changes

README.md

@@ -0,0 +1,5 @@
+Tabele with all cell information considering RAM analysis is saved in data/ as data_overview_mod.csv
+``` .py
+frame = pd.read_csv('data_overview_mod.csv')
+```
+see data_overview_mod.py for loading and filtering.

ampullary.py

@@ -1,9 +1,9 @@
+from threefish.RAM.plots import plot_suscept_matrix_data
 from threefish.RAM.reformat_cellchoice import cells_to_show_susceptibility
-from threefish.RAM.plots import ampullary_punit
 
 if __name__ == '__main__':
 
 
     cells_plot2 = cells_to_show_susceptibility(plot_choice='amp')
-    ampullary_punit(eod_metrice = False, base_extra = True, color_same = False, fr_name = '$f_{base}$', tags_individual = True, isi_delta = 5, titles=[''],
+    plot_suscept_matrix_data(titles=[''],
-                    cells_plot=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25, 1.3])#Low-CV ampullary cell,
+                             cells_plot=cells_plot2, RAM_as_punit=False, scale_val = False, add_texts = [0.25, 1.3])#Low-CV ampullary cell,

cells_suscept.py

@@ -1,8 +1,7 @@
+from threefish.RAM.plots import plot_suscept_matrix_data
 from threefish.RAM.reformat_cellchoice import cells_to_show_susceptibility
-from threefish.RAM.plot_labels import label_fr_name_rm
-from threefish.RAM.plots import ampullary_punit
 
 if __name__ == '__main__':
 
     cells_plot2 = cells_to_show_susceptibility(plot_choice='contrasts')
-    ampullary_punit(eod_metrice = False, color_same = False, base_extra = True, fr_name = label_fr_name_rm(), cells_plot=[cells_plot2[0]], isi_delta = 5, titles=[''], tags_individual = True, xlim_p = [0, 1.15])#Low-CV P-unit,
+    plot_suscept_matrix_data(cells_plot=[cells_plot2[0]], titles=[''], xlim_p = [0, 1.15])#Low-CV P-unit,

cells_suscept_high_CV.py

@@ -1,9 +1,7 @@
+from threefish.RAM.plots import plot_suscept_matrix_data
 from threefish.RAM.reformat_cellchoice import cells_to_show_susceptibility
-from threefish.RAM.plot_labels import label_fr_name_rm
-from threefish.RAM.plots import ampullary_punit
-
 
 if __name__ == '__main__':
 
     cells_plot2 = cells_to_show_susceptibility(plot_choice='contrasts')
-    ampullary_punit(base_extra = True, eod_metrice = False, color_same = False, fr_name = label_fr_name_rm(), tags_individual = True, isi_delta = 5, cells_plot=[cells_plot2[1]], titles=['', 'Ampullary cell,'], )#High-CV P-unit,
+    plot_suscept_matrix_data(cells_plot=[cells_plot2[1]], titles=['', 'Ampullary cell,'], )#High-CV P-unit,

data_overview_mod.py

@@ -1,19 +1,20 @@
-from IPython import embed
 from matplotlib import gridspec as gridspec, pyplot as plt
 import numpy as np
 
-from threefish.RAM.values import start_name, version_final
+from scipy import stats
-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.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_to_unify_invivo_nomenclature
 from threefish.load import save_visualization
 from threefish.RAM.calc_bursts import setting_overview_score
+from threefish.RAM.plot_colors import colors_overview
+from threefish.RAM.plot_grid import get_grid_4
 from threefish.RAM.plot_labels import basename_small, label_NLI_scorename2_small, label_pearson, label_stimname_small, \
     make_log_ticks
-from scipy import stats
+from threefish.RAM.plot_subplots import plt_specific_cells, scatter_with_marginals_colorcoded
+from threefish.RAM.reformat_population import load_overview_susept, update_cell_names_to_unify_invivo_nomenclature
+from threefish.RAM.values import start_name
+from threefish.reformat import exclude_nans_for_corr
+
 try:
     from plotstyle import plot_style, spines_params
 except:
@@ -29,7 +30,6 @@ def data_overview3():
     var_it2 = ''
     right = 0.85
     ws = 0.75
-    #print(right)
     grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.06,
                              hspace=0.45,  left=0.1, right=right, top=0.95)
 
@@ -37,68 +37,40 @@ def data_overview3():
     # Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
     save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__direct_']
 
-    #####################################################
-    #grid_lower_lower = gridspec.GridSpecFromSubplotSpec(1, 2, grid0[1], wspace = 0.5, hspace=0.55)#, height_ratios = [1,3]
     cell_types = [' P-unit',' Ampullary', ]#, ' P-unit',]#' P-unit',
     cell_types_name = ['P-units','Ampullary cells',]
     species = ' Apteronotus leptorhynchus'
-    burst_corr_reset = 'response_modulation'
-    burst_fraction = [1000, 1000]# 50,  # ,1,1]
     burst_fraction = [1, 1]  # ,1,1]
     burst_corr_reset = 'burst_fraction_burst_corr_individual_stim'
     redo = False
-    #embed()
     counter = 0
     tags = []
     frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
-    scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
-              'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']  # + '_diagonal_proj'
     scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
               ]  # + '_diagonal_proj'
     printing = False # print values for paper
     max_xs = [[[],[],[]],[[],[],[]]]
-    for c, cell_type_here in enumerate(cell_types):
+    for c, cell_type_here in enumerate(cell_types):# iteriert über die columns
         frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
-        #embed()
-        test = False
-        # ok das schließe ich aus weil da irgendwas in der Detektion ist, das betrifft jetzt genau 3 Zellen, also nicht so schlimm
-        #63     2018-08-14-af-invivo-1
-        #241    2018-09-05-aj-invivo-1
-        #252    2022-01-08-ah-invivo-1
         frame_file = frame_file[frame_file.cv_stim <5]
 
