374623330424542,0.001970018378635,49.69904954465432,0.002328069209402,9.134402806092888e-05,0.0015081600329058,5e-05,0.0331211905447775,1,0,-9.765625,0
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17,2012-12-20-aa-invivo-1,668.32,4.195725270757472,0.0349469498459069,0.0049449391407958,61.926544628777336,0.0017742557744246,2.0461330360757243e-05,0.0013298764071622,5e-05,0.0625906100055979,1,0,-14.84375,0
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18,2012-12-20-ab-invivo-1,738.71,109.33434336088948,0.278266740784194,0.0060093620164311,246.87907584805632,0.0011102966287199,0.0003214275666699,0.0014015863938401,5e-05,0.0879400403500039,1,0,29.6875,0
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19,2012-12-20-ac-invivo-1,744.95,5.980561640857971,0.0266121874032581,0.0027843960684635,54.35498975113278,0.0016410099311694,3.736520494044416e-05,0.0008493183014978,5e-05,0.0464782969356258,1,0,-10.9375,0
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20,2012-12-20-ad-invivo-1,759.82,47.8899415032243,0.1572111340156579,0.004412827130915,294.3089535429343,0.0019082595931216,0.0005454793744743,0.0003902027099212,5e-05,0.0657687561129787,1,0,-45.3125,0
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21,2012-12-20-ae-invivo-1,763.79,50.2191242194429,0.1230203924349023,0.0056716684601917,286.1135698773513,0.0019726034024348,0.0005025289084042,0.0004045686352769,5e-05,0.0804183995591541,1,0,-40.625,0
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22,2012-12-21-ai-invivo-1,787.12,19.552681407982867,0.0636151659491776,0.0046658522913592,178.85419634079997,0.0019037662037406,0.0002308125575111,0.0004834553836243,5e-05,0.0939907169906999,1,0,-37.5,0
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23,2012-12-21-ak-invivo-1,796.83,2.4867707329905,0.0141270344770176,0.0028616283324329,31.510428742268093,0.0003974311599786,5.775335040006332e-06,0.0006089766381869,5e-05,0.0135933352352287,1,0,-7.71484375,0
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24,2012-12-21-am-invivo-1,806.15,2.599996979076464,0.0210131719161386,0.0072703458079583,46.67063036950735,0.0007837211245351,1.411589095846406e-05,0.0011669771041571,5e-05,0.043353864209255,1,0,-11.328125,0
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25,2012-12-21-an-invivo-1,812.7,2.657969122279684,0.0126980011518428,0.0029290565793311,30.47330327087832,0.0006812296678529,2.2698725628118127e-05,0.0010815182714474,5e-05,0.0298009543582118,1,0,-6.54296875,0
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26,2013-01-08-aa-invivo-1,800.63,1.7775663328189109,0.0139724469616012,0.0003756860850739,8.567002390828673,0.0011008277277909,7.296503188131748e-05,0.0001635438226173,5e-05,0.013192502003356,1,0,-0.5859375,0
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27,2013-01-08-ab-invivo-1,800.25,54.67466209697357,0.2380256573695303,0.003147952593016,401.4746609830789,0.0032334816919704,0.0009482203053873,0.0004075044135951,5e-05,0.1060140977474878,1,0,-75.78125,0
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28,2013-02-21-ae-invivo-1,665.93,4.294576393156899,0.0349079955577279,0.0042335826224234,193.03334826738376,0.0028604383906985,0.0003229739386601,0.0012770259072818,5e-05,0.0707596447935943,1,0,-56.8359375,0
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29,2013-02-21-ag-invivo-1,658.7,12.638812661345776,0.1016749009225033,0.0035367702668509,104.74966687228977,0.0016256371684299,9.994911396704052e-05,0.0012702670089049,5e-05,0.078155035148488,1,0,-20.3125,0
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30,2013-04-10-aa-invivo-1,623.77,7.2029389446183565,0.0526419078304854,0.0022248980461019,62.65798249994464,0.0028732093372693,0.000164159124109,0.0015625668737142,5e-05,0.0635449101638369,1,0,-12.5,0
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31,2013-04-10-ac-invivo-1,684.72,2.8892039094434803,0.042548818572458,0.0016869020510687,58.09049670735142,0.0023708232278567,0.0003152371416419,0.0010783506078415,5e-05,0.0348236653456585,1,0,-15.91796875,0
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32,2013-04-17-ab-invivo-1,601.09,42.53156496468646,0.1178301462037476,0.0057660381404226,295.0137656954471,0.0021325123481165,0.0008618261070858,0.0005865905086141,5e-05,0.078251177677974,1,0,-51.5625,0
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33,2013-04-17-ac-invivo-1,597.94,6.731685804526344,0.0880636102661295,0.0050639791128441,26.766210174285348,0.0002329228437903,1.6714951095492286e-05,0.0016821794608443,5e-05,0.1005180087588643,1,0,-1.953125,0
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34,2014-01-10-ab-invivo-1,724.72,37.22921835919794,0.105865032837993,0.0077800750459636,382.4713961027781,0.0013793118050116,0.0011037648201998,0.0006409865558835,5e-05,0.0785213908371962,1,0,-86.328125,0
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35,2014-01-10-ac-invivo-1,708.44,74.22028683769639,0.2062867838543796,0.0086275878935938,522.8738003484584,0.0019099084777949,0.0010896710668221,0.0007561837419108,5e-05,0.1387768914538001,1,0,-92.96875,0
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36,2014-01-10-ae-invivo-1,670.21,14.403635240773651,0.0927322308690146,0.0051211977513466,205.9862062669372,0.0025386240396386,0.0006095586872081,0.0010368267806298,5e-05,0.0994530945069613,1,0,-51.171875,0
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37,2014-01-16-ak-invivo-1,803.26,2.3120229511581782,0.0141654897826262,0.007112977431183,24.30632120240236,0.000285109630943,4.089968833381081e-06,0.0011963117586515,5e-05,0.06057228972437,1,0,-4.98046875,0
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38,2014-01-23-ab-invivo-1,775.18,9.42831561175426,0.0224514357612552,0.0045903654403364,128.9209928230727,0.0007476859066578,0.0001912651734499,0.0004929147142214,5e-05,0.0461635400578526,1,0,-31.0546875,0
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39,2014-03-19-ad-invivo-1,681.98,69.19466420583639,0.2893760409210132,0.0054364329221975,254.4826491240454,0.0007254371985574,0.0006771079934558,0.0014615164586903,5e-05,0.1345471235735028,1,0,-13.28125,0
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40,2014-03-19-ae-invivo-1,658.18,38.918760336221055,0.2480858206656654,0.0069955407767959,411.2324502457813,0.0027246186835932,0.0007951136822738,0.0007615925807794,5e-05,0.1169994719346085,1,0,-92.1875,0
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41,2014-03-19-ah-invivo-1,635.84,56.35167256285228,0.1974715379528175,0.0081228403287698,490.6872518263162,0.0025561010713697,0.0009226463077239,0.0006702491128007,5e-05,0.1445226828597265,1,0,-100.78125,0
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42,2014-03-19-ai-invivo-1,653.3,88.77458424718674,0.2913937065789225,0.008559140638147,523.4583656536372,0.0020914527733905,0.0014209210970579,0.00066567652942,5e-05,0.1121355388450789,1,0,-78.125,0
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43,2014-03-19-aj-invivo-1,669.86,32.547605273546935,0.2456940849344553,0.0063333108683462,420.54333434151465,0.0029116982298775,0.0013842790319836,0.0009552598932444,5e-05,0.0877717308089366,1,0,-101.5625,0
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44,2014-03-25-aa-invivo-1,870.98,21.701016139817888,0.0960172639965266,0.0061302790825722,245.39704801461176,0.0013015964681189,0.0002991440776181,0.0007199971612111,5e-05,0.0733662951162392,1,0,-58.203125,0
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45,2014-03-25-af-invivo-1,837.7,37.66383751511725,0.1928208471155374,0.0055587552838484,319.58811665224687,0.0012758102274061,0.0003895600960509,0.0007605506254404,5e-05,0.0572208929359239,1,0,-65.625,0
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46,2014-06-06-ac-invivo-1,800.02,82.92925558245138,0.2497281199063106,0.0058805791567524,401.0507428419596,0.00276420009562,0.0014107594831631,0.0005758934406361,5e-05,0.1571647789884099,1,0,-43.75,0
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47,2014-06-06-ag-invivo-1,800.12,63.10829705951355,0.5313050862748335,0.0145518013760081,617.8190914088805,0.0023091597305128,0.0036613353060679,0.0009675638720757,5e-05,0.157597675961733,1,0,-134.375,0
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48,2014-12-03-ai-invivo-1,858.5,34.062575827557275,0.3244570611395585,0.0093071756342572,461.2788968601441,0.0045649933974013,0.0029183532103229,0.0006210470513751,5e-05,0.1037876538806647,1,0,-112.5,0
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49,2014-12-11-aa-invivo-1,651.29,29.028405021923344,0.3825449567612229,0.0071781591428417,259.88530639963346,0.002849966390513,0.0014916969045105,0.0010340774235971,5e-05,0.1741002240578827,1,0,-54.6875,0
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50,2014-12-11-ad-invivo-1,650.07,6.884193252440972,0.125103799166033,0.005008289539649,111.76045965688944,0.001884129168681,0.0003939483088535,0.0014575051585428,5e-05,0.1131104818008776,1,0,-28.90625,0
