improving code for plots

ampullary.py

@@ -4,8 +4,6 @@ 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=[''],
                     cells_plot=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25, 1.3])#Low-CV ampullary cell,

data_overview_mod.py

@@ -208,12 +208,10 @@ def printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_
             x = frame_file['fr_base']
             y = frame_file['ser_first']
             c = frame_file['cv_base']
-            try:
+
-                c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var,
+            c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var,
                                                                              score=score)
-            except:
+
-                print('frame file lost')
-                embed()
             corr, p_value = stats.pearsonr(x_axis, y_axis)
             pears_l = label_pearson(corr, p_value, y_axis, n=True)
             print(start_name(cell_type_here, species) + ' ' + corr_var + ' to ' + str(score) + pears_l)
@@ -328,15 +326,13 @@ def printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_
             frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='', species=species)
 
             if len(frame_file) > 0:
-                try:
+
-                    print(start_name(cell_type_here, species) + ' CV_min ' + str(
+                print(start_name(cell_type_here, species) + ' CV_min ' + str(
                         np.round(np.nanmin(frame_file.cv_base), 2)) + ' CV max ' + str(
                         np.round(np.nanmax(frame_file.cv_base), 2)) + ' FR max ' + str(
                         np.round(np.nanmin(frame_file.fr_base))) + ' FR max ' + str(
                         np.round(np.nanmax(frame_file.fr_base))))
-                except:
+
-                    print('min here')
-                    embed()
     ############################################
     #######
     # N

flowchart.py

@@ -1,14 +1,4 @@
-#from utils0import default_settings
-#from plt_RAM import model_and_data_isi, model_cells
-#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
-# from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 
-# 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
-# from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 import matplotlib.gridspec as gridspec
 
 from plotstyle import plot_style

model_and_data.py

@@ -42,7 +42,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
 
     stimulus_length = 1  # 20#550  # 30  # 15#45#0.5#1.5 15 45 100
     trials_nrs = [1]  # [100, 500, 1000, 3000, 10000, 100000, 1000000]  # 500
-    rep = 1000000  # 500000#0
     good_data, remaining = overlap_cells()
     cells_all = [good_data[0]]
 
@@ -62,17 +61,8 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
                  c_noises,
                  ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
 
-    nr = '2'
 
-    # cell_contrasts = ["2013-01-08-aa-invivo-1"]
-    # cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
-    # cells_triangl_contrast = 1
-    # cell_contrasts = 1
-
-    rows = len(cells_all)  # len(good_data)+len(cell_contrasts)
-    perc = 'perc'
     lp = 2
-    label_model = r'Nonlinearity $\frac{1}{S}$'
     for all in it.product(*iternames):
 
         var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
@@ -101,111 +91,20 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
         ##################################
         # model part
 
-        trial_nr = 100000
-        cell = '2013-01-08-aa-invivo-1'
-        cell = '2012-07-03-ak-invivo-1'
 
-        cells_given = [cell]
-        save_name_rev = find_folder_name(
-            'calc_model') + '/' + 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-            trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_revQuadrant_'
-        # for trial in trials:#.009
-        trial_nr = 1000000  #1000000
-        save_names = [
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
 
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        ]
 
 
         ##########
         # Erklärung
-        # Ich habe hier 0.009 und nicht 0.25 weil das Modell einen Fehler hat
+        # Ich habe hier 0.009 und nicht 0.25 weil das Modell sensitiver ist
-        # den Stimulus in den Daten habe ich überprüft der tatsächliche stimulus ist 2.3 Prozent
+        # ich habe das mit dem CV verifiziert dass das so stimmen sollte
-        # sollte aber 2.5 Prozent sein
-        # Im Fall von 2.5 Prozent wäre das ein Fehler von 0.36 sonst von 0.39
-        # Hier werde ich nun mit dem Fehler von 0.36 verfahren
-        # das bedeutet aber das sich den Stimulus zwar mit 0.009 ins Modell reintue später für die
-        # Susceptiblitätsberechnung sollte ich ihn aber um den Faktor 0.36 teilen
-
-        # oben habe ich einen bias factor weil die Zelle zu sensitiv gefittet ist, also passe ich das an dass die den
-        # gleichen CV und feurrate hat, wie die Zelle in der Stimulation, deswegen ist dieser Bias faktor nur oben!
-        #
-        #bias_factors = [1, 1, 1, 1]  # 0.3
-        bias_factors = [0.36, 0.36, 1, 1]  # 0.36
-        bias_factors = [1, 1, 1, 1]  # 0.36
-
-        #new
-        save_names = [
-            'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-
 