 
-        ##############################################
+        colorbar_value = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
-        # modulatoin comparison for both cell_types
+        colorbar_name = ['response_modulation','response_modulation','']#,'']#'response_modulation'
-        ################################
-        # Modulation, cell type comparison
-
 
-        x_axis = ['cv_base','cv_stim','response_modulation']#,'fr_base']#
+        x_axis = ['cv_base', 'cv_stim', 'response_modulation']  # ,'fr_base']#
-        var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
-        var_types = ['response_modulation','response_modulation','']#,'']#'response_modulation'
-        max_x = max_xs[c]
         x_axis_names = ['CV$' + basename_small() +'$','CV$' + label_stimname_small() + '$', 'Response modulation [Hz]']#$'+basename()+'$,'Fr$'+basename()+'$',]
-        #score = scores[0]
-        score_n = ['Perc99/Med', 'Perc99/Med', 'Perc99/Med']
         score = scores[c]
-        scores_here = [score,score,score]#,score]
+        y_axis_values = [score,score,score]#,score]
-        score_name = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr']#,'max(diag5Hz)/med_diagonal_proj_fr']#'Perc99/Med'
+        y_axis_name = [label_NLI_scorename2_small(), label_NLI_scorename2_small(), label_NLI_scorename2_small()]#NLI_scorename()]  # 'Fr/Med''Perc99/Med'
-        score_name = ['Fr/Med', 'Fr/Med']#'Fr/Med']  # 'Perc99/Med'
-        score_name = [label_NLI_scorename2_small(), label_NLI_scorename2_small(), label_NLI_scorename2_small()]#NLI_scorename()]  # 'Fr/Med''Perc99/Med'
         ax_j = []
         axls = []
         axss = []
-        #embed()
-        #frame_max = frame_file[frame_file[score]>5]
-        cv_name = "cv_base"
-        max_val = 1.5
-        log = ''#'logall'#''#'logy','logall'True#False
 
+        max_x = max_xs[c]
+        log = ''#'logall'#''#'logy','logall'True#False
 
-        marker = ['o']
+        for v, colorbar_name in enumerate(colorbar_name):# iteriert über die rows
-        for v, var_type in enumerate(var_types):
-            # ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
 
             axx, axy, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[v,counter])
 
@@ -107,20 +79,20 @@ def data_overview3():
                 ymin = 'no'
             else:
                 ymin = 0
-            xmin = 0
 
+            #frame.filter(like = 'fr')#wo_burstcorr
+            xmin = 0
             xlimk = None
-
             labelpad = 0.5#-1
-            cmap, _, y_axis = scatter_with_marginals_colorcoded(var_item_names[v], axs, cell_type_here, x_axis[v],
+            cmap, _, y_axis = scatter_with_marginals_colorcoded(colorbar_value[v], axs, cell_type_here, x_axis[v],
-                                                                frame_file, scores_here[v], axy, axx, ymin=ymin,
+                                                                frame_file, y_axis_values[v], axy, axx, ymin=ymin,
                                                                 xmin=xmin, burst_fraction_reset=burst_corr_reset,
-                                                                var_item=var_type, labelpad=labelpad, max_x=max_x[v],
+                                                                var_item=colorbar_name, labelpad=labelpad, max_x=max_x[v],
-                                                                xlim=xlimk, x_pos=1, burst_fraction=burst_fraction[c],
+                                                                xlim=None, x_pos=1, burst_fraction=burst_fraction[c],
                                                                 ha='right')
 
             if printing:
-                print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]])))
+                print(cell_type_here + ' median '+y_axis_values[v]+''+str(np.nanmedian(frame_file[y_axis_values[v]])))
                 print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]])))
 
             if v == 0:
@@ -129,7 +101,7 @@ def data_overview3():
 
             axx.show_spines('')
             axy.show_spines('')
-            axs.set_ylabel(score_name[v])
+            axs.set_ylabel(y_axis_name[v])
             axs.set_xlabel(x_axis_names[v], labelpad = labelpad)
             extra_lim = False
             if extra_lim:
@@ -157,19 +129,15 @@ def data_overview3():
             axy.set_yticks_blank()
 
 
-            plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, scores_here[v], marker = ['o',"s"])
+            plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, y_axis_values[v], marker = ['o',"s"])
             tags.append(axx)
-            #plt.show()
         counter += 1
-        #plt.show()
-
 
     if printing:
         printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis)
 
     show = False#True
     fig = plt.gcf()
-    #embed()
     tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
     fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
     save_visualization(pdf = True, show = show)

flowchart.py

@@ -2,18 +2,18 @@
 import matplotlib.gridspec as gridspec
 
 from plotstyle import plot_style
+from threefish.defaults import default_figsize
 from threefish.load import save_visualization
 from threefish.RAM.plots import flowchart_core
-from threefish.defaults import default_figsize
 
 
 def flowchart():
     plot_style()
-    default_figsize(column=2, length=4.7)  #5.3 5.8
+    default_figsize(column=2, length=4.7)
     grid_orig = gridspec.GridSpec(1, 1, wspace=0.15, bottom=0.07,
                                   hspace=0.1, left=0.05, right=0.96,
-                                  top=0.9)  # , height_ratios = [0.4,3]
+                                  top=0.9)
-    flowchart_core(grid_orig[0])  # grid_sheme grid_lower[3]
+    flowchart_core(grid_orig[0])
 
     save_visualization()
 

model_and_data.py

@@ -1,9 +1,7 @@
-##from update_project import **#model_and_data
 import os
 
 import numpy as np
 import pandas as pd
-from IPython import embed
 from matplotlib import gridspec, pyplot as plt
 
 from threefish.plot_subplots import plt_model_flowcharts
@@ -71,7 +69,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
         hs = 0.45
 