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51,2015-01-15-ab-invivo-1,708.7,45.11066366134594,0.3830355936726118,0.0142573616079807,437.9661974380556,0.0014288922816903,0.001889557163285,0.0009721128113178,5e-05,0.1302311388392199,1,0,-96.875,0
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52,2015-01-20-ab-invivo-1,747.35,2.388006758171008,0.0460455856570295,0.0058274300652638,125.51425676781496,0.0026078497201184,0.0005355608495797,0.0013648958604759,5e-05,0.072682482481134,1,0,-36.9140625,0
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53,2015-01-20-ac-invivo-1,749.16,3.0637076562911862,0.0361482523844888,0.0038837828744527,40.161166949813975,0.0022024851776428,0.0001775828935248,0.0010111334953581,5e-05,0.0912778908591642,1,0,-8.984375,0
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54,2015-01-20-ae-invivo-1,775.45,10.29173021176557,0.1265554462422453,0.0072071781109798,356.19007281857216,0.0021576432183898,0.0007082894532779,0.0010720005959728,5e-05,0.1036278314600582,1,0,-102.734375,0
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55,2015-01-20-af-invivo-1,778.15,17.378611201243178,0.2373397050748237,0.006486977712037,233.2942526663853,0.0040916872369868,0.0018519688539093,0.0009390077156995,5e-05,0.0987058568785954,1,0,-57.8125,0
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56,2015-01-20-ag-invivo-1,778.5,18.07439316562912,0.2669718966923193,0.0059345812215258,228.01386728096296,0.0017130481512641,0.0004828089842224,0.0007940405766277,5e-05,0.173402795420842,1,0,-55.46875,0
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57,2017-07-18-ah-invivo-1,816.18,4.492478880802049,0.0306800492065723,0.0005510176207879,11.010435284946723,0.0012660814616981,6.939326220065633e-05,0.0001048071222688,5e-05,0.0534712893535971,1,0,1.171875,0
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58,2017-07-18-ai-invivo-1,817.53,4.836173387076376,0.0459604089024203,0.0005713395854796,19.082872790172893,0.0017648069889998,0.0002954943164986,0.0003799242606729,5e-05,0.0219438187457692,1,0,-2.5390625,0
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59,2017-07-18-aj-invivo-1,818.98,62.59548468438461,0.2668879734232494,0.0063976464842525,344.9549845381489,0.0006269389203256,0.000542355661302,0.0004845893795398,5e-05,0.0884760312971245,1,0,-48.4375,0
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60,2018-01-10-al,822.43,6.53045784849496,0.0477523266938062,0.0026516323266361,20.951337676489,0.000384722118084,5.630808827711224e-05,0.0004011848987451,5e-05,0.0468008096291354,1,0,-0.09765625,0
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61,2018-01-12-af,646.31,27.48409535758441,0.0822784189321632,0.0027716752344402,72.69113348986389,0.0013786805385745,0.0001421670943929,0.0010001738040759,5e-05,0.0608936121352207,1,0,5.46875,0
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62,2018-05-08-aa-invivo-1,643.65,36.66788911232605,0.2761728222642968,0.0083357504509621,330.2402625207435,0.0021657539780422,0.0018768276776544,0.0010942851413418,5e-05,0.2116606564116055,1,0,-68.75,0
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63,2018-05-08-ab-invivo-1,646.62,66.70525377272179,0.6014654520860893,0.005682795376896,328.0465980625613,0.001823255075318,0.0009180849887618,0.0008614237691425,5e-05,0.1472296039461372,1,0,-37.5,0
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64,2018-05-08-ac-invivo-1,655.16,35.32000871480389,0.3365254255760804,0.0067732193070078,139.23166556708537,0.0004350491142382,0.000401489691558,0.0013549977866199,5e-05,0.1531910034411788,1,0,-12.5,0
|
||||
65,2018-05-08-ad-invivo-1,655.66,44.994381556842654,0.1966955414103147,0.0024283512437206,141.095538216996,0.0029968871016672,0.0004073919444425,0.0008373923698767,5e-05,0.0998422947981054,1,0,-0.390625,0
|
||||
66,2018-05-08-ae-invivo-1,649.48,29.440282257876536,0.2018789493993832,0.003910493647611,170.6160808403345,0.0021895423364158,0.0002798942878887,0.0012411447920718,5e-05,0.130171888270862,1,0,-25.78125,0
|
||||
67,2018-05-08-af-invivo-1,649.93,41.849999531343975,0.1321076268905854,0.0052475491397625,228.20359643155803,0.0014814184541785,0.000495030241084,0.0007194589371705,5e-05,0.0617800897722998,1,0,-31.25,0
|
||||
68,2018-05-08-ai-invivo-1,653.62,10.138859439469911,0.0712064900990132,0.0012599754975274,40.34961621751716,0.0027325424460168,0.0002197690747209,0.0012116953449501,5e-05,0.054392058102494,1,0,-2.34375,0
|
||||
69,2018-06-25-ad-invivo-1,840.79,53.73684452063702,0.2072325783629882,0.0041833923107559,320.2100292094566,0.0028666513530079,0.0007840761526882,0.0007822294882607,5e-05,0.0841289508219278,1,0,-48.4375,0
|
||||
70,2018-06-26-ah-invivo-1,778.22,6.703026855077471,0.0341144423335754,0.0008151238083306,16.681124499669373,0.0013215872640159,0.0001692839665525,0.0007865435959385,5e-05,0.205762993728832,1,0,1.7578125,0
|
||||
71,2020-08-12-aa-invivo-1,746.68,44.46101827995927,0.106082537177636,0.0044832342587509,235.04483921079955,0.002487994738557,0.0005967131873841,0.0008508121416945,5e-05,0.1174296496534284,1,0,-29.296875,0
|
||||
|
2001
data/models_starting_vals_transient_100_len_100small.csv
Normal file
2001
data/models_starting_vals_transient_100_len_100small.csv
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|
||||
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|
||||
|
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|
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|
||||
|
@ -0,0 +1 @@
|
||||
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|
||||
,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,112.0,113.0,114.0,115.0,116.0,117.0,118.0,119.0,120.0,121.0,122.0,123.0,124.0,125.0,126.0,127.0,128.0,129.0,130.0,131.0,132.0,133.0,134.0,135.0,136.0,137.0,138.0,139.0,140.0,141.0,142.0,143.0,144.0,145.0,146.0,147.0,148.0,149.0,150.0,151.0,152.0,153.0,154.0,155.0,156.0,157.0,158.0,159.0,160.0,161.0,162.0,163.0,164.0,165.0,166.0,167.0,168.0,169.0,170.0,171.0,172.0,173.0,174.0,175.0,176.0,177.0,178.0,179.0,180.0,181.0,182.0,183.0,184.0,185.0,186.0,187.0,188.0,189.0,190.0,191.0,192.0,193.0,194.0,195.0,196.0,197.0,198.0,199.0,200.0,201.0,202.0,203.0,204.0,205.0,206.0,207.0,208.0,209.0,210.0,211.0,212.0,213.0,214.0,215.0,216.0,217.0,218.0,219.0,220.0,221.0,222.0,223.0,224.0,225.0,226.0,227.0,228.0,229.0,230.0,231.0,232.0,233.0,234.0,235.0,236.0,237.0,238.0,239.0,240.0,241.0,242.0,243.0,244.0,245.0,246.0,247.0,248.0,249.0,250.0,251.0,252.0,253.0,254.0,255.0,256.0,257.0,258.0,259.0,260.0,261.0,262.0,263.0,264.0,265.0,266.0,267.0,268.0,269.0,270.0,271.0,272.0,273.0,274.0,275.0,276.0,277.0,278.0,279.0,280.0,281.0,282.0,283.0,284.0,285.0,286.0,287.0,288.0,289.0,290.0,291.0,292.0,293.0,294.0,295.0,296.0,297.0,298.0,299.0,isf_psd,osf_psd,io_cross,d_isf_all,d_osf_all,var_RAM,trial_nr,counter_validate,cell,file_name,eod_fr,cv,fr,fr_stim,cv_stim,ser_stim,ser_first_stim,ser_sum_stim,fr_stim_mean,cv_stim_mean,ser_stim_mean,ser_first_stim_mean,ser_sum_stim_mean
|
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@ -6,21 +6,23 @@ import pandas as pd
|
||||
from IPython import embed
|
||||
from matplotlib import gridspec, pyplot as plt
|
||||
|
||||
from threefish.plot_subplots import plt_model_flowcharts
|
||||
from threefish.values import ypos_x_modelanddata
|
||||
|
||||
try:
|
||||
from plotstyle import plot_style, spines_params
|
||||
except:
|
||||
print('plotstyle not installed')
|
||||
from threefish.RAM.plot_subplots import plot_lowpass2, plt_data_susept, plt_single_square_modl, plt_time_arrays
|
||||
from threefish.RAM.plot_subplots import plt_data_susept, plt_single_square_modl
|
||||
from threefish.RAM.values import overlap_cells, perc_model_full
|
||||
from threefish.load import resave_small_files, save_visualization
|
||||
from threefish.RAM.reformat_flowchart import get_flowchart_params
|
||||
from threefish.RAM.reformat_matrix import load_model_susept
|
||||
from threefish.core import find_folder_name
|
||||
from threefish.RAM.plot_labels import label_noise_name, nonlin_title, remove_xticks, remove_yticks, set_xlabel_arrow, \
|
||||
set_ylabel_arrow, title_find_cell_add, xlabel_xpos_y_modelanddata
|
||||
import itertools as it
|
||||
from threefish.defaults import default_figsize, default_settings
|
||||
from threefish.plot.limits import join_x, join_y, set_clim_same, set_same_ylim
|
||||
from threefish.plot.limits import set_clim_same, set_same_ylim
|
||||
|
||||
|
||||
#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
|
||||
@ -99,7 +101,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
##################################
|
||||
# model part
|
||||
|
||||
trial_nr = 500000
|
||||
trial_nr = 100000
|
||||
cell = '2013-01-08-aa-invivo-1'
|
||||
cell = '2012-07-03-ak-invivo-1'
|
||||
|
||||
@ -120,16 +122,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
|
||||
]
