diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_lh_use_corrcoeff_False.csv
index 2ac13ea..5db2e08 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_lh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_lh_use_corrcoeff_False.csv
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index cea39db..5396504 100644
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_rh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_rh_use_corrcoeff_False.csv
index a7e3b54..4a9515d 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_rh_use_corrcoeff_False.csv
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_rh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/df_corr_rh_use_corrcoeff_True.csv
index 3545560..4bc2262 100644
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_cv.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_cv.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_lvr.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_lvr.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_rates.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/exp_rates.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_cv_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_cv_arr.json
index bf5d0f7..f75d9a8 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_cv_arr.json
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_lvr_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_lvr_arr.json
index bef950c..6607120 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_lvr_arr.json
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_rates_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_rates_arr.json
index 29b9683..7a64a9a 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/ks_rates_arr.json
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/path_to_chi.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/path_to_chi.json
index 633df75..7fe4e16 100644
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_cv_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_cv_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_cv_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_cv_groundstate.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_lvr_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_lvr_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_rates_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_rates_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_rates_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/sim_rates_groundstate.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_bestfit.csv
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+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_bestfit.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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-inferiortemporal,0.5096844856355413,-0.07559208969907431,0.31594104677412854,0.26063545797589566,-0.001084909527358153,0.3672922758011444,0.4642354105308031,0.4237438866750318,1.0,0.3788426939079135,0.010059653562706536,0.40573556185846044,0.3800461312280212,0.25433826353733735,0.3415307663968523,0.9007821802418139,0.3343499477639021,0.1856216103337017,0.3282877178217263,0.5102572380622813,0.4434573682609838,0.2352333255630319,0.09563916948541798,0.03170467131420121,0.1017192682802446,0.03277034480054382,-0.08046233993599923,0.5502988340541146,0.3117469638987023,0.30612457883225147,0.512302637816013,0.18189036388731186,0.5865490195537156,0.0968187446582569
-insula,0.40216640873015214,-0.06527414055772783,0.2889284815058137,0.11753859562138295,0.026360066522726284,0.6433859554909606,0.2676006078064573,0.13629537744067477,0.3788426939079135,1.0,-0.01272257869231211,0.20129465239896435,0.7351183108675781,0.11599778754270573,0.6899678385790363,0.4252488452542463,0.41891864261242656,0.12100128422288899,0.27406422873691316,0.7382869264928569,0.818797617177277,0.09633539219540782,0.27361552296895386,0.007857387214317437,0.2522414660119873,0.003397891258931416,-0.02778175724642755,0.4885662789489022,0.3282381581058459,0.24134139686148492,0.6388436757986186,0.3537052416208574,0.721306113063102,0.3464769381410994
-isthmuscingulate,-0.03289527329929587,0.7333701615355541,-0.016694982094447763,0.05868381184263922,0.7211753670824829,-0.02850362351064524,0.06073758358116918,-0.04000300512694477,0.010059653562706536,-0.01272257869231211,1.0,0.04009264875224327,-0.02962564253367488,0.08435976089416677,-0.018012754856057595,-0.004589608482533522,0.030164222600707,0.46863974417832294,-0.03733063209405382,-0.03461282682595342,-0.04158790256684196,0.060850456809244474,0.026897248662121065,0.8532573403314695,0.006689364938636113,0.5403397714325691,0.6782001736444382,-0.012241300424525096,0.4020985045620605,0.14471344881889678,-0.024096545529008424,-0.002443156172657659,-0.0004232672661339165,-0.05752265002092807
-lateraloccipital,0.17745515095956393,-0.09744655165573209,0.09950496762369472,0.9076397622779118,0.06597430611861343,0.19247585600778352,0.8511810068455901,0.25343317992230197,0.40573556185846044,0.20129465239896435,0.04009264875224327,1.0,0.3976553707135738,0.9212848513358073,0.14819370261476147,0.31626299658066875,0.4102433106495678,0.6554360321301481,0.10358840242176759,0.4161064819038808,0.19789194453803188,0.8744688072036468,0.0865557441754621,0.005762783889288984,0.03825230485021122,0.16102356224488068,-0.10185027683915612,0.5256138560569155,0.18951376035171794,0.4219743331603593,0.2447713889284568,0.0739668682514939,0.2787264860226007,0.12757070922264305
-lateralorbitofrontal,0.2720002772219418,-0.06059481771202591,0.14698935107828348,0.30671443803792986,0.08650562153427434,0.8122802563844329,0.49368781019481145,0.17504156943223867,0.3800461312280212,0.7351183108675781,-0.02962564253367488,0.3976553707135738,1.0,0.3214608379902966,0.8939551858230858,0.35074768012424584,0.31370742847407,0.3290524260662681,0.24607020196636809,0.9501247716965624,0.8972258973260255,0.29047840994336843,0.015680284140370065,-0.016191628888754236,0.010513333350137303,0.06983440847162432,0.000387133672799772,0.640866972785131,0.3321936044943631,0.31202316601404917,0.5391260024665319,0.09508671258292394,0.8728143316599267,0.30222922393017204
-lingual,0.08010276249519502,-0.02412273313953262,0.03923670547666861,0.9221978207751867,0.11935058926225092,0.10431658817350307,0.7954757453000799,0.11366419328800734,0.25433826353733735,0.11599778754270573,0.08435976089416677,0.9212848513358073,0.3214608379902966,1.0,0.0721889160600812,0.1671273645155985,0.320358718327711,0.6756143882688282,0.032809396131070444,0.3132595281244158,0.09111863888180596,0.8985521078200969,0.06554317945833782,0.056290467686372914,0.017859326546515845,0.1636578997209973,-0.03324613111681171,0.4072963429103226,0.13280178178703003,0.33097851462369743,0.1294811378562772,0.0386847766555453,0.1824765289432025,0.07530993141330414
-medialorbitofrontal,0.23327523332168854,-0.0075953456293324575,0.1638569381715909,0.085433969572164,0.06914887760406703,0.8843466532884456,0.2124662006658063,0.14404804584728126,0.3415307663968523,0.6899678385790363,-0.018012754856057595,0.14819370261476147,0.8939551858230858,0.0721889160600812,1.0,0.33263065618506915,0.20221545100994884,0.09419383168034576,0.2674349863031084,0.9036901794867479,0.8916056355762695,0.0678990484396105,0.01816066041725926,0.020046582773819727,0.058313416527304596,0.04786490908447874,0.0660394670280156,0.5459923142650617,0.3509251935443372,0.18745071996641638,0.48134762089224004,0.08283331184121404,0.8886542281830858,0.20509553978967837
-middletemporal,0.769442439845299,-0.07928243870200885,0.32545047634196234,0.1813795666558824,-0.026546773734101425,0.3355962531926874,0.3559855160318193,0.5237378257079623,0.9007821802418139,0.4252488452542463,-0.004589608482533522,0.31626299658066875,0.35074768012424584,0.1671273645155985,0.33263065618506915,1.0,0.3802215715247784,0.10183682974375535,0.4055536362264528,0.47465953413183126,0.48101820743494134,0.16309105063725557,0.14001055946813912,0.018690865214625946,0.12638013897969386,0.0534763947705758,-0.08128238973508176,0.5235398253604554,0.33908838343793646,0.3866379416477743,0.7299736406113164,0.28103432006644663,0.5256405346419417,0.3072423194924784
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-parahippocampal,0.05910823852824639,0.4707970503729332,-0.004235202566934394,0.5834060313185848,0.6493852773749497,0.10314178991725938,0.8045152218841131,0.043791306637436,0.1856216103337017,0.12100128422288899,0.46863974417832294,0.6554360321301481,0.3290524260662681,0.6756143882688282,0.09419383168034576,0.10183682974375535,0.22804569275716652,1.0,-0.005007785610215199,0.2914138422870375,0.09953563650507428,0.6009895161504222,0.01486969703646086,0.5055192871120784,-0.04617256634958594,0.31953964980969396,0.43325060480918204,0.34663334790204414,0.3609846025500718,0.29610826124618317,0.11540541810293241,0.0009188250779024136,0.20838941213755394,0.08460773087532117
-parsopercularis,0.3736354319341176,-0.06598845423283717,0.8315014081251723,0.04838819162375322,-0.05622503824863629,0.42338374311773774,0.10796078780673336,0.27258921948200676,0.3282877178217263,0.27406422873691316,-0.03733063209405382,0.10358840242176759,0.24607020196636809,0.032809396131070444,0.2674349863031084,0.4055536362264528,0.24918677272473944,-0.005007785610215199,1.0,0.3145957724499797,0.4618682717890764,0.048874978029665846,0.057393752468116,-0.013620583047714852,0.34442844970375047,-0.000635718087103027,-0.05486327386541745,0.639672759365513,0.6747581973324802,0.19504519000082665,0.37806095820502633,0.15040991190394476,0.2936478255800944,0.2189229987234761
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+temporalpole,0.35165469277114797,0.09795999407944321,0.24500068970051692,0.05325935479785531,0.22953175742022475,0.7897308153084119,0.32237675980562447,0.10095695209671635,0.5814903281180301,0.7245729637763239,0.08554957171095653,0.15052194871826063,0.8584050976311185,0.08642518340518658,0.8988848044922821,0.5122816095740341,0.21437300123617237,0.23285846240292307,0.33006318249957517,0.9109949603266577,0.8853136515030603,0.04377626767571957,0.027624452571679267,0.11031777135965003,0.07854774381333689,0.04475381450323855,0.16983338857792113,0.5819064707988829,0.35025309233724766,0.15800082489059147,0.5670753639036483,0.15596020913669206,1.0,0.21630761161890655
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_groundstate.csv
index 28c6d93..0f1857c 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_groundstate.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed/simulated_fc_groundstate.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
-bankssts,1.0,0.02215839029073068,0.0012980535615463586,-0.000898700648350388,-0.014118323187816514,-0.009658437248322695,0.004281384325993084,0.0036812763606134924,0.0184984653189181,-0.016486481913537708,-0.006910207927415766,0.017951009902651933,0.04807400364991814,-0.019063187487746585,0.004429603110997372,0.10354205896191909,-0.0011634817345610668,-0.024646680203306785,0.010263743294388145,-0.023313871966264835,0.0038435902212676175,0.0095225426161005,0.01937191771382413,0.03904848928029523,0.004179173539599573,0.0013391298892848529,0.006910076727074197,-0.002090857720626041,0.023031517840093343,-0.02410379736790352,0.055688216341661004,0.06459878489008203,0.007483276605966389,0.0208710253975669
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_False.csv
new file mode 100644
index 0000000..70d70ed
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_False.csv
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_True.csv
new file mode 100644
index 0000000..a280b0e
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_lh_use_corrcoeff_True.csv
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_rh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_rh_use_corrcoeff_False.csv
new file mode 100644
index 0000000..8fd3c81
--- /dev/null
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_rh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/df_corr_rh_use_corrcoeff_True.csv
new file mode 100644
index 0000000..49ef100
--- /dev/null
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_cv.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_cv.npy
similarity index 99%
rename from simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_cv.npy
rename to simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_cv.npy
index a897926..53a46b0 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_cv.npy and b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_cv.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_lvr.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_lvr.npy
similarity index 99%
rename from simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_lvr.npy
rename to simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_lvr.npy
index 2bdf39e..49502c4 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/exp_lvr.npy and b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_lvr.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_rates.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_rates.npy
new file mode 100644
index 0000000..ef1f953
Binary files /dev/null and b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/exp_rates.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_cv_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_cv_arr.json
new file mode 100644
index 0000000..a7e7373
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_cv_arr.json
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_lvr_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_lvr_arr.json
new file mode 100644
index 0000000..ebe9876
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_lvr_arr.json
@@ -0,0 +1 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_rates_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_rates_arr.json
new file mode 100644
index 0000000..54235c5
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/ks_rates_arr.json
@@ -0,0 +1 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/path_to_chi.json b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/path_to_chi.json
new file mode 100644
index 0000000..e04b33f
--- /dev/null
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_cv_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_cv_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_cv_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_cv_groundstate.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_lvr_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_lvr_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_lvr_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_lvr_groundstate.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_rates_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_rates_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_rates_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/sim_rates_groundstate.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_bestfit.csv
new file mode 100644
index 0000000..ddce303
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_bestfit.csv
@@ -0,0 +1,35 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_groundstate.csv
new file mode 100644
index 0000000..333e1c4
--- /dev/null
+++ b/simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7/simulated_fc_groundstate.csv
@@ -0,0 +1,35 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh.csv
deleted file mode 100644
index 07bb981..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh.csv
+++ /dev/null
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_False.csv
deleted file mode 100644
index b6bacc8..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_False.csv
+++ /dev/null
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_True.csv
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index 4f3f2d7..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/df_corr_lh_use_corrcoeff_True.csv
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_cv_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_cv_arr.json
deleted file mode 100644
index 1c8432a..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_cv_arr.json
+++ /dev/null
@@ -1 +0,0 @@
-{"1.5": 0.4966011191923605, "1.75": 0.494239704155925, "1.25": 0.4978961876269328, "2.0": 0.49469711427718477, "2.75": 0.5141217506098631, "3.0": 0.5121550943795777, "2.5": 0.48458940423112973, "4.0": 0.8945872693822883, "1.0": 0.4843436861331355, "2.25": 0.501119286161553}
\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_lvr_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_lvr_arr.json
deleted file mode 100644
index 9f8a20e..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_lvr_arr.json
+++ /dev/null
@@ -1 +0,0 @@
-{"1.5": 0.48860124509420544, "1.75": 0.48294086489556887, "1.25": 0.4912637948799502, "2.0": 0.4959179666223439, "2.75": 0.4853281228419659, "3.0": 0.5054499221296341, "2.5": 0.49922784412490184, "4.0": 0.755468086558721, "1.0": 0.48161098976808425, "2.25": 0.5032755673644842}
\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_rates_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_rates_arr.json
deleted file mode 100644
index 156514a..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/ks_rates_arr.json
+++ /dev/null
@@ -1 +0,0 @@
-{"1.5": 0.8091180964076652, "1.75": 0.8061433650891463, "1.25": 0.8123031958712122, "2.0": 0.8078984614383188, "2.75": 0.7963266319271846, "3.0": 0.8028999152399647, "2.5": 0.8074979785223004, "4.0": 0.4537436703225466, "1.0": 0.8090531763154383, "2.25": 0.8061469995199295}
\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/path_to_chi.json b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/path_to_chi.json
deleted file mode 100644
index 17b4251..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/path_to_chi.json
+++ /dev/null
@@ -1 +0,0 @@
-{"1.5": "013292ec8fa6c9230d1c9518b71d19ad", "1.75": "01dde3bb7de102415c2c560fa65f9ee9", "1.25": "51d3f722fcea42fe722804af260521ad", "2.0": "615aa8683a7ed776eafc718065dcbd3d", "2.75": "6f68a4df981d21210c693cf61bc11bfe", "3.0": "938abb40e337c93bbb95087117e324a6", "2.5": "984f627232b71236276f8d5178c3baab", "4.0": "abab9e23cec17ce523088b36d5a4e6d8", "1.0": "ad2f36126638bf99c0418469d0bfebba", "2.25": "d9673c094027e9046fa6413950f32647"}
\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_cv_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_cv_bestfit.npy
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index aaa67d0..0000000
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_cv_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_cv_groundstate.npy
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index 2e4b860..0000000
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_lvr_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_lvr_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_lvr_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_lvr_groundstate.npy
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index de9c922..0000000
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_rates_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_rates_bestfit.npy
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_rates_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/sim_rates_groundstate.npy
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index 9c7bcd9..0000000
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_bestfit.csv
deleted file mode 100644
index 95bd98c..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_bestfit.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_groundstate.csv
deleted file mode 100644
index c9289e8..0000000
--- a/simulated_data/scaling_experiment_lichtman_chii2_different_time_consts/simulated_fc_groundstate.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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-isthmuscingulate,0.04210235496869584,0.007850032196141502,-0.003953405687039514,-0.01123965124333029,0.06361358940291718,-0.006202192869225793,-0.018827507764606143,-0.020724896954040145,-0.0037229469272263426,0.007502874264658785,1.0,0.016929804932421508,0.00989003646136484,0.04331966993646234,0.006551923285394274,0.02877443248734761,0.02893731431618471,0.0020300281680124565,-0.022029581679294457,0.03977424474854487,0.034299142221722756,-0.0006714470030187209,-0.023422208813307876,0.06110117749479023,0.0013184993479784513,0.04152765477194985,0.004966095302335648,-0.020079312738959836,-0.014674718542533867,0.00645678936716884,0.002624265076197009,-0.02539063253690326,-0.005429040917729903,0.0060057186843602015
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_False.csv
index a9f2478..85a677a 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_False.csv
@@ -1,10 +1,12 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_True.csv
index b10f6f5..74e5b55 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_lh_use_corrcoeff_True.csv
@@ -1,10 +1,12 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_False.csv
index a6b465d..c48a051 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_False.csv
@@ -1,10 +1,12 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_True.csv
index 03d97be..c286c07 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/df_corr_rh_use_corrcoeff_True.csv
@@ -1,10 +1,12 @@
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_cv.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_cv.npy
index a897926..53a46b0 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_cv.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_cv.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_lvr.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_lvr.npy
index 2bdf39e..49502c4 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_lvr.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_lvr.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_rates.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_rates.npy