         #################################
-        # data cells
+        # data cells panel A
 
         grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 0],
                                                      hspace=hs)
@@ -133,16 +131,11 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
             perc = 'perc'
 
             path = save_name + '.pkl'  # '../'+
-            # model = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name)
-
-            # full_matrix = create_full_matrix2(np.array(model), np.array(stack_rev))
-            # stack_final = get_axis_on_full_matrix(full_matrix, model)
-            # im = plt_RAM_perc(ax, perc, np.abs(model))
-
             model = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
 
             #embed()
             if len(model) > 0:
+                # panels Bii, Biii, Cii, Ciii
                 add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, model, perc,
                                                                                      titles[s],
                                                                                      width, eod_metrice=eod_metrice,
@@ -176,7 +169,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
         set_clim_same(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())#
 
         #################################################
-        # plot Flowcharts
+        # plot Flowcharts panels Bi, Ci
 
         ax_ams, ax_external = plt_model_flowcharts( a_fr, ax_external, c, cs, grid, model, stimulus_length)
 

model_full.py

@@ -1,35 +1,33 @@
 
 import sys
+import time
 
 import numpy as np
-from IPython import embed
 from matplotlib import gridspec, pyplot as plt
 
+from plotstyle import plot_style
+from threefish.core import find_code_vs_not, info
+from threefish.defaults import default_figsize
+from threefish.load import resave_small_files, save_visualization
+from threefish.plot.colorbar import colorbar_outside, rescale_colorbar_and_values
+from threefish.plot.limits import set_clim_same
 from threefish.plot.tags import tag2
 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, \
+from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_sum, label_two_deltaf1, \
-    load_b_public
+    label_two_deltaf2, nonlin_title, remove_xticks, set_xlabel_arrow, set_ylabel_arrow, title_motivation, \
-from threefish.RAM.reformat import check_var_substract_method, chose_certain_group, extract_waves, \
+    xlabel_transfer_hz
-    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.load import resave_small_files, save_visualization
+from threefish.RAM.reformat import chose_certain_group, extract_waves, \
+    get_transfer_from_model, load_cells_three, predefine_grouping_frame, restrict_cell_type, save_arrays_susept
+from threefish.RAM.reformat_matrix import convert_csv_str_to_float, load_stack_data_susept
+from threefish.RAM.save import get_transfer_for_model_full
 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 threefish.reformat import chose_mat_max_value, get_axis_on_full_matrix, get_psds_ROC, \
-from plotstyle import plot_style
+    load_b_public
-import time
-from threefish.defaults import default_diagonal_points, default_figsize
-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.plot.limits import set_clim_same
 from threefish.values import values_nfft_full_model, values_stimuluslength_model_full
 
 
@@ -48,7 +46,7 @@ def model_full():
 
 
     ##################################
-    # model square
+    # model square, panel A
     ls = '--'
     axm = plt.subplot(grid[:,0])
     axes.append(axm)
@@ -62,7 +60,7 @@ def model_full():
 
     #################
     #################
-    # power spectra model
+    # power spectra model, panel B, C, D, E
 
 
 
@@ -93,11 +91,10 @@ def model_full():
 
 
     ########################################
-    # letters for the matrix
+    # letters for the matrix in panel A
     plt_model_letters(DF1_frmult, DF2_frmult, axm, color012, color01_2, fr_noise, markers)
 
     ########################################
-    # tags
     fig = plt.gcf()
     tag2(fig, [axes[0], axes2[0], axes2[1], axes2[2], axes2[3]], xoffs=np.array([-4.2, -2.5, -2.5, -2.5, -2.5]),
          yoffs=np.array([4.55, 4.35, 4.35, 4.35, 4.35]))  # ax_ams[3],4.5
@@ -344,7 +341,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
                             divergnce, fr, pivot_chosen, max_val, max_x, max_y, mult, DF1, DF2, min_y, min_x, min_val, diff_cut = chose_mat_max_value(
                                 DF1_desired[df], DF2_desired[df], '', mult_type, eodftype, indices, cell,
                                 contrast_small,
-                                contrast_big, contrast1, dfs, start, devname_orig, contrast, autodefine=autodefine,
+                                contrast_big, contrast1, dfs, devname_orig, contrast, autodefine=autodefine,
                                 cut_matrix='cut', chose_score=chose_score)  # chose_score = 'auci02_012-auci_base_01'
                             df1.append(DF1[0])
                             df2.append(DF2[0])
@@ -421,7 +418,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
                                 ##############################################################
                                 # load plotting arrays
                                 arrays, arrays_original, spikes_pure = save_arrays_susept(
-                                    data_dir, cell, c, chirps, devs, extract, group_mean, mean_type, plot_group=0,
+                                    data_dir, cell, c, chirps, extract, group_mean, mean_type, plot_group=0,
                                     rocextra=False, sorted_on=sorted_on)
                                 ##########################################################################
                                 # part with the power spectra
@@ -574,7 +571,5 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
 
 
 if __name__ == '__main__':
-    #embed()
 
-    sys.excepthook = info
     model_full()

motivation.py

@@ -1,28 +1,16 @@
-##from update_project import **
 import numpy as np
-from IPython import embed
+from matplotlib import gridspec, pyplot as plt
 