|
||||
|
||||
save_names = [
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
] #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
|
||||
|
||||
##########
|
||||
# Erklärung
|
||||
@ -143,6 +135,65 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
|
||||
# oben habe ich einen bias factor weil die Zelle zu sensitiv gefittet ist, also passe ich das an dass die den
|
||||
# gleichen CV und feurrate hat, wie die Zelle in der Stimulation, deswegen ist dieser Bias faktor nur oben!
|
||||
#
|
||||
#bias_factors = [1, 1, 1, 1] # 0.3
|
||||
bias_factors = [0.36, 0.36, 1, 1] # 0.36
|
||||
|
||||
|
||||
#new
|
||||
save_names = [
|
||||
'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
|
||||
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
]#'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
save_names = [
|
||||
'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
|
||||
'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_100000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
|
||||
]
|
||||
|
||||
|
||||
|
||||
save_names = [
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
] #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
|
||||
save_names = [
|
||||
'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
]
|
||||
|
||||
save_names = [
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
] #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
|
||||
bias_factors = [0.36, 0.36, 1, 1]#0.36
|
||||
save_names = [
|
||||
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
@ -155,6 +206,11 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
|
||||
]
|
||||
|
||||
#
|
||||
|
||||
# 'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
#trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
@ -206,7 +262,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
fr_print=fr_print, nr=nr)
|
||||
|
||||
# if s in [1,3,5]:
|
||||
|
||||
#embed()
|
||||
ims.append(im)
|
||||
mats.append(stack_plot)
|
||||
maxs.append(np.max(np.array(stack_plot)))
|
||||
@ -232,7 +288,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
if len(cells) > 1:
|
||||
a += 1
|
||||
|
||||
set_clim_same(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())
|
||||
set_clim_same(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())#
|
||||
|
||||
#################################################
|
||||
# Flowcharts
|
||||
@ -265,161 +321,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
save_visualization(pdf=True)
|
||||
|
||||
|
||||
def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length):
|
||||
var_types = ['', 'additiv_cv_adapt_factor_scaled'] # 'additiv_cv_adapt_factor_scaled',
|
||||
##additiv_cv_adapt_factor_scaled
|
||||
a_fes = [c / 100, 0] # , 0.009
|
||||
eod_fe = [750, 750] # , 750
|
||||
ylim = [-0.5, 0.5]
|
||||
c_sigs = [0, 0.9] # , 0.9
|
||||
grid_left = [[], grid[1, 0]] # , grid[2, 0]
|
||||
ax_ams = []
|
||||
for g, grid_here in enumerate([grid[0, 2], grid[1, 2]]): # , grid[2, 0]
|
||||
grid_lowpass = gridspec.GridSpecFromSubplotSpec(4, 1,
|
||||
subplot_spec=grid_here, hspace=0.2,
|
||||
height_ratios=[1, 1, 1, 0.1])
|
||||
|
||||
models = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
|
||||
model_params = models[models['cell'] == '2012-07-03-ak-invivo-1'].iloc[0]
|
||||
cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
|
||||
eod_fr = model_params['EODf'] # .iloc[0]
|
||||
deltat = model_params.pop("deltat") # .iloc[0]
|
||||
v_offset = model_params.pop("v_offset") # .iloc[0]
|
||||
eod_fr = stack.eod_fr.iloc[0]
|
||||
|
||||
noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params(
|
||||
a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
|
||||
eod_fe=eod_fe)
|
||||
|
||||
if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
|
||||
grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1,
|
||||
subplot_spec=grid_here, height_ratios=[1, 1, 1, 0.1],
|
||||
hspace=0.05)
|
||||
|
||||
# if (np.unique(frame.RAM_afe) != 0):grid_left[g]
|
||||
|
||||
ax_external = plt_time_arrays('red', grid_lowpass2, 1, frame.RAM_afe * 100, time=time, nr=0)
|
||||
# if (np.unique(frame.RAM_noise) != 0):
|
||||
remove_xticks(ax_external)
|
||||
ax_intrinsic = plt_time_arrays('purple', grid_lowpass2, 1, frame.RAM_noise * 100, time=time, nr=1)
|
||||
ax_intrinsic.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=ax_intrinsic.transAxes)
|
||||
ax_intrinsic.show_spines('l')
|
||||
ax_external.show_spines('l')
|
||||
# ax_ams.append(axt_p2)
|
||||
# color_timeseries = 'black'
|
||||
# axt_p2.set_xlabel('Time [ms]')
|
||||
# axt_p2.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=axt_p2.transAxes)
|
||||
# ax_ams.append(axt_p2)
|
||||
vers = 'all'
|
||||
elif len(np.unique(frame.RAM_afe)) > 1:
|
||||
color_timeseries = 'red'
|
||||
nr_plot = 0
|
||||
#print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
try:
|
||||
ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
|
||||
(frame.RAM_afe + frame.RAM_noise) * 100,
|
||||
deltat, eod_fr,
|
||||
color1=color_timeseries, lw=1, extract=False)
|
||||
except:
|
||||
print('add up thing')
|
||||
embed()
|
||||
ax_external.show_spines('l')
|
||||
|
||||
ax_intrinsic = plt.subplot(grid_lowpass[1])
|
||||
ax_intrinsic.show_spines('l')
|
||||
ax_intrinsic.axhline(0, color='black', lw=0.5)
|
||||
ax_intrinsic.axhline(0, color='purple', lw=0.5)
|
||||
|
||||
remove_xticks(ax_external)
|
||||
remove_xticks(ax_intrinsic)
|
||||
join_x([ax_intrinsic, ax_external])
|
||||
join_y([ax_intrinsic, ax_external])
|
||||
vers = 'first'
|
||||
elif len(np.unique(frame.RAM_noise)) > 1:
|
||||
color_timeseries = 'purple'
|
||||
nr_plot = 1
|
||||
#print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
try:
|
||||
ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
|
||||
(frame.RAM_afe + frame.RAM_noise) * 100,
|
||||
deltat, eod_fr,
|
||||
color1=color_timeseries, lw=1, extract=False)
|
||||
except:
|
||||
print('add up thing')
|
||||
|
||||
embed()
|
||||
|
||||
ax_external = plt.subplot(grid_lowpass[0])
|
||||
ax_external.show_spines('l')
|
||||
ax_intrinsic.show_spines('l')
|
||||
|
||||
ax_external.axhline(0, color='black', lw=0.5)
|
||||
ax_external.axhline(0, color='red', lw=0.5)
|
||||
join_x([ax_intrinsic, ax_external])
|
||||
join_y([ax_intrinsic, ax_external])
|
||||
vers = 'second'
|
||||
ax_intrinsic.set_yticks_delta(6)
|
||||
ax_external.set_yticks_delta(6)
|
||||
ax_external.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_external.transAxes)
|
||||
ax_intrinsic.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_intrinsic.transAxes)
|
||||
remove_xticks(ax_intrinsic)
|
||||
# if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
|
||||
ax_external.set_xlabel('')
|
||||
# remove_yticks(ax)
|
||||
|
||||
ax_ams.append(ax_external)
|
||||
remove_xticks(ax_external)
|
||||
|
||||
ax_n, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[2]], time, noise_final_c, deltat, eod_fr,
|
||||
extract=False, color1='grey', lw=1)
|
||||
remove_yticks(ax_n)
|
||||
if g == 1:
|
||||
# ax_n.set_xlabel('Time [ms]', labelpad=-0.5)
|
||||
ax_n.text(0.5, xlabel_xpos_y_modelanddata() * 3, 'Time [ms]', transform=ax_n.transAxes, ha='center')
|
||||
else:
|
||||
remove_xticks(ax_n)
|
||||
ax_n.set_ylim(ylim)
|
||||
|
||||
if vers == 'first':
|
||||
ax_external.text(1, 1, 'RAM(' + cs[0] + ')', ha='right', color='red', transform=ax_external.transAxes)
|
||||
ax_n.text(start_pos_modeldata(), 1.1, noise_component_name(), ha='right', color='gray',
|
||||
transform=ax_n.transAxes)
|
||||
elif vers == 'second':
|
||||
ax_external.text(1, 1, 'RAM(' + cs[1] + ')', ha='right', color='red', transform=ax_external.transAxes)
|
||||
ax_intrinsic.text(start_pos_modeldata(), 1.1, signal_component_name(), ha='right', color='purple',
|
||||
transform=ax_intrinsic.transAxes)
|
||||
ax_n.text(start_pos_modeldata(), 0.9, noise_component_name(), ha='right', color='gray',
|
||||
transform=ax_n.transAxes)
|
||||
else:
|
||||
ax_n.text(start_pos_modeldata(), 0.9, noise_component_name(), ha='right', color='gray',
|
||||
transform=ax_n.transAxes)
|
||||
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
|
||||
ax_intrinsic.text(start_pos_modeldata(), 1.1, signal_component_name(), ha='right', color='purple',
|
||||
transform=ax_intrinsic.transAxes)
|
||||
ax_external.tick_params(axis='y', which='major', labelsize=8.4)
|
||||
ax_intrinsic.tick_params(axis='y', which='major', labelsize=8.4)
|
||||
ax_n.tick_params(axis='y', which='major', labelsize=8.4)
|
||||
# xtick_labelsize =
|
||||
# embed()
|
||||
return ax_ams, ax_external
|
||||
|
||||
|
||||
def start_pos_modeldata():
|
||||
return 1.03
|
||||
|
||||
|
||||
def signal_component_name():
|
||||
return r'$s_\xi(t)$' #r'$\xi_{signal}$'#signal noise'
|
||||
|
||||
|
||||
def noise_component_name(): #$\xi_{noise}$noise_name =
|
||||
return 'Intrinsic noise' ##r'$\xi_{noise}$'#'Noise component'#'intrinsic noise'
|
||||
|
||||
|
||||
def ypos_x_modelanddata():
|
||||
return -0.45
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
|
||||
|
||||
BIN
model_full.pdf
BIN
model_full.pdf
Binary file not shown.
