update

2024-06-03 14:02:22 +02:00 · 2024-06-03 14:02:22 +02:00 · 966a693de1
commit 966a693de1
parent 15d9361a06
58 changed files with 127839 additions and 120897 deletions
--- a/ampullary.pdf
+++ b/ampullary.pdf
--- a/ampullary.py
+++ b/ampullary.py
@ -1,5 +1,6 @@
-from threefish.core_cell_choice import p_units_to_show, update_cell_names
-from threefish.plot_suscept import ampullary_punit
+from threefish.RAM.reformat_cellchoice import p_units_to_show
+from threefish.RAM.reformat_population import update_cell_names
+from threefish.RAM.plots import ampullary_punit

 if __name__ == '__main__':

--- a/cells_suscept.pdf
+++ b/cells_suscept.pdf
--- a/cells_suscept.png
+++ b/cells_suscept.png
--- a/cells_suscept.py
+++ b/cells_suscept.py
@ -1,6 +1,6 @@
-from threefish.core_cell_choice import p_units_to_show
-from threefish.core_plot_labels import label_fr_name_rm
-from threefish.plot_suscept import ampullary_punit
+from threefish.RAM.reformat_cellchoice import p_units_to_show
+from threefish.RAM.plot_labels import label_fr_name_rm
+from threefish.RAM.plots import ampullary_punit

 if __name__ == '__main__':

--- a/cells_suscept_high_CV.pdf
+++ b/cells_suscept_high_CV.pdf
--- a/cells_suscept_high_CV.py
+++ b/cells_suscept_high_CV.py
@ -1,6 +1,6 @@
-from threefish.core_cell_choice import p_units_to_show
-from threefish.core_plot_labels import label_fr_name_rm
-from threefish.plot_suscept import ampullary_punit
+from threefish.RAM.reformat_cellchoice import p_units_to_show
+from threefish.RAM.plot_labels import label_fr_name_rm
+from threefish.RAM.plots import ampullary_punit


 if __name__ == '__main__':
--- a/data/flowchart___c_sig_0_a_fe_0.02.csv
+++ b/data/flowchart___c_sig_0_a_fe_0.02.csv
--- a/data/flowchart___c_sig_0_a_fe_0.csv
+++ b/data/flowchart___c_sig_0_a_fe_0.csv
--- a/data/flowchart__spikes_c_sig_0_a_fe_0.02.csv
+++ b/data/flowchart__spikes_c_sig_0_a_fe_0.02.csv
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--- a/data/flowchart__spikes_c_sig_0_a_fe_0.csv
+++ b/data/flowchart__spikes_c_sig_0_a_fe_0.csv
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--- a/data/flowchart_additiv_cv_adapt_factor_scaled__c_sig_0.9_a_fe_0.csv
+++ b/data/flowchart_additiv_cv_adapt_factor_scaled__c_sig_0.9_a_fe_0.csv
--- a/data/flowchart_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.csv
+++ b/data/flowchart_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.csv
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--- a/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1__revQuadrant_2012-07-03-ak-invivo-1.csv
+++ b/data/model_full_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1__revQuadrant_2012-07-03-ak-invivo-1.csv
--- a/data/plot_chi2-matrix.csv
+++ b/data/plot_chi2-matrix.csv
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--- a/data/plot_chi2-transfer.csv
+++ b/data/plot_chi2-transfer.csv
@ -4,61 +4,61 @@
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 0.53,(2.6896772768041193+1.3825594307673914j)
 0.54,(2.5996893495844375+1.2030311520713004j)
 0.55,(2.5304432868266873+1.0441180826244172j)
 0.56,(2.4769760689325215+0.90162255266271j)
-0.5700000000000001,(2.4358772169766887+0.7722860348840366j)
-0.5800000000000001,(2.4047599569995715+0.6535469324370443j)
+0.57,(2.4358772169766887+0.7722860348840366j)
+0.58,(2.4047599569995715+0.6535469324370443j)
 0.59,(2.381935755311294+0.5433635008775571j)
-0.6,(2.366208603211759+0.4400849301889086j)
+0.60,(2.366208603211759+0.4400849301889086j)
 0.61,(2.3567423767797515+0.3423572198993308j)
 0.62,(2.35297437800016+0.2490541333322257j)
 0.63,(2.3545591489430726+0.15922641897703302j)
@ -67,9 +67,9 @@
 0.66,(2.390589026006803-0.0969510169343469j)
 0.67,(2.413521400568194-0.17992209570037473j)
 0.68,(2.4425514258904646-0.262474254144232j)
-0.6900000000000001,(2.4783164680576775-0.3449520046360943j)
-0.7000000000000001,(2.5216540887235843-0.42759331733093936j)
-0.7100000000000001,(2.5736336300772495-0.5104972515266625j)
+0.69,(2.4783164680576775-0.3449520046360943j)
+0.70,(2.5216540887235843-0.42759331733093936j)
+0.71,(2.5736336300772495-0.5104972515266625j)
 0.72,(2.6355947578672287-0.5935707918343665j)
 0.73,(2.7091906314208365-0.6764459942936631j)
 0.74,(2.7964298339312523-0.7583546281788858j)
@ -78,24 +78,24 @@
 0.77,(3.1656789450647365-0.9801020679036082j)
 0.78,(3.3344355092637654-1.0340894606307922j)
 0.79,(3.5304660931222474-1.0675194529466192j)
-0.8,(3.754473053324657-1.070118189896796j)
+0.80,(3.754473053324657-1.070118189896796j)
 0.81,(4.003603077280335-1.028647162787452j)
-0.8200000000000001,(4.2688662898733964-0.9278487235899017j)
-0.8300000000000001,(4.532416584640468-0.7534563933615793j)
-0.8400000000000001,(4.766470205136731-0.4979489123589335j)
+0.82,(4.2688662898733964-0.9278487235899017j)
+0.83,(4.532416584640468-0.7534563933615793j)
+0.84,(4.766470205136731-0.4979489123589335j)
 0.85,(4.936686639186436-0.16795181297413753j)
 0.86,(5.011732375872379+0.21094847096187544j)
 0.87,(4.9759487215497+0.5975989158560745j)
 0.88,(4.836986225138172+0.9478937085406616j)
 0.89,(4.622265698590605+1.2294355486156572j)
-0.9,(4.367096560652767+1.4289653831818903j)
+0.90,(4.367096560652767+1.4289653831818903j)
 0.91,(4.102995773418705+1.5501313411305684j)
 0.92,(3.8518221541592137+1.6062362255566442j)
 0.93,(3.6255286548981793+1.6133715174547631j)
-0.9400000000000001,(3.4286636119750313+1.5862792911270112j)
-0.9500000000000001,(3.261251180884158+1.5367319073360208j)
-0.9600000000000001,(3.121011478591689+1.4733997382534496j)
+0.94,(3.4286636119750313+1.5862792911270112j)
+0.95,(3.261251180884158+1.5367319073360208j)
+0.96,(3.121011478591689+1.4733997382534496j)
 0.97,(3.004766091027785+1.4023438175667646j)
 0.98,(2.9092130911267517+1.327647738436902j)
 0.99,(2.831299615725355+1.2519824625812301j)
-1.0,(2.7683660420274805+1.1770461949516016j)
+1.00,(2.7683660420274805+1.1770461949516016j)
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_4500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_5000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_5000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_5500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_5500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_6000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_6000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_6500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnr_nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_6500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_100000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_100000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_1500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_2500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_250000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_250000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_3000000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_500000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_750000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
+++ b/data/trialnrcalc_RAM_model-3__nfft_whole_p_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TS_750000_a_fr_1__TrialsNr_1_2012-07-03-ak-invivo-1.csv
--- a/data_overview_mod.pdf
+++ b/data_overview_mod.pdf
--- a/data_overview_mod.py
+++ b/data_overview_mod.py
@ -2,18 +2,17 @@ from IPython import embed
 from matplotlib import gridspec as gridspec, pyplot as plt
 import numpy as np

-from threefish.core_cell_choice import update_cell_names
-from threefish.core_filenames import version_final
-from threefish.core_plot_suscept import get_grid_4, scatter_with_marginals_colorcoded
+from threefish.RAM.values import start_name, version_final
+from threefish.RAM.plot_subplots import plt_specific_cells, scatter_with_marginals_colorcoded
+from threefish.RAM.plot_grid import get_grid_4
 from threefish.defaults import default_figsize
-from threefish.core_plot_colors import colors_overview
-from threefish.core_reformat import exclude_nans_for_corr
-from threefish.core_reformat_RAM import load_overview_susept
-from threefish.core_load import save_visualization
-from threefish.core_bursts import setting_overview_score
-from threefish.core_plot_labels import basename_small, label_NLI_scorename2_small, label_pearson, label_stimname_small, \
+from threefish.RAM.plot_colors import colors_overview
+from threefish.reformat import exclude_nans_for_corr
+from threefish.RAM.reformat_population import load_overview_susept, update_cell_names
+from threefish.load import save_visualization
+from threefish.RAM.calc_bursts import setting_overview_score
+from threefish.RAM.plot_labels import basename_small, label_NLI_scorename2_small, label_pearson, label_stimname_small, \
    make_log_ticks
-from threefish.utils1_RAM import plt_specific_cells, start_name
 from scipy import stats
 try:
    from plotstyle import plot_style, spines_params
--- a/flowchart.pdf
+++ b/flowchart.pdf
--- a/flowchart.png
+++ b/flowchart.png
--- a/flowchart.py
+++ b/flowchart.py
@ -5,7 +5,6 @@
 # from threefish.utils0 import default_settings, resave_small_files
 # from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2

-from threefish.plot_suscept import flowchart_core
 # from threefish.utils0 import default_settings, resave_small_files
 # from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 # from threefish.utils0 import default_settings, resave_small_files
@ -13,8 +12,8 @@ from threefish.plot_suscept import flowchart_core
 import matplotlib.gridspec as gridspec

 from plotstyle import plot_style
-from threefish.core_load import save_visualization
-from threefish.plot_suscept import flowchart_core
+from threefish.load import save_visualization
+from threefish.RAM.plots import flowchart_core
 from threefish.defaults import default_figsize


--- a/model_and_data.pdf
+++ b/model_and_data.pdf
--- a/model_and_data.png
+++ b/model_and_data.png
--- a/model_and_data.py
+++ b/model_and_data.py
@ -10,17 +10,17 @@ try:
    from plotstyle import plot_style, spines_params
 except:
    print('plotstyle not installed')
-from threefish.core_plot_subplots import plot_lowpass2, plt_single_square_modl, plt_time_arrays
-from threefish.core_plot_suscept import perc_model_full, plt_data_susept
-from threefish.core_filenames import overlap_cells
-from threefish.core_load import resave_small_files, save_visualization
-from threefish.core_reformat_RAM import get_flowchart_params, load_model_susept
+from threefish.RAM.plot_subplots import plot_lowpass2, plt_data_susept, plt_single_square_modl, plt_time_arrays
+from threefish.RAM.values import overlap_cells, perc_model_full
+from threefish.load import resave_small_files, save_visualization
+from threefish.RAM.reformat_flowchart import get_flowchart_params
+from threefish.RAM.reformat_matrix import load_model_susept
 from threefish.core import find_folder_name
-from threefish.core_plot_labels import label_noise_name, nonlin_title, remove_xticks, remove_yticks, set_xlabel_arrow, \
+from threefish.RAM.plot_labels import label_noise_name, nonlin_title, remove_xticks, remove_yticks, set_xlabel_arrow, \
    set_ylabel_arrow, title_find_cell_add, xlabel_xpos_y_modelanddata
 import itertools as it
 from threefish.defaults import default_figsize, default_settings
-from threefish.core_plot_lims import join_x, join_y, set_clim_same, set_same_ylim
+from threefish.plot.limits import join_x, join_y, set_clim_same, set_same_ylim