-from threefish.RAM.values import find_all_dir_cells, ws_nonlin_systems
+from plotstyle import plot_style
-from threefish.reformat import chose_mat_max_value, load_b_public
 from threefish.defaults import default_figsize
-from threefish.calc_time import cr_spikes_mat
+from threefish.load import save_visualization
-from threefish.core import find_code_vs_not
-import time
-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, \
+    plot_shemes4
-    plt_coherences
+from threefish.RAM.reformat import chose_certain_group, extract_waves, load_cells_three, \
-from threefish.load import save_visualization
+    predefine_grouping_frame, save_arrays_susept
-
+from threefish.RAM.values import find_all_dir_cells, ws_nonlin_systems
-#from matplotlib import gridspec as gridspec, gridspec, mlab as ml, pyplot as plt, ticker as ticker
+from threefish.reformat import load_b_public
-from matplotlib import pyplot as plt
-from matplotlib import gridspec
-from scipy.ndimage import gaussian_filter
-
-from plotstyle import plot_style
-
 
 
 def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'],
@@ -34,20 +22,16 @@ def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'
     show = True
 
     datasets, data_dir = find_all_dir_cells()
-    DF2_desired = [-33]
+
-    DF1_desired = [133]
-    autodefine = '_dfchosen_closest_first_'
     cells = ['2021-08-03-ac-invivo-1']
     c2 = 10
     eodftype = '_psdEOD_'
-    indices = ['_allindices_']
     chirps = [
         '']  # '_ChirpsDelete3_',,'_ChirpsDelete3_'','','',''#'_ChirpsDelete3_'#''#'_ChirpsDelete3_'#'#'_ChirpsDelete2_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsCache_'
     extract = ''  # '_globalmax_'
     if len(cells) < 1:
         data_dir, cells = load_cells_three(end, data_dir=data_dir, datasets=datasets)
 
-    start = 'min'  #
     cells = ['2021-08-03-ac-invivo-1']
     ax_s = []
 
@@ -64,14 +48,14 @@ def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'
 
             b = load_b_public(c, cell, data_dir)
 
-            mt_sorted = predefine_grouping_frame(b, eodftype=eodftype, cell_name=cell)
+            frame_loaded = predefine_grouping_frame(b, eodftype=eodftype, cell_name=cell)
-            mt_sorted = mt_sorted[(mt_sorted['c2'] == c2) & (mt_sorted['c1'] == c1)]
+            frame_loaded = frame_loaded[(frame_loaded['c2'] == c2) & (frame_loaded['c1'] == c1)]
             for gg in range(len(DF1_desired)):
                 ax_w = []
 
                 ###################
                 # all trials in one
-                group_mean = group_saved_matrix(DF1_desired, DF2_desired, gg, mt_sorted)
+                group_mean = group_saved_matrix(DF1_desired, DF2_desired, gg, frame_loaded)
 
                 detection = 'MeanTrialsIndexPhaseSort'
                 mean_type = '_' + detection  # + '_' + minsetting + '_' + extend_trials + concat
@@ -79,7 +63,7 @@ def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'
                 ##############################################################
                 # load plotting arrays
                 arrays, arrays_original, spikes_pure = save_arrays_susept(
-                    data_dir, cell, c, chirps, devs, extract, group_mean, mean_type, plot_group=0,
+                    data_dir, cell, c, chirps, extract, group_mean, mean_type, plot_group=0,
                     rocextra=False, sorted_on=sorted_on, dev_desired = dev_desired)
                 ####################################################
 
@@ -92,14 +76,14 @@ def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'
                                          right=0.935, height_ratios=[0.5, 5.3])  # height_ratios=[1, 2], height_ratios = [1,6]bottom=0.25, top=0.8,
 
                 ##########################################################################
-                # plot shemes above
+                # plot shemes above (top)
                 grid00 = gridspec.GridSpecFromSubplotSpec(1, 4, wspace=0.15, hspace=0.05,
                                                          subplot_spec=grid[0, :])
 
                 plot_pictograms(ax_s, grid00)
 
                 ##########################################################################
-                # plot stimulus
+                # plot stimulus (first row)
                 grid0 = gridspec.GridSpecFromSubplotSpec(5, 4, wspace=0.15, hspace=0.35,
                                                          subplot_spec=grid[1, :],
                                                          height_ratios=[1, 0.35, 1.2, 0, 3, ])
@@ -110,7 +94,7 @@ def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, devs=['2'
                                                                                                              ylim)
 
                 ##########################################
-                # spike response
+                # spike response (bottom)
 
                 array_chosen = 1
                 smoothed_base = arrays[0][0]

nonlin_regime.py

@@ -3,36 +3,29 @@ import sys
 
 import numpy as np
 import pandas as pd
-from IPython import embed
 from matplotlib import gridspec, pyplot as plt
 
 from plotstyle import plot_style
 from threefish.calc_time import extract_am
 from threefish.core import find_code_vs_not, find_folder_name, find_project_data, info
-from threefish.defaults import default_figsize, default_ticks_talks
+from threefish.defaults import default_figsize
 from threefish.load import load_savedir, resave_small_files, save_visualization
-from threefish.plot.limits import join_x, join_y, set_same_ylim
+from threefish.plot.limits import set_same_ylim
 from threefish.RAM.calc_fft import log_calc_psd
 from threefish.RAM.calc_model import chose_old_vs_new_model
 from threefish.RAM.core import find_load_function
-from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_f_eod_name_core_rm, \
+from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_fbasename_small, label_sum, \
-    label_fbasename_small, label_sum, \
-    onebeat_cond, \
     remove_yticks
 from threefish.RAM.plot_subplots import colors_suscept_paper_dots, plt_spikes_ROC, recalc_fr_to_DF1
-from threefish.RAM.values import val_cm_to_inch, vals_model_full
+from threefish.RAM.values import vals_model_full
-from threefish.twobeat.calc_model import calc_roc_amp_core_cocktail
+from threefish.twobeat.calc_model import calc_threefish_model
-from threefish.twobeat.colors import colors_susept, twobeat_cond
+from threefish.twobeat.colors import colors_susept
-from threefish.twobeat.labels import f_stable_name, f_vary_name
 from threefish.twobeat.reformat import c_dist_recalc_func, c_dist_recalc_here, dist_recalc_phaselockingchapter, \
-    find_dfs, \
+    find_dfs
-    get_frame_cell_params
+from threefish.twobeat.subplots import plt_psd_saturation, plt_single_trace, plt_stim_saturation, power_spectrum_name
-from threefish.twobeat.subplots import plt_psd_saturation, plt_single_trace, plt_stim_saturation, plt_vmem_saturation, \
-    power_spectrum_name
-from threefish.values import values_nfft_full_model, values_stimuluslength_model_full
 