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
+        trial_nr = 100000
-            '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(
+        cell = '2012-07-03-ak-invivo-1'
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        ]#'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        save_names = [
-            'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
-            'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
-
-            'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_100000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_old_fit_',
-
-        ]
-
-
-
-        save_names = [
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        ]  #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
-        save_names = [
-            'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-3__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-            'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-3__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_'+str(trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        ]
-
-        save_names = [
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_afe_0.023_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        ]  #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
-        bias_factors = [0.36, 0.36, 1, 1]#0.36
-        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(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-            'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-                trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
 
-        ]
+        cells_given = [cell]
+        trial_nr = 1000000  #1000000
         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(
@@ -214,30 +113,14 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
             'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
             'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
                 trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
         ]
-
-        #
-
-        #            'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
-        #trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
-        #'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-        #'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
-
         nrs_s = [3, 4, 8, 9]  #, 10, 11
-        #embed()
-        tr_name = trial_nr / 1000000
-        if tr_name == 1:
-            tr_name = 1  #'$c=1\,\%$','$c=0\,\%$'
         c = 2.5
         cs = ['$c=%.1f$' % c + '$\,\%$', '$c=0\,\%$']
         titles = ['Model\n$N=11$', 'Model\n' + '$N=10^6$',
                   'Model\,(' + label_noise_name().lower() + ')' + '\n' + '$N=11$',
                   'Model\,(' + label_noise_name().lower() + ')' + '\n' + '$N=10^6$'
                   ]  #%#%s$' % (tr_name) + '\,million'
-        #'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=1\,\%$',$N=%s $' % (tr_name) +'\,million'
-        #          'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ '
         ax_model = []
 
         for s, sav_name in enumerate(save_names):
@@ -265,11 +148,8 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
                                                                                      width, eod_metrice=eod_metrice,
                                                                                      titles_plot=True,
                                                                                      resize=True,
-                                                                                     bias_factor=bias_factors[s],
                                                                                      fr_print=fr_print, nr=nr)
 
-                # if s in [1,3,5]:
-                #embed()
                 ims.append(im)
                 mats.append(stack_plot)
                 maxs.append(np.max(np.array(stack_plot)))
@@ -278,7 +158,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
                 row = 2
                 ax_external.set_xticks_delta(100)
                 ax_external.set_yticks_delta(100)
-                # cbar[0].set_label(nonlin_title(add_nonlin_title))  # , labelpad=100
                 cbar.set_label(nonlin_title(' [' + add_nonlin_title), labelpad=lp)  # rotation=270,
 
                 if s in np.arange(col - 1, 100, col):  # | (s == 0)
@@ -288,7 +167,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
 
                 if s >= row * col - col:
                     set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
-                    # ax.set_xlabel(F1_xlabel(), labelpad=20)
                 else:
                     remove_xticks(ax_external)
 
@@ -298,32 +176,20 @@ 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())#
 
         #################################################
-        # Flowcharts
+        # plot Flowcharts
 
-        ax_ams, ax_external = plt_model_flowcharts(a_fr, ax_external, c, cs, grid, model, stimulus_length)
+        ax_ams, ax_external = plt_model_flowcharts( a_fr, ax_external, c, cs, grid, model, stimulus_length)
 
         set_same_ylim(ax_ams, up='up')
 
         axes = np.concatenate([ax_data, ax_model])
 