index 3f977ab..ef1f953 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_rates.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/exp_rates.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_cv_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_cv_arr.json
index 92c0cb7..3802417 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_cv_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_cv_arr.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_lvr_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_lvr_arr.json
index 56399cd..caebc4f 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_lvr_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_lvr_arr.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_rates_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_rates_arr.json
index 6475190..9c76ec6 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_rates_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/ks_rates_arr.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/path_to_chi.json b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/path_to_chi.json
index 8acf9ba..0946a8a 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/path_to_chi.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/path_to_chi.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_bestfit.npy
index 459a905..46b7bf3 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_groundstate.npy
index 5f97cc1..fd5332a 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_cv_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_bestfit.npy
index eefb4c3..f2ca492 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_groundstate.npy
index 7d81ec5..896494a 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_lvr_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_bestfit.npy
index e9e00de..55e0e64 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_groundstate.npy
index 4598e5e..0fdbed9 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/sim_rates_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_bestfit.csv
index 59d84eb..3758b57 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_bestfit.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_bestfit.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_groundstate.csv
index d681d66..f5797bb 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_groundstate.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_distributed_params/simulated_fc_groundstate.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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-fusiform,0.012993274999608473,0.006221469440278447,0.016450460696439743,0.012747890467056123,0.012691262249094774,0.0002567054254964741,1.0,-0.0056248799200497536,0.026402750228438465,0.021468824226596083,-0.0017441162657566781,0.010414266276289596,0.013179288835516112,0.0127568421272007,0.007316441037125791,0.008688669796152234,0.008708603904296616,0.009265612914587357,0.004055395468871752,-0.01212036781809649,-0.01470154647530903,-0.003678799090466883,-0.00976770950948988,-0.011508385580302176,0.013714615787710467,0.008276325285801618,0.019304413449249897,0.017712459915958118,-0.012361849330519441,-0.0017844366897062055,0.03143023431897313,0.008160072019410148,0.01809779994897794,0.00863585176993614
-inferiorparietal,0.008103788497319832,0.0026729496791815317,0.0029006921071867667,0.01561567539394055,-0.019227732012599832,-0.0029185052667071052,-0.0056248799200497536,1.0,-0.0005612394141283204,0.012752464152942605,-0.008834619275215791,-0.004587304301197736,0.007739865572323987,-0.009998166351394373,-0.010801967975304977,0.008131292165757108,-0.025814379752553997,0.00278071122690524,0.005669415660969742,0.003223385087683026,0.0007743246023428977,0.0035581582138845503,-0.00925669890625095,0.003022638903951591,0.002636175773990616,0.00560694541130399,0.00021666184503937074,0.004499947182891647,0.030151844937397632,0.00485758273047432,0.009102404378729065,0.020822751493336365,-0.0008997485033976985,0.014886060735395319
-inferiortemporal,0.00854390458432845,-0.01698685486697888,0.00881205152921139,0.0008033847151932832,-0.002191375689678143,-0.011587350386902279,0.026402750228438465,-0.0005612394141283204,1.0,0.038845929344375786,0.021740790349340972,0.019846800859950765,0.009270187197728152,-0.015432738250500136,0.0298277988353104,0.021135516753496512,-0.014175234864465341,-0.0058846331303781365,0.003744447463887514,0.016287190556796734,0.009343323346091639,-0.0047938803333577055,-0.0003811024347951046,-0.014114391860470664,-0.020680258506201744,-0.003348822163814453,-0.021799016218432787,0.009080127225868906,0.020414482560833506,0.021818632971593976,0.025943364655377006,0.0016130037464488142,0.030026783558604546,-0.0018405788537609721
-insula,-0.011061251900428334,0.01057428630147953,0.0017993331829179462,0.027032747728884757,-0.0039865286608481544,0.0036428548179560627,0.021468824226596083,0.012752464152942605,0.038845929344375786,1.0,0.003468516891601191,-0.008440281800221798,-0.002826099149140075,0.011943461561340542,0.017427922117598138,-0.00387569402288859,0.0037780413502792615,0.013536504442320095,-0.000348839206722591,0.01882049936580119,0.006479215266864734,0.008614271219342254,0.008801662302711864,0.0092171670441384,-0.0023273849407837483,-0.00817830845782427,-0.0031440633621440625,-0.004684123141236732,-0.015395930086326474,0.007748487162700109,0.0007878990873271023,0.028417506448015002,0.010457170461884222,-0.01050096468669488
-isthmuscingulate,-0.007461798829947566,-0.0018712087049258276,0.002744628676265741,-0.016336365041947836,0.009791064367708517,0.011284904739427122,-0.0017441162657566781,-0.008834619275215791,0.021740790349340972,0.003468516891601191,1.0,-0.007229476051490148,0.003113440103025393,-0.018020051888227598,0.0005636094543357108,0.021096234128252938,-0.011307186386581337,0.001656272698017748,0.01444434089634749,-0.001414619036992641,-0.00039328684644916024,-0.003587036778640523,-0.01277479664394396,0.009860177868382529,-0.0015813886500647555,0.032396515561180186,0.00860343178150599,-0.0032271572592611217,-0.0009175761844573082,0.007461215899427338,0.008568845090497181,-0.0012577553014206414,0.0013757755730311046,0.011978598459088987
-lateraloccipital,0.020894247343241114,-0.018925289418470114,0.0038340205903270564,-0.0030797045215658417,-0.02457862429023792,-0.009646986611456448,0.010414266276289596,-0.004587304301197736,0.019846800859950765,-0.008440281800221798,-0.007229476051490148,1.0,5.435232018449105e-05,0.007868005342077756,-0.02023438530527444,0.00208673098608553,-0.005044916043007211,0.006497868854386745,0.0019171272617404696,0.013001330772616873,0.0060675257193391645,0.004478008317884227,0.0003136718735090266,-0.01319428011876029,0.008189920454614301,-0.001039774035112164,-0.018007544652637784,0.0017041944684215117,0.001145495381909759,-0.004689906359238769,0.005320514389984167,-0.008759038164319454,-0.003010563031566027,-0.006177625578691321
-lateralorbitofrontal,-0.006503126075134049,-0.009536011527572935,-0.008951405061363975,-0.0347077159275236,-0.0029746138293891408,-0.000305449328549252,0.013179288835516112,0.007739865572323987,0.009270187197728152,-0.002826099149140075,0.003113440103025393,5.435232018449105e-05,1.0,-0.009162109136851794,0.014923882672053164,-0.03221196412918354,-0.010342285484622597,-0.009525835637050927,-0.023438098050553934,0.0052025557281297884,-0.013122228777820877,0.00782222234775734,-0.001797192000313137,-0.004150389621542711,-0.002486663482866487,0.02340172538850594,-0.008048024775345724,-0.010293955086349383,0.014616388681977645,0.0058965094639114495,0.005589730542759582,0.013728502409803422,-0.0002040694371671392,-0.0029967970980266326
-lingual,-0.006899889545406015,-0.00455683728405277,0.00929494351797571,0.0355077917353322,-0.0004040927341277097,-0.017009819205791435,0.0127568421272007,-0.009998166351394373,-0.015432738250500136,0.011943461561340542,-0.018020051888227598,0.007868005342077756,-0.009162109136851794,1.0,-0.0069625446632530265,-0.0006490327803566592,-0.0049135599642326075,-0.00042246814140771226,0.010815332530285356,-0.007221871334549382,0.039157457678759965,0.04977219353594703,0.016334187919589058,-0.0005175833486130563,0.009517562868089758,0.03054391085297116,0.013426852786673726,-0.0045400122714839685,-0.004719160312720846,-0.000930536337242792,-0.00047034939715538026,0.007663980969974357,0.003512293842809215,0.0032148353899384024
-medialorbitofrontal,-0.007987292525179287,-0.0026848319027849184,-0.00520390422051413,0.019880108417494855,0.0017291122106036897,0.025389225022277657,0.007316441037125791,-0.010801967975304977,0.0298277988353104,0.017427922117598138,0.0005636094543357108,-0.02023438530527444,0.014923882672053164,-0.0069625446632530265,1.0,0.005094485162220393,0.009272016370565838,0.0019496576445344525,-0.023163675500714315,-0.000774086181683257,-0.004815647041600088,-0.008107482339665946,0.007560498082874125,0.02069722631363699,-0.0009677886384989457,0.0010943819040056828,0.0027548120831336733,-0.009048128153848176,-0.003471172643941053,-0.013441158485105978,-0.009137365966628957,-0.0008703994803418892,0.018775788892601668,-0.008008736240930799
-middletemporal,0.014047941819719313,0.012615364333125899,-0.014128658480719876,0.006420629168135134,0.003986336162499016,-0.013659415169607253,0.008688669796152234,0.008131292165757108,0.021135516753496512,-0.00387569402288859,0.021096234128252938,0.00208673098608553,-0.03221196412918354,-0.0006490327803566592,0.005094485162220393,1.0,-0.008295298777370468,-0.0007478387977398896,0.009061451330850921,-0.005685650990172524,0.011239941424088712,-0.006343394830414853,0.0026366026307191944,0.0017156681136915194,-0.0007795085087004199,-0.009352728628601837,-0.019733433571600217,0.01410607435177991,-0.00018530815407087509,0.017021152641996856,-0.0014692684465937471,-0.011809941868264548,6.522982931895298e-05,6.288684226798427e-05
-paracentral,0.003922218378395873,0.0030148326061536636,-0.0032115371462529303,-0.0014206889252022302,0.0016873062702520967,0.02728829112285905,0.008708603904296616,-0.025814379752553997,-0.014175234864465341,0.0037780413502792615,-0.011307186386581337,-0.005044916043007211,-0.010342285484622597,-0.0049135599642326075,0.009272016370565838,-0.008295298777370468,1.0,-0.00783965735172204,-0.0003898412468048488,-0.0030098267041644964,-0.016816211446720034,-0.009645037601027454,0.02588381867788264,-0.004425857301774425,0.028660152520088668,0.003259741929335416,-0.007242674137721263,0.00019880701185273392,-0.008224914464058252,-0.008368721363162772,-0.010210293660596727,0.008550643646460115,-0.009204133043293743,-0.0047429945308483604
-parahippocampal,-0.020276439177684548,0.00295007089874338,-0.006690751528494673,-0.00838089796716318,0.02101562978485764,0.023551590431787454,0.009265612914587357,0.00278071122690524,-0.0058846331303781365,0.013536504442320095,0.001656272698017748,0.006497868854386745,-0.009525835637050927,-0.00042246814140771226,0.0019496576445344525,-0.0007478387977398896,-0.00783965735172204,1.0,0.003986991342321872,0.0061196199081051525,0.01182107447552227,-0.010700872964768267,-0.005857327333530903,-0.003945629270173921,0.007297209510984736,0.0066675040093235995,0.00974465037839311,0.007122613288354284,0.0064275911982205865,-0.00493555419955155,-0.006931864238245148,0.016972458165874133,-0.016015414093899727,0.004167331089293754
-parsopercularis,0.004384534933154065,0.018682950589044185,0.004421475532295471,-0.009725308288735524,0.008569179935157345,0.01883211729264955,0.004055395468871752,0.005669415660969742,0.003744447463887514,-0.000348839206722591,0.01444434089634749,0.0019171272617404696,-0.023438098050553934,0.010815332530285356,-0.023163675500714315,0.009061451330850921,-0.0003898412468048488,0.003986991342321872,1.0,-0.00270029377228297,-0.001506802122620544,-0.027561668711986724,0.006273980569160358,-0.003573232116518378,-0.0018419090938220977,0.032193099073426656,-0.001985798135231121,0.006977794249997565,0.014963787788790008,0.04328076181445388,-0.0023744089617113696,-0.0029367035819062833,0.004074981564027065,-0.010422788213373719
-parsorbitalis,0.02769087251692912,0.0010975403177062107,-0.0004618567630933704,0.010523469451664687,0.0070920453243936785,0.004587300002548063,-0.01212036781809649,0.003223385087683026,0.016287190556796734,0.01882049936580119,-0.001414619036992641,0.013001330772616873,0.0052025557281297884,-0.007221871334549382,-0.000774086181683257,-0.005685650990172524,-0.0030098267041644964,0.0061196199081051525,-0.00270029377228297,1.0,-0.016653335856271864,-0.00930426012973154,-0.006687163942470622,-0.0005901072429513903,0.0074060610815420555,-0.00822283172439337,-0.007486310451055234,0.0030675594666248686,-0.0013962964402580629,0.00917449460331146,0.0028213455755955897,-0.006007612337287527,0.027272636395669375,-0.014454270878677988
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-pericalcarine,0.01824862593877653,-0.006671293444806003,0.0018944975633802912,0.05434602273688877,0.0019427805163270682,0.0008960294688225014,-0.003678799090466883,0.0035581582138845503,-0.0047938803333577055,0.008614271219342254,-0.003587036778640523,0.004478008317884227,0.00782222234775734,0.04977219353594703,-0.008107482339665946,-0.006343394830414853,-0.009645037601027454,-0.010700872964768267,-0.027561668711986724,-0.00930426012973154,0.0036807654127286094,1.0,0.010084190197796872,-0.012970554554824041,0.019354837500898038,-0.0023259623462372828,0.004970255004264715,-0.006010616083177366,-0.0087315630595645,-0.003190724261690343,-0.014185416489159084,0.01371479584438376,-0.009878931068170454,-0.00774294206569832
-postcentral,-0.008464531643664449,0.0014990815970962231,0.0006225824949047078,-0.0073999094751295005,0.0014445289846819814,7.477301702080279e-05,-0.00976770950948988,-0.00925669890625095,-0.0003811024347951046,0.008801662302711864,-0.01277479664394396,0.0003136718735090266,-0.001797192000313137,0.016334187919589058,0.007560498082874125,0.0026366026307191944,0.02588381867788264,-0.005857327333530903,0.006273980569160358,-0.006687163942470622,-0.019721651879686965,0.010084190197796872,1.0,0.002905190024034243,0.04172949949589307,0.007125856245512988,-0.0057055210200370665,-0.0010621908901091558,0.006029477831343171,0.004790289261795464,0.007805898202490076,0.05188947972717404,-0.0037095418562335804,0.0011704539826968895
-posteriorcingulate,0.02378867177496528,0.027348782522496055,-0.009510769848638589,0.028928461027902815,0.0031714275964229706,-0.021616253711666228,-0.011508385580302176,0.003022638903951591,-0.014114391860470664,0.0092171670441384,0.009860177868382529,-0.01319428011876029,-0.004150389621542711,-0.0005175833486130563,0.02069722631363699,0.0017156681136915194,-0.004425857301774425,-0.003945629270173921,-0.003573232116518378,-0.0005901072429513903,-0.004762273177518511,-0.012970554554824041,0.002905190024034243,1.0,0.005175584136023232,0.01806722751174192,0.01613181878588608,0.011570142005611109,0.016621384213713012,0.0009820854976292856,-0.006667021181736878,-0.024982425097845807,0.00569250774210277,0.015278145238438035
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_False.csv
index 8e7e800..8956559 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_False.csv
@@ -1,5 +1,7 @@
-1.0,0.39471258202865794
-1.25,0.4008688166784357
-1.5,0.412611934286404
-1.75,0.5248065031611584
-2.0,0.2547347828696828
+,0
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+1.25,0.4009867711680793
+1.5,0.41138722954763524
+1.75,0.5268776039107825
+2.0,0.48486682324099917
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_True.csv
index a734c1b..bfebd71 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_lh_use_corrcoeff_True.csv
@@ -1,5 +1,7 @@
-1.0,0.45062973784225685
-1.25,0.4553830258090757
-1.5,0.48066781671889786
-1.75,0.42168502615405035
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+,0
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+1.25,0.46018053812877996
+1.5,0.4878563353942622
+1.75,0.4087915823984392
+2.0,0.32140593973294496
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_False.csv
index 734f962..ecbe3fe 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_False.csv
@@ -1,5 +1,7 @@
-1.0,0.39580267616037823
-1.25,0.4026436332096207
-1.5,0.4151095201136285
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_True.csv
index 290f486..8ba7069 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/df_corr_rh_use_corrcoeff_True.csv
@@ -1,5 +1,7 @@
-1.0,0.44548272698470576
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_cv.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_cv.npy
index a897926..53a46b0 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_cv.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_cv.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_lvr.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_lvr.npy
index 2bdf39e..49502c4 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_lvr.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_lvr.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_rates.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_rates.npy
index 3f977ab..ef1f953 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_rates.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/exp_rates.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_cv_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_cv_arr.json
index 4163032..8d804ce 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_cv_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_cv_arr.json
@@ -1 +1 @@
-{"1.5": 0.4075078566692465, "1.25": 0.41108070575878153, "1.0": 0.4138047462228829, "2.0": 0.4439099601819372, "1.75": 0.690044830086779}
\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_lvr_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_lvr_arr.json
index 6f8e44d..1feac0c 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_lvr_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_lvr_arr.json
@@ -1 +1 @@
-{"1.5": 0.39495340002673296, "1.25": 0.393968226715414, "1.0": 0.39243240916205724, "2.0": 0.8662365517415959, "1.75": 0.7974777551441637}
\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_rates_arr.json b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_rates_arr.json
index c8f17e9..d7e5436 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_rates_arr.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/ks_rates_arr.json
@@ -1 +1 @@
-{"1.5": 0.9231253497192522, "1.25": 0.9234123775273747, "1.0": 0.923980968249118, "2.0": 0.5955962206155109, "1.75": 0.913408242300987}
\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/path_to_chi.json b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/path_to_chi.json
index 12dca22..48f2bc6 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/path_to_chi.json
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/path_to_chi.json
@@ -1 +1 @@
-{"1.5": "08e8618517e6e057db688d6257680da6", "1.25": "35022b8c38368b83fb671cfc1087d83f", "1.0": "59cc1c6418e57a0cac22d007f9e0467b", "2.0": "7d86a0be95d45469743dffa2b2809b8a", "1.75": "dbbc477edceb3323b0883c89f8d7d955"}
\ No newline at end of file
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\ No newline at end of file
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_bestfit.npy
index cca2693..68c1500 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_groundstate.npy
index c15e9df..2d6a21f 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_cv_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_bestfit.npy
index 2fc37b0..0e23c41 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_groundstate.npy
index d10e5d8..12bfa02 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_lvr_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_bestfit.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_bestfit.npy
index 73cdcd6..53fdfbe 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_bestfit.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_bestfit.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_groundstate.npy b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_groundstate.npy
index 7c738c4..cbb2bc2 100644
Binary files a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_groundstate.npy and b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/sim_rates_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_bestfit.csv
index 3050c58..ce49298 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_bestfit.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_bestfit.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
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-cuneus,0.25920020024478446,0.13306166246288728,0.17025514902107433,1.0,0.20422116943781277,0.22232226613208556,0.4961543701595272,0.34683014595481637,0.24992452731790232,0.355450474820631,0.26939685534693125,0.9023208335858289,0.376688269223575,0.8674315659749745,0.23564840176693014,0.2769926671063517,0.4689053689494709,0.47874270726586754,0.08655394756774464,0.3853226884998505,0.15366758024107202,0.9391756289108257,0.31968871737496174,0.19310983883426405,0.23741206492942385,0.4806636971118698,0.14642246890560945,0.27860359334451,0.2432607253452224,0.4586309670426167,0.3586520948688015,0.31083588191384354,0.2570886854036263,0.3190956383555456
-entorhinal,0.24427685384210554,0.8179617545486099,0.04944208789791546,0.20422116943781277,1.0,0.203567021847963,0.4171462902464062,0.26149993117413534,0.31511073817579577,0.33698087816747385,0.7167712740656795,0.27449707323966205,0.3365140105087376,0.18603097613208636,0.2994999657664254,0.3162600543702062,0.4279134440220489,0.8496058723819061,0.02291711615611406,0.389933032345904,0.09759960648072825,0.18895662202749325,0.24494608196446702,0.6763987817825972,0.09643131347415762,0.48532016963333247,0.8294136503551073,0.18729056815815165,0.2811822090928965,0.3060267906847169,0.361626090108671,0.24135976446228127,0.45295416973299046,0.2921852034090207