 
-def nonlin_regime0(yposs=[450, 450, 450], show=True):
+def nonlin_regime0():
 
 
     results_diff = pd.DataFrame()
@@ -74,13 +67,13 @@ def nonlin_regime0(yposs=[450, 450, 450], show=True):
                                                   subplot_spec=grid0[0], height_ratios=[1.5,1],)  # height_ratios=[2,1],
 
         ############################
-        # overview
+        # overview, panel E
         grid_down = gridspec.GridSpecFromSubplotSpec(1, 1,
                                                    hspace=0.75,
                                                    wspace=0.1,
                                                    subplot_spec=grid00[1])  # 1.2hspace=0.4,wspace=0.2,len(chirps)
         ax_u1, c_dist_recalc, c_nrs, color0, color01, color012, color01_2, color02, eodf, f_counter, frame_cell, frame_cell_orig, freq1, freq2, i, sampling = plt_amplitudes_for_contrasts(
-            c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs)
+            c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs = [[220, 220, 220, 220]])
 
 
         ##############################
@@ -105,19 +98,18 @@ def nonlin_regime0(yposs=[450, 450, 450], show=True):
         model_cells, reshuffled = chose_old_vs_new_model(model_fit=model_fit)
         axe_all = []
 
-        for c_nn, c_nr in enumerate(c_nrs):
-
-            #################################
-            # hier wurden die psds vorgespeichert weil die lange brauchen
-
-            a_f2s = [c_nrs_orig[c_nn]]
         xlimp = (0, 300)
-            runs = 1
         n = 1
         dev = 0.001
         dev_name = ['05']
         a_fr = 1
         a = 0
+        for c_nn, c_nr in enumerate(c_nrs):
+            a_f2s = [c_nrs_orig[c_nn]]
+            #################################
+            # hier wurden die psds vorgespeichert weil die lange brauchen
+
+
 
 
 
@@ -150,38 +142,23 @@ def nonlin_regime0(yposs=[450, 450, 450], show=True):
             nfft_here = stimulus_length_here * 20000
             a_f2s = [c_nrs_orig[c_nn]]
 
-            _, arrays_spikes, arrays_stim, _, _, _, _, arrays, names, _, _ = calc_roc_amp_core_cocktail([freq1 + eodf],
+            # v_mems, arrays_spikes, arrays_stim, frame_diff, position_diff, arrays_convovled, names, p_array, ff
-                                                                                                        [freq2 + eodf],
+            _, spikes_arrays_all, stimulus_arrays_all, _, _, convovled_arrays, threefish_names, _, _ = calc_threefish_model(
-                                                                                                        auci_wo, auci_w,
+                results_diff, position_diff, model_cells, cell_here, trials_nr=trials_nr,
-                                                                                                        results_diff,
+                stimulus_length=stimulus_length_here, freq1=[freq1 + eodf], freq2=[freq2 + eodf], a_f2s=a_f2s, a_f1s=[
-                                                                                                        a_f2s,
-                                                                                                        trials_nr,
-                                                                                                        nfft_here, a_fr,
-                                                                                                        stimulus_length_here,
-                                                                                                        model_cells,
-                                                                                                        position_diff,
-                                                                                                        dev, cell_here,
-                                                                                                        a_f1s=[
                     c_nrs_orig[
-                                                                                                                c_nn]],
+                        c_nn]], a_fr=a_fr, dev=dev, dev_name=dev_name, n=n, reshuffled=reshuffled, nfft=nfft_here,
-                                                                                                        dev_name=dev_name,
+                psd_min_amps='_minamps_', mean_choice='', printing_cell=False)#means_different
-                                                                                                        min_amps='_minamps_',
-                                                                                                        n=n,
-                                                                                                        reshuffled=reshuffled,
-                                                                                                        mean_choice='',
-                                                                                                        printing_cell=False)
 
             ##################################################################
-            # preprocessing der arrays
+            # plot panels A, B, C, D
-            time = np.arange(0, len(arrays[a][0]) / sampling, 1 / sampling)
+            time = np.arange(0, len(convovled_arrays[a][0]) / sampling, 1 / sampling)
             time = time * 1000
-            arrays_time = [arrays[3]]  # [v_mems[1],v_mems[3]]#[1,2]#[1::]
+            # die arrays die rausgegeben werden sind für das threefish problem und hier nehmen wir nur den 012, also wo alle fische dabei sind
-            arrays_here = [arrays[3]]  # [arrays[1],arrays[3]]#arrays[1::]#
+            stimuli_arrays = [stimulus_arrays_all[3]]  #1:: [stimulus_arrays_all[1],stimulus_arrays_all[3]]#
-            arrays_st = [arrays_stim[3]]  #1:: [arrays_stim[1],arrays_stim[3]]#
+            spike_arrays = [spikes_arrays_all[3]]  # [spikes_arrays_all[1],spikes_arrays_all[3]]#spikes_arrays_all[1::]
-            arrays_sp = [arrays_spikes[3]]  # [arrays_spikes[1],arrays_spikes[3]]#arrays_spikes[1::]
-            colors_array_here = ['grey', 'grey', 'grey']  # colors_array[1::]
             p_arrays_all.append(p_arrays_here)
-            for a in range(len(arrays_here)):
+            for a in range(len(stimuli_arrays)):
 
                 grid_pt = gridspec.GridSpecFromSubplotSpec(3, 1,
                                                            hspace=0.3,
@@ -193,36 +170,43 @@ def nonlin_regime0(yposs=[450, 450, 450], show=True):
 