 #from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
--- a/model_full.pdf
+++ b/model_full.pdf
--- a/model_full.png
+++ b/model_full.png
--- a/model_full.py
+++ b/model_full.py
@ -2,35 +2,31 @@
 import sys

 import numpy as np
-from IPython import embed
 from matplotlib import gridspec, pyplot as plt

-from threefish.core_calc_fft import create_full_matrix2, get_transfer_from_timesignal
-from threefish.core_reformat import chose_mat_max_value, get_axis_on_full_matrix, get_psds_ROC, \
+from threefish.RAM.calc_fft import create_full_matrix2
+from threefish.reformat import chose_mat_max_value, get_axis_on_full_matrix, get_psds_ROC, \
    load_b_public
-from threefish.core_reformat_RAM import convert_csv_str_to_float, get_transfer_for_model_full, get_transfer_from_model, \
-    load_stack_data_susept
-from threefish.core_plot_subplots import colors_suscept_paper_dots, letters_for_full_model, plt_model_full_model2, \
+from threefish.RAM.reformat import check_var_substract_method, chose_certain_group, extract_waves, \
+    get_transfer_from_model, load_cells_three, predefine_grouping_frame, restrict_cell_type, save_arrays_susept
+from threefish.RAM.save import get_transfer_for_model_full
+from threefish.RAM.reformat_matrix import convert_csv_str_to_float, load_stack_data_susept
+from threefish.RAM.plot_subplots import colors_suscept_paper_dots, letters_for_full_model, plt_model_big, \
+    plt_model_full_model2, \
    plt_model_letters, \
    plt_psds_ROC, plt_RAM_perc, recalc_fr_to_DF1
-from threefish.core_load import resave_small_files, save_visualization
-from threefish.core_values import vals_model_full
-from threefish.core_cell_choice import find_all_dir_cells
-from threefish.core_filenames import version_final
-from threefish.core_plot_suscept import perc_model_full, plt_model_big
+from threefish.load import resave_small_files, save_visualization
+from threefish.RAM.values import find_all_dir_cells, perc_model_full, vals_model_full, version_final
 from threefish.core import find_code_vs_not, info
 from plotstyle import plot_style
 import time
 from threefish.defaults import default_diagonal_points, default_figsize
-from threefish.core_plot_colorbar import colorbar_outside, rescale_colorbar_and_values
-from threefish.utils1_RAM import check_var_substract_method, chose_certain_group, \
-    extract_waves, load_cells_three, predefine_grouping_frame, restrict_cell_type, \
-    save_arrays_susept
-from threefish.core_plot_labels import label_deltaf1, label_deltaf2, label_diff, label_sum, \
+from threefish.plot.colorbar import colorbar_outside, rescale_colorbar_and_values
+from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_sum, \
    nonlin_title, remove_xticks, set_xlabel_arrow, set_ylabel_arrow, title_motivation, label_two_deltaf1, \
    label_two_deltaf2, \
    xlabel_transfer_hz
-from threefish.core_plot_lims import set_clim_same
+from threefish.plot.limits import set_clim_same


 #from utils_test import test_spikes_clusters
@ -62,7 +58,8 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s

    cell = '2012-07-03-ak-invivo-1'

-    perc, im, stack_final, stack_saved  = plt_model_big(axm, ls = ls,  pos_rel=-0.12, lw = 0.75, cell = cell, lines = True)
+    perc, im, stack_final, stack_saved  = plt_model_big(axm, ls = ls,  pos_rel=-0.12, lw = 0.75, cell = cell, lines = True,
+                                                        save_name_given = 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_4000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV')
    #embed()
    fr_waves = 139
    color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
@ -112,8 +109,7 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s
        trials_nr = 100  # 100


-        frame = get_transfer_for_model_full([a_size], 0,cell, deltat,
-                                                                                                       eod_fr,
+        frame = get_transfer_for_model_full([a_size], 0,cell, deltat,eod_fr,
                                                                                                       model_params,
                                                                                                       stimulus_length,
                                                                                                       trials_nr,
@ -196,10 +192,15 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_s

        pos_df1 = np.argmin(np.abs(f_same - np.abs(df1_recalc)))
        pos_df2 = np.argmin(np.abs(f_same - np.abs(df2_recalc)))
-        axc.text(df1_recalc, np.mean(transfer[pos_df1-array_len:pos_df1+array_len]),
-                letters[f], color=color01, ha='center',
-                va='center', zorder = 100)  # , alpha = alphas[f]
-        axc.text(df2_recalc, np.mean(transfer[pos_df2-array_len:pos_df2+array_len]),
+        if letters[f] == 'C':
+            axc.text(df1_recalc, np.max(transfer[pos_df1 - array_len:pos_df1 + array_len]) + 5000,
+                     'C,D', color=color01, ha='center',
+                     va='center', zorder=100)  # , alpha = alphas[f]
+        if (letters[f] not in ['C', 'D']):
+            axc.text(df1_recalc, np.max(transfer[pos_df1-array_len:pos_df1+array_len])+3500,
+                    letters[f], color=color01, ha='center',
+                    va='center', zorder = 100)  # , alpha = alphas[f]
+        axc.text(df2_recalc, np.max(transfer[pos_df2-array_len:pos_df2+array_len])+2500,
                letters[f], color=color02, ha='center',
                va='center', zorder = 100)  # , alpha = alphas[f]

--- a/motivation.pdf
+++ b/motivation.pdf
--- a/motivation.py
+++ b/motivation.py
@ -1,17 +1,19 @@
 ##from update_project import **
 import numpy as np

-from threefish.core_cell_choice import find_all_dir_cells
-from threefish.core_reformat import chose_mat_max_value, load_b_public
-from threefish.core_plot_suscept import plt_coherences
+from threefish.RAM.values import find_all_dir_cells, ws_nonlin_systems
+from threefish.reformat import chose_mat_max_value, load_b_public
 from threefish.defaults import default_figsize
-from threefish.core_time import cr_spikes_mat
+from threefish.calc_time import cr_spikes_mat
 from threefish.core import find_code_vs_not
 import time
-from threefish.utils1_RAM import check_var_substract_method, chose_certain_group, circle_plot, extract_waves, load_cells_three, predefine_grouping_frame, restrict_cell_type, save_arrays_susept, ws_nonlin_systems
-from threefish.core_plot_labels import title_motivation
-from threefish.core_plot_subplots import colors_suscept_paper_dots, plot_arrays_ROC_psd_single3, plot_shemes4
-from threefish.core_load import save_visualization
+from threefish.RAM.reformat import check_var_substract_method, chose_certain_group, extract_waves, load_cells_three, \
+    predefine_grouping_frame, restrict_cell_type, save_arrays_susept
+from threefish.RAM.plot_labels import title_motivation
+from threefish.RAM.plot_subplots import circle_plot, colors_suscept_paper_dots, plot_arrays_ROC_psd_single3, \
+    plot_shemes4, \
+    plt_coherences
+from threefish.load import save_visualization

 #from matplotlib import gridspec as gridspec, gridspec, mlab as ml, pyplot as plt, ticker as ticker
 from matplotlib import pyplot as plt
--- a/plot_chi2.pdf
+++ b/plot_chi2.pdf
--- a/plot_chi2.png
+++ b/plot_chi2.png
--- a/plot_chi2.py
+++ b/plot_chi2.py
@ -5,18 +5,19 @@ import mpmath as mp
 import matplotlib.pyplot as plt
 import pandas as pd
 from IPython import embed
-from mpl_toolkits import mplot3d
-from matplotlib import projections, rc
+from matplotlib import rc

 from threefish.core import find_code_vs_not
-from threefish.core_load import save_visualization
-from threefish.core_plot_colorbar import colorbar_outside
-from threefish.core_plot_labels import nonlin_title
-from threefish.core_plot_lims import set_clim_same
-from threefish.core_plot_suscept import perc_model_full
-from threefish.core_reformat_RAM import find_model_names_susept
+from threefish.load import save_visualization
+from threefish.plot.colorbar import colorbar_outside
+from threefish.plot.limits import set_clim_same
+from threefish.plot.tags import tag2
+from threefish.RAM.plot_labels import nonlin_title, set_xlabel_arrow, set_ylabel_arrow
+from threefish.RAM.plot_subplots import plt_RAM_perc
+from threefish.RAM.reformat_matrix import convert_csv_str_to_float, find_model_names_susept
 from threefish.defaults import default_figsize, default_settings
-from threefish.plotstyle import plot_style
+from threefish.plot.plotstyle import plot_style
+from threefish.RAM.values import perc_model_full

 rc('text', usetex=True)

@ -70,8 +71,9 @@ if __name__ == '__main__':
    load_function, name2, save_name = find_model_names_susept(save_name2)
    versions_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not()

+    redo = False
    if versions_comp == 'develop':
-        if not os.path.exists(name1):
+        if (not os.path.exists(name1)) | (redo == True):
            mu = 1.1
            D = 0.001

@ -111,19 +113,27 @@ if __name__ == '__main__':
                    chi2_data[f2][f1] = chi2
                    chi1_data[f1] =  chi1_1