-        axes = [ax_ams[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ]  #ax_ams[2], axes[5], axes[6],
+        axes = [ax_ams[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ]
-        #axd1 = plt.subplot(grid[1, 1])
-        #axd2 = plt.subplot(grid[2, 1])
-        #ax_data.extend([,])
-        #axd1.show_spines('')
-        #axd2.show_spines('')
-        #embed()
-        #axes = [[ax_ams[0],ax_data[0],axes[2], axes[3]],[ax_ams[1],axd1,axes[4], axes[5]],[axd2,axd2, axes[6],  axes[7]]]
 
-        fig.tag([ax_data], xoffs=-3, yoffs=1.6)  # ax_ams[3],
+        fig.tag([ax_data], xoffs=-3, yoffs=1.6)
-        fig.tag([axes[0:3]], xoffs=-3, yoffs=1.6)  # ax_ams[3],
+        fig.tag([axes[0:3]], xoffs=-3, yoffs=1.6)
-        #fig.tag([[axes[4]]], xoffs=-3, yoffs=1.6, minor_index=0)  # ax_ams[3],
+        fig.tag([axes[3:6]], xoffs=-3, yoffs=1.6)
-        fig.tag([axes[3:6]], xoffs=-3, yoffs=1.6)  #, major_index = 1, minor_index = 2 ax_ams[3],
-        #fig.tag([[axes[7]]], xoffs=-3, yoffs=1.6, major_index=2,minor_index=0)  # ax_ams[3],
-        #fig.tag([axes[8::]], xoffs=-3, yoffs=1.6, major_index=2, minor_index=2)  # ax_ams[3],
-        #fig.tag([axes[7::]], xoffs=-3, yoffs=1.6)  # ax_ams[3],
 
-        #fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],
 
         save_visualization(pdf=True)
 

model_full.py

@@ -88,7 +88,7 @@ def model_full():
                                                         stimulus_length=length, plus_q=plus_q, stack_saved = stack_saved,
                                                         diagonal=diagonal, runs=1, nfft = nfft, xlim_psd =  xlim_psd,
                                                         cells=[cell], dev_spikes ='original', markers = markers, DF1_frmult = DF1_frmult, DF2_frmult = DF2_frmult,
-                                                        log = log, ms = ms, models_name = models_name, adjust_factor_outside = adjust_factor_outside, array_len = array_len, f_same = f_same, transfer = transfer, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1])  #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
+                                                        log = log, ms = ms, models_name = models_name, adjust_factor_outside = adjust_factor_outside, array_len = array_len, clip_on = False, trials_nr_all= [1, 1, 1, 1, 1])  #arrays_len a_f1s=[0.02]"2012-12-13-an-invivo-1"'2013-01-08-aa-invivo-1'
 
 
 
@@ -146,7 +146,7 @@ def do_trasfer_in_model_full(a_size, cell, do_transfer, grid, models_name, stack
             a_fes = [a_size, a_size, 0]  # alpha\alpha0.1,
             trials_nr = 100  # 100
 
-            frame = get_transfer_for_model_full([a_size], 0, cell, deltat, eod_fr,
+            frame = get_transfer_for_model_full(cell, deltat, eod_fr,
                                                 model_params,
                                                 stimulus_length,
                                                 trials_nr,
@@ -208,7 +208,7 @@ def plt_data_matrix(axes, grid, ls, lw, perc):
     #                    stack_final_rev['snippets'].unique()[0], stack_here=stack_final_rev)  #
     mat, add_nonlin_title, resize_val = rescale_colorbar_and_values(mat)
     mat_rev, add_nonlin_title, resize_val = rescale_colorbar_and_values(mat_rev, resize_val=resize_val)
-    # try:
+
     full_matrix = create_full_matrix2(np.array(mat), np.array(mat_rev))
     # except:
     #    print('full matrix something')
@@ -376,7 +376,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
                         for gg in range(len(DF1_desired)):
 