-frontalpole,0.38225339686699317,0.2188317547043474,0.46201232656000685,0.22232226613208556,0.203567021847963,1.0,0.34247604386751124,0.45959382136156895,0.38903161966444977,0.6482313107692768,0.20597586417466285,0.3212436525136638,0.7188195684474572,0.20463065396600585,0.8057206316463066,0.4526011794987546,0.4621347319532668,0.2747706686976014,0.5040862555965354,0.6530180350081377,0.6107616208458885,0.21843111734788762,0.3466312934948179,0.23086496664055534,0.4027829325850063,0.27864771606378136,0.2702729084879542,0.8082088919551872,0.7174513174766177,0.4276453952959963,0.5495987626922374,0.37600206572001443,0.5464087942096566,0.44736426122479767
-fusiform,0.5461884802913584,0.2579664505300496,0.15830837465137987,0.4961543701595272,0.4171462902464062,0.34247604386751124,1.0,0.5453566444835882,0.7518897491087391,0.5715765942120188,0.2674507416550682,0.6810823287093128,0.5461878607154331,0.5463682469346486,0.44165795841536487,0.6836011420431515,0.5428114783191009,0.6730321269778475,0.07711962606502137,0.6703849014166291,0.19923644410676247,0.5064491544720975,0.46228183450679516,0.26496623758594745,0.25889937846953165,0.4236315343426856,0.27952866939964627,0.378370413751492,0.307023699831442,0.5242076073600975,0.647117669125204,0.44788714253452044,0.68326122555523,0.4799692168095336
-inferiorparietal,0.6276572986488248,0.2389378276027858,0.3784302299990613,0.34683014595481637,0.26149993117413534,0.45959382136156895,0.5453566444835882,1.0,0.6161677457682705,0.728447084821942,0.22194243994020962,0.4966864755363495,0.6006402223625876,0.29502603865567195,0.49741253149928993,0.7341167510922434,0.6721734184492695,0.39209710639706025,0.3063194217183419,0.6973747848762627,0.43300107601346866,0.3114847803092527,0.5636450867089882,0.26721601713268844,0.427733972060503,0.43888945004659474,0.27106132970818547,0.5919041703772419,0.5058170094905233,0.725881181113548,0.7686220192632038,0.5816679114209086,0.5597331931268577,0.6231263504158289
-inferiortemporal,0.665063368471788,0.25825403442431444,0.1711981899037632,0.24992452731790232,0.31511073817579577,0.38903161966444977,0.7518897491087391,0.6161677457682705,1.0,0.5759605497348169,0.2051264866995258,0.43745145661187995,0.5189441026696393,0.24923672694959145,0.48831750530366547,0.918625166008174,0.47559289514039454,0.4254661217931036,0.11560799256991773,0.6676813786064801,0.24862258728597625,0.2374429978036303,0.43282306419865096,0.26647210620232753,0.2414077034202743,0.31448526783165925,0.27890363825431685,0.4122660999254985,0.3099789326785217,0.43434850135599395,0.6891781661740792,0.4176097388930111,0.7671034778630327,0.4375927733522989
-insula,0.6077568716869035,0.2967712326603441,0.4354052126969347,0.355450474820631,0.33698087816747385,0.6482313107692768,0.5715765942120188,0.728447084821942,0.5759605497348169,1.0,0.2528921548813632,0.48852763292004286,0.8501551284018635,0.3017130723809848,0.7949715706408588,0.6953224926480375,0.7245074179048125,0.44304941906621226,0.37209516542149185,0.8698545741872631,0.5400738021173608,0.31204501072307056,0.6801366385343888,0.29686916101865585,0.5201945667158775,0.44405945487941445,0.35718231728187855,0.7047680033147916,0.6248994316349832,0.716008071847206,0.8847108945100652,0.6745367543868835,0.7989306179963732,0.7388873011038124
-isthmuscingulate,0.1628497228581159,0.58589724290061,0.06194561576178705,0.26939685534693125,0.7167712740656795,0.20597586417466285,0.2674507416550682,0.22194243994020962,0.2051264866995258,0.2528921548813632,1.0,0.2728143521978154,0.26378105101797245,0.20801680842988135,0.22113410311324425,0.21314619788924632,0.5276353428431746,0.6511284386800966,0.049899973179387616,0.26069703974073477,0.1027972483222129,0.24287113179594508,0.17463771739890138,0.6555107879628117,0.08603045736029807,0.5881857642966783,0.5916877377068851,0.17919979295195712,0.2614769062034388,0.27355336044966444,0.2563164118022751,0.16229922411530964,0.28091092193462247,0.214173585671223
-lateraloccipital,0.4007182414507264,0.17421478824667794,0.20431064327394863,0.9023208335858289,0.27449707323966205,0.3212436525136638,0.6810823287093128,0.4966864755363495,0.43745145661187995,0.48852763292004286,0.2728143521978154,1.0,0.5027616725810207,0.8819624356384077,0.35200106709390283,0.4579012258318369,0.5587543108404492,0.5921597823591145,0.11858103491183451,0.5466890590759247,0.21562861466536706,0.9176414343425074,0.4159422557203122,0.22448257038044736,0.28936132949339866,0.5276651968867672,0.1953428093373152,0.3818312000652686,0.3179407182594881,0.5604905796389125,0.5137133483332881,0.4089800847442526,0.41714751128301236,0.42918007830660243
-lateralorbitofrontal,0.4634563817950367,0.29520636123275945,0.3396794415732043,0.376688269223575,0.3365140105087376,0.7188195684474572,0.5461878607154331,0.6006402223625876,0.5189441026696393,0.8501551284018635,0.26378105101797245,0.5027616725810207,1.0,0.3553577873323116,0.9026618836429418,0.5781342831438752,0.5572973322868584,0.4651832624892444,0.340299517107883,0.8902964498978763,0.5353029377400343,0.3548208175205602,0.475250212883054,0.30987316938347453,0.37518332061144116,0.3800372715120096,0.35643506096001787,0.6801085689678923,0.5542540322550442,0.5603364975694862,0.7087494351533044,0.4840753797128924,0.8066927573604238,0.5589872116657839
-lingual,0.24553532698085476,0.11222691306285343,0.14968973370459646,0.8674315659749745,0.18603097613208636,0.20463065396600585,0.5463682469346486,0.29502603865567195,0.24923672694959145,0.3017130723809848,0.20801680842988135,0.8819624356384077,0.3553577873323116,1.0,0.2114156750250499,0.2610728819987492,0.3843097598721381,0.4972433342841811,0.06999274127329395,0.37056465923518056,0.12866087665098627,0.8998006143181354,0.26494101508020573,0.1502587838442843,0.19485277294931141,0.40153675882185946,0.12069056784732712,0.24907092134586858,0.2037101134733676,0.3866447168399219,0.3197164938278634,0.26539684719045376,0.2344460776108479,0.2898720141705592
-medialorbitofrontal,0.4264866793570891,0.2965734899893439,0.3578098369942562,0.23564840176693014,0.2994999657664254,0.8057206316463066,0.44165795841536487,0.49741253149928993,0.48831750530366547,0.7949715706408588,0.22113410311324425,0.35200106709390283,0.9026618836429418,0.2114156750250499,1.0,0.5384593251841879,0.4777775109736133,0.3542244296271427,0.3998458139129111,0.8721924039446788,0.5730705100919483,0.21752305328589883,0.4045735888787948,0.29254805361741576,0.3574614067584987,0.2928525235363444,0.3635371048792303,0.7184886758292113,0.6099837192942295,0.4404054371744677,0.6541649973622006,0.41964124266201425,0.8153978776503448,0.520291778380241
-middletemporal,0.8224413535247693,0.2828685745318191,0.2974304188476464,0.2769926671063517,0.3162600543702062,0.4526011794987546,0.6836011420431515,0.7341167510922434,0.918625166008174,0.6953224926480375,0.21314619788924632,0.4579012258318369,0.5781342831438752,0.2610728819987492,0.5384593251841879,1.0,0.6013433056676344,0.42969595608531896,0.20108644990501062,0.696892931507228,0.32390982623389647,0.254615495661404,0.5795955670488224,0.2872968566967455,0.3966983244355325,0.3549967246459564,0.30598867513641104,0.5077958062479453,0.42169398234500965,0.5417610483019473,0.8551642197079193,0.6203935215706311,0.7360851212481377,0.6525416848409523
-paracentral,0.5539539263974357,0.4018418894502632,0.44927573691378586,0.4689053689494709,0.4279134440220489,0.4621347319532668,0.5428114783191009,0.6721734184492695,0.47559289514039454,0.7245074179048125,0.5276353428431746,0.5587543108404492,0.5572973322868584,0.3843097598721381,0.4777775109736133,0.6013433056676344,1.0,0.5804627586147154,0.331533663024429,0.6120873945886276,0.4103331888821866,0.4247034687041998,0.6672174268477656,0.5044540138896193,0.5410522612772042,0.7007481602287158,0.41974625847875485,0.5857741508170953,0.6111831503619573,0.722811283744813,0.7657638107460277,0.6534147132669087,0.5088598743851802,0.6666538558277119
-parahippocampal,0.3490986665264114,0.6754425366209285,0.11652717126219828,0.47874270726586754,0.8496058723819061,0.2747706686976014,0.6730321269778475,0.39209710639706025,0.4254661217931036,0.44304941906621226,0.6511284386800966,0.5921597823591145,0.4651832624892444,0.4972433342841811,0.3542244296271427,0.42969595608531896,0.5804627586147154,1.0,0.054339174621100425,0.525690651872817,0.15038613467586634,0.4837408959643396,0.3492335582123946,0.6337294434010059,0.18172635131765102,0.5755505043535663,0.673223431265599,0.2847948988153029,0.330404632864591,0.451177317235605,0.48595395950770864,0.3481512809072176,0.5076380252719879,0.39724228556803054
-parsopercularis,0.18414789686236324,0.10579155325549218,0.7596783462826592,0.08655394756774464,0.02291711615611406,0.5040862555965354,0.07711962606502137,0.3063194217183419,0.11560799256991773,0.37209516542149185,0.049899973179387616,0.11858103491183451,0.340299517107883,0.06999274127329395,0.3998458139129111,0.20108644990501062,0.331533663024429,0.054339174621100425,1.0,0.36922528743728467,0.9225289512871951,0.08634898079263138,0.1419792164315354,0.1095307777845101,0.5509563712141832,0.1493589393393112,0.13978292939086387,0.7885914243435973,0.8007272464577133,0.260713815613999,0.2839363528706824,0.20958625462599445,0.18986600845743443,0.260369795493514
-parsorbitalis,0.5365351208783107,0.33892804739629734,0.35548612747319136,0.3853226884998505,0.389933032345904,0.6530180350081377,0.6703849014166291,0.6973747848762627,0.6676813786064801,0.8698545741872631,0.26069703974073477,0.5466890590759247,0.8902964498978763,0.37056465923518056,0.8721924039446788,0.696892931507228,0.6120873945886276,0.525690651872817,0.36922528743728467,1.0,0.5692529380797248,0.3639898229257221,0.5144393035598627,0.33258297522095204,0.3927109190679934,0.4207290598887624,0.405390960767536,0.7078630015009261,0.5819616265863888,0.6164823582431717,0.7793729203699866,0.5043406688601195,0.9109208441854336,0.5976709339753749
-parstriangularis,0.26800974659361315,0.1681973417205085,0.664917701735048,0.15366758024107202,0.09759960648072825,0.6107616208458885,0.19923644410676247,0.43300107601346866,0.24862258728597625,0.5400738021173608,0.1027972483222129,0.21562861466536706,0.5353029377400343,0.12866087665098627,0.5730705100919483,0.32390982623389647,0.4103331888821866,0.15038613467586634,0.9225289512871951,0.5692529380797248,1.0,0.14544671501342832,0.22709992775545138,0.18237847643535138,0.497684595418662,0.21792727007577495,0.21108468196558722,0.8600125644796336,0.8029631160196506,0.35896106439527736,0.40752900887652027,0.2804752402066031,0.3809931185492262,0.3415387846836498
-pericalcarine,0.23963747382343478,0.1119069029128707,0.1628935326792991,0.9391756289108257,0.18895662202749325,0.21843111734788762,0.5064491544720975,0.3114847803092527,0.2374429978036303,0.31204501072307056,0.24287113179594508,0.9176414343425074,0.3548208175205602,0.8998006143181354,0.21752305328589883,0.254615495661404,0.4247034687041998,0.4837408959643396,0.08634898079263138,0.3639898229257221,0.14544671501342832,1.0,0.27881941211459804,0.17198819316898387,0.2217040762615117,0.4478782931202907,0.12157085056823992,0.26966600234983606,0.22724446085443947,0.40979485365397983,0.3199299851687793,0.2732285456659619,0.2293797609234999,0.28686522103125117
-postcentral,0.6242128271247911,0.2075657139040724,0.31915790927942334,0.31968871737496174,0.24494608196446702,0.3466312934948179,0.46228183450679516,0.5636450867089882,0.43282306419865096,0.6801366385343888,0.17463771739890138,0.4159422557203122,0.475250212883054,0.26494101508020573,0.4045735888787948,0.5795955670488224,0.6672174268477656,0.3492335582123946,0.1419792164315354,0.5144393035598627,0.22709992775545138,0.27881941211459804,1.0,0.20848306494689628,0.6289998584434309,0.31614066606758434,0.23254525191284212,0.4469143537773071,0.40174435103257844,0.5849950763906513,0.7444538772448546,0.7979284944711647,0.45626959332455064,0.6930219860999679
-posteriorcingulate,0.2041867015495389,0.8665192636499265,0.09617805041951848,0.19310983883426405,0.6763987817825972,0.23086496664055534,0.26496623758594745,0.26721601713268844,0.26647210620232753,0.29686916101865585,0.6555107879628117,0.22448257038044736,0.30987316938347453,0.1502587838442843,0.29254805361741576,0.2872968566967455,0.5044540138896193,0.6337294434010059,0.1095307777845101,0.33258297522095204,0.18237847643535138,0.17198819316898387,0.20848306494689628,1.0,0.10956736664095477,0.5180792480543751,0.8016268679946692,0.23508981122750142,0.3327039278580139,0.24258066581580176,0.2977366477284212,0.20296062553672195,0.3482518894368833,0.22849951490049641
-precentral,0.4392382765329814,0.10729142351387233,0.8044915128055632,0.23741206492942385,0.09643131347415762,0.4027829325850063,0.25889937846953165,0.427733972060503,0.2414077034202743,0.5201945667158775,0.08603045736029807,0.28936132949339866,0.37518332061144116,0.19485277294931141,0.3574614067584987,0.3966983244355325,0.5410522612772042,0.18172635131765102,0.5509563712141832,0.3927109190679934,0.497684595418662,0.2217040762615117,0.6289998584434309,0.10956736664095477,1.0,0.21773822030712475,0.1340366476899496,0.642862709697688,0.6378358992150327,0.4366581545553132,0.5534207909399208,0.6247152188768005,0.26765822065537176,0.5407355225109124
-precuneus,0.2894069635874698,0.4751979112279008,0.19838004071541968,0.4806636971118698,0.48532016963333247,0.27864771606378136,0.4236315343426856,0.43888945004659474,0.31448526783165925,0.44405945487941445,0.5881857642966783,0.5276651968867672,0.3800372715120096,0.40153675882185946,0.2928525235363444,0.3549967246459564,0.7007481602287158,0.5755505043535663,0.1493589393393112,0.4207290598887624,0.21792727007577495,0.4478782931202907,0.31614066606758434,0.5180792480543751,0.21773822030712475,1.0,0.5104958036262457,0.3357469482439344,0.3942973724101861,0.6028838450331725,0.465564801754842,0.3155111183866718,0.3563872720103128,0.40477984222763347
-rostralanteriorcingulate,0.2110239919742447,0.960396017775941,0.1307310110888031,0.14642246890560945,0.8294136503551073,0.2702729084879542,0.27952866939964627,0.27106132970818547,0.27890363825431685,0.35718231728187855,0.5916877377068851,0.1953428093373152,0.35643506096001787,0.12069056784732712,0.3635371048792303,0.30598867513641104,0.41974625847875485,0.673223431265599,0.13978292939086387,0.405390960767536,0.21108468196558722,0.12157085056823992,0.23254525191284212,0.8016268679946692,0.1340366476899496,0.5104958036262457,1.0,0.28837831402958236,0.4091815287781141,0.29467639916868904,0.34932111428978374,0.21795379013821092,0.438348746796807,0.2747877989597672
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diff --git a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_groundstate.csv
index f03f74c..65d92ae 100644
--- a/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_groundstate.csv
+++ b/simulated_data/scaling_experiment_lichtman_chii2_smaller_g/simulated_fc_groundstate.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
-bankssts,1.0,0.01833390956016188,0.028779660022448854,0.024174073103753097,0.008765898824097219,-0.002810650884268653,0.03597580138279759,0.07373614138390024,0.0506764437753173,0.0066491322352143145,-0.017981498632706195,0.02136167141245999,-0.0242497739383269,0.010789201307795778,0.00044806628250475733,0.16503282556211993,0.03768714000524412,0.023070332247146195,0.030141354278524043,-0.009903059649475937,-0.03947254774308927,-0.01019465603082587,0.01629970612954531,-0.022162641744111134,0.0012785281490219661,-0.00082591279247435,0.02467408629185861,0.03525509277399497,0.016068406760900312,0.04299612277519848,0.11404443518001067,0.10687222073654118,0.023513186649686182,0.032766190052898714
-caudalanteriorcingulate,0.01833390956016188,1.0,-0.002533045883338621,-0.00818161192544738,-0.03586603856658748,0.02129585488359328,-0.011849905415489938,-0.004313033307301459,0.00033622955027620964,-0.005930988533438661,-0.015473792555311451,0.0033066775909613914,0.025132659932988615,-0.0032235207706206087,-0.001326119377166432,-0.007826698191249625,0.0022493274018652795,-0.019873286612580976,-0.0017201228295872749,0.010837238785506248,0.0035290871103200432,0.013485538846699846,-0.03634154159026602,0.0743173475021432,-0.018658784112117372,0.03705709954297879,0.09823274292855179,0.02569337563312466,0.08476545592796374,-0.0020634827810836536,0.0034442400688228054,-0.0041431385091699685,-0.019671311669435976,0.015981798520327918
-caudalmiddlefrontal,0.028779660022448854,-0.002533045883338621,1.0,0.0010052175899856236,0.011888326979615374,0.04656807272538316,0.023321911621998336,0.018059441706324703,0.03156717658328866,0.009675473305828497,-0.006258181161898564,0.04574445287004988,0.0035528229054630346,0.0050516604932955315,0.02261546110273485,0.03258447225623403,0.02372226908978309,-0.021743995411209544,0.05752681107392326,0.03229270467148169,0.01575955089898543,-0.0016128082532651945,0.022136069674314893,0.006476774521221134,0.09573656672418705,-0.0033889438427883575,0.0181900761153214,0.13952825774853586,0.11434508666325024,0.026176130861407514,-0.009952759603562896,0.01173013740063101,-0.01327591944460409,-0.02437806454966842
-cuneus,0.024174073103753097,-0.00818161192544738,0.0010052175899856236,1.0,-0.005411276688959979,-0.011297925637019951,0.0009413006891010312,0.045700472435874474,-0.009408503817946117,-0.06643085756424598,0.017949066971596518,0.1040821855432395,0.01673001858887889,0.20271277859849746,-0.018623978531854712,-0.012676125722035634,0.01891848922164892,-0.013848632598415775,-1.3778882621616186e-05,-0.02002187889680687,-0.02477463198015546,0.5817122933990742,0.01030826040248548,0.006152257166032566,-0.003469337223286907,0.08292648756814909,-0.00726301986485175,0.012339362525879735,-0.016877825588138422,0.10587852485501387,0.03359194890273012,-0.0007307436944215381,0.03311514743564753,0.04275463793249696
-entorhinal,0.008765898824097219,-0.03586603856658748,0.011888326979615374,-0.005411276688959979,1.0,0.012592606291299796,0.007910555376742208,0.021406184015013163,-0.0007903577849590982,-0.013356066792604124,0.036411972740795946,-0.003240723033790142,0.008861385210683772,0.0373826041698055,-0.00015289412695347498,0.023197399830639873,-0.006440145876191123,0.17430976545691562,-0.027439668943765726,-0.00882975314661517,-0.01441101051372092,0.008395342040980922,-0.015083928694484135,-0.029617690737464727,0.004187309134652388,-0.0023265111728133763,-0.01724283953224138,0.014089721198090698,-0.028370291268463594,-0.004486075402951715,-0.00364451752346524,-0.005273700462089347,0.08022481598472665,-0.012088934938302933
-frontalpole,-0.002810650884268653,0.02129585488359328,0.04656807272538316,-0.011297925637019951,0.012592606291299796,1.0,-0.0003195078760068218,-0.017012215463928233,0.019390861003418212,0.038199774675405244,-0.03177884806562494,0.00495033149030658,0.0195410655054469,0.005102087143649381,0.2184175742293354,0.022817731950268292,-0.020615307667880684,0.0034607552295618346,0.034636411269378345,0.001870759792187567,0.007903249299893913,-0.01076801873659102,0.027898631664404337,-0.026099669857034533,-0.015470198592529925,-0.041256439859085774,0.020288703965889103,0.0950405019357738,0.0015533331980037706,-0.043776413491669997,0.006157218988244456,-0.02017342544578186,0.012781960776275799,-0.07263918524500254
-fusiform,0.03597580138279759,-0.011849905415489938,0.023321911621998336,0.0009413006891010312,0.007910555376742208,-0.0003195078760068218,1.0,0.03650581950079524,0.16450451123038415,-0.021690437906546116,-0.02487279586572733,0.14595248223775467,0.0033125221429471286,0.06086262542237831,-0.010320704217230067,0.043024034079676664,0.0006735757959371727,0.19070602403707396,0.01413838428409349,-0.005196767105515468,0.014984513608481827,-0.004456096071047756,0.017725044201986045,0.020779564825529985,-0.028019485008913853,0.03343744967460528,-0.004206321241790524,-0.006130093905596297,0.007072546351812815,0.023285010546889348,0.015508367675659836,0.028381191972091473,0.03940473500414242,-0.006592638653277761
-inferiorparietal,0.07373614138390024,-0.004313033307301459,0.018059441706324703,0.045700472435874474,0.021406184015013163,-0.017012215463928233,0.03650581950079524,1.0,0.07200972224784029,0.00821923314130354,0.021150217053252998,0.09460416257525513,0.022736198514127972,0.013700321834621568,0.013388910012012355,0.10531158474078645,-0.013934287872649217,0.02366691983581889,0.009528083323747563,0.016364916881523132,0.0036451604662987953,0.000469004499402603,0.0645388899105692,0.005280599005721873,0.06094396008122382,0.05014980247927774,0.0015784048431367445,0.028415773134539687,-0.0003380093558504002,0.10931223211282712,0.052140792957291925,0.13646389807299572,-0.010317593631773104,-0.011942180593792462
-inferiortemporal,0.0506764437753173,0.00033622955027620964,0.03156717658328866,-0.009408503817946117,-0.0007903577849590982,0.019390861003418212,0.16450451123038415,0.07200972224784029,1.0,-0.027458747577473006,0.012857113387068875,0.0979201652750175,0.0023264482266420155,-0.001662062841858172,0.014553252682655693,0.21160458493123183,0.010027595406506006,0.053998004243804396,-0.00013857978276030485,0.027101302688789557,0.022820317136675906,-0.026938291836828433,0.025772175800644608,0.0204424423191431,-0.004405134088288091,0.012008180416415227,0.016718766278046922,0.03152375060783,-0.007614786685079336,-0.014029258390106235,0.0416179787381934,0.015178587867259476,-0.00515402233428514,-0.0084113967748335
-insula,0.0066491322352143145,-0.005930988533438661,0.009675473305828497,-0.06643085756424598,-0.013356066792604124,0.038199774675405244,-0.021690437906546116,0.00821923314130354,-0.027458747577473006,1.0,-0.012214226159160029,0.02656860147894856,0.06326684485869584,-0.044569461462136174,0.039873475276765304,0.01792355525555674,-0.018913946771392787,-0.021049338679028445,0.030574661998399713,0.05830690541183426,0.03930218588536927,-0.059593405187959525,-0.02496488377571808,0.03257143076061208,-0.03171714621345584,-0.00543824043196887,0.03624103112571968,0.003263568152565499,0.013026982981094191,0.08163201501403292,0.08066955466614809,0.040505305707523656,0.12178839926687539,0.09183543844818098