                 ########################################
                 # plot stimulus
-                plt_stim_nonlin(arrays_st, axe_all, axes, c_nn, c_nrs, colors_array_here, grid_pt, i, time, xlim)
+                plt_stim_nonlin(stimuli_arrays, axe_all, axes, c_nn, c_nrs, ['grey'], grid_pt, i, time, xlim)
 
                 #############################
                 # plot spikes
-                plt_spikes_nonlin(a, arrays_sp, axps, axss, c_nn, grid_pt, xlim)
+                plt_spikes_nonlin(a, spike_arrays, axss, c_nn, grid_pt, xlim)
+
+                #############################
+                # preallocate psd ax
+                axp = plt.subplot(grid_pt[-1])
+                axps.append(axp)
 
         f_counter += 1
 
         ####################
-        # plot psds
+        # plot psds, panels A, B, C, D, bottom
-        a = plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color01_2, color02,
+        plt_psd_nonlin(axps, c_nrs, color0, color01, color012, color01_2, color02,
-                           colors_array_here, ff_p, frame_cell_orig, freq1, freq2, log, p_arrays_all, xlimp)
+                           ['grey'], ff_p, frame_cell_orig, freq1, freq2, log, p_arrays_all, xlimp)
 
+        ###################
+        # limits
         axps[0].get_shared_y_axes().join(*axps)
         axps[0].get_shared_x_axes().join(*axps)
         set_same_ylim(axe_all)
         set_same_ylim(axps)
+
         fig = plt.gcf()
         fig.tag([axes[0], axes[1], axes[2], axes[3], ax_u1], xoffs=-2.3, yoffs=1.4)
-        save_visualization(cell_here, show)
+        save_visualization(cell_here, show=True)
 
 
-def plt_spikes_nonlin(a, arrays_sp, axps, axss, c_nn, grid_pt, xlim):
+def plt_spikes_nonlin(a, arrays_sp, axss, c_nn, grid_pt, xlim):
     axs = plt.subplot(grid_pt[1])
     plt_spikes_ROC(axs, 'grey', np.array(arrays_sp[a], dtype=object) * 1000, xlim, lw=1)
     if c_nn == 0:
         axs.xscalebar(0.15, -0.1, 30, 'ms', va='right', ha='bottom')
     axss.append(axs)
-    axp = plt.subplot(grid_pt[-1])
+
-    axps.append(axp)
 
 
 def plt_stim_nonlin(arrays_st, axe_all, axes, c_nn, c_nrs, colors_array_here, grid_pt, i, time, xlim):
@@ -244,11 +228,11 @@ def plt_stim_nonlin(arrays_st, axe_all, axes, c_nn, c_nrs, colors_array_here, gr
     axe_all.append(axe)
 
 
-def plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color01_2, color02, colors_array_here, ff_p,
+def plt_psd_nonlin(axps, c_nrs, color0, color01, color012, color01_2, color02, colors_array_here, ff_p,
                    frame_cell_orig, freq1, freq2, log, p_arrays_all, xlimp):
     pps = []
     for c_nn, c_nr in enumerate(c_nrs):
-        for a in range(len(arrays_here)):
+        for a in range(len(p_arrays_all[c_nn])):
 
             axps_here = [[axps[0], axps[1], axps[2], axps[3]]]  # [axps[3], axps[4], axps[5]
             axp = axps_here[a][c_nn]
@@ -263,6 +247,7 @@ def plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color
                       label_sum(), label_diff(), label_fbasename_small(), label_fbasename_small()]
             freqs = [np.abs(freq1), np.abs(freq2), np.abs(freq1) + np.abs(freq2), np.abs(np.abs(freq1) - np.abs(freq2)),
                      fr]
+
             plt_psd_saturation(pp, ff_p, a, axp, colors_array_here, freqs=freqs,
                                colors_peaks=colors_peaks, xlim=xlimp,
                                markeredgecolor=markeredgecolors, labels=labels, log=log, add_log=5)
@@ -289,7 +274,6 @@ def plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color
                 else:
                     axp.set_ylabel(power_spectrum_name())
             axp.set_xlabel('Frequency [Hz]')
-    return a
 