+            #embed()
+            #chi2_data.index = chi2_data.index.map(lambda x: "{:.2f}".format(float(x)))
+            #chi2_data.columns = chi2_data.columns.map(lambda x: "{:.2f}".format(float(x)))
            frame = pd.DataFrame(chi1_data, index=f1_range, columns = ['transfer'])
+            frame.index = frame.index.map(lambda x: "{:.2f}".format(float(x)))
            frame.to_csv(name1)
+
            frame2 = pd.DataFrame(chi2_data, index=f2_range, columns = f1_range)
+            frame2.index = frame2.index.map(lambda x: "{:.2f}".format(float(x)))
+            frame2.columns = frame2.columns.map(lambda x: "{:.2f}".format(float(x)))
            frame2.to_csv(name2)
+
            # name1 =
            print('transfer embed')
        else:
-            frame = pd.read_csv(name1)
-            frame2 = pd.read_csv(name2)
+            frame = pd.read_csv(name1, index_col=0)
+            frame2 = pd.read_csv(name2, index_col=0)
    else:
-        frame = pd.read_csv(name1)
-        frame2 = pd.read_csv(name2)
-
+        frame = pd.read_csv(name1, index_col = 0)
+        frame2 = pd.read_csv(name2, index_col = 0)
+    #embed()
    # colorbar plot of chi_2
    #def vary_contrasts_big_with_tuning3(freqs=[(39.5, -135.5)], cells_here='2011-10-25-ad-invivo-1'):
    default_settings()  # ts=13, ls=13, fs=13, lw = 0.7
@ -134,15 +144,36 @@ if __name__ == '__main__':
    fig1, ax = plt.subplots(1,2)
    plt.subplots_adjust(bottom=0.2, wspace=0.35, left=0.04, right = 0.97)
    kwargs = {'cmap': 'turbo', 'extent': [frame2.columns[0], frame2.columns[-1], frame2.index[0], frame2.index[-1]]}#x_min - 0.5*dx, x_max + 0.5*dx, y_min - 0.5*dy, y_max + 0.5*dy
-    im = ax[0].imshow(np.abs(frame2), **kwargs, origin='lower')
-    clim = im.get_clim()
-    im.set_clim(0, clim[-1])
+    #im = ax[0].imshow(np.abs(frame2), **kwargs, origin='lower')
+    #clim = im.get_clim()
+    #im.set_clim(0, clim[-1])
+    #embed()
+    frame2.columns = frame2.index
+    new_keys, stack_plot = convert_csv_str_to_float(frame2)
+    im = plt_RAM_perc(ax[0], 'perc', np.abs(stack_plot))
+    set_clim_same([im],
+                  mats=[np.abs(stack_plot)],
+                  lim_type='up',
+                  nr_clim='perc',
+                  clims='',
+                  percnr=perc_model_full())
+
+    pos_rel = -0.12
+    #set_xlabel_arrow(ax[0], xpos=0.95, ypos=pos_rel)
+    #set_ylabel_arrow(ax[0], xpos=pos_rel, ypos=0.95)
+    cbar, left, bottom, width, height = colorbar_outside(ax[0],
+                                                         im,
+                                                         add=5,
+                                                         width=0.01)
+    #cbar.set_label(nonlin_title(add_nonlin_title=' ['),
+    #               rotation=90,
+    #               labelpad=8)
    #set_clim_same([im], mats=[np.abs(frame2)], lim_type='up', nr_clim='perc', clims='', percnr=99)#perc_model_full()
    #plt.colorbar(pos, fraction=0.046)#, pad=0.04

    ax[0].set_xlabel(r'$f_{1}$')
    ax[0].set_ylabel(r'$f_{2}$')
-    ax[0].set_title(r'$|\chi_{2}(\omega_{1}, \omega_{2})|$' + ': ' + r'$\mu =$ ' + str(mu) + ', D = ' + str(D) + ', ' + r'$r_{0} =$ ' + str(round(r0,3)), pad = 10)
+    #ax[0].set_title(r'$|\chi_{2}(\omega_{1}, \omega_{2})|$' + ': ' + r'$\mu =$ ' + str(mu) + ', D = ' + str(D) + ', ' + r'$r_{0} =$ ' + str(round(r0,3)), pad = 10)
    ax[0].set_xticks_delta(0.2)
    ax[0].set_xticks_delta(0.2)
    ax[0].set_xlim(frame2.columns[0]-0.01, frame2.columns[-1])
@ -151,12 +182,18 @@ if __name__ == '__main__':
    cbar.set_label(r'$|\chi_{2}|$', rotation=90, labelpad=8)

    #embed()
+    #new_keys, frame0 = convert_csv_str_to_float(frame)
+    frame = frame.astype(complex)
    ax[1].plot(frame.index, np.abs(frame['transfer']), color = 'black')
    ax[1].set_xlabel(r'$f_{1}$')
    ax[1].set_ylabel(r'$|\chi_{1}|$')
+    ax[1].set_xlim(0, frame.index[-1])
    ylim = ax[1].get_ylim()
    ax[1].set_ylim(0, ylim[-1])

+    fig = plt.gcf()
+    tag2(axes = ax, xoffs=[-0.5, -5.5], yoffs=1.7)  # ax_ams[3],
+
    save_visualization()
    #plt.show()

--- a/susceptibility1.tex
+++ b/susceptibility1.tex
@ -522,14 +522,14 @@ Nonlinear processes are key to neuronal information processing. Decision making

 \notejb{Wo genau soll die Einleitung hinzielen? Das ist bis jetzt eine Ansammlung von Aussagen zum Thema nonlinearities und irgendwie schleicht sich da der Fisch noch rein.}

-\notejb{What are the main findings of the manuscript? (i) 2nd order susceptibility can be measured using RAM stimuli, (ii) we see a bit of nonlinearity in low-CV P-units and strong one in ampullary cells. (ii) We find stuff, that matches the theoretical predictions (Voronekov) (iii) AMs with carrier (auditory) (iv) There is an estimation problem because of low N and RAM stimulus introducing linearizing noise. (v) noise split gives a good estimate that translates well to pure sine wave stimulation.}
+\notejb{What are the main findings of the manuscript? (i) 2nd order susceptibility can be measured using RAM stimuli, (ii) we see a bit of calc_nonlinearity_model in low-CV P-units and strong one in ampullary cells. (ii) We find stuff, that matches the theoretical predictions (Voronekov) (iii) AMs with carrier (auditory) (iv) There is an estimation problem because of low N and RAM stimulus introducing linearizing noise. (v) noise split gives a good estimate that translates well to pure sine wave stimulation.}

 \notejb{Strong aspects are (i) how to estimate 2nd order susceptibilities, (ii) what do they tell us about relevant stimuli, (iii) nonlinearities show up only for very specific frequencies}

 \notejb{For the estimation problem we need to cite work that also measured higher order Wiener kernels and filters (we need to find the Andrew French paper, who else? Gabbiani? John Miller?). For nonlinear encoding we need to talk about linear-nonlinear models (Chinchilinsky, Gollisch, Jan Clemens) versus Wiener series (French). And we find nonlinear responses in neurons that have been considered as quite linear, but only at specific frequency combinations and low signal amplitudes.}


-\noteab{The nonlinearity of a system has been accessed with the use of wiener kernels \cite{French1973,French1976}, measuring the system response to white noise stimulation. Besides that the nonlinearity of a system has been addressed by pure sinewave simulation, considering the Fourier transform of the Volterra series \cite{Victor1977,Victor1980,Shapley1979}. The estimates of the nonlinearity with both methods, white noise and sinewave stimulation, was shown to yield similar results \cite{Vitor1979}. Nonlinearity was investigated, not addressing the system properties, but focusing on the quadratic phase coupling of the two input frequencies \cite{Nikias1993, Neiman2011fish}. With these approaches nonlinearity at the sum of two input frequencies was quantified in retinal cells \cite{Shapley1979} for stimuli with small amplitudes, in ampullary cells \cite{Neiman2011fish}, in the EEG of sleep \cite{Barnett1971,Bullock1997} and in mechanorecetors \cite{French1976}. Second-order responses have been quantified in not amplitude modulated \cite{Neiman2011fish} and amplitude modulated systems \cite{Victor1977,Victor1980,Shapley1979}.}
+\noteab{The calc_nonlinearity_model of a system has been accessed with the use of wiener kernels \cite{French1973,French1976}, measuring the system response to white noise stimulation. Besides that the calc_nonlinearity_model of a system has been addressed by pure sinewave simulation, considering the Fourier transform of the Volterra series \cite{Victor1977,Victor1980,Shapley1979}. The estimates of the calc_nonlinearity_model with both methods, white noise and sinewave stimulation, was shown to yield similar results \cite{Vitor1979}. Nonlinearity was investigated, not addressing the system properties, but focusing on the quadratic phase coupling of the two input frequencies \cite{Nikias1993, Neiman2011fish}. With these approaches calc_nonlinearity_model at the sum of two input frequencies was quantified in retinal cells \cite{Shapley1979} for stimuli with small amplitudes, in ampullary cells \cite{Neiman2011fish}, in the EEG of sleep \cite{Barnett1971,Bullock1997} and in mechanorecetors \cite{French1976}. Second-order responses have been quantified in not amplitude modulated \cite{Neiman2011fish} and amplitude modulated systems \cite{Victor1977,Victor1980,Shapley1979}.}

 \begin{figure*}[t]
  \includegraphics[width=\columnwidth]{plot_chi2}
@ -539,7 +539,7 @@ Nonlinear processes are key to neuronal information processing. Decision making

 \noteab{Ich weiß nicht ob du in die Literatur reingeschaut hast, aber so eine Dreiecksstruktur finden wir schon in früheren Arbeiten! Vor allen in denen von Viktor.}