-isthmuscingulate,-0.017981498632706195,-0.015473792555311451,-0.006258181161898564,0.017949066971596518,0.036411972740795946,-0.03177884806562494,-0.02487279586572733,0.021150217053252998,0.012857113387068875,-0.012214226159160029,1.0,0.00966748818768906,-0.016042260111810026,0.05828138596384979,0.028184126903818982,0.022613855927179894,0.028971931022825324,0.026993463950080985,-0.02297410745770978,0.01749675729270983,0.024462429870008556,-0.021363490045197397,0.03799492887135408,0.05177260547302192,0.005108143726930867,0.16946153382034662,-0.03427358765614605,0.002633708390548954,-0.028362846259895793,0.029025362465044658,-0.0011798576270627226,0.03416151631296468,0.007197040468827895,-0.034663468184423174
-lateraloccipital,0.02136167141245999,0.0033066775909613914,0.04574445287004988,0.1040821855432395,-0.003240723033790142,0.00495033149030658,0.14595248223775467,0.09460416257525513,0.0979201652750175,0.02656860147894856,0.00966748818768906,1.0,0.027605448339029743,0.10204327497251527,0.003658098076354771,0.04566744766553621,-0.00038400599303008756,0.056134710054332966,0.05336024462690816,0.010459893164157477,0.03520167356255421,0.06640290994717636,0.01374701438202712,0.005010109982676257,-0.002606489705224648,0.033913281505036606,0.018896922882184634,0.03438767764913295,-0.009937442493363879,0.06995724954184529,0.015047183110806918,-0.010266973445731508,0.029894454131154637,0.014970038414567751
-lateralorbitofrontal,-0.0242497739383269,0.025132659932988615,0.0035528229054630346,0.01673001858887889,0.008861385210683772,0.0195410655054469,0.0033125221429471286,0.022736198514127972,0.0023264482266420155,0.06326684485869584,-0.016042260111810026,0.027605448339029743,1.0,-0.013001098992190124,0.14653004818036355,0.0029981213789220776,0.012278482567525529,0.007127971378310777,0.0008863067923789779,0.05659918656784927,-0.0037992480643911346,0.021284862468551623,0.02259897275945773,-0.008265539609493541,-0.06204740138449481,0.005432973480823276,0.029313543802565453,0.01580717566530804,0.010163501313063494,0.016658236851206064,-0.0008381149380023638,-0.03801334901614331,0.06393169643344687,-0.0013887247680115006
-lingual,0.010789201307795778,-0.0032235207706206087,0.0050516604932955315,0.20271277859849746,0.0373826041698055,0.005102087143649381,0.06086262542237831,0.013700321834621568,-0.001662062841858172,-0.044569461462136174,0.05828138596384979,0.10204327497251527,-0.013001098992190124,1.0,-0.001666457425529141,0.0069595718469776125,0.021659782167983335,0.1138814855246851,-0.030983036149701354,-0.0004239454774812506,-0.026804648604254265,0.3107942962904962,0.00801198545195275,0.054429103935338174,0.009825141211530202,0.10776012254852647,-0.007075161095946261,0.0035977394084342287,-0.03250747632918142,0.07027270941053956,-0.002706864676289171,-0.0027644360773861755,0.028875025767021528,0.01541616724482552
-medialorbitofrontal,0.00044806628250475733,-0.001326119377166432,0.02261546110273485,-0.018623978531854712,-0.00015289412695347498,0.2184175742293354,-0.010320704217230067,0.013388910012012355,0.014553252682655693,0.039873475276765304,0.028184126903818982,0.003658098076354771,0.14653004818036355,-0.001666457425529141,1.0,-0.021854067832812202,0.059409855378194015,-0.005554734314410207,0.022659548349740672,0.026468392769784608,0.016874738930416543,-0.003726695685478779,0.039897566526907194,-0.012255047873571779,-0.007857231039806047,0.024443473831981075,0.0966108133652171,0.04947596216805005,0.004101040817005757,-0.0027718033528015737,0.027449567059114437,-0.011570776488169286,0.08982072849533487,-0.02342332968723478
-middletemporal,0.16503282556211993,-0.007826698191249625,0.03258447225623403,-0.012676125722035634,0.023197399830639873,0.022817731950268292,0.043024034079676664,0.10531158474078645,0.21160458493123183,0.01792355525555674,0.022613855927179894,0.04566744766553621,0.0029981213789220776,0.0069595718469776125,-0.021854067832812202,1.0,0.022475840990293455,0.027396214741365676,-0.009412589076419748,0.012787962231000123,-0.0009948472588081744,-0.02689135044052715,0.010504469929274899,-0.021146127190229364,0.02447928955349789,0.03856940244888035,-0.0054909927787584025,0.0016806563882523753,-0.0226476075118595,0.02578492594715745,0.07466514217459584,0.037514998552011174,0.017439113942070802,-0.04133269885246149
-paracentral,0.03768714000524412,0.0022493274018652795,0.02372226908978309,0.01891848922164892,-0.006440145876191123,-0.020615307667880684,0.0006735757959371727,-0.013934287872649217,0.010027595406506006,-0.018913946771392787,0.028971931022825324,-0.00038400599303008756,0.012278482567525529,0.021659782167983335,0.059409855378194015,0.022475840990293455,1.0,0.02090713724659678,-0.028817069810579958,0.03622106574209665,-0.018553655079699284,-0.03741141288987793,0.07509587673688346,0.07151600040392223,0.010483947237700216,0.09599380994016882,0.004704090634032258,0.05248928670030205,0.013167776082435904,0.0456501813302778,-0.016331306463856818,-0.03532744686202798,0.048137423618948315,-0.02928992873314157
-parahippocampal,0.023070332247146195,-0.019873286612580976,-0.021743995411209544,-0.013848632598415775,0.17430976545691562,0.0034607552295618346,0.19070602403707396,0.02366691983581889,0.053998004243804396,-0.021049338679028445,0.026993463950080985,0.056134710054332966,0.007127971378310777,0.1138814855246851,-0.005554734314410207,0.027396214741365676,0.02090713724659678,1.0,0.006954143860723059,-0.01224142746083937,-0.04552101231829874,-0.016411031894199738,-0.005166520987560468,0.04037485534016303,-0.022037809931364782,0.060999331367695774,0.008589305014321542,0.00828671274712708,0.0149385462506639,0.006895787857430635,0.00033419395083133537,-0.01445976580699667,0.04487014419128752,0.0037776475432964676
-parsopercularis,0.030141354278524043,-0.0017201228295872749,0.05752681107392326,-1.3778882621616186e-05,-0.027439668943765726,0.034636411269378345,0.01413838428409349,0.009528083323747563,-0.00013857978276030485,0.030574661998399713,-0.02297410745770978,0.05336024462690816,0.0008863067923789779,-0.030983036149701354,0.022659548349740672,-0.009412589076419748,-0.028817069810579958,0.006954143860723059,1.0,-0.0134097979667962,0.1333601050283572,-0.009720771011340845,0.007652646363051609,0.007078943113131859,0.005769359137695762,-0.03407729524784111,0.026804811378337193,0.056642797956245154,0.04778777105216759,-0.004101656593169588,0.011143070782070205,0.013344039260996093,0.013141940388846847,-0.01299819816374411
-parsorbitalis,-0.009903059649475937,0.010837238785506248,0.03229270467148169,-0.02002187889680687,-0.00882975314661517,0.001870759792187567,-0.005196767105515468,0.016364916881523132,0.027101302688789557,0.05830690541183426,0.01749675729270983,0.010459893164157477,0.05659918656784927,-0.0004239454774812506,0.026468392769784608,0.012787962231000123,0.03622106574209665,-0.01224142746083937,-0.0134097979667962,1.0,0.10904913420384382,-0.011708117160385592,0.029635179458580213,0.005871796829822905,0.01115521707829352,0.029776044154946543,-0.0016573429006331009,0.0727435069222917,0.023179824451255906,0.00897358271075259,0.004632492931686103,0.016014840682357912,0.03085262927474796,-0.031687657369109286
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_False.csv
index 5eb4ae9..ad98b6f 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_False.csv
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_True.csv
index 890f36b..c548071 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_lh_use_corrcoeff_True.csv
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_False.csv b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_False.csv
index ef720eb..3f09a6b 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_False.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_False.csv
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_True.csv b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_True.csv
index fc9bb88..c40b5bd 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_True.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/df_corr_rh_use_corrcoeff_True.csv
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/exp_cv.npy b/simulated_data/scaling_experiment_lower_e_to_i/exp_cv.npy
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/exp_lvr.npy b/simulated_data/scaling_experiment_lower_e_to_i/exp_lvr.npy
index 2bdf39e..49502c4 100644
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/exp_rates.npy b/simulated_data/scaling_experiment_lower_e_to_i/exp_rates.npy
index 3f977ab..ef1f953 100644
Binary files a/simulated_data/scaling_experiment_lower_e_to_i/exp_rates.npy and b/simulated_data/scaling_experiment_lower_e_to_i/exp_rates.npy differ
diff --git a/simulated_data/scaling_experiment_lower_e_to_i/ks_cv_arr.json b/simulated_data/scaling_experiment_lower_e_to_i/ks_cv_arr.json
index 03a69a7..0b7fdb3 100644
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/ks_lvr_arr.json b/simulated_data/scaling_experiment_lower_e_to_i/ks_lvr_arr.json
index ddd9ba1..4959e1f 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/ks_lvr_arr.json
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/ks_rates_arr.json b/simulated_data/scaling_experiment_lower_e_to_i/ks_rates_arr.json
index 66904a7..7b158a6 100644
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diff --git a/simulated_data/scaling_experiment_lower_e_to_i/path_to_chi.json b/simulated_data/scaling_experiment_lower_e_to_i/path_to_chi.json
index 4a69eda..e637c2f 100644
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index e8144e0..d941c94 100644
Binary files a/simulated_data/scaling_experiment_lower_e_to_i/sim_rates_groundstate.npy and b/simulated_data/scaling_experiment_lower_e_to_i/sim_rates_groundstate.npy differ
diff --git a/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_bestfit.csv b/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_bestfit.csv
index aef0f4f..f4a9c18 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_bestfit.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_bestfit.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
-bankssts,1.0,0.01182015713619466,0.07626544381060725,0.07109666709784473,0.14130951324028762,0.10729634976456046,0.17054857835991544,0.3217055308067932,0.2024476673857833,0.18991646341608273,0.025341141432132848,0.247219389872685,0.13897374816679592,0.09313559557165363,0.1033401162783108,0.33894516674471603,0.13650116224844477,0.16355243089005939,0.0979770536787097,0.10175860175431566,0.12323296086325701,0.04847113293862434,0.10659164377836478,0.05452064926009502,0.04888957478515542,0.08059332625565775,0.07413085356977901,0.09527852383885949,0.0434801918134946,0.18397601462946342,0.45942324446928295,0.24685175618354038,0.2043406173271379,0.3933099337147779
-caudalanteriorcingulate,0.01182015713619466,1.0,0.6693346467732934,0.10977747255751591,0.12043833364097488,0.362150670626326,0.015292058976574903,0.12222092053316172,0.0717171920886517,0.3744100065336827,0.2848559389255887,0.19230203097846227,0.36310333145288515,0.13630253198762207,0.3576505526895929,0.03712790432765859,0.5963402645528432,0.10088920915900522,0.4007113860843464,0.4765309029634356,0.35941011078621854,0.09896329401797212,0.22398095577697316,0.6021369917188615,0.16534747619507098,0.346357111078955,0.5742702827722024,0.4729506151772601,0.8046617409219601,0.2831860604895614,0.07657459135450294,0.16874957711691208,0.2629420687511205,0.12927382423124947
-caudalmiddlefrontal,0.07626544381060725,0.6693346467732934,1.0,0.10877482747800281,0.10362119242258415,0.42070538018881337,0.03300218212289705,0.2894271764226242,0.10247101776572339,0.47598080170761403,0.13148977281316268,0.24482135425610282,0.40821375030498397,0.14411082137025155,0.3986346933078203,0.10210321218675888,0.6089812414744056,0.08438573709162879,0.5897191867709721,0.5334705320342246,0.5215013023655067,0.09433748383185092,0.27880114532479877,0.34199023188712413,0.4020487231081246,0.27303640515460226,0.5464045059292016,0.6897497827008454,0.7869167344460447,0.3681628456787026,0.1524808275589462,0.2679108150132592,0.33974761724479835,0.19597051229930101
-cuneus,0.07109666709784473,0.10977747255751591,0.10877482747800281,1.0,0.16160945628144696,0.12789312758354807,0.08383249950671197,0.1148267928797484,0.05744774407974801,0.11836713852759158,0.16869734077448292,0.4624157333376761,0.1321082108629401,0.5194994288002309,0.12305766306338191,0.06313141984578668,0.2776868571297032,0.13830882899031746,0.12393783971370738,0.14845557535476397,0.11141757046584401,0.7494094808733509,0.08931977640476164,0.15120911795866948,0.0669588661741241,0.1858763037181067,0.09648527392854553,0.12282785593419933,0.12722708136594768,0.1229466159537541,0.041169097541278774,0.12216279086603851,0.12938080771887261,0.03990065236289647
-entorhinal,0.14130951324028762,0.12043833364097488,0.10362119242258415,0.16160945628144696,1.0,0.11770746627589866,0.5433963914352998,0.24120644305738706,0.20532222103517178,0.20422106197567874,0.28356994446205014,0.629175967934984,0.18632210817054357,0.3546095073438407,0.14499181707018524,0.14673762905737575,0.20833158755314166,0.7414412100864197,0.11639108571368431,0.1551252439593657,0.12887351342102749,0.2160005158874572,0.10863550660455011,0.18837487128948274,0.10448673429942364,0.246261950018988,0.12689567819046574,0.0904387186336417,0.08496265627808929,0.22351220160253885,0.11479665361364479,0.14799124638486486,0.39422871525916997,0.11096727211700862
-frontalpole,0.10729634976456046,0.362150670626326,0.42070538018881337,0.12789312758354807,0.11770746627589866,1.0,0.0001422225238365332,0.24783979503948983,0.06614127206345127,0.6260481576922073,0.13868786568078845,0.24484720922417552,0.7036443338010303,0.15945796319306824,0.8581664723358495,0.14727913295591497,0.4810816575278437,0.08845332539505943,0.4889529121756437,0.6772679624251706,0.5516955935454118,0.10637302843482874,0.17496441444202904,0.29307175421517034,0.04003132857249361,0.2356476486777612,0.6752879094959395,0.6027755368813849,0.4688699934240239,0.33610907558920294,0.2314568474216194,0.24665853014236963,0.5616542422955237,0.2482555792975834
-fusiform,0.17054857835991544,0.015292058976574903,0.03300218212289705,0.08383249950671197,0.5433963914352998,0.0001422225238365332,1.0,0.2066842550605628,0.4921676138697439,0.034043004294390555,0.1250868047475091,0.5601955305592614,0.02877243881603317,0.23931882086072198,0.018257312421350588,0.2534680656752861,0.06234796163767114,0.6664842439465493,0.04598067696926799,0.0007226386658379671,-0.004193625648883801,0.09223923761259586,0.07596563264240543,0.03385629283532565,0.08328829038578314,0.09185639695011694,0.0337679199125173,-0.0009770226677675278,0.009844176634571497,0.126405780984387,0.12578926898238907,0.09212176483802755,0.15343404592029336,0.1334624341926756
-inferiorparietal,0.3217055308067932,0.12222092053316172,0.2894271764226242,0.1148267928797484,0.24120644305738706,0.24783979503948983,0.2066842550605628,1.0,0.22023072210821046,0.3315327208715125,0.06654233759804708,0.48425917271222796,0.30162739652894255,0.20445823941236527,0.24185664037592303,0.3068922218922893,0.3456870399091107,0.20725581178255628,0.22031570635585535,0.26749267729019705,0.2676967047715266,0.12117358516521748,0.24085872897239052,0.12262936310895153,0.2005037350854099,0.20328330604954653,0.1727666794343544,0.25436497969894317,0.17454665889962628,0.43938052304066116,0.307288716059734,0.36694460837811416,0.3223454287585018,0.30348212556418563
-inferiortemporal,0.2024476673857833,0.0717171920886517,0.10247101776572339,0.05744774407974801,0.20532222103517178,0.06614127206345127,0.4921676138697439,0.22023072210821046,1.0,0.09311915456593307,0.07877268728833704,0.3530475063001392,0.07900756991143155,0.09885695190704206,0.08027357825435737,0.5175062259841826,0.10031158987289185,0.23993005955865132,0.0915678845750921,0.06086974099575812,0.06782608801230844,0.047150454553570316,0.05493816025424037,0.03992559238540712,0.03729509191523022,0.045217272396963784,0.10159281898542245,0.0775425352827011,0.09312694020936933,0.09715174168063109,0.23581784027269032,0.10700011639720444,0.1840748301646081,0.18814078890108288
-insula,0.18991646341608273,0.3744100065336827,0.47598080170761403,0.11836713852759158,0.20422106197567874,0.6260481576922073,0.034043004294390555,0.3315327208715125,0.09311915456593307,1.0,0.1539975906878599,0.3000862140998961,0.6828785031426762,0.177995838340243,0.6556507378241366,0.14207175702563274,0.4857987344847827,0.13940763448929064,0.5047963685743047,0.7012086557518592,0.6591821545806309,0.13664425845371142,0.1922305712922809,0.2826496029134585,0.08664778373803313,0.25448674025839707,0.5434678942537327,0.5367115028803164,0.46034170503601524,0.3705265933225481,0.36995277684746924,0.2767669696247844,0.6449294981585278,0.41358274424338715
-isthmuscingulate,0.025341141432132848,0.2848559389255887,0.13148977281316268,0.16869734077448292,0.28356994446205014,0.13868786568078845,0.1250868047475091,0.06654233759804708,0.07877268728833704,0.1539975906878599,1.0,0.23918646870307536,0.15140556269936734,0.21891521539023276,0.1359629972040044,0.04201543381851731,0.26331009459515714,0.27180408891717944,0.10496503183190208,0.15887266860049065,0.11234153589859622,0.16811377263505722,0.07956334041234915,0.47119143715751605,0.07983615703537879,0.4336500459228378,0.17492598718537,0.12862398935154895,0.15278675599647318,0.14233317666691392,0.04918735794789743,0.0685715387427234,0.15292197028938884,0.01606946623760166
-lateraloccipital,0.247219389872685,0.19230203097846227,0.24482135425610282,0.4624157333376761,0.629175967934984,0.24484720922417552,0.5601955305592614,0.48425917271222796,0.3530475063001392,0.3000862140998961,0.23918646870307536,1.0,0.30442124149612526,0.6071386860797815,0.2555698213693418,0.28347455974903046,0.44475014870270857,0.5690787350078694,0.2657768540084243,0.26213038789162746,0.243980011992138,0.4754544962754553,0.23295828360984114,0.2426229027574394,0.18175212895441867,0.36007305402240375,0.2046742876887581,0.23665426150828692,0.20257328036557834,0.4067784825287202,0.2121320576716067,0.29235789730569073,0.40691849605604924,0.2170342710219927
-lateralorbitofrontal,0.13897374816679592,0.36310333145288515,0.40821375030498397,0.1321082108629401,0.18632210817054357,0.7036443338010303,0.02877243881603317,0.30162739652894255,0.07900756991143155,0.6828785031426762,0.15140556269936734,0.30442124149612526,1.0,0.17355974748957875,0.8158019965924543,0.1616385033477942,0.474132337731144,0.13039339584934678,0.5101475831020684,0.8216812142608737,0.6243848908469309,0.12440595561281755,0.19967360702211612,0.2941626882431978,0.07611621300914784,0.2483255484477544,0.5540757157437666,0.5888159966267553,0.4439074148641587,0.3475190223132704,0.27160825015905193,0.257639773977654,0.6495830951251811,0.3063661025800218
-lingual,0.09313559557165363,0.13630253198762207,0.14411082137025155,0.5194994288002309,0.3546095073438407,0.15945796319306824,0.23931882086072198,0.20445823941236527,0.09885695190704206,0.177995838340243,0.21891521539023276,0.6071386860797815,0.17355974748957875,1.0,0.16334363577377572,0.10027214818272054,0.2975705555012017,0.32425590670018273,0.18063092810966722,0.16896609655452108,0.17858974654833074,0.5724359579151879,0.13430783972665972,0.2206204701054929,0.09328123629607718,0.2453261497598562,0.14271197721621923,0.16451692117705055,0.1285715466530874,0.2128855295050318,0.07888207758140359,0.15598502470106906,0.2119775523584905,0.08739075175636836
-medialorbitofrontal,0.1033401162783108,0.3576505526895929,0.3986346933078203,0.12305766306338191,0.14499181707018524,0.8581664723358495,0.018257312421350588,0.24185664037592303,0.08027357825435737,0.6556507378241366,0.1359629972040044,0.2555698213693418,0.8158019965924543,0.16334363577377572,1.0,0.15849482039196286,0.46218901251573224,0.09771367004688736,0.4979993974715895,0.7658081006262842,0.5956636789773264,0.10908496676769107,0.1673503648498502,0.30132134920492354,0.05007020285566962,0.24334535765102266,0.7106274164738777,0.5787610180068856,0.44551495184304196,0.32715923304841815,0.22675292070391964,0.22678651525819052,0.6385967448259734,0.2602950536215881
-middletemporal,0.33894516674471603,0.03712790432765859,0.10210321218675888,0.06313141984578668,0.14673762905737575,0.14727913295591497,0.2534680656752861,0.3068922218922893,0.5175062259841826,0.14207175702563274,0.04201543381851731,0.28347455974903046,0.1616385033477942,0.10027214818272054,0.15849482039196286,1.0,0.14380147000336924,0.14753816901712802,0.1120681528871812,0.12108657448772811,0.11874562935098779,0.029805641439608215,0.06560603490001862,0.0409544419579916,0.00811362441281089,0.07199892133032344,0.1148612208111909,0.12346146391273924,0.07508747430718198,0.1593891060206555,0.3694722798329746,0.20533785961402415,0.20153486903921417,0.21817150638174473
-paracentral,0.13650116224844477,0.5963402645528432,0.6089812414744056,0.2776868571297032,0.20833158755314166,0.4810816575278437,0.06234796163767114,0.3456870399091107,0.10031158987289185,0.4857987344847827,0.26331009459515714,0.44475014870270857,0.474132337731144,0.2975705555012017,0.46218901251573224,0.14380147000336924,1.0,0.15903454062153372,0.5378324589158429,0.5450738423729239,0.5019908760485352,0.24895530286653844,0.4219117207731816,0.6180263670548126,0.33961805344488827,0.5163294410895183,0.5013973865982041,0.5372072637060911,0.5934757264291476,0.5401322864286192,0.17932412906091724,0.4236959751436403,0.40414016880892845,0.23132832017952284
