updating figures

data/model_and_data__spikes_c_sig_0_a_fe_0.009.csv

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data/model_and_data__spikes_c_sig_0_a_fe_0.025.csv

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data/model_and_data_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.009.csv

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data/model_and_data_additiv_cv_adapt_factor_scaled_spikes_c_sig_0.9_a_fe_0.csv

@@ -1,240 +1,240 @@
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model_and_data.py

@@ -138,7 +138,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
         #
         #bias_factors = [1, 1, 1, 1]  # 0.3
         bias_factors = [0.36, 0.36, 1, 1]  # 0.36
-
+        bias_factors = [1, 1, 1, 1]  # 0.36
 
         #new
         save_names = [
@@ -195,6 +195,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
 
         ]  #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
         bias_factors = [0.36, 0.36, 1, 1]#0.36
+        bias_factors = [1, 1, 1, 1]
         save_names = [
             'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
             'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(

nonlin_regime.py

@@ -7,13 +7,17 @@ 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_folder_name, info
 from threefish.defaults import default_figsize, default_ticks_talks
 from threefish.load import load_savedir, save_visualization
 from threefish.plot.limits import join_x, join_y, set_same_ylim
 from threefish.RAM.calc_fft import log_calc_psd
 from threefish.RAM.calc_model import chose_old_vs_new_model
-from threefish.RAM.plot_labels import label_f_eod_name_core_rm, onebeat_cond, remove_yticks
+from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_f_eod_name_core_rm, \
+    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.twobeat.calc_model import calc_roc_amp_core_cocktail
@@ -206,6 +210,8 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
                         print('grid search problem4')
                         embed()
 
+                    labels = [label_deltaf1(), label_deltaf2(),
+                               label_sum(), label_diff(), label_fbasename_small()]
                     plt_single_trace([], ax_u1, frame_cell_orig, freq1, freq2,
                                      scores=np.array(scores)[index], labels=np.array(labels)[index],
                                      colors=np.array(colors)[index],
@@ -262,31 +268,41 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
 
             #################################
             # arrays plot
-            save_dir = load_savedir(level=0).split('/')[0]
+
+
+            trials_nr = 1
+            nfft_here = values_nfft_full_model()
+            stimulus_length_here = 100  # values_stimuluslength_model_full()
+            nfft_here = 20 * 20000
+            a_f2s = [c_nrs_orig[c_nn]]
+            a_f2s = [c_nrs_orig[c_nn]]
+
+            save_dir = load_savedir(level=0).split('/')[0] +'_afe_'+str(a_f2s[0])+'_nfft_'+str(nfft_here)+'_len_' + str(stimulus_length_here)
+
             name_psd = save_dir + '_psd.npy'
             name_psd_f = save_dir + '_psdf.npy'
 
-            do = False
-            if ((not os.path.exists(name_psd)) | (redo == True)) & do:
-                if log != 'log':
-                    stimulus_length_here = 0.5
-                    stimulus_length_here = values_stimuluslength_model_full()
-                    nfft_here = 32768
-                    nfft_here = stimulus_length_here*20000#values_nfft_full_model()
-                else:
-                    stimulus_length_here = 50
-                    stimulus_length_here = values_stimuluslength_model_full()
-                    trials_nr = 1
-                    nfft_here = values_nfft_full_model()
-
+            do = True
+            if ((not os.path.exists(name_psd)) | (redo == True)) | (do == True):
+
+                # psd generierung
+
+                _, _, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
+                    [freq1 + eodf], [freq2 + eodf], datapoints, auci_wo, auci_w, results_diff,
+                    a_f2s,
+                    fish_jammer, trials_nr, nfft_here, us_name, gain, runs, a_fr, nfft_for_morph,
+                    beat,
+                    printing,
+                    stimulus_length_here,
+                    model_cells, position_diff, dev, cell_here, dev_name=dev_name,
+                    a_f1s=[c_nrs_orig[c_nn]],
+                    n=n,
+                    reshuffled=reshuffled, min_amps=min_amps, mean_choice='first')
+                np.save(name_psd, p_arrays_p)
+                np.save(name_psd_f, ff_p)
             else:
-                nfft_here = 2 ** 14
-                stimulus_length_here = 0.5
-
-                stimulus_length_here = 10#values_stimuluslength_model_full()
-                trials_nr = 10
-                nfft_here = values_nfft_full_model()
-                nfft_here = stimulus_length_here * 20000
+                ff_p = np.load(name_psd_f)  # p_arrays_p
+                p_arrays_p = np.load(name_psd)  # p_arrays_p
 