                         
-                            # try:
+
                             grid0 = gridspec.GridSpecFromSubplotSpec(len(DF1_desired), 1, wspace=0.15, hspace=0.35,
                                                                      subplot_spec=grid)
                             t3 = time.time()
@@ -390,7 +390,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
                             grouped = mt_sorted.groupby(
                                 ['c1', 'c2', 'm1, m2'],
                                 as_index=False)
-                            # try:
+
                             grouped_mean = chose_certain_group(DF1_desired[gg],
                                                                DF2_desired[gg], grouped,
                                                                several=True, emb=False,
@@ -423,19 +423,6 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
                                 arrays, arrays_original, spikes_pure = save_arrays_susept(
                                     data_dir, cell, c, chirps, devs, extract, group_mean, mean_type, plot_group=0,
                                     rocextra=False, sorted_on=sorted_on)
-                                ####################################################
-
-                                ####################################################
-                                # hier checken wir ob für diesen einen Punkt das funkioniert mit der standardabweichung
-                            
-                                #try:
-                                #    check_var_substract_method(spikes_pure)
-                                #except:
-                                #    print('var checking not possible')
-                                # fig = plt.figure()
-                                # grid = gridspec.GridSpec(2, 1, wspace=0.7, left=0.05, top=0.95, bottom=0.15,
-                                #                         right=0.98)
-
                                 ##########################################################################
                                 # part with the power spectra
 

plot_chi2.py

@@ -1,20 +1,14 @@
-import os
-
 import numpy as np
-import mpmath as mp
 import matplotlib.pyplot as plt
-import pandas as pd
-from IPython import embed
 from matplotlib import rc
 
-from threefish.core import find_code_vs_not
 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.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, find_model_names_susept
+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
@@ -22,120 +16,14 @@ from threefish.RAM.values import perc_model_full
 rc('text', usetex=True)
 