-parahippocampal,0.16355243089005939,0.10088920915900522,0.08438573709162879,0.13830882899031746,0.7414412100864197,0.08845332539505943,0.6664842439465493,0.20725581178255628,0.23993005955865132,0.13940763448929064,0.27180408891717944,0.5690787350078694,0.13039339584934678,0.32425590670018273,0.09771367004688736,0.14753816901712802,0.15903454062153372,1.0,0.08178323726988604,0.09447868035649326,0.06656121549791176,0.17467462504990083,0.0976930280117136,0.17481064947712344,0.09813714922833686,0.21964376791275791,0.09381422476485358,0.06566333109835842,0.05705978367857833,0.20505391456033115,0.11025807609105184,0.10916895956750106,0.227344242857361,0.10418431459059213
-parsopercularis,0.0979770536787097,0.4007113860843464,0.5897191867709721,0.12393783971370738,0.11639108571368431,0.4889529121756437,0.04598067696926799,0.22031570635585535,0.0915678845750921,0.5047963685743047,0.10496503183190208,0.2657768540084243,0.5101475831020684,0.18063092810966722,0.4979993974715895,0.1120681528871812,0.5378324589158429,0.08178323726988604,1.0,0.6381459866238233,0.717884511623567,0.10201777576490789,0.19208794012720784,0.31231338922883073,0.165129004122872,0.23949723641173476,0.46356356371780655,0.6475088058477542,0.5380631402956122,0.3117613632112506,0.1859094250228802,0.22003637882536875,0.4066590371888866,0.25049685770416896
-parsorbitalis,0.10175860175431566,0.4765309029634356,0.5334705320342246,0.14845557535476397,0.1551252439593657,0.6772679624251706,0.0007226386658379671,0.26749267729019705,0.06086974099575812,0.7012086557518592,0.15887266860049065,0.26213038789162746,0.8216812142608737,0.16896609655452108,0.7658081006262842,0.12108657448772811,0.5450738423729239,0.09447868035649326,0.6381459866238233,1.0,0.7643157033697044,0.12314203384851412,0.20573391901390745,0.3748274649728721,0.10121845769940258,0.27352700244043554,0.6190570874600271,0.7254279934307195,0.6064906945725204,0.3477097550579736,0.22955017444601278,0.23683570085957312,0.6248693651027897,0.2715113562073556
-parstriangularis,0.12323296086325701,0.35941011078621854,0.5215013023655067,0.11141757046584401,0.12887351342102749,0.5516955935454118,-0.004193625648883801,0.2676967047715266,0.06782608801230844,0.6591821545806309,0.11234153589859622,0.243980011992138,0.6243848908469309,0.17858974654833074,0.5956636789773264,0.11874562935098779,0.5019908760485352,0.06656121549791176,0.717884511623567,0.7643157033697044,1.0,0.11929132486655881,0.1901743433421769,0.293472376668223,0.07829999696226916,0.24804635230520938,0.5245010941296242,0.6964961414120537,0.49362398290322446,0.33125606413252223,0.23552332556908842,0.21112146890853117,0.5142644994967787,0.2809631571653606
-pericalcarine,0.04847113293862434,0.09896329401797212,0.09433748383185092,0.7494094808733509,0.2160005158874572,0.10637302843482874,0.09223923761259586,0.12117358516521748,0.047150454553570316,0.13664425845371142,0.16811377263505722,0.4754544962754553,0.12440595561281755,0.5724359579151879,0.10908496676769107,0.029805641439608215,0.24895530286653844,0.17467462504990083,0.10201777576490789,0.12314203384851412,0.11929132486655881,1.0,0.09853100383654773,0.16983490746424534,0.07103084685381134,0.1571913655965414,0.10022920306940532,0.11321648650975207,0.09179333904258152,0.08513025706146629,0.046507567330707024,0.11118603028222387,0.15415024523373178,0.04765442906943732
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+superiortemporal,0.5838874486805424,0.11659870905099334,0.1792357438806467,0.05947449508398588,0.23172323787502544,0.21371995526824666,0.21920864526155417,0.35833549373179574,0.3373605268396769,0.41478023934019664,0.09612593242712911,0.3013854077850445,0.3148243265596369,0.1516703413775839,0.25036638763543007,0.4844068814337347,0.217151616888114,0.17578148654733294,0.174383321158125,0.28013786544691305,0.1962346244954344,0.029793769896701867,0.17678070278504832,0.100612250571808,0.08099826145728795,0.10230811865455736,0.15461930937587537,0.17808808296718132,0.13532795630294506,0.2490330299803788,1.0,0.30987760072823,0.39720962701374685,0.7231047895958309
+supramarginal,0.2990865587755271,0.24114413180133826,0.35865845772972765,0.1487730738235027,0.21320162745223492,0.24398583995731588,0.18422178305867284,0.38241660410321154,0.2200613526295951,0.3345210850775656,0.12413331393872643,0.3549902075668067,0.276894744822709,0.20760155542478112,0.26302507314686757,0.28376740862820543,0.4838472964256414,0.20472473146929793,0.2869020754187068,0.3011480583374357,0.266848963874109,0.10948781104853171,0.549491063538396,0.251057287407888,0.36442692839846036,0.19343587020601558,0.1951435687125982,0.2715336636691718,0.25486176984591846,0.3394795252262817,0.30987760072823,1.0,0.3207489678915231,0.40593360384233435
+temporalpole,0.3348324128222183,0.3399323083841094,0.4420971319361898,0.10506024740598582,0.41821830382745956,0.5290751569431669,0.3003205434446373,0.4758788385190032,0.33455376647179536,0.6449151278228497,0.1748267123515317,0.48535714516304485,0.6580987194833211,0.2613483820555415,0.6287343951316255,0.34912868415381354,0.4229912582304774,0.3033201472713878,0.4524712834253586,0.6360369663455867,0.4947191019724733,0.08372864952461564,0.21672116563103347,0.24142966431803412,0.13160041542449188,0.20384766520153175,0.46524185585635836,0.5193882702837155,0.43283831031327097,0.3600955534669839,0.39720962701374685,0.3207489678915231,1.0,0.45019504263241644
+transversetemporal,0.5052128904640022,0.22228672365516686,0.3141058389804591,0.11885529149879585,0.3003194744610029,0.28038557635974093,0.2561104875998396,0.41203185119753993,0.3095245437691705,0.4880750169932505,0.13868089002317124,0.39100401242110366,0.39210946982382,0.22192898884342332,0.3251555900183635,0.3942447879686912,0.37373401045341414,0.24365051758383127,0.2963011557863596,0.3905245430545975,0.3122810119839356,0.06332934642771151,0.2874210790439096,0.1977980131871392,0.17811376162401946,0.17873576416051348,0.2129899958763143,0.29049058172940556,0.2506407752309402,0.33531742925693697,0.7231047895958309,0.40593360384233435,0.45019504263241644,1.0
diff --git a/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_groundstate.csv b/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_groundstate.csv
index 614dfac..106f167 100644
--- a/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_groundstate.csv
+++ b/simulated_data/scaling_experiment_lower_e_to_i/simulated_fc_groundstate.csv
@@ -1,35 +1,35 @@
 area,bankssts,caudalanteriorcingulate,caudalmiddlefrontal,cuneus,entorhinal,frontalpole,fusiform,inferiorparietal,inferiortemporal,insula,isthmuscingulate,lateraloccipital,lateralorbitofrontal,lingual,medialorbitofrontal,middletemporal,paracentral,parahippocampal,parsopercularis,parsorbitalis,parstriangularis,pericalcarine,postcentral,posteriorcingulate,precentral,precuneus,rostralanteriorcingulate,rostralmiddlefrontal,superiorfrontal,superiorparietal,superiortemporal,supramarginal,temporalpole,transversetemporal
-bankssts,1.0,0.005663505763912862,-0.004422715131333313,-0.026820526752884516,-0.021271748050562905,0.021324465812976808,0.024369380623817872,0.014990296315404637,0.014691905663231105,0.03558222215169223,0.02504266694034003,0.0007210250436691357,0.017388635629895947,-0.021842285190464068,0.0004518361790158974,0.07726436892882263,0.036757924148717265,-0.013971469378905296,-0.024995980808259523,0.0026863137034358656,0.014405896035348562,-0.036326121674572394,0.03044984444031466,0.00022553464743118112,0.022324112703213193,-0.011256442568174983,0.003578866367475268,0.01912567793066142,0.011007641010176357,0.001641211156508128,0.07494810952682777,0.05128735487500831,0.026330186358414526,0.08510741060310531
-caudalanteriorcingulate,0.005663505763912862,1.0,0.004361260127512423,0.008933567217560053,-0.006146742114284308,0.00929029688246688,0.005491190582915044,-0.010123559085988918,-0.02634760030032233,0.023914817896638633,0.0405515964942036,0.0020736596326061704,0.015406791569677603,0.019170732564596827,0.012001862712655853,0.014087550000823217,0.036700494731171075,0.017227977751140367,-0.005836202641340089,-0.007180772834261616,-0.01321674314003614,-0.006642669990574016,0.002543337872289798,0.050057575460325465,0.002621049227992213,0.032374725139402184,0.05692218949302662,-0.014453510552699627,0.028623434693507307,0.017545640930852282,0.026030383318270296,-0.028594235190805617,-0.01821344254767,0.017698878505487077
-caudalmiddlefrontal,-0.004422715131333313,0.004361260127512423,1.0,-0.025745023128411166,-0.002680947467370326,0.02159686731556239,-0.010880720880669938,0.023507207544407435,-0.008365341234684794,-0.02520281429918114,-0.003493766743377807,-0.009767119394257819,0.025223686597183485,-0.018605583448806893,0.04328088375115921,0.018514911767844745,-0.011318688094371351,0.0028997774231574545,0.06074746738560193,-0.005295396238909146,0.03198721306786642,-0.04068044159915602,0.005607027520313235,0.0045497104576922185,0.05748227397516119,-0.0006110381232260077,-0.010950415118843997,0.059697270889543505,0.06870114899183892,-0.002301802182840366,0.007135217921784489,-0.007673127763246919,0.018084450661696956,-0.031538634887093436
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-entorhinal,-0.021271748050562905,-0.006146742114284308,-0.002680947467370326,-0.0006597700064267093,1.0,-0.012377286788554334,0.012400617789657559,-0.01790910187042235,-0.008106866030996559,0.01495764960271528,-0.006187051265237415,0.00615629365494996,0.0007916874908733576,0.012672063871928515,0.010516581805499888,-0.010077216009580808,7.755531669567497e-05,0.06242062657462911,-0.01964443310743621,-0.01260891619064248,0.015515513606810529,0.015763279785514377,-0.0021213694521778218,-0.006970897437992648,0.008829251771344008,0.015448128637989967,0.016215284930977597,0.004637924299740429,0.01833879057655846,-0.005227974366021713,0.00999315924434966,0.007158187785197566,0.020352819736888506,0.0057896821604700305
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-inferiortemporal,0.014691905663231105,-0.02634760030032233,-0.008365341234684794,-0.033748535763732886,-0.008106866030996559,-0.01678717734818892,0.0701974403314659,0.08402323322162052,1.0,-0.018719837082511112,-0.03055614132595212,0.039550767824082414,0.04589274355605312,-0.02300504504800117,0.026131140768381907,0.08726414706231755,-0.01213815004926996,-0.011586511871270543,0.017127039810534962,0.00858105323882888,-0.0026140050013871226,-0.039205390095321944,0.01570633239689774,-0.03920171958322664,0.006556841417623154,-0.04945292111582142,-0.0025533235181353768,-0.008847867323245577,0.013048082441886644,0.0023531975076561897,0.006649927834221702,0.018521234295256005,0.008461350919373896,-0.024014933547435967
-insula,0.03558222215169223,0.023914817896638633,-0.02520281429918114,0.024859892001103535,0.01495764960271528,-0.001193290412629607,-0.020428828141725773,0.0021627726528664994,-0.018719837082511112,1.0,0.030159094181565912,0.001138667331456621,0.015015944992890046,0.023886540732144173,0.030995128130842743,0.013545847793778047,0.046437689201588826,-0.0058055027844629795,-0.028768919485749483,0.009362837117419236,0.029863587668907673,0.005313265682763985,-0.0014359251546014003,-0.0069060454707966625,0.0018124510950072023,-0.015433925717459138,0.010401855492867738,-0.04816435735755132,0.010859066350063952,0.01313399075155077,0.05703192564203263,0.0472774821692858,0.051744649740372795,0.058727824477028415
-isthmuscingulate,0.02504266694034003,0.0405515964942036,-0.003493766743377807,0.08128035434530777,-0.006187051265237415,-0.025436135508441423,0.006625618495852543,-0.02935222681670485,-0.03055614132595212,0.030159094181565912,1.0,-0.014467865854387543,-0.029033861408442347,0.08577896076177102,-0.020191040511433615,-0.002320376656657433,0.0406462675945759,0.0746586899415722,-0.01953994953624427,0.0021939089651868532,-0.0013338971244741913,0.06258573693254114,0.020408626036871165,0.07820496030683402,0.010451855949368987,0.10079493915709299,-0.004034123059902062,-0.02730959766690842,0.016063496422768025,0.06323309298226626,-0.00411874435238929,0.02759233323305537,-0.010305223522301461,-0.01336810972898897
-lateraloccipital,0.0007210250436691357,0.0020736596326061704,-0.009767119394257819,0.008041362939250243,0.00615629365494996,0.016358932189353338,0.04120796198224421,0.05069832753164967,0.039550767824082414,0.001138667331456621,-0.014467865854387543,1.0,0.010777123864985456,0.06115515829848708,-0.0111317972814505,0.005678420191249686,-0.026577886124882506,-0.0038019478205671593,0.039559167750373866,0.00040688272410807484,0.02040697532358378,0.042936855687033024,0.0022551419579126693,0.009887807522196635,0.01314690606639176,0.016178713398289832,0.020044780209880857,0.01511728181835326,0.008961805047471134,0.04072736246064469,0.013788689354165556,0.021599214716610058,-0.018161195694399244,0.018041776746656534
-lateralorbitofrontal,0.017388635629895947,0.015406791569677603,0.025223686597183485,-0.012723608312129415,0.0007916874908733576,0.03362351735164242,0.00048702199325317185,0.028905549318470367,0.04589274355605312,0.015015944992890046,-0.029033861408442347,0.010777123864985456,1.0,0.03919002418918106,0.09400533454930116,0.013576717890201005,0.005627187203497244,0.003901510056717041,0.0075117264812829395,0.047394446056290326,-0.009134497350000806,0.007027001218383339,-0.009507472932681334,0.024670584605701287,0.011043582504664093,0.011351812164048333,0.03772921167367413,0.01608137179024291,-0.0022665962438875057,-0.02059023491977813,0.026050558141374146,-0.00993391399093187,0.05705475200721441,0.038439105154976984
-lingual,-0.021842285190464068,0.019170732564596827,-0.018605583448806893,0.10061779686975772,0.012672063871928515,0.004711088727762258,0.04396193778763291,-0.047834495741093684,-0.02300504504800117,0.023886540732144173,0.08577896076177102,0.06115515829848708,0.03919002418918106,1.0,0.02521572273184694,-0.04418807124244299,0.0025586307113297916,0.05338458684249057,0.02387012599303529,0.022278953186098332,0.014796329451498282,0.19858847517129566,-0.03223042065223139,0.02952573397512954,-0.011135748390765799,0.059948419534454254,-0.003258111474557293,-0.03852568460730209,0.024827909936788246,0.022235493995744118,-0.014598107770893865,-0.04198645481588673,0.026916306389765835,-0.010020241867777432
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+precentral,0.0017117115659891588,0.02209866560720769,0.038684072584175465,0.01892720575380715,-0.01480510606787709,-0.006553208528638111,0.017654100281232012,0.022526419406709804,-0.014576739569631342,0.023787471275361773,0.014497370920540617,-0.011465715200609624,0.019502761626872103,0.0015160523069332014,0.01449518035497967,0.017793749702523638,0.006923152406148936,-0.0087883265437039,0.030749366825545653,0.005947227056040361,-0.006604631374879175,0.019949183917995827,0.09525280681558183,-0.00014243720107389932,1.0,0.012535388156025869,0.01787018778524274,0.01867819414734632,0.029709704756797878,0.009641424354415036,0.03302957199354103,0.019372754513362924,0.007206766070989548,0.009384798750730039
+precuneus,-0.05941672266255327,0.03539533721156103,-0.01789111163157482,0.11699811544970529,0.0051578819608386955,-0.014928038878074748,0.0033660557776306497,0.054296781876763255,0.020622984944505252,-0.01633810830833009,0.06978252598064738,0.05185527774422255,0.014661636928078354,0.05197084772712172,0.005155043315319,-0.02805747281494882,0.032924648748803145,0.023876100568514417,-0.01011409475136569,-0.01565002146834509,-0.008038805999495444,0.050544784476379424,0.018382052202894232,0.032684395189460404,0.012535388156025869,1.0,0.01980471253268634,-0.04239931030724534,-0.013555693905470546,0.11465071073596528,0.007305788419035785,0.027922090776413704,-0.026536784750855684,-0.00137606214281113
+rostralanteriorcingulate,0.02732299950580762,0.038403793985864285,0.002503433858252751,0.013579151899527136,-0.027428289824902013,-0.002801055879137844,0.012430669240580982,-0.00126371900853851,0.02745721420062344,0.012713339052748499,0.028104385817412796,0.013714472107760492,-0.006802761769529463,0.030954604136320327,0.03688553729946493,-0.004344093532796697,0.014532290578759908,0.028659689086079923,0.011678583036409161,0.022187078672917793,0.005635311392554314,-0.003229108344805963,0.02244895680602372,0.023152519399724666,0.01787018778524274,0.01980471253268634,1.0,2.286216888767902e-05,0.016710940330762508,0.0030765914262712076,0.01274032523354943,0.013909234246253944,0.019141615444759418,0.009219313639696101
+rostralmiddlefrontal,0.03645712233696257,-0.011620269754608857,0.07748207951500223,0.007528120033995741,-0.007263844037246399,0.05643956139622222,-0.006406800695781333,-0.002945190412972615,-0.003857712941849555,0.007315181648846343,-0.016222163141891996,-0.003581426727530524,0.031080784134013273,0.023870665152160842,0.052843220376598436,0.011444287862430694,0.01874072011549254,0.03517627498549717,0.04678772875417987,0.056998753095015106,0.08633197524122203,0.018009895933428416,-0.029273098843796943,0.01512987188135261,0.01867819414734632,-0.04239931030724534,2.286216888767902e-05,1.0,0.036742679869307356,-0.016579406447109767,-0.030774671704922645,0.016392039835656267,0.0024223262648996545,-0.00712163000442117
+superiorfrontal,0.025184168978940415,0.02035715658755702,0.03479570094798684,-0.006251465943610464,-0.015570893839988203,-0.0003336257626043953,0.011059214631219606,-0.03607922798993071,-0.03698574301658402,0.03709454330342238,0.035644401458406215,0.00016157221122284522,0.022365939000050346,0.0038908467028674542,0.0183455761893284,-0.07061426786657925,0.014290812961817244,0.009844463256265196,-0.010855672850062018,-0.007407503976787681,-0.018705588125986983,0.01210790620051702,0.012129655477214682,0.008839160404842217,0.029709704756797878,-0.013555693905470546,0.016710940330762508,0.036742679869307356,1.0,-0.021506331070496078,0.017367465508884267,-0.009409971743090115,0.02365806540549998,-0.0154472413142454
+superiorparietal,-0.0027877278269583146,0.004630772333948807,-0.014229909098317175,0.09088415191636409,0.009118494842669404,-0.019198763168985992,0.0036992837150741204,0.0714575959224222,0.00677359692451607,0.02726903024070897,0.015902922811537205,0.03110474348053601,0.02679362282368884,0.009695878335469041,-0.017931717525200865,-0.02175795098633674,0.038030460447712224,0.01287630706048317,0.01444826573787335,-0.01073239948758183,0.016467539566287503,0.04094238156860776,0.0323074331940756,0.004002259949073418,0.009641424354415036,0.11465071073596528,0.0030765914262712076,-0.016579406447109767,-0.021506331070496078,1.0,-0.005226569589183085,0.02722119264614856,-0.04255698062584152,0.014644859759504641
+superiortemporal,0.05114530555130905,-0.022637903484860254,0.009414539394740773,-0.0230787104205793,-0.005167392108255299,0.007870988911764056,0.01689852817618079,0.035900856270614796,0.02925848949801568,0.01678777558261575,-0.01718293836821461,-0.00694689567046992,-0.023299343533684146,-0.01185443742700909,-0.0024649106330233064,0.06113730945235296,0.011317414829623581,-0.010052846891241057,0.00501556772690052,-0.009229489460457263,0.01016516139707049,-0.024424911568865018,-0.006418171429760575,-0.023960072190688814,0.03302957199354103,0.007305788419035785,0.01274032523354943,-0.030774671704922645,0.017367465508884267,-0.005226569589183085,1.0,0.08644957755694327,0.04617580617067424,0.15026254449576365
+supramarginal,0.07161407685370634,-0.025038369567399948,0.009525674010452217,0.015942594520333767,-0.01791629921268684,0.01265141349544399,0.017737233817876015,0.11229342177024342,-0.008690409509216656,0.039489330192883795,-0.03027890037094973,0.017803886923237516,0.015093512520265891,0.014923681816076671,0.032962900127471925,0.06966166565384187,-0.014264954947561295,0.006839352751095849,-0.027419671497164476,0.009460732474868847,0.02886606558937534,0.0022789539733079982,0.108087653846501,-0.012319457706743775,0.019372754513362924,0.027922090776413704,0.013909234246253944,0.016392039835656267,-0.009409971743090115,0.02722119264614856,0.08644957755694327,1.0,-0.0037764203259246336,0.057956540614759064
+temporalpole,-0.010025649807220052,0.017822933472418895,0.00972005895911689,-0.018388434375426334,0.02016359686242814,0.016223691122513106,0.008979474454212334,-0.01984811156665397,-0.01223923017012562,0.04939915266155487,-0.0033315180721205603,-0.009685715062954015,0.04421419712577075,-0.02760995866658619,0.030974690385640415,0.015608831742810067,-0.019332172662368478,0.008140415251597172,0.03490047367661172,-0.02829833556999033,0.03936440433058558,-0.037168550487912844,-0.0171847505480451,0.04032570971972448,0.007206766070989548,-0.026536784750855684,0.019141615444759418,0.0024223262648996545,0.02365806540549998,-0.04255698062584152,0.04617580617067424,-0.0037764203259246336,1.0,0.021883086182962048
+transversetemporal,0.03190651470195851,-0.0014179939712298888,-0.00962019198710638,0.02009269200480009,-0.00914272339140918,-0.027109033799422266,-0.012172685044813386,0.04255075757848438,-0.015451307545505145,0.054125943031404535,-0.0023505970101046266,-0.025034213706144146,0.008346485601553644,0.0027485069580441097,-0.00953395848827475,-0.008065383364030688,0.025585145659487787,-0.01984395883548756,0.008037374396927175,0.01974433965726119,-0.007508087751581766,-0.02105731618254809,0.021164639587790083,0.005728434792116156,0.009384798750730039,-0.00137606214281113,0.009219313639696101,-0.00712163000442117,-0.0154472413142454,0.014644859759504641,0.15026254449576365,0.057956540614759064,0.021883086182962048,1.0
diff --git a/src/compute_scaling_experiments.py b/src/compute_scaling_experiments.py
index b625f22..30c82ba 100644
--- a/src/compute_scaling_experiments.py
+++ b/src/compute_scaling_experiments.py
@@ -14,10 +14,10 @@
 experiments = [
         'lower_etoi',
         'lichtman_chii2_smaller_g',
-        'lichtman_chii2_different_time_consts',
         'lichtman_chii2_distributed_params',
         'lichtman_chii2_different_seed',
-        'lichtman_chii2_random_seeds'
+        'lichtman_chii2_random_seeds',
+        'lichtman_chii2_different_seed_factor10per7'
         ]
 