-While the encoding of signals can often be well described by linear models in the sensory periphery\cite{Machens2001}, this is not true for many upstream neurons. Rather, nonlinear processes are implemented to extract special stimulus features\cite{Adelson1985,Gabbiani1996,Olshausen1996,Gollisch2009}. In active electrosensation, the self-generated electric field (electric organ discharge, EOD) that is quasi sinusoidal in wavetype electric fish acts as the carrier signal that is amplitude modulated in the context of communication\cite{Walz2014, Henninger2018, Benda2020}, object detection and navigation\cite{Fotowat2013, Nelson1999}. In social contexts, the interference of the EODs of two interacting animals result in a characteristic periodic amplitude modulation, the so-called beat. The beat amplitude is defined by the smaller EOD amplitude, its frequency is defined as the difference between the two EOD frequencies ($\Delta f = f-\feod{}$, valid for $f < \feod{}/2$)\cite{Barayeu2023}. Cutaneous electroreceptor organs that are distributed over the bodies of these fish \cite{Carr1982} are tuned to the own field\cite{Hopkins1976,Viancour1979}. Probability-type electroreceptor afferents (P-units) innervate these organs via ribbon synapses\cite{Szabo1965, Wachtel1966} and project to the hindbrain where they trifurcate and synapse onto pyramidal cells in the electrosensory lateral line lobe (ELL)\cite{Krahe2014}. The P-units of the gymnotiform electric fish \lepto{} encode such amplitude modulations (AMs) by modulation of their firing rate\cite{Gabbiani1996}. They fire probabilistically but phase-locked to the own EOD and the skipping of cycles leads to their characteristic multimodal interspike-interval distribution. Even though the extraction of the AM itself requires a nonlinearity\cite{Middleton2006,Stamper2012Envelope,Savard2011,Barayeu2023} encoding the time-course of the AM is linear over a wide range of AM amplitudes and frequencies\cite{Xu1996,Benda2005,Gussin2007,Grewe2017,Savard2011}. In the context of social signalling among three fish we observe an AM of the AM, also referred to as second-order envelope or just social envelope\cite{Middleton2006, Savard2011, Stamper2012Envelope}. Encoding this again requires nonlinearities\cite{Middleton2006} and it was shown that a subpopulation of P-units are sensitive to envelopes\cite{Savard2011} and exhibit nonlinearities e.g. when driven by strong stimuli\cite{Nelson1997,Chacron2004}. 
+While the encoding of signals can often be well described by linear models in the sensory periphery\cite{Machens2001}, this is not true for many upstream neurons. Rather, nonlinear processes are implemented to extract special stimulus features\cite{Adelson1985,Gabbiani1996,Olshausen1996,Gollisch2009}. In active electrosensation, the self-generated electric field (electric organ discharge, EOD) that is quasi sinusoidal in wavetype electric fish acts as the carrier signal that is amplitude modulated in the context of communication\cite{Walz2014, Henninger2018, Benda2020}, object detection and navigation\cite{Fotowat2013, Nelson1999}. In social contexts, the interference of the EODs of two interacting animals result in a characteristic periodic amplitude modulation, the so-called beat. The beat amplitude is defined by the smaller EOD amplitude, its frequency is defined as the difference between the two EOD frequencies ($\Delta f = f-\feod{}$, valid for $f < \feod{}/2$)\cite{Barayeu2023}. Cutaneous electroreceptor organs that are distributed over the bodies of these fish \cite{Carr1982} are tuned to the own field\cite{Hopkins1976,Viancour1979}. Probability-type electroreceptor afferents (P-units) innervate these organs via ribbon synapses\cite{Szabo1965, Wachtel1966} and project to the hindbrain where they trifurcate and synapse onto pyramidal cells in the electrosensory lateral line lobe (ELL)\cite{Krahe2014}. The P-units of the gymnotiform electric fish \lepto{} encode such amplitude modulations (AMs) by modulation of their firing rate\cite{Gabbiani1996}. They fire probabilistically but phase-locked to the own EOD and the skipping of cycles leads to their characteristic multimodal interspike-interval distribution. Even though the extraction of the AM itself requires a calc_nonlinearity_model\cite{Middleton2006,Stamper2012Envelope,Savard2011,Barayeu2023} encoding the time-course of the AM is linear over a wide range of AM amplitudes and frequencies\cite{Xu1996,Benda2005,Gussin2007,Grewe2017,Savard2011}. In the context of social signalling among three fish we observe an AM of the AM, also referred to as second-order envelope or just social envelope\cite{Middleton2006, Savard2011, Stamper2012Envelope}. Encoding this again requires nonlinearities\cite{Middleton2006} and it was shown that a subpopulation of P-units are sensitive to envelopes\cite{Savard2011} and exhibit nonlinearities e.g. when driven by strong stimuli\cite{Nelson1997,Chacron2004}.

 \begin{figure*}[t]
  \includegraphics[width=\columnwidth]{motivation}
@ -549,7 +549,7 @@ While the encoding of signals can often be well described by linear models in th

 Recent theoretical and modelling work on leaky integrate-and-fire (LIF) model neurons revealed specific pattern of nonlinear interactions when driven with weak sinewaves\cite{Voronenko2017}. This situation is reminiscent of the real fish's situation in which three electric fish interact. The active electrosensory system is then exposed to the two AMs arising from the interactions with either foreign fish's field. Previous recordings in the natural habitat showed interactions of three animals in which two animals interacted closely (strong signals) and were interrupted by an intruding animal (weak signal). The intruder was detected at spatial distances that led to an extremely faint intruder signal while the strong signal of the other animal is present\cite{Henninger2018}. When the receiver fish with EOD frequency \feod{} is alone, the P-unit fire action potentials as a baseline response $r_{0}$ at a spontaneous baseline rate \fbase{}. Accordingly, a peak at \fbase{} is present in the power spectrum of the neuronal response (\subfigrefb{fig:motivation}{A}, bottom). Phase-locking to the own field also leads to a representation of \feod{} in the P-unit firing rate (see \figref{fig:cells_suscept}\panel{B})\cite{Sinz2020}. The beat frequency when two fish interact is also represented in the respective responses and accordingly, there is a peak in power spectrum at $\Delta f_1$ or $\Delta f_2$ (\subfigrefb{fig:motivation}{B, C}, respectively). When three fish encounter, all their waveforms interfere with both beat frequencies being present in the superimposed signal (\subfigrefb{fig:motivation}{D}, second row). The power spectrum of the neuronal activity contains both previously seen beat peaks, but also nonlinear interaction peaks at the sum frequency \bsum{} and the difference frequency \bdiff{} (\subfigrefb{fig:motivation}{D}, bottom), thus the response of the system is not equal to the input (\subfigrefb{fig:motivation}{D}, top). The appearing difference peak is known as the social envelope\cite{Stamper2012Envelope, Savard2011}. The neuron shown here, clearly encodes the envelope. Whether P-units in general encode envelopes has been subject of controversy, some works do not consider P-units as envelope encoders\cite{Middleton2006}, while others identify some P-units as envelope encoders\cite{Savard2011}. 

-The P-unit responses can be partially explained by simple linear filters. The linear relation of the cellular response and the stimulus can be captured by the first-order susceptibility (or transfer function). As in Volterra series, higher-order terms describe the nonlinear interactions. We quantify the nonlinearity of P-unit encoding by estimating the second-order susceptibility from white-noise responses\cite{Nikias1993,Neiman2011fish, Voronenko2017,Egerland2020}. We address whether the theory still holds for neurons that do not encode the foreign signals directly but respond to the AM of a carrier. For this study we re-use a large set of recordings of afferents of both the active (P-units) and the passive electrosensory (ampullary cells) to work out which cellular features enhance nonlinear encoding. 
+The P-unit responses can be partially explained by simple linear filters. The linear relation of the cellular response and the stimulus can be captured by the first-order susceptibility (or transfer function). As in Volterra series, higher-order terms describe the nonlinear interactions. We quantify the calc_nonlinearity_model of P-unit encoding by estimating the second-order susceptibility from white-noise responses\cite{Nikias1993,Neiman2011fish, Voronenko2017,Egerland2020}. We address whether the theory still holds for neurons that do not encode the foreign signals directly but respond to the AM of a carrier. For this study we re-use a large set of recordings of afferents of both the active (P-units) and the passive electrosensory (ampullary cells) to work out which cellular features enhance nonlinear encoding.



@ -574,7 +574,7 @@ The second-order susceptibility, \Eqnref{eq:susceptibility},  quantifies amplitu


 % DAS GEHOERT IN DIE DISKUSSION:
-% To estimate the second-order susceptibility from the P-unit responses to RAM stimuli, the noise stimulus was set in relation to the corresponding neuronal response in the Fourier domain, resulting in a matrix in which the nonlinearity at the sum frequency \fsum{} in the firing rate is depicted for two noise frequencies \fone{} and \ftwo{} (\Eqnref{eq:susceptibility}, \subfigrefb{fig:cells_suscept}{E--F}). The decomposition of the RAM stimulus into the frequency components allows us to approximate the beat frequencies $\Delta f$, occurring in case of pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}). Thus, the nonlinearity accessed with the RAM stimulation at \fsum{} (\subfigrefb{fig:cells_suscept}{E}) is similar to the nonlinearity appearing during pure sine-wave stimulation at \bsum{} (orange peak, \subfigrefb{fig:motivation}{D}).
+% To estimate the second-order susceptibility from the P-unit responses to RAM stimuli, the noise stimulus was set in relation to the corresponding neuronal response in the Fourier domain, resulting in a matrix in which the calc_nonlinearity_model at the sum frequency \fsum{} in the firing rate is depicted for two noise frequencies \fone{} and \ftwo{} (\Eqnref{eq:susceptibility}, \subfigrefb{fig:cells_suscept}{E--F}). The decomposition of the RAM stimulus into the frequency components allows us to approximate the beat frequencies $\Delta f$, occurring in case of pure sine-wave stimulation (\subfigrefb{fig:motivation}{D}). Thus, the calc_nonlinearity_model accessed with the RAM stimulation at \fsum{} (\subfigrefb{fig:cells_suscept}{E}) is similar to the calc_nonlinearity_model appearing during pure sine-wave stimulation at \bsum{} (orange peak, \subfigrefb{fig:motivation}{D}).

 For the low-CV P-unit we observe a band of slightly elevated second-order susceptibility for the low RAM contrast at \fsumb{} (yellowish anti-diagonal between pink edges, \subfigrefb{fig:cells_suscept}{E}). This structure vanishes for the stronger stimulus (\subfigref{fig:cells_suscept}{F}). Further, the overall level of the second-order susceptibility is reduced with increasing stimulus strength. To quantify the structural changes in the susceptibility matrices we projected susceptibility values onto the diagonal by taking the mean over all anti-diagonals (\subfigrefb{fig:cells_suscept}{G}). For the low RAM contrast this projected second-order susceptibility indeed has a small peak at \fbase{} (\subfigrefb{fig:cells_suscept}{G}, dot on top line). For the higher RAM contrast, however, this peak vanishes and the overall level of the second-order susceptibility is reduced (\subfigrefb{fig:cells_suscept}{G}). The reason behind this reduction is that a RAM with a higher contrast is not only a stimulus with an increased amplitude, but also increases the total noise in the system. Increased noise is known to linearize signal transmission \cite{Longtin1993, Chialvo1997,  Roddey2000, Voronenko2017} and thus the second-order susceptibility is expected to decrease.

@ -615,7 +615,7 @@ With high levels of intrinsic noise, we would not expect the nonlinear response


 \subsection*{Second-order susceptibility can explain nonlinear peaks in pure sinewave stimulation}
-We estimated the second-order susceptibility of P-unit responses using RAM stimuli. In particular, we found pronounced nonlinear responses in the limit of weak stimulus amplitudes. How do these findings relate to the situation of two pure sinewave stimuli with finite amplitudes that approximates the interference of EODs of real animals? For the P-units the relevant signals are the beat frequencies \bone{} and \btwo{} that arise from the interference of either of the two foreign EODs with the receiving fish's own EOD (\figref{fig:motivation}). In the introductory example, the response power spectrum showed peaks from nonlinear interactions at the sum of the two beat frequencies (orange marker, \subfigrefb{fig:motivation}{D}) and at the difference between the two beat frequencies (red marker, \subfigrefb{fig:motivation}{D}). In this example, $\Delta f_{2}$ was similar to \fbase{}, corresponding to the horizontal line of the second-order susceptibility estimated for a vanishing external RAM stimulus (\subfigrefb{model_and_data}\,\panel[iii]{C}). In the three-fish example, there was a second nonlinearity at the difference between the two beat frequencies (red dot, \subfigrefb{fig:motivation}{D}), that is not covered by the so-far shown part of the second-order susceptibility (\subfigrefb{model_and_data}\,\panel[iii]{C}), in which only the response at the sum of the two stimulus frequencies is addressed. % less prominent,
+We estimated the second-order susceptibility of P-unit responses using RAM stimuli. In particular, we found pronounced nonlinear responses in the limit of weak stimulus amplitudes. How do these findings relate to the situation of two pure sinewave stimuli with finite amplitudes that approximates the interference of EODs of real animals? For the P-units the relevant signals are the beat frequencies \bone{} and \btwo{} that arise from the interference of either of the two foreign EODs with the receiving fish's own EOD (\figref{fig:motivation}). In the introductory example, the response power spectrum showed peaks from nonlinear interactions at the sum of the two beat frequencies (orange marker, \subfigrefb{fig:motivation}{D}) and at the difference between the two beat frequencies (red marker, \subfigrefb{fig:motivation}{D}). In this example, $\Delta f_{2}$ was similar to \fbase{}, corresponding to the horizontal line of the second-order susceptibility estimated for a vanishing external RAM stimulus (\subfigrefb{model_and_data}\,\panel[iii]{C}). In the three-fish example, there was a second calc_nonlinearity_model at the difference between the two beat frequencies (red dot, \subfigrefb{fig:motivation}{D}), that is not covered by the so-far shown part of the second-order susceptibility (\subfigrefb{model_and_data}\,\panel[iii]{C}), in which only the response at the sum of the two stimulus frequencies is addressed. % less prominent,