BIN
model_full.png
BIN
model_full.png
Binary file not shown.
|
Before Width: | Height: | Size: 2.3 KiB After Width: | Height: | Size: 178 KiB |
@ -2,6 +2,7 @@
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
from IPython import embed
|
||||
from matplotlib import gridspec, pyplot as plt
|
||||
|
||||
from threefish.RAM.calc_fft import create_full_matrix2
|
||||
@ -11,7 +12,7 @@ from threefish.RAM.reformat import check_var_substract_method, chose_certain_gro
|
||||
get_transfer_from_model, load_cells_three, predefine_grouping_frame, restrict_cell_type, save_arrays_susept
|
||||
from threefish.RAM.save import get_transfer_for_model_full
|
||||
from threefish.RAM.reformat_matrix import convert_csv_str_to_float, load_stack_data_susept
|
||||
from threefish.RAM.plot_subplots import adjust_factor_outside_nonlin, colors_suscept_paper_dots, letters_for_full_model, \
|
||||
from threefish.RAM.plot_subplots import colors_suscept_paper_dots, letters_for_full_model, \
|
||||
plt_model_big, \
|
||||
plt_model_full_model2, \
|
||||
plt_model_letters, \
|
||||
@ -28,6 +29,7 @@ from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff,
|
||||
label_two_deltaf2, \
|
||||
xlabel_transfer_hz
|
||||
from threefish.plot.limits import set_clim_same
|
||||
from threefish.values import values_nfft_full_model, values_stimuluslength_model_full
|
||||
|
||||
|
||||
#from utils_test import test_spikes_clusters
|
||||
@ -35,16 +37,16 @@ from threefish.plot.limits import set_clim_same
|
||||
|
||||
def model_full():
|
||||
|
||||
plot_style()
|
||||
default_figsize(column=2, length=3.5)#2.7
|
||||
grid = gridspec.GridSpec(2, 2, wspace=0.55, bottom = 0.15, height_ratios = [2, 5], width_ratios = [1.2, 1], hspace=0.7, top=0.92, left=0.06, right=0.98)#hspace=0.25,
|
||||
|
||||
|
||||
plot_style()
|
||||
default_figsize(column=2, length=3.5)#2.7
|
||||
grid = gridspec.GridSpec(2, 2, wspace=0.55, bottom = 0.15, height_ratios = [2, 5], width_ratios = [1.2, 1], hspace=0.7, top=0.88, left=0.06, right=0.98)#hspace=0.25,top=0.92
|
||||
|
||||
|
||||
axes = []
|
||||
a_size = 0.0095#0.0065#0.0065und 0.0005 für das lineare das ist vielleicht für das nichtlienare 0.0065#0.0085#0.01#0.0085#125 # 0.0025# 0.01 0.005 und davor 0.025, funktioniert gut 0.0085
|
||||
adjust_factor_outside = 3#adjust_factor_outside_nonlin()
|
||||
a_size = 0.02#6#0.0095#0.01#15#0.0095#0.0065#0.0065und 0.0005 für das lineare das ist vielleicht für das nichtlienare 0.0065#0.0085#0.01#0.0085#125 # 0.0025# 0.01 0.005 und davor 0.025, funktioniert gut 0.0085
|
||||
adjust_factor_outside = 1#adjust_factor_outside_nonlin()
|
||||
|
||||
|
||||
|
||||
@ -57,17 +59,25 @@ def model_full():
|
||||
axes.append(axm)
|
||||
|
||||
cell = '2012-07-03-ak-invivo-1'
|
||||
|
||||
cell = "2013-01-08-aa-invivo-1"
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_4000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV'
|
||||
tr = 500000
|
||||
save_name_given = 'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_'
|
||||
save_name_given = 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_1000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV'
|
||||
#save_name_given = 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_4000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV'
|
||||
perc, im, stack_final, stack_saved = plt_model_big(axm, ls = ls, pos_rel=-0.12, lw = 0.75, cell = cell, lines = True,
|
||||
save_name_given = 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_4000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV')
|
||||
save_name_given = save_name_given)
|
||||
#embed()
|
||||
fr_waves = 139
|
||||
color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
|
||||
DF1_frmult, DF2_frmult = vals_model_full(val=0.30833333333333335)
|
||||
|
||||
|
||||
models_name = "models_big_fit_d_right.csv"#_big_fit
|
||||
#############################################
|
||||
# plot coherence
|
||||
#embed()
|
||||
do_transfer = False
|
||||
if do_transfer:
|
||||
loaded_from_calc_RAM_model = False
|
||||
if loaded_from_calc_RAM_model:
|
||||
cross = get_transfer_from_model(stack_saved)
|
||||
@ -83,16 +93,16 @@ def model_full():
|
||||
# frequently used value
|
||||
# so harmonic is the right one since p-change and wave is some relict so take just harmonic
|
||||
|
||||
models = resave_small_files("models_big_fit_d_right.csv",
|
||||
models = resave_small_files(models_name,
|
||||
load_folder='calc_model_core')
|
||||
flowchart_cell = '2012-07-03-ak-invivo-1'
|
||||
#flowchart_cell = '2012-07-03-ak-invivo-1'
|
||||
|
||||
model_params = models[models['cell'] == flowchart_cell].iloc[0]
|
||||
print('cell=' + str(flowchart_cell))
|
||||
model_params = models[models['cell'] == flowchart_cell].iloc[0]
|
||||
print('cell=' + str(flowchart_cell))
|
||||
model_params = models[models['cell'] == cell].iloc[0]
|
||||
print('cell=' + str(cell))
|
||||
model_params = models[models['cell'] == cell].iloc[0]
|
||||
print('cell=' + str(cell))
|
||||
# amp_frame = pd.read_csv('peak_amplitudes_power.csv')
|
||||
cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
|
||||
model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
|
||||
eod_fr = model_params['EODf'] # .iloc[0]
|
||||
deltat = model_params.pop("deltat") # .iloc[0]
|
||||
v_offset = model_params.pop("v_offset") # .iloc[0]
|
||||
@ -114,26 +124,32 @@ def model_full():
|
||||
stimulus_length,
|
||||
trials_nr,
|
||||
v_offset,
|
||||
c_sig = 0.9,
|
||||
c_sig = 0.9,redo =False,
|
||||
var_type='additiv_cv_adapt_factor_scaled')
|
||||
|
||||
axc = plt.subplot(grid[0, 1])
|
||||
f_same = frame.index
|
||||
#embed()
|
||||
try:
|
||||
transfer = frame['0']
|
||||
except:
|
||||
transfer = frame[0]
|
||||
axc.plot(f_same[f_same< xlim[-1]], np.abs(transfer[f_same< xlim[-1]])**power, color = 'black', zorder = 100)
|
||||
|
||||
axc.set_xlim(xlim)
|
||||
ylim_here = axc.get_ylim()
|
||||
#if c == 0:
|
||||
axc.set_ylabel(r'$|\chi_1|^2$')
|
||||
|
||||
else:
|
||||
f_same = False
|
||||
transfer = []
|
||||
|
||||
|
||||
#################
|
||||
# power spectra data
|
||||
log = 'log'#log'log'#'log'#'log'#'log'#'log'
|
||||
log = 'log'#'log'#log'log'#'log'#'log'#'log'#'log'
|
||||
ylim_log = (-14.2, 3)#(-14.2, 3)
|
||||
nfft = 20000#2 ** 13
|
||||
nfft = values_nfft_full_model()#length = values_stimuluslength_model_full()
|
||||
xlim_psd = [0, 300]
|
||||
|
||||
DF1_desired_orig = [133, 166]#33
|
||||
@ -149,7 +165,7 @@ def model_full():
|
||||
|
||||
#0.16666666666666666
|
||||
grid0 = gridspec.GridSpecFromSubplotSpec(2, 2, wspace=0.15, hspace=0.4,
|
||||
subplot_spec=grid[1,1])
|
||||
subplot_spec=grid[:,1])
|
||||
|
||||
|
||||
|
||||
@ -164,18 +180,20 @@ def model_full():
|
||||
diagonal = 'diagonal1'
|
||||
diagonal = ''
|
||||
plus_q = 'plus' # 'minus'#'plus'##'minus'
|
||||
length = 100#2*40 # 5
|
||||
length = values_stimuluslength_model_full()
|
||||
reshuffled = '' # ,
|
||||
array_len = 2
|
||||
alphas = [1,0.5]
|
||||
#a_size = 0.0065#0.0125#25#0.04#0.015
|
||||
|
||||
#embed()
|
||||
# ATTENTION: Diese Zelle ('2012-07-03-ak-invivo-1') braucht längere Abschnitte, mindsetesn 5 Sekunden damit das Powerspectrum nicht so niosy ist!