 if len(sys.argv) > 1 and sys.argv[1] in experiments:
@@ -25,7 +25,7 @@
 else:
     simulation = 'lichtman_chii2_different_seed'
 
-# base_path = os.path.join(os.getcwd(), 'out/')
+base_path = os.path.join(os.getcwd(), 'out/')
 
 if simulation == 'lichtman_chii2_different_seed':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_seed as state
@@ -52,7 +52,6 @@
             '6cc8213fba1b6ff7935d0505491045ea/',
             'aa3e9b1f3ef704504bee98665a215178/',
             ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lichtman_chii2_random_seeds/'
     sim_path = '4772f0b020c9f3310f4096a6db758343/'
     ana_path = 'fff793e841649603c59db1822e566c93/'
 elif simulation == 'lichtman_chii2_random_seeds':
@@ -71,7 +70,6 @@
             '6cc8213fba1b6ff7935d0505491045ea/',
             'aa3e9b1f3ef704504bee98665a215178/',
             ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lichtman_chii2_random_seeds/'
     sim_path = '4772f0b020c9f3310f4096a6db758343/' # 2903
     ana_path = 'fff793e841649603c59db1822e566c93/'
 elif simulation == 'lower_etoi':
@@ -92,7 +90,6 @@
             '51753977f483813e58131899e2c2c472/',
             'be5b9b57a154b97777664d015cc16cfc/'
             ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lower_etoi/'
     sim_path = '4772f0b020c9f3310f4096a6db758343/'
     ana_path = '875fb14936529840c819d2bdc86ff14f/'
 elif simulation == 'lichtman_chii2_smaller_g':
@@ -106,26 +103,6 @@
             'e1f0481943c7dd4937436abc3b0ba8ec/',
             'f6de3f7c02703fa1dde65ef14e61171b/'    
             ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lichtman_chii2_smaller_g/'
-    sim_path = '4772f0b020c9f3310f4096a6db758343/'
-    ana_path = '1931d354c159b2a01cba9c4bd0c9ed46/'
-elif simulation == 'lichtman_chii2_different_time_consts':
-    from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_time_consts as state
-    save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_time_consts'
-    interesting_paths = [
-            '00d934bd2694ad6970f9c524616cdf30/',
-            '5ba365e8ec002d2786d214345aca891f/',
-            '7f378a020da9f7aba1d783b820e6acce/',
-            'd1826785ec16aaad0f0c83144c86fb70/',
-            '3d611f90e1fff6060ddbf9a058d1459b/',
-            '5e69f9756903bc364c83ffe5e3ad480a/',
-            '8cd1d42a86c90996650fa98a9982d221/',
-            'ebcc993ae9d4a5c2a11124f94128e1a4/',
-            '56452204319c31bd9e0debcbc03f8f2c/',
-            '75ec59f25c4826f9e2de3447a63fa61d/',
-            'c23ff0bd24504b81b276fc3bcb964431/'
-            ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lichtman_chii2_different_time_consts/'
     sim_path = '4772f0b020c9f3310f4096a6db758343/'
     ana_path = '1931d354c159b2a01cba9c4bd0c9ed46/'
 elif simulation == 'lichtman_chii2_distributed_params':
@@ -144,7 +121,51 @@
             '70029618054442dffacb000422702166/',
             'b2a35418096820454aed549ab4d7a215/'
             ]
-    base_path = '/p/scratch/cjinb33/shimoura1/python3.9/humam_lichtman_chii2_distributed_params/'
+    sim_path = '4772f0b020c9f3310f4096a6db758343/'
+    ana_path = '1931d354c159b2a01cba9c4bd0c9ed46/'
+elif simulation == 'lichtman_chii2_different_seed_factor10per7':
+    from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_seed_factor10per7 as state
+    save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7'
+    interesting_paths = [
+            '16de0d322e4b222e6f4713ba662c41da/',
+            '66335d4198126b33771291d5467fbf27/',
+            '95c2a43bd22a1f27ce65ff551564b557/',
+            'd4731e112855447f384827a1281528c5/',
+            '19abb84050e7cfadf9fcdf10d5bb0a34/',
+            'd9f98fdf4f25bd770b5a8b3afae31697/',
+            '69bb1d367ce878bd9bc7ae7cc46185b9/',
+            'a44832b116875b8cc03d1a77b7278d7e/',
+            '293fc6e4537603ce6e867f563c05049b/',
+            '6bc32c0f64d91ae502f07d91b92da12b/',
+            'aa3b81bb3f3795c81c9a5b3454e95494/',
+            'dff92fb0f6b0e3aa5718b6b43d340a49/',
+            '2f75bb3dd5920776a7a720916cd28f71/',
+            '6e68141c0b412bd68ecf23ae798d00fe/',
+            'ab01c8287e7f70ccc3d3a43801247165/',
+            'e3e1c4a3db89d42249965451312aeb75/',
+            '300340dca17d9378bf64236bb25f3ae3/',
+            '73717e152e908b8ac0831e06ce8c8ffb/',
+            'ac304a4d73d44a7e6cda7676c1ff374a/',
+            '3619e6131dbfdb258bba41b919f79541/',
+            '75065f05aea926f0e4153664f4f9212c/',
+            'bc84db13bd75614bd36a563498c142c9/',
+            'e891197ebe3eeebb4e555f72273ab92c/',
+            '4e3a98b5e43c004f49deba5fe35023f4/',
+            '89bd4af3f1044072c5d4b1e634e4d517/',
+            'c114dc8dd3981cebd93568871ec1a53a/',
+            '58320586de2820a50f47b93962e70f48/',
+            '8aca6e3e2da9e7f78559bd9323850983/',
+            'c56b28a915a568e44d505296b2f3f83d/',
+            'e958fa00425fa488a97292bf48d930af/',
+            'e977991b439cc72a0a77154cc97703cb/',
+            '5fd951e40a387f8bc20ac7fbcc4f2d5d/',
+            'cce7ba1da76cc43ec1d64c7034a78128/',
+            'f24a0ce0a69a0fe8ed15bcca21db420e/',
+            '642431ea0f4d4cd7885d71f1b2ca9fdf/',
+            '93b288add2929814291c19ee2b445e6f/',
+            'ce89dacc4fe37ff7873c0a1576ed79d9/',
+            'f8d202967ba7cab69ef473165aaa9f74/'
+            ]
     sim_path = '4772f0b020c9f3310f4096a6db758343/'
     ana_path = '1931d354c159b2a01cba9c4bd0c9ed46/'
 