 
-def FiringRate(mu, D, tau_ref, vR, vT):
-    x_start = (mu - vT)/mp.sqrt(2.0*D)
-    x_end = (mu - vR)/mp.sqrt(2.0*D)
-    dx = 0.0001
-    r = 0.0
-
-    for i in np.arange(x_start, x_end, dx):
-        integrand = mp.exp(i**2) * mp.erfc(i)
-        r += integrand*dx
-    
-    return 1.0/(tau_ref + mp.sqrt(mp.pi)*r)
-
-def Susceptibility_1(omega, r0, mu, D, tau_ref, vR, vT):
-    delta = (vR**2 - vT**2 + 2.0*mu*(vT - vR))/(4.0*D)
-    a = (r0 * omega*1.0j)/(mp.sqrt(D) * (omega*1.0j - 1.0))
-    b = mp.pcfd(omega*1.0j - 1.0, (mu - vT)/mp.sqrt(D)) - mp.exp(delta) * mp.pcfd(omega*1.0j - 1.0, (mu - vR)/mp.sqrt(D))
-    c = mp.pcfd(omega*1.0j, (mu - vT)/mp.sqrt(D)) - mp.exp(delta) * mp.exp(omega*1.0j*tau_ref) * mp.pcfd(omega*1.0j, (mu - vR)/mp.sqrt(D))
-    return a * b/c
-
-def Susceptibility_2(omega1, omega2, chi1_1, chi1_2, r0, mu, D, tau_ref, vR, vT):
-    delta = (vR**2 - vT**2 + 2.0*mu*(vT - vR))/(4.0*D)
-    a1 = r0*(1.0 - omega1*1.0j - omega2*1.0j)*(omega1*1.0j + omega2*1.0j)/(2.0*D*(omega1*1.0j - 1.0)*(omega2*1.0j - 1.0))
-    a2 = (omega1*1.0j + omega2*1.0j)/(2.0*mp.sqrt(D))
-    a3 = chi1_1/(omega2*1.0j - 1.0) + chi1_2/(omega1*1.0j - 1.0)
-    b1 = mp.pcfd(omega1*1.0j + omega2*1.0j - 2.0, (mu - vT)/mp.sqrt(D)) - mp.exp(delta) * mp.pcfd(omega1*1.0j + omega2*1.0j - 2.0, (mu - vR)/mp.sqrt(D))
-    b2 = mp.pcfd(omega1*1.0j + omega2*1.0j - 1.0, (mu - vT)/mp.sqrt(D))
-    b3 = mp.exp(delta) * mp.pcfd(omega1*1.0j + omega2*1.0j - 1.0, (mu - vR)/mp.sqrt(D))
-    c = mp.pcfd(omega1*1.0j + omega2*1.0j, (mu - vT)/mp.sqrt(D)) - mp.exp(delta) * mp.exp(1.0j*(omega1 + omega2)*tau_ref) * mp.pcfd(omega1*1.0j + omega2*1.0j, (mu - vR)/mp.sqrt(D))
-
-    return a1 * b1/c + a2*a3*b2/c - a2*a3*b3/c
-
-
-def calc_nonlin_analytic_values():
-
-    save_name = '-transfer'
-    load_function, name1, save_name = find_model_names_susept(save_name)
-    save_name2 = '-matrix'
-    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)) | (redo == True):
-            frame, frame2 = develop_analytic_nonlin(name1, name2)
-        else:
-            frame = pd.read_csv(name1, index_col=0)
-            frame2 = pd.read_csv(name2, index_col=0)
-    else:
-        frame = pd.read_csv(name1, index_col=0)
-        frame2 = pd.read_csv(name2, index_col=0)
-    return frame, frame2
-
-
-def develop_analytic_nonlin(name1, name2):
-    # LIF model in dimensionless units: dv/dt = -v + mu + sqrt(2D)*xi
-    ## v: membrane voltage
-    ## mu: mean input voltage
-    ## D: noise intensity
-    ## xi: white Gaussian noise
-    ## tau_mem = 1 (membrane time constant, skipped here)
-    mu = 1.1
-    D = 0.001
-    # threshold voltage
-    vT = 1.0
-    # reset voltage
-    vR = 0.0
-    # refractory period
-    tau_ref = 0.0
-    # stationary firing rate
-    r0 = FiringRate(mu, D, tau_ref, vR, vT)
-    x_min = 0.01
-    x_max = 1.0
-    dx = 0.01
-    y_min = 0.01
-    y_max = 1.0
-    dy = 0.01
-    f1_range = np.arange(x_min, x_max + 0.5 * dx, dx)
-    f2_range = np.arange(y_min, y_max + 0.5 * dy, dy)
-    chi1_data = np.zeros(f2_range.size, dtype=complex)
-    chi2_data = np.zeros((f2_range.size, f1_range.size), dtype=complex)
-    for f2 in range(f2_range.size):
-        omega2 = 2.0 * np.pi * f2_range[f2]
-        chi1_2 = Susceptibility_1(omega2, r0, mu, D, tau_ref, vR, vT)
-        print('Step: ' + str(f2 + 1) + ' von ' + str(int((y_max - y_min) / dy) + 1))
-        chi1_data[f2] = chi1_2
-
-        for f1 in range(f1_range.size):
-            omega1 = 2.0 * np.pi * f1_range[f1]
-            chi1_1 = Susceptibility_1(omega1, r0, mu, D, tau_ref, vR, vT)
-            chi2 = Susceptibility_2(omega1, omega2, chi1_1, chi1_2, r0, mu, D, tau_ref, vR, vT)
-            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')
-    return frame, frame2
-
-
 def plot_chi2():
-    global ax, frame2
     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)
     fig1, ax = plt.subplots(1, 2)
     plt.subplots_adjust(bottom=0.2, wspace=0.35, left=0.1, right=0.92)
+
     ###################################################
     # plot transfer function
     frame = frame.astype(complex)
@@ -145,6 +33,7 @@ def plot_chi2():
     ax[0].set_xlim(0, frame.index[-1])
     ylim = ax[0].get_ylim()
     ax[0].set_ylim(0, ylim[-1])
+
     ###################################################
     # plot matrix
     frame2.columns = frame2.index