|
||||
#print('started')
|
||||
fr_noise, eod_fr_mm, axes2 = plt_model_full_model2(grid0, stack_final = stack_final, reshuffled=reshuffled, dev=0.0005, a_f1s=[a_size], af_2 = a_size,
|
||||
stimulus_length=length, plus_q=plus_q, stack_saved = stack_saved,
|
||||
diagonal=diagonal, runs=1, nfft = nfft, xlim_psd = xlim_psd,
|
||||
cells=[cell], dev_spikes ='original', markers = markers, DF1_frmult = DF1_frmult, DF2_frmult = DF2_frmult,
|
||||
log = log, ms = ms, adjust_factor_outside = adjust_factor_outside, array_len = array_len, f_same = f_same, transfer = transfer, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1]) #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
|
||||
log = log, ms = ms, models_name = models_name, adjust_factor_outside = adjust_factor_outside, array_len = array_len, f_same = f_same, transfer = transfer, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1]) #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
|
||||
|
||||
|
||||
|
||||
@ -185,6 +203,16 @@ def model_full():
|
||||
plt_model_letters(DF1_frmult, DF2_frmult, axm, color012, color01_2, fr_noise, markers)
|
||||
|
||||
# letters for the transfer function
|
||||
if do_transfer:
|
||||
transferletters(DF1_frmult, DF2_frmult, array_len, axc, color01, color02, f_same, fr_noise, transfer)
|
||||
|
||||
fig = plt.gcf()#[axes[0], axes[3], axes[4]]
|
||||
fig.tag([axes[0], axes2[0], axes2[1], axes2[2], axes2[3]], xoffs=-4.5, yoffs=1.35) # ax_ams[3],
|
||||
#plt.show()
|
||||
save_visualization()
|
||||
|
||||
|
||||
def transferletters(DF1_frmult, DF2_frmult, array_len, axc, color01, color02, f_same, fr_noise, transfer):
|
||||
letters = letters_for_full_model()
|
||||
for f in range(len(letters)):
|
||||
df1_recalc = recalc_fr_to_DF1(DF1_frmult, f, fr_noise)
|
||||
@ -204,11 +232,6 @@ def model_full():
|
||||
letters[f], color=color02, ha='center',
|
||||
va='center', zorder=100) # , alpha = alphas[f]
|
||||
|
||||
fig = plt.gcf()#[axes[0], axes[3], axes[4]]
|
||||
fig.tag([axes[0], axc, axes2[0], axes2[1], axes2[2], axes2[3]], xoffs=-4.5, yoffs=1.2) # ax_ams[3],
|
||||
plt.show()
|
||||
save_visualization()
|
||||
|
||||
|
||||
def plt_data_matrix(axes, grid, ls, lw, perc):
|
||||
ax = plt.subplot(grid[0])
|
||||
|
||||
BIN
nonlin_regime.pdf
Normal file
BIN
nonlin_regime.pdf
Normal file
Binary file not shown.
BIN
nonlin_regime.png
Normal file
BIN
nonlin_regime.png
Normal file
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nonlin_regime.py
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nonlin_regime.py
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import os
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import sys
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import numpy as np
|
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import pandas as pd
|
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from IPython import embed
|
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from matplotlib import gridspec, pyplot as plt
|
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|
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from plotstyle import plot_style
|
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from threefish.core import find_folder_name, info
|
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from threefish.defaults import default_figsize, default_ticks_talks
|
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from threefish.load import load_savedir, save_visualization
|
||||
from threefish.plot.limits import join_x, join_y, set_same_ylim
|
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from threefish.RAM.calc_fft import log_calc_psd
|
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from threefish.RAM.calc_model import chose_old_vs_new_model
|
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from threefish.RAM.plot_labels import label_f_eod_name_core_rm, onebeat_cond, remove_yticks
|
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from threefish.RAM.plot_subplots import colors_suscept_paper_dots, plt_spikes_ROC, recalc_fr_to_DF1
|
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from threefish.RAM.values import val_cm_to_inch, vals_model_full
|
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from threefish.twobeat.calc_model import calc_roc_amp_core_cocktail
|
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from threefish.twobeat.colors import colors_susept, twobeat_cond
|
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from threefish.twobeat.labels import f_stable_name, f_vary_name
|
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from threefish.twobeat.reformat import c_dist_recalc_func, c_dist_recalc_here, dist_recalc_phaselockingchapter, \
|
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find_dfs, \
|
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get_frame_cell_params
|
||||
from threefish.twobeat.subplots import plt_psd_saturation, plt_single_trace, plt_stim_saturation, plt_vmem_saturation, \
|
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power_spectrum_name
|
||||
from threefish.values import values_nfft_full_model, values_stimuluslength_model_full
|
||||
|
||||
|
||||
def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False, beat='',
|
||||
nfft_for_morph=4096 * 4,
|
||||
gain=1,
|
||||
cells_here=["2013-01-08-aa-invivo-1"], fish_jammer='Alepto', us_name='',
|
||||
show=True):
|
||||
runs = 1
|
||||
n = 1
|
||||
|
||||
dev = 0.001
|
||||
|
||||
#reshuffled = 'reshuffled' # ,
|
||||
|
||||
# standard combination with intruder small
|
||||
a_f2s = [0.1]
|
||||
|
||||
min_amps = '_minamps_'
|
||||
dev_name = ['05']
|
||||
|
||||
#model_cells = pd.read_csv(find_folder_name('calc_model_core') + "/models_big_fit_d_right.csv")
|
||||
|
||||
#if len(cells_here) < 1:
|
||||
# cells_here = np.array(model_cells.cell)
|
||||
|
||||
a_fr = 1
|
||||
a = 0
|
||||
trials_nrs = [5]
|
||||
|
||||
datapoints = 1000
|
||||
stimulus_length = 2
|
||||
results_diff = pd.DataFrame()
|
||||
position_diff = 0
|
||||
plot_style()
|
||||
|
||||
default_figsize(column=2, length=7.5)
|
||||
|
||||
|
||||
########################################
|
||||
# für das model_full, die Freuqnezen
|
||||
DF1_frmult, DF2_frmult = vals_model_full(val=0.30833333333333335)
|
||||
frame_cvs = pd.read_csv(find_folder_name('calc_base')+'/csv_model_data.csv')
|
||||
frame_cell = frame_cvs[frame_cvs.cell == '2012-07-03-ak-invivo-1']
|
||||
#embed()
|
||||
for d in range(len(DF1_frmult)):
|
||||
#DF2_frmult[d] = str(DF2_frmult[d])+'Fr'
|
||||
#DF1_frmult[d] = str(DF1_frmult[d]) + 'Fr'
|
||||
DF2_frmult[d] = recalc_fr_to_DF1(DF2_frmult, d, frame_cell.fr_data.iloc[0])
|
||||
DF1_frmult[d] = recalc_fr_to_DF1(DF1_frmult, d, frame_cell.fr_data.iloc[0])
|
||||
##DF2_frmult[d] = recalc_fr_to_DF1(DF2_frmult, d, frame_cell.fr_data.iloc[0])
|
||||
freqs = [(DF1_frmult[2], DF2_frmult[2])]
|
||||
|
||||
|
||||
|
||||
|
||||
# sachen die ich variieren will
|
||||
###########################################
|
||||
|
||||
auci_wo = []
|
||||
auci_w = []
|
||||
|
||||
nfft = 32768
|
||||
cells_here = ['2012-07-03-ak-invivo-1']
|
||||
for cell_here in cells_here:
|
||||
|
||||
###########################################
|
||||
# über die frequenzen hinweg
|
||||
|
||||
for freq1, freq2 in freqs: # das ist
|
||||
full_names = [
|
||||
'calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_C2_0.1_C1Len_50_FirstC1_0.0001_LastC1_1.0_FRrelavtiv__start_0.0001_end_1_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_point_1temporal',