 \begin{figure*}[tp]
  \includegraphics[width=\columnwidth]{model_full}
@ -632,7 +632,7 @@ Is it possible based on the second-order susceptibility estimated by means of RA

 \begin{figure*}[tp]
  \includegraphics[width=\columnwidth]{data_overview_mod}
-  \caption{\label{fig:data_overview} Nonlinear responses in P-units and ampullary cells. The second-order susceptibility is condensed into the peakedness of the nonlinearity, \nli{} \Eqnref{eq:nli_equation}, that relates the amplitude of the projected susceptibility at a cell's baseline firing rate to its median (see \subfigrefb{fig:cells_suscept}{G}). Each of the recorded neurons contributes at maximum with two stimulus contrasts. Black squares and circles highlight recordings conducted in a single cell. Squares in  \panel{A, C, E} correspond to the cell in \figrefb{fig:cells_suscept} and circles to the cell in \figrefb{fig:cells_suscept_high_CV}. Squares in \panel{B, D, F} correspond to the cell in \figrefb{fig:ampullary}. \figitem{A, B} There is a negative correlation between the CV during baseline and \nli.  \figitem{C, D} There is a negative correlation between the CV during stimulation and \nli. \figitem{E, F} \nli{} is plotted against the response modulation, (see methods), an indicator of the subjective stimulus strength for a cell. There is a negative correlation between response modulation and \nli. Restricting the analysis to the weakest stimulus that was presented to each unique neuron, does not change the results. The number of unique neurons is 221 for P-units and 45 for ampullary cells.
+  \caption{\label{fig:data_overview} Nonlinear responses in P-units and ampullary cells. The second-order susceptibility is condensed into the peakedness of the calc_nonlinearity_model, \nli{} \Eqnref{eq:nli_equation}, that relates the amplitude of the projected susceptibility at a cell's baseline firing rate to its median (see \subfigrefb{fig:cells_suscept}{G}). Each of the recorded neurons contributes at maximum with two stimulus contrasts. Black squares and circles highlight recordings conducted in a single cell. Squares in  \panel{A, C, E} correspond to the cell in \figrefb{fig:cells_suscept} and circles to the cell in \figrefb{fig:cells_suscept_high_CV}. Squares in \panel{B, D, F} correspond to the cell in \figrefb{fig:ampullary}. \figitem{A, B} There is a negative correlation between the CV during baseline and \nli.  \figitem{C, D} There is a negative correlation between the CV during stimulation and \nli. \figitem{E, F} \nli{} is plotted against the response modulation, (see methods), an indicator of the subjective stimulus strength for a cell. There is a negative correlation between response modulation and \nli. Restricting the analysis to the weakest stimulus that was presented to each unique neuron, does not change the results. The number of unique neurons is 221 for P-units and 45 for ampullary cells.
    % The two example P-units shown before are highlighted with dark markers in \subfigrefb{fig:data_overview}{A, C, E} (squares -- \figrefb{fig:cells_suscept}, circles --  \figrefb{fig:cells_suscept_high_CV}).
    % Several of the recorded neurons contribute with two samples to the population analysis as their responses have been recorded to two different contrast of the same RAM stimulus. Higher stimulus contrasts lead to a stronger drive and thus stronger response modulations (see color code bar in \subfigref{fig:data_overview}{A}, see methods).
    % The example cell shown above (\figref{fig:ampullary}) was recorded at two different stimulus intensities and the \nli{} values are highlighted with black squares.
@ -640,8 +640,8 @@ Is it possible based on the second-order susceptibility estimated by means of RA
 \end{figure*}

 %\Eqnref{response_modulation}
-\subsection*{Low CVs and weak stimuli are associated with strong nonlinearity}
-All the statements about nonlinear encoding in p-type and ampullary electroreceptor afferents based on single cell examples shown above are supported by the analysis of our pool of 221 P-units and 47 ampullary afferents recorded in 71 specimen. For a comparison across cells we summarize the structure of the second-order susceptibilities in a single number, the peakedness of the nonlinearity \nli{} \Eqnref{eq:nli_equation}, that characterizes the size of the expected peak of the projections of a \suscept{} matrix onto its diagonal at the baseline firing rate (e.g. \subfigref{fig:cells_suscept}{G}). \nli{} assumes high values when the peak at \fbase{} is pronounced relative to the median of projections onto the diagonal and is small when there is no distinct peak. The \nli{} values of the P-unit population depend weakly on the CV of the baseline ISI distribution. Cells with lower baseline CVs tend to have more pronounced peaks in their projections than those that have high baseline CVs (\subfigrefb{fig:data_overview}{A}). This negative correlation is more pronounced against the CV measured during stimulation (\subfigrefb{fig:data_overview}{C}).
+\subsection*{Low CVs and weak stimuli are associated with strong calc_nonlinearity_model}
+All the statements about nonlinear encoding in p-type and ampullary electroreceptor afferents based on single cell examples shown above are supported by the analysis of our pool of 221 P-units and 47 ampullary afferents recorded in 71 specimen. For a comparison across cells we summarize the structure of the second-order susceptibilities in a single number, the peakedness of the calc_nonlinearity_model \nli{} \Eqnref{eq:nli_equation}, that characterizes the size of the expected peak of the projections of a \suscept{} matrix onto its diagonal at the baseline firing rate (e.g. \subfigref{fig:cells_suscept}{G}). \nli{} assumes high values when the peak at \fbase{} is pronounced relative to the median of projections onto the diagonal and is small when there is no distinct peak. The \nli{} values of the P-unit population depend weakly on the CV of the baseline ISI distribution. Cells with lower baseline CVs tend to have more pronounced peaks in their projections than those that have high baseline CVs (\subfigrefb{fig:data_overview}{A}). This negative correlation is more pronounced against the CV measured during stimulation (\subfigrefb{fig:data_overview}{C}).

 The effective stimulus strength also plays an important role. We quantify the effect a stimulus has on a cell's response by the response modulation --- the standard deviation of a cell's firing rate in response to a RAM stimulus. P-units are heterogeneous in their sensitivity, their response modulations to the same stimulus contrast vary a lot \cite{Grewe2017}. Cells with weak responses to a stimulus, be it an insensitive cell or a weak stimulus, have higher \nli{} values and thus a more pronounced ridge in the second-order susceptibility at \fsumb{} in comparison to strongly responding cells that basically have flat second-order susceptibilities (\subfigrefb{fig:data_overview}{E}). How pronounced nonlinear response components are in P-units thus depends on the baseline CV (a proxy for the internal noise level), and both the CV and response strength during stimulation (effective output noise). 
 %(Pearson's $r=-0.35$, $p<0.001$)221 P-units and 47 (Pearson's $r=-0.16$, $p<0.01$)
@ -652,7 +652,7 @@ The population of ampullary cells is generally more homogeneous, with lower base

 \section*{Discussion}

-Nonlinearities are ubiquitous in nervous systems, they are essential to extract certain features such as amplitude modulations of a carrier. Here, we analyzed the nonlinearity in primary electroreceptor afferents of the weakly electric fish \lepto{}. Under natural stimulus conditions, when the electric fields of two or more animals produce interference patterns that contain information of the context of the encounter, such nonlinear encoding is required to enable responding to AMs or also so-called envelopes. The work presented here is inspired by observations of electric fish interactions in the natural habitat. In these observed electrosensory cocktail-parties, a second male is intruding the conversations of a courting dyad. The courting male detects the intruder at a large distance where the foreign electric signal is strongly attenuated due to spatial distance and tiny compared to the signal emitted by the close-by female. Such a three-animal situation is exemplified in \figref{fig:motivation} for a rather specific combination of interfering EODs. There, we observe that the electroreceptor response contains components that result from nonlinear interference (\subfigref{fig:motivation}{D}) that might help to solve this detection task. Based on theoretical work we work out the circumstances under which electroreceptor afferents show such nonlinearities.
+Nonlinearities are ubiquitous in nervous systems, they are essential to extract certain features such as amplitude modulations of a carrier. Here, we analyzed the calc_nonlinearity_model in primary electroreceptor afferents of the weakly electric fish \lepto{}. Under natural stimulus conditions, when the electric fields of two or more animals produce interference patterns that contain information of the context of the encounter, such nonlinear encoding is required to enable responding to AMs or also so-called envelopes. The work presented here is inspired by observations of electric fish interactions in the natural habitat. In these observed electrosensory cocktail-parties, a second male is intruding the conversations of a courting dyad. The courting male detects the intruder at a large distance where the foreign electric signal is strongly attenuated due to spatial distance and tiny compared to the signal emitted by the close-by female. Such a three-animal situation is exemplified in \figref{fig:motivation} for a rather specific combination of interfering EODs. There, we observe that the electroreceptor response contains components that result from nonlinear interference (\subfigref{fig:motivation}{D}) that might help to solve this detection task. Based on theoretical work we work out the circumstances under which electroreceptor afferents show such nonlinearities.


 %\,\panel[iii]{C}
@ -660,9 +660,9 @@ Nonlinearities are ubiquitous in nervous systems, they are essential to extract
 Theoretical work\cite{Voronenko2017} explained analytically the occurrence of nonlinear products when a LIF model neuron is stimulated with pure sine-waves. To investigate whether the same mechanisms occur in electroreceptor afferents which are driven by AMs of a carrier and not by pure sine-waves, we followed the previous approach and quantified the second-order susceptibility from responses to white-noise stimuli \cite{Voronenko2017, Egerland2020, Neiman2011fish,Nikias1993}. We expected to see elevated second-order susceptibility where either of the foreign signals matches the baseline firing rate ($f_1=f_{base}$ or $f_2=f_{base}$) or when the sum equals the baseline firing rate of the neuron (\fsumb{}) creating a triangular pattern of elevated \suscept{} e.g.\,\subfigref{model_and_data}\,\panel[iii]{C}. Indeed, we find traces of the same nonlinearities in the neuronal responses of p-type electroreceptor afferents. The nonlinear pattern observed in the experimental data, however, matches to the expectations only partially and only in a subset of neurons (\figsref{fig:cells_suscept} and\,\ref{fig:ampullary}). Nevertheless, the theory holds also for systems that are driven by AMs of a carrier and is thus more widely applicable.