 
 def load_psds_for_nonlin_regime(a_f2s, a_fr, auci_w, auci_wo, c_nn, c_nrs_orig, cell_here, dev,
@@ -338,9 +322,28 @@ def develop_save_nonlin_psd_values(a_f2s, a_fr, auci_w, auci_wo, c_nn, c_nrs_ori
                                    name_psd_f, nfft_here, position_diff, reshuffled,
                                    results_diff, stimulus_length_here, trials_nr, xlimp):
     # psd generierung
-    _, _, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
+    _, _, arrays_stim, results_diff, position_diff, arrays, names, p_arrays_p, ff_p = calc_threefish_model(results_diff,
-        [freq1 + eodf], [freq2 + eodf], auci_wo, auci_w, results_diff, a_f2s, trials_nr, nfft_here, a_fr,
+                                                                                                           position_diff,
-        stimulus_length_here, model_cells, position_diff, dev, cell_here,a_f1s=[c_nrs_orig[c_nn]], dev_name=dev_name, min_amps='_minamps_', n=n, reshuffled=reshuffled, mean_choice='first')
+                                                                                                           model_cells,
+                                                                                                           cell_here,
+                                                                                                           trials_nr=trials_nr,
+                                                                                                           stimulus_length=stimulus_length_here,
+                                                                                                           freq1=[
+                                                                                                               freq1 + eodf],
+                                                                                                           freq2=[
+                                                                                                               freq2 + eodf],
+                                                                                                           a_f2s=a_f2s,
+                                                                                                           a_f1s=[
+                                                                                                               c_nrs_orig[
+                                                                                                                   c_nn]],
+                                                                                                           a_fr=a_fr,
+                                                                                                           dev=dev,
+                                                                                                           dev_name=dev_name,
+                                                                                                           n=n,
+                                                                                                           reshuffled=reshuffled,
+                                                                                                           nfft=nfft_here,
+                                                                                                           psd_min_amps='_minamps_',
+                                                                                                           mean_choice='first')
     p_arrays_here = [p_arrays_p[3]]
     # embed()
     for p in range(len(p_arrays_here)):
@@ -351,7 +354,7 @@ def develop_save_nonlin_psd_values(a_f2s, a_fr, auci_w, auci_wo, c_nn, c_nrs_ori
     return auci_w, auci_wo, ff_p, p_arrays_here, position_diff, results_diff
 
 
-def plt_amplitudes_for_contrasts(c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs):
+def plt_amplitudes_for_contrasts(c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs = [[220, 220, 220, 220]]):
     full_names = [
         'calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_af_coupled__C1Len_100_FirstC1_0.0001_LastC1_0.3_FRrelavtiv__start_0.0001_end_0.3_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_not_log__point_1_fft2_temporal']
     for i, full_name in enumerate(full_names):
@@ -450,4 +453,4 @@ def load_nonlin_regime_values(full_name):
 
 if __name__ == '__main__':
     sys.excepthook = info
-    nonlin_regime0(yposs = [[220, 220, 220, 220]],)#, [430,470], [200,200], [200,200, 200, 200], [200,200, 200, 200], [200,200, 200, 200]
+    nonlin_regime0()

plot_chi2.py

@@ -1,23 +1,25 @@
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib import rc
 
+from threefish.defaults import default_figsize, default_settings
 from threefish.load import save_visualization
 from threefish.plot.colorbar import colorbar_outside
 from threefish.plot.limits import set_clim_same
+from threefish.plot.plotstyle import plot_style
 from threefish.plot.tags import tag2
 from threefish.RAM.calc_analytics import calc_nonlin_analytic_values
 from threefish.RAM.plot_subplots import plt_RAM_perc
 from threefish.RAM.reformat_matrix import convert_csv_str_to_float
-from threefish.defaults import default_figsize, default_settings
-from threefish.plot.plotstyle import plot_style
 from threefish.RAM.values import perc_model_full
 
 rc('text', usetex=True)
 
 
 def plot_chi2():
-    frame, frame2 = calc_nonlin_analytic_values()
+
+
+
     default_settings()  # ts=13, ls=13, fs=13, lw = 0.7
     plot_style()
     default_figsize(column=2, length=2.5)
@@ -25,19 +27,22 @@ def plot_chi2():
     plt.subplots_adjust(bottom=0.2, wspace=0.35, left=0.1, right=0.92)
 
     ###################################################
-    # plot transfer function
+    transfer_values, suscept_values = calc_nonlin_analytic_values()
-    frame = frame.astype(complex)
+
-    ax[0].plot(frame.index, np.abs(frame['transfer']), color='black')
+    ###################################################
+    # plot transfer function, panel A
+    transfer_values = transfer_values.astype(complex)
+    ax[0].plot(transfer_values.index, np.abs(transfer_values['transfer']), color='black')
     ax[0].set_xlabel(r'$f_{1}$')
     ax[0].set_ylabel(r'$|\chi_{1}|$')
-    ax[0].set_xlim(0, frame.index[-1])
+    ax[0].set_xlim(0, transfer_values.index[-1])
     ylim = ax[0].get_ylim()
     ax[0].set_ylim(0, ylim[-1])
 
     ###################################################
-    # plot matrix
+    # plot matrix, panel B
-    frame2.columns = frame2.index
+    suscept_values.columns = suscept_values.index
-    new_keys, stack_plot = convert_csv_str_to_float(frame2)
+    new_keys, stack_plot = convert_csv_str_to_float(suscept_values)
     im = plt_RAM_perc(ax[1], 'perc', np.abs(stack_plot))
     set_clim_same([im],
                   mats=[np.abs(stack_plot)],
@@ -45,17 +50,13 @@ def plot_chi2():
                   nr_clim='perc',
                   clims='',
                   percnr=perc_model_full())
-    pos_rel = -0.12
+
-    cbar, left, bottom, width, height = colorbar_outside(ax[1],
-                                                         im,
-                                                         add=5,
-                                                         width=0.01)
     ax[1].set_xlabel(r'$f_{1}$')
     ax[1].set_ylabel(r'$f_{2}$')
     ax[1].set_xticks_delta(0.2)
     ax[1].set_xticks_delta(0.2)
-    ax[1].set_xlim(frame2.columns[0] - 0.01, frame2.columns[-1])
+    ax[1].set_xlim(suscept_values.columns[0] - 0.01, suscept_values.columns[-1])
-    ax[1].set_ylim(frame2.columns[0] - 0.01, frame2.columns[-1])
+    ax[1].set_ylim(suscept_values.columns[0] - 0.01, suscept_values.columns[-1])
     cbar, left, bottom, width, height = colorbar_outside(ax[1], im, add=5, width=0.01)
     cbar.set_label(r'$|\chi_{2}|$', rotation=90, labelpad=8)
     tag2(axes=ax, xoffs=[-0.5, -5.5], yoffs=1.7)  # ax_ams[3],
@@ -64,8 +65,6 @@ def plot_chi2():
 
 if __name__ == '__main__':
 