|
||||
'calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_C2_0.1_C1Len_50_FirstC1_0.0001_LastC1_1.0_FRrelavtiv__start_0.0001_end_1_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_point_1_old_fit_temporal']
|
||||
|
||||
full_names = ['calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_af_coupled__C1Len_50_FirstC1_0.0001_LastC1_1.0_FRrelavtiv__start_0.0001_end_1_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_point_1temporal',
|
||||
'calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_af_coupled__C1Len_50_FirstC1_0.0001_LastC1_1.0_FRrelavtiv__start_0.0001_end_1_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_point_1_old_fit_temporal']
|
||||
|
||||
c_grouped = ['c1'] # , 'c2']
|
||||
c_nrs_orig = [0.02, 0.2] # 0.0002, 0.05, 0.5
|
||||
trials_nr = 20 # 20
|
||||
redo = False # True
|
||||
log = 'log' # 'log'
|
||||
grid0 = gridspec.GridSpec(1, 1, bottom=0.08, top=0.93, left=0.11,
|
||||
right=0.95, wspace=0.04) #
|
||||
grid00 = gridspec.GridSpecFromSubplotSpec(2, 1,
|
||||
wspace=0.04, hspace=0.1,
|
||||
subplot_spec=grid0[0], height_ratios=[1.5,1],) # height_ratios=[2,1],
|
||||
|
||||
grid_up = gridspec.GridSpecFromSubplotSpec(len(c_nrs_orig) + 1, 3,
|
||||
hspace=0.75,
|
||||
wspace=0.1, height_ratios=[1, 1, 0.7],
|
||||
subplot_spec=grid00[
|
||||
0]) # 1.2hspace=0.4,wspace=0.2,len(chirps)
|
||||
grid_down = gridspec.GridSpecFromSubplotSpec(1, 2,
|
||||
hspace=0.75,
|
||||
wspace=0.1,
|
||||
subplot_spec=grid00[1]) # 1.2hspace=0.4,wspace=0.2,len(chirps)
|
||||
for i, full_name in enumerate(full_names):
|
||||
frame = pd.read_csv(find_folder_name('calc_cocktailparty') + '/' + full_name + '.csv')
|
||||
|
||||
frame_cell_orig = frame[(frame.cell == cell_here)]
|
||||
if len(frame_cell_orig) > 0:
|
||||
try:
|
||||
pass
|
||||
except:
|
||||
print('min thing')
|
||||
embed()
|
||||
|
||||
get_frame_cell_params(c_grouped, cell_here, frame, frame_cell_orig)
|
||||
|
||||
#################################################################
|
||||
|
||||
# calc_model_amp_freqs-F1_750-975-25_F2_500-775-25_C2_0.1_C1Len_20_FirstC1_0.0001_LastC1_1.0_StimLen_25_nfft_32768_trialsnr_1_absolut_power_1temporal.csv
|
||||
# devs_extra = ['stim','stim_rec','stim_am','original','05']#['original','05']
|
||||
|
||||
# da implementiere ich das jetzt für eine Zelle
|
||||
# wo wir den einezlnen Punkt und Kontraste variieren
|
||||
|
||||
f_counter = 0
|
||||
|
||||
frame_cell_orig, df1s, df2s, f1s, f2s = find_dfs(frame_cell_orig)
|
||||
|
||||
eodf = frame_cell_orig.f0.unique()[0]
|
||||
|
||||
f = -1
|
||||
|
||||
f += 1
|
||||
|
||||
#######################################################################################
|
||||
# übersicht
|
||||
|
||||
frame_cell = frame_cell_orig[
|
||||
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
||||
if len(frame_cell) < 1:
|
||||
freq1 = frame_cell_orig.iloc[(np.argmin(np.abs(frame_cell_orig.df1 - freq1)))].df1
|
||||
freq2 = frame_cell_orig.iloc[(np.argmin(np.abs(frame_cell_orig.df2 - freq2)))].df2
|
||||
|
||||
frame_cell = frame_cell_orig[
|
||||
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
||||
print('Tuning curve needed for F1' + str(frame_cell.f1.unique()) + ' F2' + str(
|
||||
frame_cell.f2.unique()) + ' for cell ' + str(cell_here))
|
||||
|
||||
labels, alpha, color01, color01_012, color02, color02_012, colors, colors_array, linestyles, scores, linewidths = colors_susept(
|
||||
add='_mean_original', nr=4)
|
||||
|
||||
#print(cell_here + ' F1' + str(freq1) + ' F2 ' + str(freq2))
|
||||
|
||||
sampling = 20000
|
||||
|
||||
c_dist_recalc = dist_recalc_phaselockingchapter()
|
||||
|
||||
c_nrs = c_dist_recalc_func(frame_cell, c_nrs=c_nrs_orig, cell=cell_here,
|
||||
c_dist_recalc=c_dist_recalc)
|
||||
if not c_dist_recalc:
|
||||
c_nrs = np.array(c_nrs) * 100
|
||||
|
||||
|
||||
|
||||
letters = ['A', 'B']
|
||||
|
||||
indexes = [[0, 1, 2, 3]]
|
||||
#scores_all = [scores]
|
||||
#embed()
|
||||
#
|
||||
scores = ['amp_B1_012_mean_original', 'amp_B2_012_mean_original', 'amp_B1+B2_012_mean_original', 'amp_B1-B2_012_mean_original']
|
||||
color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
|
||||
colors = [color01, color02, color012, color01_2]
|
||||
linestyles = ['-','-','-','-']
|
||||
#frame_cell_orig['amp_B1+B2_012_mean_original']
|
||||
#frame_cell_orig['amp_B1-B2_012_mean_original']
|
||||
#for i, index in enumerate(indexes):
|
||||
index = [0, 1, 2, 3]
|
||||
try:
|
||||
ax_u1 = plt.subplot(grid_down[0, i])
|
||||
except:
|
||||
print('grid search problem4')
|
||||
embed()
|
||||
if 'old_fit' in full_name:
|
||||
ax_u1.set_title('old fit (before 2021)')
|
||||
else:
|
||||
ax_u1.set_title('new fit (after 2021)')
|
||||
plt_single_trace([], ax_u1, frame_cell_orig, freq1, freq2,
|
||||
scores=np.array(scores)[index], labels=np.array(labels)[index],
|
||||
colors=np.array(colors)[index],
|
||||
linestyles=np.array(linestyles)[index],
|
||||
linewidths=np.array(linewidths)[index],
|
||||
alpha=np.array(alpha)[index],
|
||||
thesum=False, B_replace='F', default_colors=False,
|
||||
c_dist_recalc=c_dist_recalc)
|
||||
#ax_us.append(ax_u1)
|
||||
frame_cell = frame_cell_orig[
|
||||
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
||||
c1 = c_dist_recalc_here(c_dist_recalc, frame_cell)
|
||||
ax_u1.set_xlim(0, 50)
|
||||
if i != 0:
|
||||
ax_u1.set_ylabel('')
|
||||
remove_yticks(ax_u1)
|
||||
if i < 2:
|
||||
ax_u1.fill_between(c1, frame_cell[np.array(scores)[index][0]],
|
||||
frame_cell[np.array(scores)[index][1]], color='grey',
|
||||
alpha=0.1)
|
||||
|
||||
ax_u1.scatter(c_nrs, (np.array(yposs[i]) - 35) * np.ones(len(c_nrs)), color='black',
|
||||
marker='v',
|
||||
clip_on=False)
|
||||
|
||||
#
|
||||
for c_nn, c_nr in enumerate(c_nrs):
|
||||
ax_u1.text(c_nr, yposs[i][c_nn] + 50, letters[c_nn], color='black', ha='center',
|
||||
va='top')
|
||||
# ax_u1.plot([c_nr, c_nr], [0, 435], color='black', linewidth=0.8, clip_on=False)
|
||||
|
||||
|
||||
start = 200 # 1000
|
||||
mults_period = 3
|
||||
xlim = [start, start + (mults_period * 1000 / np.min([np.abs(freq1), np.abs(freq2)]))]
|
||||
|
||||
#axts_all = []
|
||||
#axps_all = []
|
||||
#ax_us = []
|
||||
|
||||
# über die kontraste gehen
|
||||
axts = []
|
||||
axps = []
|
||||
axes = []
|
||||
|
||||
p_arrays_all = []
|
||||
model_fit = '_old_fit_' # ''#'_old_fit_'#''#'_old_fit_'#''#'_old_fit_'#''###'_old_fit_'
|
||||
model_cells, reshuffled = chose_old_vs_new_model(model_fit=model_fit)
|
||||
|
||||
for c_nn, c_nr in enumerate(c_nrs):
|
||||
|
||||
#################################
|
||||
# arrays plot
|
||||
save_dir = load_savedir(level=0).split('/')[0]
|
||||
name_psd = save_dir + '_psd.npy'
|
||||
name_psd_f = save_dir + '_psdf.npy'
|
||||
|
||||
do = False
|
||||
if ((not os.path.exists(name_psd)) | (redo == True)) & do:
|
||||
if log != 'log':
|
||||
stimulus_length_here = 0.5
|
||||
stimulus_length_here = values_stimuluslength_model_full()
|
||||
nfft_here = 32768
|
||||
nfft_here = values_nfft_full_model()
|
||||
else:
|
||||
stimulus_length_here = 50
|
||||
stimulus_length_here = values_stimuluslength_model_full()
|
||||
trials_nr = 1
|
||||
nfft_here = values_nfft_full_model()
|
||||
|
||||
else:
|
||||
nfft_here = 2 ** 14
|
||||
stimulus_length_here = 0.5
|
||||
|
||||
# #
|
||||
#
|
||||
|
||||
_, arrays_spikes, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
|
||||
[freq1 + eodf], [freq2 + eodf], datapoints, auci_wo, auci_w, results_diff,
|
||||
a_f2s,
|
||||
fish_jammer, trials_nr, nfft_here, us_name, gain, runs, a_fr, nfft_for_morph,