 \subsection*{Intrinsic noise limits nonlinear responses}
-Only those P-units that exhibit low coefficients of variation (CV) of the interspike-interval distribution (\subfigref{fig:cells_suscept}{A}) in their unperturbed baseline response show the expected nonlinerities (\subfigref{fig:data_overview}{A}). Such low-CV cells are rare among the 221 P-units that we used in this study. The afferents of the passive electrosensory system, the ampullary cells, however have generally lower CVs and show a much clearer nonlinearity pattern than the low-CV P-unit exemplified here (compare \figsref{fig:cells_suscept} and \ref{fig:ampullary}). The single ampullary cell featured in \figref{fig:ampullary} is a representative of the majority of ampullary cells analyzed here. All ampullary cells have CVs below 0.4 with a median around 0.12 and the observed \nli{}s are 10-fold higher than in P-units.
+Only those P-units that exhibit low coefficients of variation (CV) of the interspike-interval distribution (\subfigref{fig:cells_suscept}{A}) in their unperturbed baseline response show the expected nonlinerities (\subfigref{fig:data_overview}{A}). Such low-CV cells are rare among the 221 P-units that we used in this study. The afferents of the passive electrosensory system, the ampullary cells, however have generally lower CVs and show a much clearer calc_nonlinearity_model pattern than the low-CV P-unit exemplified here (compare \figsref{fig:cells_suscept} and \ref{fig:ampullary}). The single ampullary cell featured in \figref{fig:ampullary} is a representative of the majority of ampullary cells analyzed here. All ampullary cells have CVs below 0.4 with a median around 0.12 and the observed \nli{}s are 10-fold higher than in P-units.

-The CV serves as a proxy for the intrinsic noise in the cells. In both cell types, we observe a negative correlation between \nli{} and the CV, indicating that it is the level of intrinsic noise that plays a role here. These findings are in line with previous studies that propose that noise linearizes the system\cite{Roddey2000, Chialvo1997, Voronenko2017}. More intrinsic noise has been demonstrated to increase the CV and reduce nonlinear phase-locking in vestibular afferents\cite{Schneider2011}. Reduced noise, on the other hand, has been associated with stronger nonlinearity in pyramidal cells of the ELL\cite{Chacron2006}. Further support for the notion of noise limiting the nonlinearity comes from our P-unit LIF model that faithfully reproduces P-unit activity\cite{Barayeu2023}. We can use this model and apply a noise-split \cite{Lindner2022} based on the to the Furutsu-Novikov theorem\cite{Novikov1965,Furutsu1963}, to increase the signal-to-noise ratio in the cell while keeping the overall response variability constant (see methods). Treating 90\,\% of the total noise as signal and simulating large numbers of trial uncovers the full nonlinear structure (\figref{model_and_data}) seen in LIF neurons and the analytical derivations when driven with sine-wave stimuli\cite{Voronenko2017}.
+The CV serves as a proxy for the intrinsic noise in the cells. In both cell types, we observe a negative correlation between \nli{} and the CV, indicating that it is the level of intrinsic noise that plays a role here. These findings are in line with previous studies that propose that noise linearizes the system\cite{Roddey2000, Chialvo1997, Voronenko2017}. More intrinsic noise has been demonstrated to increase the CV and reduce nonlinear phase-locking in vestibular afferents\cite{Schneider2011}. Reduced noise, on the other hand, has been associated with stronger calc_nonlinearity_model in pyramidal cells of the ELL\cite{Chacron2006}. Further support for the notion of noise limiting the calc_nonlinearity_model comes from our P-unit LIF model that faithfully reproduces P-unit activity\cite{Barayeu2023}. We can use this model and apply a noise-split \cite{Lindner2022} based on the to the Furutsu-Novikov theorem\cite{Novikov1965,Furutsu1963}, to increase the signal-to-noise ratio in the cell while keeping the overall response variability constant (see methods). Treating 90\,\% of the total noise as signal and simulating large numbers of trial uncovers the full nonlinear structure (\figref{model_and_data}) seen in LIF neurons and the analytical derivations when driven with sine-wave stimuli\cite{Voronenko2017}.

 %
 \subsection*{Noise stimulation approximates the real three-fish interaction}
@ -670,31 +670,31 @@ Our analysis is based on the neuronal responses to white noise stimulus sequence

 In the natural situation, the stimuli are periodic signals defined by the difference frequencies. How well can we extrapolate from the white noise analysis to the pure sinewave situation? \notejg{Predictions from the X2 matrix and the equations in Voronekov}

-In contrast to the situation with individual frequencies (direct sine-waves or sinusoidal AMs) during noise stimulation the total power of the stimulus is equally distributed on all frequencies leading to a weaker signal-to-noise ratio. This explains that the nonlinearity pattern in the electroreceptor recordings only partially matches the expectation (\figsref{fig:cells_suscept},\,\ref{fig:ampullary}) while the single-frequency stimulation shows nonlinear interference when the individual stimulus frequencies ($f_1, f_2, \Delta f_1, \Delta f_2$) match the baseline firing rate (\figref{fig:motivation}, \subfigref{fig:model_full}{B--E}). With the noise-splitting trick, we could show that in low-CV cells, that have a low-CV and are not subject to strong stimulation, the full nonlinearity pattern is present but covered by the intrinsic noise. We thus conclude that the presence of the anti-diagonal pattern in the \suscept{} matrix is sufficient to conclude that the same nonlinear interactions happen here. This also validates the application of the white noise approach to characterize the full \suscept{} matrix instead of using all combinations of individual frequencies.
+In contrast to the situation with individual frequencies (direct sine-waves or sinusoidal AMs) during noise stimulation the total power of the stimulus is equally distributed on all frequencies leading to a weaker signal-to-noise ratio. This explains that the calc_nonlinearity_model pattern in the electroreceptor recordings only partially matches the expectation (\figsref{fig:cells_suscept},\,\ref{fig:ampullary}) while the single-frequency stimulation shows nonlinear interference when the individual stimulus frequencies ($f_1, f_2, \Delta f_1, \Delta f_2$) match the baseline firing rate (\figref{fig:motivation}, \subfigref{fig:model_full}{B--E}). With the noise-splitting trick, we could show that in low-CV cells, that have a low-CV and are not subject to strong stimulation, the full calc_nonlinearity_model pattern is present but covered by the intrinsic noise. We thus conclude that the presence of the anti-diagonal pattern in the \suscept{} matrix is sufficient to conclude that the same nonlinear interactions happen here. This also validates the application of the white noise approach to characterize the full \suscept{} matrix instead of using all combinations of individual frequencies.



-% The nonlinearity of ampullary cells in paddlefish \cite{Neiman2011fish} has been previously accessed with bandpass limited white noise. 
+% The calc_nonlinearity_model of ampullary cells in paddlefish \cite{Neiman2011fish} has been previously accessed with bandpass limited white noise.

-% Here it was demonstrated that the second-order susceptibility for the two RAM noise input frequencies \fone{} and \ftwo{} can approximate a three-fish setting, where the driving force for the P-unit are two beats with frequencies \bone{} and \btwo{}. This was confirmed by a low-CV P-unit, where nonlinearities in the P-unit response occurred at the sum and difference of the beat frequencies for pure sine-wave stimulation (\figrefb{fig:motivation}). In this P-unit the nonlinearity appeared in a three-wave setting with \bone{} being close to \fbase{}, corresponding to a frequency combination on the vertical line at \foneb{} in the second-order susceptibility (\subfigrefb{fig:model_full}{B}). This implies that even if only the diagonal structure can be accessed with noise stimulation in the second-order susceptibility in an experiment (\subfigrefb{fig:model_full}{A}), it can be taken as an indicator that the whole nonlinear structure should be present during pure sine-wave stimulation in the same cell. With this RAM stimulation was demonstrated to be an effective method to scan the three-fish or two-beat plane and estimate the whole theoretically predicted nonlinear structure in experimentally recorded cells.
+% Here it was demonstrated that the second-order susceptibility for the two RAM noise input frequencies \fone{} and \ftwo{} can approximate a three-fish setting, where the driving force for the P-unit are two beats with frequencies \bone{} and \btwo{}. This was confirmed by a low-CV P-unit, where nonlinearities in the P-unit response occurred at the sum and difference of the beat frequencies for pure sine-wave stimulation (\figrefb{fig:motivation}). In this P-unit the calc_nonlinearity_model appeared in a three-wave setting with \bone{} being close to \fbase{}, corresponding to a frequency combination on the vertical line at \foneb{} in the second-order susceptibility (\subfigrefb{fig:model_full}{B}). This implies that even if only the diagonal structure can be accessed with noise stimulation in the second-order susceptibility in an experiment (\subfigrefb{fig:model_full}{A}), it can be taken as an indicator that the whole nonlinear structure should be present during pure sine-wave stimulation in the same cell. With this RAM stimulation was demonstrated to be an effective method to scan the three-fish or two-beat plane and estimate the whole theoretically predicted nonlinear structure in experimentally recorded cells.

 \subsection*{Selective readout versus integration of heterogeneous populations}% Nonlinearity might be influenced once integrating from a P-unit population with heterogeneous baseline properties}%Heterogeneity of P-units might influence 

 The observed nonlinear effects might facilitate the detectability of faint signals during a three fish setting, the electrosensory cocktail party. These nonlinear effects are, however, very specific with respect to the relation of stimulus frequencies and the P-unit baseline frequency. The EOD frequencies of the interacting fish would be drawn from the distributions of EOD frequencies found in male and female fish\cite{Hopkins1974Eigen, Meyer1987, Henninger2018, Henninger2020}. To be behaviorally relevant the faint signal detection would require reliable neuronal signaling irrespective of the individual EOD frequencies. 
 P-units, however are very heterogeneous in their baseline firing properties\cite{Grewe2017, Hladnik2023}. The baseline firing rates vary in wide ranges (50--450\,Hz). This range covers substantial parts of the beat frequencies that may occur during animal interactions which is limited to frequencies below the Nyquist frequency (0 -- \feod/2)\cite{Barayeu2023}. It is thus likely that there are P-units which match approximately to the specificities of the different encounters. 