             #    #
             #
@@ -309,25 +325,7 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
                 n=n,
                 reshuffled=reshuffled, min_amps=min_amps, mean_choice = '')
 
-            ###########################
-            # psd generierung
-            trials_nr = 1
-            nfft_here = values_nfft_full_model()
-            stimulus_length_here = 100  # values_stimuluslength_model_full()
-            nfft_here = stimulus_length_here * 20000
-            a_f2s = [c_nrs_orig[c_nn]]
-            a_f2s = [c_nrs_orig[c_nn]]
-            _, _, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
-                [freq1 + eodf], [freq2 + eodf], datapoints, auci_wo, auci_w, results_diff,
-                a_f2s,
-                fish_jammer, trials_nr, nfft_here, us_name, gain, runs, a_fr, nfft_for_morph,
-                beat,
-                printing,
-                stimulus_length_here,
-                model_cells, position_diff, dev, cell_here, dev_name=dev_name,
-                a_f1s=[c_nrs_orig[c_nn]],
-                n=n,
-                reshuffled=reshuffled, min_amps=min_amps, mean_choice = 'first')
+
 
             ##################################################################
             # ff_p, arrays, names, p_arrays_p, arrays_spikes, arrays_stim,
@@ -360,19 +358,20 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
                     freqs = [np.abs(freq2)]
                 else:
                     freqs = [np.abs(freq1), np.abs(freq2)]
-                grid_pt = gridspec.GridSpecFromSubplotSpec(2, 1,
+                grid_pt = gridspec.GridSpecFromSubplotSpec(3, 1,
                                                            hspace=0.2,
                                                            wspace=0.2,
-                                                           subplot_spec=grid_up[a, c_nn], height_ratios = [1,2]
+                                                           subplot_spec=grid_up[a, c_nn], height_ratios = [1, 1,2]
                                                           )  # hspace=0.4,wspace=0.2,len(chirps)
 
                 stim = False
-                if stim:
-                    axe = plt.subplot(grid_pt[0])
-                    axes.append(axe)
-                    plt_stim_saturation(a, [], arrays_st, axe, colors_array_here, f,
-                                        f_counter, names, time,
-                                        xlim=xlim)  # np.array(arrays_sp)*1000
+                #if stim:
+                axe = plt.subplot(grid_pt[0])
+                axes.append(axe)
+                am, time_am = extract_am(arrays_st, time, extract='',norm = False)
+                plt_stim_saturation(a, [], am, axe, colors_array_here, f,
+                                    f_counter, names, time,
+                                    xlim=xlim)  # np.array(arrays_sp)*1000
 
                 a_f2_cm = c_dist_recalc_func(frame_cell, c_nrs=[a_f2s[0]], cell=cell_here,
                                              c_dist_recalc=c_dist_recalc)
@@ -393,7 +392,7 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
                 #############################
 
 
-                axs = plt.subplot(grid_pt[0])
+                axs = plt.subplot(grid_pt[1])
 
                 plt_spikes_ROC(axs, 'grey', np.array(arrays_sp[a]) * 1000, xlim, lw=1)
 

susceptibility1.tex.bak

@@ -885,10 +885,6 @@ CVs in P-units can range up to 1.5 \citep{Grewe2017, Hladnik2023}. We show the s
 
 \begin{figure*}[t]
   \includegraphics[width=\columnwidth]{trialnr}
-  \notejb{ylabel: absolute value of $chi_2$!}
-  \notejb{sort the legend labels!}
-  \notejb{one of the 99.99\% percentiles is enough}
-  \notejb{add $10^7$ tick on x-axis}
   \caption{\label{fig:trialnr} Dependence of the estimate of the second-order susceptibility on the number of trials $\n$. While the estimate of the noise floor (10th and 90th percentile) of the $|\chi_2(f_1, f_2)|$ matrix does not saturate yet, the estimates of the high values in the matrix that make up the characteristic ridges saturate for $N>10^6$. The model used has the identifier 2013-01-08-aa.
   }
 \end{figure*}