updating figures

data/models_d_right.csv

@@ -0,0 +1,73 @@
+,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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+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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+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
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data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_8500000_a_fr_1__TrialsNr_1_2013-01-08-aa-invivo-1.csv

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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

data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_9000000_a_fr_1__TrialsNr_1_2013-01-08-aa-invivo-1.csv

@@ -0,0 +1 @@
+,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

data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_9500000_a_fr_1__TrialsNr_1_2013-01-08-aa-invivo-1.csv

@@ -0,0 +1 @@
+,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

model_and_data.py

@@ -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')

model_full.py

@@ -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
+    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 = 3#adjust_factor_outside_nonlin()
+    adjust_factor_outside = 1#adjust_factor_outside_nonlin()
 
 
 
@@ -57,83 +59,97 @@ 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
-    loaded_from_calc_RAM_model = False
+    #embed()
-    if loaded_from_calc_RAM_model:
+    do_transfer = False
-        cross = get_transfer_from_model(stack_saved)
+    if do_transfer:
-        axc = plt.subplot(grid[0, 1])
+        loaded_from_calc_RAM_model = False
-        axc.plot(stack_saved.index, cross, color = 'black')
+        if loaded_from_calc_RAM_model:
-        axc.set_xlabel('Frequency [Hz]')
+            cross = get_transfer_from_model(stack_saved)
-        axc.set_ylabel(xlabel_transfer_hz())
+            axc = plt.subplot(grid[0, 1])
+            axc.plot(stack_saved.index, cross, color = 'black')
+            axc.set_xlabel('Frequency [Hz]')
+            axc.set_ylabel(xlabel_transfer_hz())
+        else:
+            ############################################################
+            #calculate the noise induced tranferfunction
+            # start_eod_emitter --  the lowest frequency of the emmiter fish, default 20
+
+            # 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_name,
+                                        load_folder='calc_model_core')
+            #flowchart_cell = '2012-07-03-ak-invivo-1'
+
+            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')
+            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]
+            stimulus_length = 1
+
+            power = 1
+            xlim = [0,300]
+            c_sigs = [0.9]  # '',0,
+            var_types = [
+                         ]  # ''#'additiv_cv_adapt_factor_scaled'
+            #            'additiv_visual_d_4_scaled', '']
+            #a_f = 0.009  # 2
+            a_fes = [a_size, a_size, 0]  # alpha\alpha0.1,
+            trials_nr = 100  # 100
+
+
+            frame = get_transfer_for_model_full([a_size], 0,cell, deltat,eod_fr,
+                                                                                                           model_params,
+                                                                                                           stimulus_length,
+                                                                                                           trials_nr,
+                                                                                                           v_offset,
+                                                                                                           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
-        #calculate the noise induced tranferfunction
+        transfer = []
-        # start_eod_emitter --  the lowest frequency of the emmiter fish, default 20
-
-        # 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",
-                                    load_folder='calc_model_core')
-        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))
-        # amp_frame = pd.read_csv('peak_amplitudes_power.csv')
-        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]
-        stimulus_length = 1
-
-        power = 1
-        xlim = [0,300]
-        c_sigs = [0.9]  # '',0,
-        var_types = [
-                     ]  # ''#'additiv_cv_adapt_factor_scaled'
-        #            'additiv_visual_d_4_scaled', '']
-        #a_f = 0.009  # 2
-        a_fes = [a_size, a_size, 0]  # alpha\alpha0.1,
-        trials_nr = 100  # 100
-
-
-        frame = get_transfer_for_model_full([a_size], 0,cell, deltat,eod_fr,
-                                                                                                       model_params,
-                                                                                                       stimulus_length,
-                                                                                                       trials_nr,
-                                                                                                       v_offset,
-                                                                                                       c_sig = 0.9,
-                                                                                                       var_type='additiv_cv_adapt_factor_scaled')
-
-        axc = plt.subplot(grid[0, 1])
-        f_same = frame.index
-        #embed()
-        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)
-        #if c == 0:
-        axc.set_ylabel(r'$|\chi_1|^2$')
-
 
 
     #################
     # 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)
@@ -197,17 +225,12 @@ def model_full():
                      'C,D', color=color01, ha='center',
                      va='center', zorder=100)  # , alpha = alphas[f]
         if (letters[f] not in ['C', 'D']):
-            axc.text(df1_recalc, np.max(transfer[pos_df1-array_len:pos_df1+array_len])+3500,
+            axc.text(df1_recalc, np.max(transfer[pos_df1 - array_len:pos_df1 + array_len]) + 3500,
-                    letters[f], color=color01, ha='center',
+                     letters[f], color=color01, ha='center',
-                    va='center', zorder = 100)  # , alpha = alphas[f]
+                     va='center', zorder=100)  # , alpha = alphas[f]
-        axc.text(df2_recalc, np.max(transfer[pos_df2-array_len:pos_df2+array_len])+2500,
+        axc.text(df2_recalc, np.max(transfer[pos_df2 - array_len:pos_df2 + array_len]) + 2500,
-                letters[f], color=color02, ha='center',
+                 letters[f], color=color02, ha='center',
-                va='center', zorder = 100)  # , alpha = alphas[f]
+                 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):

nonlin_regime.py

@@ -0,0 +1,484 @@
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+from IPython import embed
+from matplotlib import gridspec, pyplot as plt
+
+from plotstyle import plot_style
+from threefish.core import find_folder_name, info
+from threefish.defaults import default_figsize, default_ticks_talks
+from threefish.load import load_savedir, save_visualization
+from threefish.plot.limits import join_x, join_y, set_same_ylim
+from threefish.RAM.calc_fft import log_calc_psd
+from threefish.RAM.calc_model import chose_old_vs_new_model
+from threefish.RAM.plot_labels import label_f_eod_name_core_rm, onebeat_cond, remove_yticks
+from threefish.RAM.plot_subplots import colors_suscept_paper_dots, plt_spikes_ROC, recalc_fr_to_DF1
+from threefish.RAM.values import val_cm_to_inch, vals_model_full
+from threefish.twobeat.calc_model import calc_roc_amp_core_cocktail
+from threefish.twobeat.colors import colors_susept, twobeat_cond
+from threefish.twobeat.labels import f_stable_name, f_vary_name
+from threefish.twobeat.reformat import c_dist_recalc_func, c_dist_recalc_here, dist_recalc_phaselockingchapter, \
+    find_dfs, \
+    get_frame_cell_params
+from threefish.twobeat.subplots import plt_psd_saturation, plt_single_trace, plt_stim_saturation, plt_vmem_saturation, \
+    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]

susceptibility1.tex

@@ -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}).

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, ]
+    alphas = [0.5]#, 1, 1]
-    colors = ['black', 'red']
+    colors = ['black']#, 'black','red']
-    for s in range(2):
+    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])
+        #if s == 0:
-        ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s])
+
+        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)