-On the other hand, the nonlinearity was found only in low-CV P-units (with white noise stimulation). The CVs are also very heterogeneous (0.1--1.4, \figref{fig:data_overview}\panel{A}) in our sample. Only a small fraction of the P-units has a sufficiently low level of intrinsic noise and will exhibit nonlinear responses. The P-units project to the ELL\cite{Krahe2014} and the integrating pyramidal cells in the different segments receive inputs in the range of 10s to 1000s of neurons\cite{Maler2009a}. Since the receptive fields of the pyramidal neurons are patches of adjacent receptors on the fish's body surface \cite{Bastian2002, Maler2009a, Haggard2023} and the input heterogeneity does not depend on the location of the receptor on the fish body \cite{Hladnik2023} the pyramidal cell input population will be heterogeneous. Heterogeneity was shown to be generally advantageous for the encoding in this\cite{Hladnik2023} and other systems\cite{Padmanabhan2010, Beiran2018}. At the same time it contradicts the apparent need for a selective readout of low-CV cells to maintain information arising through nonlinear interactions. 
+On the other hand, the calc_nonlinearity_model was found only in low-CV P-units (with white noise stimulation). The CVs are also very heterogeneous (0.1--1.4, \figref{fig:data_overview}\panel{A}) in our sample. Only a small fraction of the P-units has a sufficiently low level of intrinsic noise and will exhibit nonlinear responses. The P-units project to the ELL\cite{Krahe2014} and the integrating pyramidal cells in the different segments receive inputs in the range of 10s to 1000s of neurons\cite{Maler2009a}. Since the receptive fields of the pyramidal neurons are patches of adjacent receptors on the fish's body surface \cite{Bastian2002, Maler2009a, Haggard2023} and the input heterogeneity does not depend on the location of the receptor on the fish body \cite{Hladnik2023} the pyramidal cell input population will be heterogeneous. Heterogeneity was shown to be generally advantageous for the encoding in this\cite{Hladnik2023} and other systems\cite{Padmanabhan2010, Beiran2018}. At the same time it contradicts the apparent need for a selective readout of low-CV cells to maintain information arising through nonlinear interactions.
 A possible readout mechanism should be the topic of future studies that need to take into account that the nonlinearities are stronger in pure sine-wave stimulation and the fraction of cells that show it under naturalistic stimulation might be larger than expected from the distribution of CVs.

 \subsection*{Behavioral relevance of nonlinear interactions}
 The behavioral relevance of the weak signal detection in P-units is evident from the courtship context observed in freely interacting animals\cite{Henninger2018}. Outside courtship behavior, the encoding of secondary or social envelopes is a common need\cite{Stamper2012Envelope}. In a previous study it was demonstrated that information about low-frequency secondary envelopes would not be present in P-units' responses but would arise thorough nonlinear processing downstream in the ELL \cite{Middleton2006,Middleton2007}. Based on our work we would predict that only a small subset of cells, with low CVs, should encode the social envelopes under white noise stimulation. An absence of low-CVs cells in the population analyzed in the previous studies could explain their conclusions. On the other hand, another study showed that P-units with strong nonlinearities, low firing rates and high CVs could indeed encode social envelopes\cite{Savard2011}. These findings are in contrast to the previously mentioned work\cite{Middleton2007} and, at first glance, also to our results. The missing link, that has not been considered in this work, might be the bursting of P-units, the repeated firing of spikes after one EOD period interleaved with longer intervals of quietness\cite{Chacron2004}. Bursting was not explicitly addressed in the previous work, still the reported high CVs of the envelope encoding P-units indicate a higher rate of bursting\cite{Savard2011}. How bursts influence the second-order susceptibility of P-units will be addressed in following works (in preparation). Note that in this work we operated in a regime of weak stimuli and that the envelope encoding addressed in \cite{Savard2011,Middleton2007} operates in a regime of strong stimuli, where the firing rate is saturated. The exact transition from the nonlinearities in a regime of weak stimuli to a regime of strong stimuli could be addressed in further P-unit studies. 

-Sinusoidal AMs are relevant in interactions with a few fish. We can understand the noise as the presence of many animals with individual EOD frequencies at the same time. Under noise stimulation, nonlinearities were demonstrated to be strong for weak stimuli but were shown to decrease for stronger noise stimuli (\figrefb{fig:cells_suscept}). As long as the noise signal is weak, those fish are distant and the nonlinearity is maintained. An increasing stimulus amplitude would indicate that many fish are close to the receiver and a decrease of nonlinear effects can be observed. These findings imply that the nonlinear effects arising in the presence of three fish decline the more fish join. \lepto{} usually prefers small groups of fish\cite{Stamper2010}. Thus, the described second-order susceptibility might still be behaviorally relevant under natural conditions. The decline of nonlinear effects when several fish are present might be an adaptive process reducing the number of frequencies represented in its primary sensory afferents to a minimum. Such representation would still leave room to create nonlinear effects at later processing steps in higher-order neurons.
+Sinusoidal AMs are relevant in interactions with a few fish. We can understand the noise as the presence of many animals with individual EOD frequencies at the same time. Under noise stimulation, nonlinearities were demonstrated to be strong for weak stimuli but were shown to decrease for stronger noise stimuli (\figrefb{fig:cells_suscept}). As long as the noise signal is weak, those fish are distant and the calc_nonlinearity_model is maintained. An increasing stimulus amplitude would indicate that many fish are close to the receiver and a decrease of nonlinear effects can be observed. These findings imply that the nonlinear effects arising in the presence of three fish decline the more fish join. \lepto{} usually prefers small groups of fish\cite{Stamper2010}. Thus, the described second-order susceptibility might still be behaviorally relevant under natural conditions. The decline of nonlinear effects when several fish are present might be an adaptive process reducing the number of frequencies represented in its primary sensory afferents to a minimum. Such representation would still leave room to create nonlinear effects at later processing steps in higher-order neurons.

-The afferents of the passive electrosensory system, the ampullary cells, were found to exhibit much stronger nonlinearities than P-units (\figref{fig:data_overview}). The adequate stimulus for this system is a direct stimulation not an amplitude modulation. In this sense the ampullary cells are closer to the LIF models used by Voroneko and colleagues\cite{Voronenko2017} and we can thus expect that the same nonlinear mechanisms are at work here. For the ampullary system, the sinewave stimulation is not as relevant as for the P-unit system. Ampullary cells encode low-frequency exogenous electric signals such as muscle potentials induced by prey movement\cite{Kalmijn1974, Engelmann2010, Neiman2011fish}. The simultaneous muscle activity of a swarm of prey (such as \textit{Daphnia}) resembles Gaussian white noise\cite{Neiman2011fish}, similar to the stimuli used here. Our results show some similarities with the analyses by Neiman and Russel\cite{Neiman2011fish} who study the encoding in ampullary afferents in the paddlefish. There, the power spectrum of the spontaneous activity also shows a peak at the baseline frequency (internal oscillator) but also at the oscillation frequency measured at the epithelium and interactions of both. Most of the latter disappear in the case of external stimulation, though. Here we find only peaks at the baseline frequency of the neuron and its harmonics. There are interesting similarities and dissimilarities; stimulus encoding in the paddlefish as well as in the brown ghost is very linear for low frequencies and there are nonlinearities in both systems. Linear encoding in the paddlefish shows a gap in the spectrum at the frequency of the epitheliar oscillation, instead the nonlinear response is very pronounced there. In \lepto{}, the dominating frequency under baseline conditions is the baseline firing rate, and we do see increased nonlinearity in this frequency range. The baseline frequency, however, is outside the linear coding range\cite{Grewe2017} while it is within the linear coding range in paddlefish\cite{Neiman2011fish}. Interestingly, the nonlinear response in the paddlefish ampullaries increases with stimulus intensity while it disappears in our case (\subfigrefb{fig:data_overview}{F}). The population of ampullary cells is much more homogeneous with respect to the baseline rate (131$\pm$29\,Hz) and stimulus encoding properties than the P-units\cite{Grewe2017}. This implies that, if the stimulus contains the appropriate frequency components that sum up to the baseline rate, there should be a nonlinear response at a frequency that is similar for the full population of ampullary cells (the baseline frequency) that is outside the linear coding range. Postsynaptic cells integrating ampullary input might be able to extract this nonlinear response from the input population. How such nonlinear effects in ampullary cells might influence prey detection should be addressed in further studies.
+The afferents of the passive electrosensory system, the ampullary cells, were found to exhibit much stronger nonlinearities than P-units (\figref{fig:data_overview}). The adequate stimulus for this system is a direct stimulation not an amplitude modulation. In this sense the ampullary cells are closer to the LIF models used by Voroneko and colleagues\cite{Voronenko2017} and we can thus expect that the same nonlinear mechanisms are at work here. For the ampullary system, the sinewave stimulation is not as relevant as for the P-unit system. Ampullary cells encode low-frequency exogenous electric signals such as muscle potentials induced by prey movement\cite{Kalmijn1974, Engelmann2010, Neiman2011fish}. The simultaneous muscle activity of a swarm of prey (such as \textit{Daphnia}) resembles Gaussian white noise\cite{Neiman2011fish}, similar to the stimuli used here. Our results show some similarities with the analyses by Neiman and Russel\cite{Neiman2011fish} who study the encoding in ampullary afferents in the paddlefish. There, the power spectrum of the spontaneous activity also shows a peak at the baseline frequency (internal oscillator) but also at the oscillation frequency measured at the epithelium and interactions of both. Most of the latter disappear in the case of external stimulation, though. Here we find only peaks at the baseline frequency of the neuron and its harmonics. There are interesting similarities and dissimilarities; stimulus encoding in the paddlefish as well as in the brown ghost is very linear for low frequencies and there are nonlinearities in both systems. Linear encoding in the paddlefish shows a gap in the spectrum at the frequency of the epitheliar oscillation, instead the nonlinear response is very pronounced there. In \lepto{}, the dominating frequency under baseline conditions is the baseline firing rate, and we do see increased calc_nonlinearity_model in this frequency range. The baseline frequency, however, is outside the linear coding range\cite{Grewe2017} while it is within the linear coding range in paddlefish\cite{Neiman2011fish}. Interestingly, the nonlinear response in the paddlefish ampullaries increases with stimulus intensity while it disappears in our case (\subfigrefb{fig:data_overview}{F}). The population of ampullary cells is much more homogeneous with respect to the baseline rate (131$\pm$29\,Hz) and stimulus encoding properties than the P-units\cite{Grewe2017}. This implies that, if the stimulus contains the appropriate frequency components that sum up to the baseline rate, there should be a nonlinear response at a frequency that is similar for the full population of ampullary cells (the baseline frequency) that is outside the linear coding range. Postsynaptic cells integrating ampullary input might be able to extract this nonlinear response from the input population. How such nonlinear effects in ampullary cells might influence prey detection should be addressed in further studies.

 \subsection*{Conclusion}
-We have demonstrated that there are pronounced nonlinear responses in the primary electrosensory afferences of the weakly electric fish \lepto{}, systems that are very often characterized using linear methods. The observed nonlinearities match the expectations from previous theoretical studies\cite{Voronenko2017}. We can confirm that the theory applies also to systems that are encoding amplitude modulations of a carrier signal. Comparisons of P-units and ampullary cells showed that it is the level of intrinsic noise that determines how strongly nonlinear the system acts. Using the second-oder susceptibility estimated from the responses to white noise stimuli provides an easy way to determine the nonlinearity of the system under study. P-units share several features with mammalian
+We have demonstrated that there are pronounced nonlinear responses in the primary electrosensory afferences of the weakly electric fish \lepto{}, systems that are very often characterized using linear methods. The observed nonlinearities match the expectations from previous theoretical studies\cite{Voronenko2017}. We can confirm that the theory applies also to systems that are encoding amplitude modulations of a carrier signal. Comparisons of P-units and ampullary cells showed that it is the level of intrinsic noise that determines how strongly nonlinear the system acts. Using the second-oder susceptibility estimated from the responses to white noise stimuli provides an easy way to determine the calc_nonlinearity_model of the system under study. P-units share several features with mammalian
 auditory nerve fibers and such nonlinear effects might also be expected in the auditory system during the encoding of amplitude modulations \cite{Joris2004}.