|
||||
beat,
|
||||
printing,
|
||||
stimulus_length_here,
|
||||
model_cells, position_diff, dev, cell_here, dev_name=dev_name,
|
||||
a_f1s=[c_nrs_orig[c_nn]],
|
||||
n=n,
|
||||
reshuffled=reshuffled, min_amps=min_amps)
|
||||
# ff_p, arrays, names, p_arrays_p, arrays_spikes, arrays_stim,
|
||||
p_arrays_here = p_arrays_p[1::]
|
||||
xlimp = (0, 300)
|
||||
for p in range(len(p_arrays_here)):
|
||||
p_arrays_here[p][0] = p_arrays_here[p][0][ff_p < xlimp[1]]
|
||||
|
||||
ff_p = ff_p[ff_p < xlimp[1]]
|
||||
|
||||
time = np.arange(0, len(arrays[a][0]) / sampling, 1 / sampling)
|
||||
time = time * 1000
|
||||
|
||||
# plot the first array
|
||||
arrays_time = arrays[1::] # [v_mems[1],v_mems[3]]#[1,2]#[1::]
|
||||
arrays_here = arrays[1::] # [arrays[1],arrays[3]]#arrays[1::]#
|
||||
|
||||
arrays_st = arrays_stim[1::] # [arrays_stim[1],arrays_stim[3]]#
|
||||
arrays_sp = arrays_spikes[
|
||||
1::] # [arrays_spikes[1],arrays_spikes[3]]#arrays_spikes[1::]
|
||||
colors_array_here = ['grey', 'grey', 'grey'] # colors_array[1::]
|
||||
|
||||
p_arrays_all.append(p_arrays_here)
|
||||
for a in range(len(arrays_here)):
|
||||
print('a' + str(a))
|
||||
|
||||
if a == 0:
|
||||
freqs = [np.abs(freq1)] # ], np.abs(freq2)],
|
||||
elif a == 1:
|
||||
freqs = [np.abs(freq2)]
|
||||
else:
|
||||
freqs = [np.abs(freq1), np.abs(freq2)]
|
||||
grid_pt = gridspec.GridSpecFromSubplotSpec(5, 1,
|
||||
hspace=0.3,
|
||||
wspace=0.2,
|
||||
subplot_spec=grid_up[c_nn, a],
|
||||
height_ratios=[1, 0.7, 1, 0.25,
|
||||
2.5]) # hspace=0.4,wspace=0.2,len(chirps)
|
||||
|
||||
axe = plt.subplot(grid_pt[0])
|
||||
axes.append(axe)
|
||||
plt_stim_saturation(a, [], arrays_st, axe, colors_array_here, f,
|
||||
f_counter, names, time,
|
||||
xlim=xlim) # np.array(arrays_sp)*1000
|
||||
|
||||
a_f2_cm = c_dist_recalc_func(frame_cell, c_nrs=[a_f2s[0]], cell=cell_here,
|
||||
c_dist_recalc=c_dist_recalc)
|
||||
|
||||
if not c_dist_recalc:
|
||||
a_f2_cm = np.array(a_f2_cm) * 100
|
||||
|
||||
if a == 2: # if (a_f1s[0] != 0) & (a_f2s[0] != 0):
|
||||
fish = 'Three fish: $' + label_f_eod_name_core_rm() + '$\,\&\,' + f_vary_name() + '\,\&\,' + f_stable_name() # + '$'#' $\Delta '$\Delta$
|
||||
beat_here = twobeat_cond(big=True, double=True,
|
||||
cond=False) + '\,' + f_vary_name(
|
||||
freq=int(freq1), delta=True) + ',\,$c_{1}=%s$' % (
|
||||
int(np.round(c_nrs[c_nn]))) + '$\%$' + '\n' + f_stable_name(
|
||||
freq=int(freq2), delta=True) + ',\,$c_{2}=%s$' % (
|
||||
int(np.round(a_f2_cm[0]))) + '$\%$' # +'$'
|
||||
|
||||
title_name = fish + '\n' + beat_here # +c1+c2
|
||||
elif a == 0: # elif (a_f1s[0] != 0):
|
||||
beat_here = ' ' + onebeat_cond(big=True, double=True,
|
||||
cond=False) + '\,' + f_vary_name(
|
||||
freq=int(freq1), delta=True) # +'$' + ' $\Delta '
|
||||
fish = 'Two fish: $' + label_f_eod_name_core_rm() + '$\,\&\,' + f_vary_name() # +'$'
|
||||
c1 = ',\,$c_{1}=%s$' % (int(np.round(c_nrs[c_nn]))) + '$\%$ \n '
|
||||
title_name = fish + '\n' + beat_here + c1 # +'cm'+'cm'+'cm'
|
||||
elif a == 1: # elif (a_f2s[0] != 0):
|
||||
beat_here = ' ' + onebeat_cond(big=True, double=True,
|
||||
cond=False) + '\,' + f_stable_name(
|
||||
freq=int(freq2),
|
||||
delta=True) # +'$'
|
||||
fish = '\n Two fish: $' + label_f_eod_name_core_rm() + '$\,\&\,' + f_stable_name() # +'$'
|
||||
c1 = ',\,$c_{2}=%s$' % (int(np.round(a_f2_cm[0]))) + '$\%$ \n'
|
||||
title_name = fish + '\n' + beat_here + c1 # +'cm'
|
||||
|
||||
axe.text(1, 1.1, title_name, va='bottom', ha='right',
|
||||
transform=axe.transAxes)
|
||||
|
||||
#############################
|
||||
|
||||
axs = plt.subplot(grid_pt[1])
|
||||
|
||||
plt_spikes_ROC(axs, 'grey', np.array(arrays_sp[a]) * 1000, xlim, lw=1)
|
||||
|
||||
#############################
|
||||
axt = plt.subplot(grid_pt[2])
|
||||
axts.append(axt)
|
||||
plt_vmem_saturation(a, arrays_sp, arrays_time, axt, colors_array_here, f,
|
||||
time, xlim=xlim)
|
||||
|
||||
axp = plt.subplot(grid_pt[-1])
|
||||
axps.append(axp)
|
||||
|
||||
if a == 0:
|
||||
axt.show_spines('')
|
||||
axt.xscalebar(0.1, -0.1, 10, 'ms', va='right', ha='bottom')
|
||||
axt.yscalebar(-0.02, 0.35, 600, 'Hz', va='left', ha='top')
|
||||
|
||||
f_counter += 1
|
||||
|
||||
if (not os.path.exists(name_psd)) | (redo == True):
|
||||
np.save(name_psd, p_arrays_all)
|
||||
np.save(name_psd_f, ff_p)
|
||||
else:
|
||||
ff_p = np.load(name_psd_f) # p_arrays_p
|
||||
p_arrays_all = np.load(name_psd) # p_arrays_p
|
||||
|
||||
for c_nn, c_nr in enumerate(c_nrs):
|
||||
for a in range(len(arrays_here)):
|
||||
|
||||
axps_here = [[axps[0], axps[1], axps[2]], [axps[3], axps[4], axps[5]]]
|
||||
axp = axps_here[c_nn][a]
|
||||
pp = log_calc_psd(log, p_arrays_all[c_nn][a][0],
|
||||
np.nanmax(p_arrays_all))
|
||||
|
||||
markeredgecolors = []
|
||||
if a == 0:
|
||||
colors_peaks = [color01] # , 'red']
|
||||
freqs = [np.abs(freq1)] # ], np.abs(freq2)],
|
||||
elif a == 1:
|
||||
colors_peaks = [color02] # , 'red']
|
||||
freqs = [np.abs(freq2)]
|
||||
else:
|
||||
colors_peaks = [color01_012, color02_012] # , 'red']
|
||||
freqs = [np.abs(freq1), np.abs(freq2)]
|
||||
markeredgecolors = [color01, color02]
|
||||
colors_peaks = [color01, color02, color012, color01_2]
|
||||
markeredgecolors = [color01, color02, color012, color01_2]
|
||||
freqs = [np.abs(freq1), np.abs(freq2), np.abs(freq1)+np.abs(freq2), np.abs(np.abs(freq1)-np.abs(freq2))]
|
||||
plt_psd_saturation(pp, ff_p, a, axp, colors_array_here, freqs=freqs,
|
||||
colors_peaks=colors_peaks, xlim=xlimp,
|
||||
markeredgecolor=markeredgecolors, )
|
||||
|
||||
if log:
|
||||
scalebar = False
|
||||
if scalebar:
|
||||
axp.show_spines('b')
|
||||
if a == 0:
|
||||
axp.yscalebar(-0.05, 0.5, 20, 'dB', va='center', ha='left')
|
||||
axp.set_ylim(-33, 5)
|
||||
else:
|
||||
|
||||
axp.show_spines('lb')
|
||||
if a == 0:
|
||||
axp.set_ylabel('dB') # , va='center', ha='left'
|
||||
else:
|
||||
remove_yticks(axp)
|
||||
axp.set_ylim(-39, 5)
|
||||
else:
|
||||
axp.show_spines('lb')
|
||||
if a != 0:
|
||||
remove_yticks(axp)
|
||||
else:
|
||||
axp.set_ylabel(power_spectrum_name())
|
||||
axp.set_xlabel('Frequency [Hz]')
|
||||
|
||||
#a#xts_all.extend(axts)
|
||||
#axps_all.extend(axps)
|
||||
|
||||
#ax_us[-1].legend(loc=(-2.22, 1.2), ncol=2, handlelength=2.5) # -0.07loc=(0.4,1)
|
||||
|
||||
#axts_all[0].get_shared_y_axes().join(*axts_all)
|
||||
#axts_all[0].get_shared_x_axes().join(*axts_all)
|
||||
#axps_all[0].get_shared_y_axes().join(*axps_all)
|
||||
#axps_all[0].get_shared_x_axes().join(*axps_all)
|
||||
axts[0].get_shared_y_axes().join(*axts)
|
||||
axts[0].get_shared_x_axes().join(*axts)
|
||||
axps[0].get_shared_y_axes().join(*axps)
|
||||
axps[0].get_shared_x_axes().join(*axps)
|
||||
join_y(axts)
|
||||
set_same_ylim(axts)
|
||||
set_same_ylim(axps)
|
||||
join_x(axts)
|
||||
#join_x(ax_us)
|
||||
#join_y(ax_us)
|
||||
fig = plt.gcf()
|
||||
|
||||
fig.tag([[axes[0], axes[1], axes[2]]], xoffs=0, yoffs=3.7)
|
||||
fig.tag([[axes[3], axes[4], axes[5]]], xoffs=0, yoffs=3.7)
|
||||
|
||||
#fig.tag([ax_us[0], ax_us[1], ax_us[2]], xoffs=-2.3, yoffs=1.4)
|
||||
|
||||
save_visualization(cell_here, show)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#embed()
|
||||
sys.excepthook = info
|
||||
nonlin_regime(yposs = [[170,180], [200,200]],)#, [430,470]
|
||||
BIN
nonlin_regime_psd.npy
Normal file
BIN
nonlin_regime_psd.npy
Normal file
Binary file not shown.
BIN
nonlin_regime_psdf.npy
Normal file
BIN
nonlin_regime_psdf.npy
Normal file
Binary file not shown.