diff --git a/src/compute_scaling_experiments_helpers.py b/src/compute_scaling_experiments_helpers.py
index 90db2c6..e7a1dd9 100644
--- a/src/compute_scaling_experiments_helpers.py
+++ b/src/compute_scaling_experiments_helpers.py
@@ -17,11 +17,6 @@
         'bestfit': 'f358fd7d2844f0a6e3e806352af2fe2f',  #chi=1.75, overall best agreement
         }
 
-state_scaling_lichtman_chii2_different_time_consts = {
-        'groundstate': 'd1826785ec16aaad0f0c83144c86fb70',
-        'bestfit': '75ec59f25c4826f9e2de3447a63fa61d',
-        }
-
 state_scaling_lichtman_chii2_distributed_params = {
         'groundstate': 'c207557ee4ca705ba40627631f44d6f7',
         'bestfit': '105ccb56b38cd990b839889f12953a38',
@@ -37,6 +32,11 @@
         'bestfit': '8c49a09f51f44fbb036531ce0719b5ba',
         }
 
+state_scaling_lichtman_chii2_different_seed_factor10per7 = {
+        'groundstate': '4e3a98b5e43c004f49deba5fe35023f4',
+        'bestfit': 'bc84db13bd75614bd36a563498c142c9',
+        }
+
 left_ordering = {'isthmuscingulate': 'DMN',
   'medialorbitofrontal': 'DMN',
   'posteriorcingulate': 'DMN',
diff --git a/src/default_ana_params.py b/src/default_ana_params.py
index 81497bb..93d1725 100644
--- a/src/default_ana_params.py
+++ b/src/default_ana_params.py
@@ -6,30 +6,30 @@
 
 params['mplstyles'] = 'misc/mplstyles/report_plots_master.mplstyle'
 params['extension'] = 'png'
-params['seed'] = 2106
+params['seed'] = 2903
 params['python_sort'] = True
 
 params['rate_histogram'] = {
-    'binsize': 1.,  # in ms
+    'binsize': 1.,    # in ms
     't_start': 500.,  # in ms
-    't_stop': None  # in ms
+    't_stop': None    # in ms
 }
 
 params['meanFiringRate'] = {
     't_start': 500.,  # in ms
-    't_stop': None  # in ms
+    't_stop': None    # in ms
 }
 
 params['individualFiringRate'] = {
     't_start': 500.,  # in ms
-    't_stop': 2500.  # in ms
+    't_stop': None    # in ms
 }
 
 params['correlation_coefficient'] = {
     'subsample': 2000,
-    'tbin': 1.,  # in ms
+    'tbin': 1.,    # in ms
     'tmin': 500.,  # in ms
-    'tmax': None  # in ms
+    'tmax': None   # in ms
 }
 
 params['compute_louvain'] = {
@@ -38,13 +38,13 @@
 
 params['cv'] = {
     't_start': 500.,  # in ms
-    't_stop': None,  # in ms
+    't_stop': None,   # in ms
     'min_spikes': 10
 }
 
 params['lvr'] = {
     't_start': 500.,  # in ms
-    't_stop': None,  # in ms
+    't_stop': None,   # in ms
     'min_spikes': 10
 }
 
@@ -69,7 +69,7 @@
 }
 
 params['plotBOLD'] = {
-    'tmin': 500.,  # in ms
+    'tmin': 500.,   # in ms
     'stepSize': 1.  # in ms
 }
 
diff --git a/src/figure_scaling_experiments.py b/src/figure_scaling_experiments.py
index ef38801..51ecbab 100644
--- a/src/figure_scaling_experiments.py
+++ b/src/figure_scaling_experiments.py
@@ -12,9 +12,9 @@
 experiments = [
         'lower_etoi',
         'lichtman_chii2_smaller_g',
-        'lichtman_chii2_different_time_consts',
         'lichtman_chii2_distributed_params',
-        'lichtman_chii2_different_seed'
+        'lichtman_chii2_different_seed',
+        'lichtman_chii2_different_seed_factor10per7'
         ]
 
 if len(sys.argv) > 1 and sys.argv[1] in experiments:
@@ -34,15 +34,15 @@
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_smaller_g as state
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_smaller_g'
     name_extension = '_lichtman_chiI2_smaller_g'
-elif simulation == 'lichtman_chii2_different_time_consts':
-    from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_time_consts as state
-    save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_time_consts'
-    name_extension = '_lichtman_chiI2_different_time_consts'
 elif simulation == 'lichtman_chii2_distributed_params':
     from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_distributed_params as state
     save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_distributed_params'
     name_extension = '_lichtman_chiI2_distributed_params'
-
+elif simulation == 'lichtman_chii2_different_seed_factor10per7':
+    from compute_scaling_experiments_helpers import state_scaling_lichtman_chii2_different_seed_factor10per7 as state
+    save_data_dir = 'simulated_data/scaling_experiment_lichtman_chii2_different_seed_factor10per7'
+    name_extension = '_lichtman_chiI2_different_seed_factor10per7'
+    
 linewidth=2.
 