-
-
     plot_chi2()
 
 

trialnr.py

@@ -2,15 +2,15 @@ import os
 
 import numpy as np
 import pandas as pd
-from IPython import embed
 from matplotlib import pyplot as plt
+
 from plotstyle import plot_style
-from threefish.RAM.plot_labels import title_find_cell_add
-from threefish.defaults import default_figsize, default_settings
-from threefish.load import save_visualization
-from threefish.RAM.reformat_matrix import get_stack, load_model_susept
 from threefish.core import find_folder_name
+from threefish.defaults import default_figsize
+from threefish.load import save_visualization
 from threefish.RAM.calc_model import trial_nrs_ram_model
+from threefish.RAM.plot_labels import title_find_cell_add
+from threefish.RAM.reformat_matrix import get_stack, load_model_susept
 
 
 ##from update_project import *
@@ -33,23 +33,20 @@ def trialnr():
     ##################################
     # model part
 
-    cell = '2013-01-08-aa-invivo-1'
+    cells = ['2013-01-08-aa-invivo-1']
-    cells_given = [cell]
     trial_nrs_here = trial_nrs_ram_model()
 
 
 
     fig, ax = plt.subplots(1, 1)
     ax = [ax]
-    alphas = [1]#, 1, 1]
+
-    colors = ['black']#, 'black','red']
+    perc05, perc90, perc95 = get_lines_trialnr(cells, trial_nrs_here)
-    for s in range(len(colors)):
-        perc05, perc90, perc95 = get_lines_trialnr(cell, cells_given, s, trial_nrs_here)
 
     #######################
-        # plot 99the percentiel
+    # plot 99the percentile
-        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')
-        ax[0].scatter(trial_nrs_here, perc95, color = colors[s], clip_on = False, alpha = alphas[s])
+    ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False)
     ax[0].set_xscale('log')#colors[s]
     ax[0].set_yscale('log')
     ax[0].set_xlabel('Trials [$N$]')
@@ -57,11 +54,11 @@ def trialnr():
     ax[0].set_xlim(8, 10000000)
 
     ############################################
-        # plot percentile
+    # plot percentiles
-        ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha=alphas[s])
+    ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile')
-        ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s])
+    ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile')
-        ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s])
+    ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False)
-        ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s])
+    ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False)
     ax[0].legend()
 
 
@@ -70,7 +67,7 @@ def trialnr():
     save_visualization(pdf=True)
 
 
-def get_lines_trialnr(cell, cells_given, s, trial_nrs_here):
+def get_lines_trialnr(cells_given, trial_nrs_here):
     stacks = []
     perc95 = []
     perc90 = []
@@ -88,29 +85,23 @@ def get_lines_trialnr(cell, cells_given, s, 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']
-
+        save_name = find_folder_name('calc_model') + '/' + save_names[0]
-        save_name = save_names[s]
-
-
-        save_name = find_folder_name('calc_model') + '/' + save_name
-
         cell_add, cells_save = title_find_cell_add(cells_given)
-
         path = save_name + '.pkl'  # '../'+
 
-        stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
+        suscept_matrix = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
+
+        if len(suscept_matrix) > 0:
+            model_show, stack_plot, stack_plot_wo_norm = get_stack(cells_given[0], suscept_matrix)
 
-        if len(stack) > 0:
-            model_show, stack_plot, stack_plot_wo_norm = get_stack(cell, stack)
             stacks.append(stack_plot)
-            if 'counter_validate' in stack.keys():
+            if 'counter_validate' in suscept_matrix.keys():
-                counter.append(stack['counter_validate'].iloc[0])
+                counter.append(suscept_matrix['counter_validate'].iloc[0])
             else:
                 counter.append(float('nan'))
-            cv_stims.append(stack['cv_stim_mean'].iloc[0])
+            cv_stims.append(suscept_matrix['cv_stim_mean'].iloc[0])
-            fr_stims.append(stack['fr_stim_mean'].iloc[0])
+            fr_stims.append(suscept_matrix['fr_stim_mean'].iloc[0])
-            vars.append(stack['var_RAM'].iloc[0])
+            vars.append(suscept_matrix['var_RAM'].iloc[0])
-
             perc95.append(np.percentile(stack_plot, 99.9))  # 99.99
             perc90.append(np.percentile(stack_plot, 90))
             perc05.append(np.percentile(stack_plot, 10))
@@ -129,7 +120,6 @@ def get_lines_trialnr(cell, cells_given, s, trial_nrs_here):
             perc90.append(float('nan'))
             perc05.append(float('nan'))
             median.append(float('nan'))
-
             stacks_wo_norm.append([])
             perc95_wo_norm.append(float('nan'))
             perc05_wo_norm.append(float('nan'))
@@ -168,11 +158,9 @@ def print_table_for_methods():
 
 if __name__ == '__main__':
 
-    plot_style()
-    default_settings(lw=0.5, column=2, length=3.35)  #8.5
 
     ##########################
-    # hier printen wir die table Werte zum kopieren in den Text
+    # hier kann man die tabellen Werte zum kopieren in den Text printen lassen
     print_table_for_methods()
 
     trialnr()