 \section*{Methods}
@ -803,10 +803,10 @@ The second-order susceptibility was calculated by dividing the higher-order cros
 %   \chi_{2}(\omega_{1}, \omega_{2}) = \frac{TN \sum_{n=1}^N \int_{0}^{T} dt\,r_{n}(t) e^{-i(\omega_{1}+\omega_{2})t} \int_{0}^{T}dt'\,s_{n}(t')e^{i \omega_{1}t'} \int_{0}^{T} dt''\,s_{n}(t'')e^{i \omega_{2}t''}}{2 \sum_{n=1}^N \int_{0}^{T} dt\, s_{n}(t)e^{-i \omega_{1}t} \int_{0}^{T} dt'\,s_{n}(t')e^{i \omega_{1}t'} \sum_{n=1}^N \int_{0}^{T} dt\,s_{n}(t)e^{-i \omega_{2}t} \int_{0}^{T} dt'\,s_{n}(t')e^{i \omega_{2}t'}}
 % 	\end{split}
 % \end{equation}
-The absolute value of a second-order susceptibility matrix is visualized in \figrefb{fig:model_full}. There the upper right and the lower left quadrants characterize the nonlinearity in the response $x(t)$ at the sum frequency of the two input frequencies. The lower right and upper left quadrants characterize the nonlinearity in the response $x(t)$ at the difference of the input frequencies.
+The absolute value of a second-order susceptibility matrix is visualized in \figrefb{fig:model_full}. There the upper right and the lower left quadrants characterize the calc_nonlinearity_model in the response $x(t)$ at the sum frequency of the two input frequencies. The lower right and upper left quadrants characterize the calc_nonlinearity_model in the response $x(t)$ at the difference of the input frequencies.

 \paragraph{Nonlinearity index}\label{projected_method}
-We expect to see nonlinear susceptibility when $\omega_1 + \omega_2 = \fbase{}$. To characterize this we calculated the peakedness of the nonlinearity (PNL) as
+We expect to see nonlinear susceptibility when $\omega_1 + \omega_2 = \fbase{}$. To characterize this we calculated the peakedness of the calc_nonlinearity_model (PNL) as
  \begin{equation}
    \label{eq:nli_equation}
    \nli{} = \frac{ \max D(\fbase{}-5\,\rm{Hz} \leq f \leq \fbase{}+5\,\rm{Hz})}{\mathrm{med}(D(f))}
@ -858,7 +858,7 @@ Whenever the membrane voltage $V_m(t)$ crossed the spiking threshold $\theta=1$
  V_m(t) \ge \theta \; : \left\{ \begin{array}{rcl} V_m & \mapsto & 0 \\ A  & \mapsto & A + \Delta A/\tau_A \end{array} \right.
 \end{equation}

-% The static nonlinearity $f(V_m)$ was equal to zero for the LIF. In the case of an exponential integrate-and-fire model (EIF), this function was set to
+% The static calc_nonlinearity_model $f(V_m)$ was equal to zero for the LIF. In the case of an exponential integrate-and-fire model (EIF), this function was set to
 % \begin{equation}
 %   \label{eifnl}
 %   f(V_m)= \Delta_V \text{e}^{\frac{V_m-1}{\Delta_V}}
@ -892,7 +892,7 @@ The random amplitude modulation (RAM) input to the model was created by drawing
 \end{equation}
 From each simulation run, the first second was discarded and the analysis was based on the last second of the data. The resulting spectra thus have a spectral resolution of 1\,Hz.
 % \subsection{Second-order susceptibility analysis of the model}
-% %\subsubsection{Model second-order nonlinearity}
+% %\subsubsection{Model second-order calc_nonlinearity_model}

 % The second-order susceptibility in the model was calculated with \Eqnref{eq:susceptibility}, resulting in matrices as in \figrefb{model_and_data} and \figrefb{fig:model_full}. For this, the model neuron was presented the input $x(t)$ for 2\,s, with the first second being dismissed as the transient. The second-order susceptibility calculation was performed on the last second, resulting in a frequency resolution of 1\,Hz. 

@ -977,7 +977,7 @@ In the here used model a small portion of the original noise was assigned to the


 \paragraph*{S1 Second-order susceptibility of high-CV P-unit}
-CVs in P-units can range up to 1.5 \cite{Grewe2017, Hladnik2023}. We show the same analysis as in \figrefb{fig:cells_suscept} for an example higher-CV P-unit. Similar to the low-CV cell, high-CV P-units fire at multiples of the EOD period (\subfigrefb{fig:cells_suscept_high_CV}{A}). In contrast to low-CV P-units, however, the higher CV characterizes the noisier baseline firing pattern and the peak at \fbase{} is less pronounced in the power spectrum of the baseline activity (\subfigrefb{fig:cells_suscept_high_CV}{B}). High-CV P-units do not exhibit a clear nonlinear structure related to \fbase{} neither in the second-order susceptibility matrices (\subfigrefb{fig:cells_suscept_high_CV}{E--F}), nor in the projected diagonals (\subfigrefb{fig:cells_suscept_high_CV}{G}). The overall level of nonlinearity, however shows the same dependence on the stimulus contrast. It is much reduced for high-contrast stimuli that drive the neuron much stronger (\subfigrefb{fig:cells_suscept_high_CV}{F}). 
+CVs in P-units can range up to 1.5 \cite{Grewe2017, Hladnik2023}. We show the same analysis as in \figrefb{fig:cells_suscept} for an example higher-CV P-unit. Similar to the low-CV cell, high-CV P-units fire at multiples of the EOD period (\subfigrefb{fig:cells_suscept_high_CV}{A}). In contrast to low-CV P-units, however, the higher CV characterizes the noisier baseline firing pattern and the peak at \fbase{} is less pronounced in the power spectrum of the baseline activity (\subfigrefb{fig:cells_suscept_high_CV}{B}). High-CV P-units do not exhibit a clear nonlinear structure related to \fbase{} neither in the second-order susceptibility matrices (\subfigrefb{fig:cells_suscept_high_CV}{E--F}), nor in the projected diagonals (\subfigrefb{fig:cells_suscept_high_CV}{G}). The overall level of calc_nonlinearity_model, however shows the same dependence on the stimulus contrast. It is much reduced for high-contrast stimuli that drive the neuron much stronger (\subfigrefb{fig:cells_suscept_high_CV}{F}).

 \label{S1:highcvpunit}
 \begin{figure*}[!ht]
--- a/trialnr.pdf
+++ b/trialnr.pdf
--- a/trialnr.png
+++ b/trialnr.png
--- a/trialnr.py
+++ b/trialnr.py
@ -2,17 +2,16 @@ import os

 import numpy as np
 import pandas as pd
-from IPython import embed
 from matplotlib import pyplot as plt
 from plotstyle import plot_style
-from threefish.core_plot_labels import title_find_cell_add
+from threefish.RAM.plot_labels import title_find_cell_add
 from threefish.defaults import default_figsize, default_settings
-from threefish.core_filenames import overlap_cells
-from threefish.core_load import resave_small_files, save_visualization
-from threefish.core_reformat_RAM import get_stack, load_model_susept
+from threefish.RAM.values import overlap_cells
+from threefish.load import resave_small_files, save_visualization
+from threefish.RAM.reformat_matrix import get_stack, load_model_susept
 from threefish.core import find_folder_name
-from threefish.utils1_RAM import trial_nrs_ram_model
-import itertools as it
+from threefish.RAM.calc_model import trial_nrs_ram_model
+

 ##from update_project import *

@ -104,7 +103,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext

    fig, ax = plt.subplots(1, 1)
    ax = [ax]
-    alphas = [1, 0.5]
+    alphas = [0.5, 1, ]
    colors = ['black', 'red']
    for s in range(2):
        stacks = []
@ -122,10 +121,10 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
        counter = []
        for tr in trial_nrs_here:
            save_names = [
-                'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                    tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
                'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                    tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV']
+                    tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV','calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
+                    tr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
+                ]
            nrs_s = [3, 4, 8, 9]#, 10, 11
            save_name = save_names[s]
            #embed()
@ -162,7 +161,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
                    vars.append(stack['var_RAM'].iloc[0])

                    perc95.append(np.percentile(stack_plot,99.99))
-                    perc90.append(np.percentile(stack_plot, 10))
+                    perc90.append(np.percentile(stack_plot, 90))
                    perc05.append(np.percentile(stack_plot, 10))
                    median.append(np.percentile(stack_plot, 50))
                    stacks_wo_norm.append(stack_plot_wo_norm)
@ -187,30 +186,33 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
                    counter.append(float('nan'))
        # vars, cv_stims, fr_stims
        print((np.array(counter)+1)/trial_nrs_here)
-        embed()
+        print((np.array(counter)) / trial_nrs_here)
+        #embed()
        #ax.plot(trial_nrs_here, perc05, color = 'grey')
-        ax[0].plot(trial_nrs_here, perc95, color = colors[s], clip_on = False, label = '99.99th percentile', alpha = alphas[s])
+        ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile', alpha = alphas[s])
        #ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
        #ax.scatter(trial_nrs_here, perc05, color = 'grey')
-        ax[0].scatter(trial_nrs_here, perc95, color = colors[s], clip_on = False, alpha = alphas[s])
-        ax[0].set_xscale('log')
+        ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s])
+        ax[0].set_xscale('log')#colors[s]
        ax[0].set_yscale('log')
        ax[0].set_xlabel('Trials [$N$]')
        ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
-        ax[0].legend()
+

        ############################################
-        ax[0].plot(trial_nrs_here, perc05, color='grey', clip_on=False, label = '10th percentile', alpha = alphas[s])
-        ax[0].scatter(trial_nrs_here, perc05, color='grey', clip_on=False, alpha = alphas[s])
+        if s == 1:
+            ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s])
+            ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s])


-        ax[0].plot(trial_nrs_here, perc05, color='grey', clip_on=False, label='10th percentile', alpha = alphas[s])
-        ax[0].scatter(trial_nrs_here, perc05, color='grey', clip_on=False, alpha = alphas[s])
-        #ax[0].set_xscale('log')
-        #ax[0].set_yscale('log')
-        #ax[0].set_xlabel('Trials [$N$]')
-        #ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
-        #ax[0].legend()
+            ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha = alphas[s])
+            ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s])
+            ax[0].legend()
+            #ax[0].set_xscale('log')
+            #ax[0].set_yscale('log')
+            #ax[0].set_xlabel('Trials [$N$]')
+            #ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
+            #ax[0].legend()

        '''        ax = plt.subplot(1,3,2)
        ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
@ -246,6 +248,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext

    #fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],

+    #plt.show()
    save_visualization(pdf=True)