 colors_state = {
diff --git a/src/figure_temporal_hierarchy.py b/src/figure_temporal_hierarchy.py
new file mode 100644
index 0000000..5262da1
--- /dev/null
+++ b/src/figure_temporal_hierarchy.py
@@ -0,0 +1,399 @@
+""" 
+Generates the matrix part of the Temporal Hierarchy figure, plus shows 
+the hierarchy in 3d by opening a link to the Scalable Brain Atlas in a
+browser window.
+
+Author: Rembrandt Bakker & other authors from the publications
+https://doi.org/10.1101/2023.03.23.533968
+and
+https://doi.org/10.1371/journal.pcbi.1006359
+""" 
+
+"""
+python module imports
+"""
+import os
+from os.path import join as path_join
+
+import copy
+import json
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import to_hex
+from matplotlib.colors import LinearSegmentedColormap
+import numpy as np
+import pandas as pd
+from scipy import signal
+from scipy.stats import median_abs_deviation
+
+# to organize areas by hierarchy
+from scipy.optimize import minimize
+
+# to show brain in Scalable Brain Atlas Composer
+import webbrowser
+
+"""
+global parameters
+"""
+filepath = os.path.join(os.getcwd(), 'out/8c49a09f51f44fbb036531ce0719b5ba/4772f0b020c9f3310f4096a6db758343/7f5b8ecbfce8346314b050b58bbab1cf/')
+filename = 'rate_histogram_areas.pkl'
+spikeRateDataPickleFile = path_join(filepath, filename)
+diagnosticPlots = False
+
+# What part of the simulation to use (12500 ms available)
+t_total = 12500
+t_start = 2500 # to ignore the transient
+t_stop = t_total
+
+# gross ordering of the brain into four lobes
+right_ordering_four_lobes = {
+    # Occipital Lobe
+    'cuneus': 'occipital',
+    'lateraloccipital': 'occipital',
+    'lingual': 'occipital',
+    'pericalcarine': 'occipital',
+
+    # Temporal Lobe
+    'superiortemporal': 'temporal',
+    'middletemporal': 'temporal',
+    'inferiortemporal': 'temporal',
+    'entorhinal': 'temporal',
+    'parahippocampal': 'temporal',
+    'temporalpole': 'temporal',
+    'transversetemporal': 'temporal',
+    'bankssts': 'temporal',
+    'fusiform': 'temporal',  # Note: fusiform can be considered part of both occipital and temporal lobes
+    'insula': 'temporal',  # Included with temporal lobe in this coarse division
+
+
+    # Parietal Lobe
+    'precuneus': 'parietal',
+    'supramarginal': 'parietal',
+    'inferiorparietal': 'parietal',
+    'superiorparietal': 'parietal',
+    'postcentral': 'parietal',
+    'posteriorcingulate': 'parietal',  # Often associated with parietal lobe functions
+    'isthmuscingulate': 'parietal',  # Can be considered part of parietal lobe
+
+    # Frontal Lobe
+    'medialorbitofrontal': 'frontal',
+    'lateralorbitofrontal': 'frontal',
+    'parsopercularis': 'frontal',
+    'parsorbitalis': 'frontal',
+    'parstriangularis': 'frontal',
+    'rostralmiddlefrontal': 'frontal',
+    'caudalmiddlefrontal': 'frontal',
+    'superiorfrontal': 'frontal',
+    'frontalpole': 'frontal',
+    'precentral': 'frontal',
+    'paracentral': 'frontal', # check this one
+    'rostralanteriorcingulate': 'frontal',  # Associated with frontal lobe functions
+    'caudalanteriorcingulate': 'frontal'  # Associated with frontal lobe functions
+}
+
+right_ordering = right_ordering_four_lobes
+
+"""
+Load area-averaged firing rates, based on spike trains from a model run
+"""
+rate_per_area = pd.read_pickle(spikeRateDataPickleFile)
+rate_per_area = rate_per_area*1000 # convert to Hz
+
+# Remove the transient
+for area in rate_per_area.index:
+    rate_per_area[area] = rate_per_area[area][t_start:t_stop]
+
+time = range(len(rate_per_area.iloc[0]))
+
+# Area list
+area_list = rate_per_area.index
+
+# Area-averaged firing rates as a matrix
+rate_values = np.array(rate_per_area.tolist())
+
+"""
+Routine to select a peak from the cross correlation function.
+Positive/negative peaks are treated separately.
+Smaller (absolute) delays are preferred, larger delays are only accepted
+if peak is more than a standard deviation taller.
+"""
+# source for confidence intervals: https://faculty.ksu.edu.sa/sites/default/files/probability_and_statistics_for_engineering_and_the_sciences.pdf
+def selectPeak(cc,lags,sd):
+    peaks, props = signal.find_peaks(cc,height=sd,distance=5)
+    def sortFunc(i):
+        return np.abs(lags[i])
+    bestPeaks = {}
+    bestHeights = {}
+    for delaySign in ['neg','pos']:
+        if delaySign == 'pos':
+            sortedPeaks = sorted(peaks[lags[peaks]>=0],key=sortFunc)
+        else: 
+            sortedPeaks = sorted(peaks[lags[peaks]<0],key=sortFunc)
+            
+        bestPeak = np.nan
+        bestHeight = np.nan
+        if len(sortedPeaks):
+            idx = sortedPeaks[0]
+            bestPeak = lags[idx]
+            bestHeight = cc[idx]
+            for idx in sortedPeaks[1:]:
+                if cc[idx]>bestHeight+sd:
+                    bestPeak = lags[idx]
+                    bestHeight = cc[idx]
+                prev = idx
+        bestPeaks[delaySign] = bestPeak
+        bestHeights[delaySign] = bestHeight
+    return bestPeaks,bestHeights
+
+"""
+Compute the cross-correlation functions between all pairs of brain areas
+"""
+max_lag = 50
+peak_matrix = np.zeros((len(area_list), len(area_list)))
+for i,area in enumerate(area_list):
+    print(area)
+    diagnosticPlots = ()
+    for j,other_area in enumerate(area_list):
+        signal1 = rate_per_area[area]
+        signal1 = signal1-signal1.mean()
+        signal1 /= signal1.std()
+        signal2 = rate_per_area[other_area]
+        signal2 = signal2-signal2.mean()
+        signal2 /= signal2.std()
+        
+        N = len(signal1)
+        nChunks = 9
+        chunkSize = N//nChunks
+
+        # Now divide the timeseries into 9 chunks 
+        # and compute the crosscorrelation function for each chunk 
+        cc_chunks = []
+        for c in range(nChunks):
+            # autocorrelation method
+            chunk1 = signal1[c*chunkSize:(c+1)*chunkSize]
+            chunk1 = (chunk1-chunk1.mean())/chunk1.std()
+            chunk2 = signal2[c*chunkSize:(c+1)*chunkSize]
+            chunk2 = (chunk2-chunk2.mean())/chunk2.std()
+            cc = signal.correlate(chunk1, chunk2, mode='full')/chunkSize
+            lags = signal.correlation_lags(chunk1.size, chunk2.size, mode='full')
+            lag_indices = np.where((lags <= max_lag) & (lags >= -max_lag))
+            cc = cc[lag_indices]
+            cc_chunks.append(cc)
+
+        # Also compute the crosscorrelation function for the full data
+        cc_full = signal.correlate(signal1, signal2, mode='full')/N
+        lags = signal.correlation_lags(signal1.size, signal2.size, mode='full')
+        lag_indices = np.where((lags <= max_lag) & (lags >= -max_lag))
+        cc_full = cc_full[lag_indices]
+        lags_full = lags[lag_indices]
+
+        # Use the 9 chunks to compute the standard deviation of the cross-correlation functions
+        cc_chunks_np = np.array(cc_chunks)
+        cc_chunks_np -= cc_full # cc_full is used as an estimator for the average of cc_chunks
+        cc_chunk_std = cc_chunks_np.flatten().std()
+        
+        # The standard deviation of the full data cross correlation function is lower than that of the chunks
+        cc_full_std = cc_chunk_std/np.sqrt(nChunks)
+        
+        bestPeaks_full,bestHeights_full = selectPeak(cc_full,lags_full,cc_full_std)
+        if diagnosticPlots:
+            fig,ax = plt.subplots(1,1,figsize=[10,6])
+            ax.plot(lags_full, cc_full, label=f'{area} vs. ' + other_area, color='orange',linewidth=5)
+            ax.vlines(x=bestPeaks_full['neg'], ymin=cc_full.min(), ymax=cc_full.max(), color="orange",linewidth=5)
+            ax.vlines(x=bestPeaks_full['pos'], ymin=cc_full.min(), ymax=cc_full.max(), color="orange",linewidth=5)
+
+        # Independently compute the best negative/best positive delay peak for each chunk
+        bestNegPeak_chunks = []
+        bestPosPeak_chunks = []
+        for c in range(nChunks):
+            cc = cc_chunks[c]
+            lags = signal.correlation_lags(chunkSize, chunkSize, mode='full')
+            lag_indices = np.where((lags <= max_lag) & (lags >= -max_lag))
+            lags = lags[lag_indices]
+            bestPeaks,bestHeights = selectPeak(cc,lags,cc_chunk_std)
+
+            bestNegPeak_chunks.append(bestPeaks['neg'])
+            bestPosPeak_chunks.append(bestPeaks['pos'])
+            if diagnosticPlots:
+                ax.plot(lags, cc, label=f'{area} vs. ' + other_area, color='steelblue')
+                ax.vlines(x=bestPeaks['neg'], ymin=cc.min(), ymax=cc.max(), color="firebrick")
+                ax.vlines(x=bestPeaks['pos'], ymin=cc.min(), ymax=cc.max(), color="green")
+        
+        medNeg = np.median(bestNegPeak_chunks)
+        medPos = np.median(bestPosPeak_chunks)
+        madNeg = median_abs_deviation(bestNegPeak_chunks)
+        madPos = median_abs_deviation(bestPosPeak_chunks)
+
+        # Decide which peaks are significant:
+        # 1. the peaks obtained for the chunks should not be too divergent,
+        # 2. the median delay of the peak for the chunks should be close to the estimated delay of the full data
+        # 3. the delay should not exceed 30
+        peak_matrix[i][j] = None
+        acceptNeg = madNeg <= 4 and np.abs(bestPeaks_full['neg']-medNeg) <= 3 and np.abs(bestPeaks_full['neg'])<30
+        acceptPos = madPos <= 4 and np.abs(bestPeaks_full['pos']-medPos) <= 3 and np.abs(bestPeaks_full['pos'])<30
+        if acceptNeg and acceptPos:
+            if bestHeights_full['neg']>bestHeights_full['pos']+2*cc_full_std:
+                # negative delay peak is significantly higher than positive delay peak
+                peak_matrix[i][j] = bestPeaks_full['neg']
+            elif bestHeights_full['pos']>bestHeights_full['neg']+2*cc_full_std:
+                # positive delay peak is significantly higher than negative delay peak
+                peak_matrix[i][j] = bestPeaks_full['pos']
+            else:
+                peak_matrix[i][j] = None # no clear largest peak, undecided
+        else:
+            if acceptNeg:
+                peak_matrix[i][j] = bestPeaks_full['neg']
+            if acceptPos:
+                peak_matrix[i][j] = bestPeaks_full['pos']
+
+        if diagnosticPlots:
+            mn = cc_full.min()
+            mx=cc_full.max()
+            mn = mn-0.5*(mx-mn)
+            mx = mx+0.5*(mx-mn)
+            if not acceptNeg:
+                ax.plot([lags[0],0,np.nan,lags[0],0],[mn,mx,np.nan,mx,mn], color='red',linewidth=5)
+            if not acceptPos:
+                ax.plot([0,lags[-1],np.nan,0,lags[-1]],[mn,mx,np.nan,mx,mn], color='red',linewidth=5)
+
+            ax.set_xlabel("Time delay (s)")
+            ax.set_ylabel("Cross correlation (-)")
+            ax.set_title(f'{area} vs {other_area}: delay {peak_matrix[i][j]}, {madNeg}+{madPos}')
+            plt.show()
+    
+            plt.close(fig)
+
+peak_matrix = pd.DataFrame(peak_matrix, index=area_list, columns=area_list)
+
+"""
+Routines for Hierarchical Clustering
+"""
+def dev(i, j, hierarchy, cc_matrix):
+    """
+    Deviation function for the linear programming algorithm
+    determining the hierarchy.
+    """
+    if np.isnan(cc_matrix[i][j]):
+        return 0
+    else:
+        return (hierarchy[i] - hierarchy[j] - cc_matrix[i][j])
+
+def hier_dev(hierarchy, cc_matrix):
+    deviation = 0.
+    for i in range(hierarchy.size):
+        for j in range(hierarchy.size):
+            deviation += (dev(i, j, hierarchy, cc_matrix)) ** 2
+    return np.sqrt(deviation)
+
+def create_hierarchy(cc_matrix, areas):
+    """
+    Determined the hierarchy for a given set of areas and their
+    cross-correlation peak matrix.
+    """
+    res = minimize(hier_dev, np.random.rand(
+        cc_matrix[0].size), args=(cc_matrix,))
+    hierarchy = res['x']
+    index_transformation = np.argsort(hierarchy)
+    hierarchical_areas = copy.copy(areas)
+    hierarchical_areas = np.array(hierarchical_areas)
+    hierarchical_areas = hierarchical_areas[index_transformation]
+    hierarchy = hierarchy[index_transformation]
+    # Map hierarchy onto [0,1] interval
+    hierarchy -= np.min(hierarchy)
+    hierarchy /= np.max(hierarchy)
+    return res, hierarchy, hierarchical_areas, index_transformation
+
+"""
+Perform Hierarchical Clustering
+"""
+cc_matrix = peak_matrix.to_numpy() # Rows: Source area, Cols: Target area
+res, hierarchy, hierarchical_areas, index_transformation = create_hierarchy(cc_matrix, area_list)
+
+hierarchy_to_area_list = []
+for area in area_list:
+    hierarchy_to_area_list.append(
+        np.where(hierarchical_areas == area)[0][0]
+    )
+
+cc_matrix_hier = cc_matrix[index_transformation][:, index_transformation]
+cc_matrix_hier_masked = np.ma.masked_where(np.isnan(cc_matrix_hier), cc_matrix_hier)
+    
+"""
+Plot the Hierarchical Clustering matrix, with brain areas on the x- and y-axes,
+x-symbols at NaN-value locations
+"""
+# Define colors for each group of brainregions
+group_colors = {
+    'frontal': '#AAFFAA', # green
+    'occipital': '#EEFFEE', # light grey
+    'temporal': '#BBBBFF', # blue
+    'parietal': '#DDDDDD', # darker grey
+}
+
+colormap = 'seismic'
+
+fig, ax = plt.subplots(figsize=(8, 6.5))
+
+ax.set_xlabel('Source area\n$\longrightarrow$ temporal hierarchy $\longrightarrow$')
+ax.set_ylabel('$\longrightarrow$ temporal hierarchy $\longrightarrow$\nTarget area')
+
+vlim = np.nanmax(np.abs(peak_matrix))
+im = ax.pcolormesh(
+    cc_matrix_hier_masked[:, :],
+    cmap = colormap,
+    vmin = -vlim,
+    vmax = vlim 
+    )
+
+ax.set_xticks(np.arange(0, len(hierarchical_areas), 1) + 0.5)
+ax.set_yticks(np.arange(0, len(hierarchical_areas), 1) + 0.5)
+
+ax.set_xticklabels(hierarchical_areas[:], rotation='vertical')
+ax.set_yticklabels(hierarchical_areas[:])
+
+# Add background color to tick labels
+xtick_labels = ax.get_xticklabels()
+ytick_labels = ax.get_yticklabels()
+
+for label in xtick_labels:
+    area = label.get_text()
+    group = right_ordering[area]
+    label.set_backgroundcolor(group_colors[group])
+    label.set_bbox(dict(facecolor=group_colors[group], edgecolor='none', pad=0))  # Add padding
+
+for label in ytick_labels:
+    area = label.get_text()
+    group = right_ordering[area]
+    label.set_backgroundcolor(group_colors[group])
+    label.set_bbox(dict(facecolor=group_colors[group], edgecolor='none', pad=0))  # Add padding
+
+xNan,yNan = np.nonzero(np.isnan(cc_matrix_hier[:, :]))
+plt.plot(yNan+0.5,xNan+0.5,'x',color='gray')
+plt.plot(np.arange(len(area_list))+0.5,np.arange(len(area_list))+0.5,'.',color='gray')
+
+cbar = plt.colorbar(im, ax=ax)
+cbar.set_label(r'Delay, positive when source leads target ($\mathrm{ms}$)')
+
+plt.savefig("figures/figure_temporal_hierarchy_matrix.pdf")
+
+# Prepare urls to display hierarchies in SBA computer
+rescale = matplotlib.colors.Normalize(vmin=0, vmax=len(hierarchical_areas)-1)
+cdict = {'red':   [[0.0,  1.0, 1.0],
+                   [1.0,  0.0, 0.0]],
+         'green': [[0.0,  1.0, 1.0],
+                   [1.0, 0.0, 0.0]],
+         'blue':  [[0.0,  1.0, 1.0],
+                   [1.0,  0.2, 0.2]]}
+mycmap = LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256)
+
+urls = {}
+for surface in ['gm','infl','wm']:
+   coloredRegions = { f'{source}(L,{surface})':[to_hex(mycmap(rescale(j)))[1:],1] for j,source in enumerate(hierarchical_areas)}
+   coloredRegions[f'unknown(L,{surface})'] = ["000000",1]
+   url = f'https://neuroinformatics.nl/sba-alpha/www/composer/?template=WKBetal10&scene={{"regions":{json.dumps(coloredRegions)},"background":"ffffff"}}'
+   url = url.replace('"','%22')
+   urls[surface] = url
+   
+# Show hierarchy in SBA Composer as whitematter (use wm), grey matter (use gm) or inflated (use infl) surface
+webbrowser.open(urls['infl'])
+