@ -467,7 +467,7 @@ In contrast, a high-CV P-unit (CV$_{\text{base}}=0.4$) does not exhibit pronounc
|
||||
|
||||
\begin{figure*}[t]
|
||||
\includegraphics[width=\columnwidth]{ampullary}
|
||||
\caption{\label{fig:ampullary} Linear and nonlinear stimulus encoding in an ampullary afferent (cell identifier ``2012-04-26-ae"). \figitem{A} Interspike interval (ISI) distribution of the cell's baseline activity. The very low CV of the ISIs indicates almost perfect periodic spiking. \figitem{B} Power spectral density of baseline activity with peaks at the cell's baseline firing rate and its harmonics. \figitem{C} Bad-limited white noise stimulus (top, with cutoff frequency of 150\,Hz) added to the fish's self-generated electric field and spike raster of the evoked responses (bottom) for two stimulus contrasts as indicated (right). \figitem{D} Gain of the transfer function, \Eqnref{linearencoding_methods}, of the responses to stimulation with 2\,\% (light green) and 20\,\% contrast (dark green). \figitem{E, F} Absolute value of the second-order susceptibility, \Eqnref{eq:susceptibility}, for both stimulus contrasts as indicated. Pink triangles indicate baseline firing rate. \figitem{G} Projections of the second-order susceptibilities in \panel{E, F} onto the diagonal. \notejb{`` Wurden die ampullaeren auch auf 10s ausgewertet?.''}
|
||||
\caption{\label{fig:ampullary} Linear and nonlinear stimulus encoding in an ampullary afferent (cell identifier ``2012-04-26-ae"). \figitem{A} Interspike interval (ISI) distribution of the cell's baseline activity. The very low CV of the ISIs indicates almost perfect periodic spiking. \figitem{B} Power spectral density of baseline activity with peaks at the cell's baseline firing rate and its harmonics. \figitem{C} Bad-limited white noise stimulus (top, with cutoff frequency of 150\,Hz) added to the fish's self-generated electric field and spike raster of the evoked responses (bottom) for two stimulus contrasts as indicated (right). \figitem{D} Gain of the transfer function, \Eqnref{linearencoding_methods}, of the responses to stimulation with 2\,\% (light green) and 20\,\% contrast (dark green). \figitem{E, F} Absolute value of the second-order susceptibility, \Eqnref{eq:susceptibility}, for both stimulus contrasts as indicated. Pink triangles indicate baseline firing rate. \figitem{G} Projections of the second-order susceptibilities in \panel{E, F} onto the diagonal. \notejb{`` Wurden die ampullaeren auch auf 10s ausgewertet?.''}\notesr{ja}
|
||||
}
|
||||
\end{figure*}
|
||||
|
||||
@ -480,7 +480,7 @@ In the example recordings shown above (\figsrefb{fig:cells_suscept} and \fref{fi
|
||||
|
||||
\begin{figure*}[t]
|
||||
\includegraphics[width=\columnwidth]{model_and_data}
|
||||
\caption{\label{model_and_data} Estimation of second-order susceptibilities in the limit of weak stimuli. \figitem{A} \suscept{} estimated from $N=11$ trials \notejb{of duration XXX?} of an electrophysiological recording of another low-CV P-unit (cell 2012-07-03-ak, $\fbase=120$\,Hz, CV=0.20) driven with a weak RAM stimulus with contrast 2.5\,\%. Pink edges mark baseline firing rate where enhanced nonlinear responses are expected. \figitem[i]{B} \textit{Standard condition} of model simulations with intrinsic noise (bottom) and a RAM stimulus (top). \notejb{Since the model overestimated the sensitivity of the real P-unit, we adjusted the RAM contrast to 0.009\,\%, such that the resulting spike trains had the same CV as the electrophysiolgical recorded P-unit during the 2.5\,\% contrast stimulation (see table~\ref{modelparams} for model parameters).} \figitem[ii]{B} \suscept{} estimated from simulations of the cell's LIF model counterpart (cell 2012-07-03-ak, table~\ref{modelparams}) based on the same number of trials as in the electrophysiological recording. \figitem[iii]{B} Same as \panel[ii]{B} but using $10^6$ stimulus repetitions. \figitem[i-iii]{C} Same as in \panel[i-iii]{B} but in the \textit{noise split} condition: there is no external RAM signal driving the model. Instead, a large part (90\,\%) of the total intrinsic noise is treated as signal and is presented as an equivalent amplitude modulation (\signalnoise, center), while the intrinsic noise is reduced to 10\,\% of its original strength (see methods for details). In addition to one million trials, this reveals the full expected structure of the second-order susceptibility.}
|
||||
\caption{\label{model_and_data} Estimation of second-order susceptibilities in the limit of weak stimuli. \figitem{A} \suscept{} estimated from $N=11$ trials \notejb{of duration XXX?} \notesr{0.5\,s} of an electrophysiological recording of another low-CV P-unit (cell 2012-07-03-ak, $\fbase=120$\,Hz, CV=0.20) driven with a weak RAM stimulus with contrast 2.5\,\%. Pink edges mark baseline firing rate where enhanced nonlinear responses are expected. \figitem[i]{B} \textit{Standard condition} of model simulations with intrinsic noise (bottom) and a RAM stimulus (top). \notejb{Since the model overestimated the sensitivity of the real P-unit, we adjusted the RAM contrast to 0.009\,\%, such that the resulting spike trains had the same CV as the electrophysiolgical recorded P-unit during the 2.5\,\% contrast stimulation (see table~\ref{modelparams} for model parameters).} \figitem[ii]{B} \suscept{} estimated from simulations of the cell's LIF model counterpart (cell 2012-07-03-ak, table~\ref{modelparams}) based on the same number of trials as in the electrophysiological recording. \figitem[iii]{B} Same as \panel[ii]{B} but using $10^6$ stimulus repetitions. \figitem[i-iii]{C} Same as in \panel[i-iii]{B} but in the \textit{noise split} condition: there is no external RAM signal driving the model. Instead, a large part (90\,\%) of the total intrinsic noise is treated as signal and is presented as an equivalent amplitude modulation (\signalnoise, center), while the intrinsic noise is reduced to 10\,\% of its original strength (see methods for details). In addition to one million trials, this reveals the full expected structure of the second-order susceptibility.}
|
||||
\end{figure*}
|
||||
|
||||
One reason could be simply too little data for a good estimate of the second-order susceptibility. Electrophysiological recordings are limited in time, and therefore responses to only a limited number of trials, i.e. repetitions of the same RAM stimulus, are available. As a consequence, the cross-spectra, \Eqnref{eq:crosshigh}, are insufficiently averaged and the full structure of the second-order susceptibility might be hidden in finite-data noise. This experimental limitation can be overcome by using a computational model for the P-unit, a stochastic leaky integrate-and-fire model with adaptation current and dendritic preprocessing, and parameters fitted to the experimentally recorded P-unit (\figrefb{flowchart}) \citep{Barayeu2023}. The model faithfully reproduces the second-order susceptibility of a low-CV cell estimated from the same low number of trials as in the experiment ($\n{}=11$, compare \panel{A} and \panel[ii]{B} in \figrefb{model_and_data}).
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
BIN
trialnr.pdf
Normal file
BIN
trialnr.pdf
Normal file
Binary file not shown.
BIN
trialnr.png
BIN
trialnr.png
Binary file not shown.
|
Before Width: | Height: | Size: 25 KiB After Width: | Height: | Size: 25 KiB |
30
trialnr.py
30
trialnr.py
@ -2,6 +2,7 @@ import os
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from IPython import embed
|
||||
from matplotlib import pyplot as plt
|
||||
from plotstyle import plot_style
|
||||
from threefish.RAM.plot_labels import title_find_cell_add
|
||||
@ -84,8 +85,10 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
# model part
|
||||
|
||||
trial_nr = 500000
|
||||
|
||||
|
||||
cell = '2013-01-08-aa-invivo-1'
|
||||
cell = '2012-07-03-ak-invivo-1'
|
||||
#cell = '2012-07-03-ak-invivo-1'
|
||||
#print('cell'+str(cell))
|
||||
cells_given = [cell]
|
||||
save_name_rev = find_folder_name(
|
||||
@ -103,9 +106,9 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
|
||||
fig, ax = plt.subplots(1, 1)
|
||||
ax = [ax]
|
||||
alphas = [0.5, 1, ]
|
||||
colors = ['black', 'red']
|
||||
for s in range(2):
|
||||
alphas = [0.5]#, 1, 1]
|
||||
colors = ['black']#, 'black','red']
|
||||
for s in range(len(colors)):
|
||||
stacks = []
|
||||
perc95 = []
|
||||
perc90 = []
|
||||
@ -124,7 +127,10 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV','calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
|
||||
|
||||
]
|
||||
save_names = ['calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV']
|
||||
nrs_s = [3, 4, 8, 9]#, 10, 11
|
||||
save_name = save_names[s]
|
||||
tr_name = trial_nr/1000000
|
||||
@ -138,6 +144,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
|
||||
path = save_name + '.pkl' # '../'+
|
||||
|
||||
#embed()
|
||||
stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
|
||||
|
||||
if len(stack)> 0:
|
||||
@ -151,7 +158,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
fr_stims.append(stack['fr_stim_mean'].iloc[0])
|
||||
vars.append(stack['var_RAM'].iloc[0])
|
||||
|
||||
perc95.append(np.percentile(stack_plot,99.99))
|
||||
perc95.append(np.percentile(stack_plot,99.9))#99.99
|
||||
perc90.append(np.percentile(stack_plot, 90))
|
||||
perc05.append(np.percentile(stack_plot, 10))
|
||||
median.append(np.percentile(stack_plot, 50))
|
||||
@ -176,8 +183,11 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
median_wo_norm.append(float('nan'))
|
||||
counter.append(float('nan'))
|
||||
|
||||
ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s])
|
||||
#if s == 0:
|
||||
|
||||
ax[0].plot(trial_nrs_here, perc95, color = colors[s], clip_on = False, label = '99.99th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc95, color = colors[s], clip_on = False, alpha = alphas[s])
|
||||
#embed()
|
||||
ax[0].set_xscale('log')#colors[s]
|
||||
ax[0].set_yscale('log')
|
||||
ax[0].set_xlabel('Trials [$N$]')
|
||||
@ -185,13 +195,17 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
|
||||
|
||||
############################################
|
||||
if s == 1:
|
||||
if s == 0:
|
||||
ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s])
|
||||
ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s])
|
||||
ax[0].legend()
|
||||
|
||||
|
||||
#ax[1].plot(trial_nrs_here,cv_stims/ trial_nrs_here, color = colors[s])
|
||||
#embed()
|
||||
|
||||
plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95)
|
||||
|
||||
save_visualization(pdf=True)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user