#from plt_RAM import plt_squares_bursts_single_psd from utils_suseptibility import * def plt_squares_bursts_single_psd2(col = 6, cells = ['2012-07-12-ap-invivo-1', '2018-06-26-ah-invivo-1', '2012-12-20-ae-invivo-1', '2012-06-27-ah-invivo-1', '2011-10-25-ad-invivo-1', '2012-12-21-ai-invivo-1', '2012-05-10-ad-invivo-1', '2012-12-20-ad-invivo-1', '2012-04-20-ak-invivo-1', '2012-12-13-ah-invivo-1', '2012-12-20-ab-invivo-1'],nr_clim=10, many=False, width=0.02, row='no', HZ50=True, fs=8, hs=0.39, redo=False, nffts=['whole'], powers=[1], col_desired=2, var_items=['contrasts'], show=False, contrasts=[0], noises_added=[''], fft_i='forward', fft_o='forward', spikes_unit='Hz', mV_unit='mV', D_extraction_method=['additiv_visual_d_4_scaled'], internal_noise=['eRAM'], external_noise=['eRAM'], level_extraction=['_RAMdadjusted'], cut_off2=300, repeats=[1000000], receiver_contrast=[1], dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],burst_corrs = [''], clims='all', restrict='restrict', label=r'$\frac{1}{mV^2S}$'): plot_style() duration_noise = '_short', formula = 'code' ##'formula' # ,int(2 ** 16) int(2 ** 16), int(2 ** 15), 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 stimulus_type = '_StimulusOrig_' # ,# # ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1, variant = 'sinz' mimick = 'no' cell_recording_save_name = '' trans = 1 # 5 aa = 0 for burst_corr, cell, var_type, stim_type_afe, trials_stim, 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, in it.product( burst_corrs, cells, D_extraction_method, external_noise , repeats, internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal, c_noises, ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ): #print(cell, burst_corr, cell, var_type, stim_type_afe, trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, cut_off1,trial_nrs) # print(trial_nrs, stim_type_noise, trials_stim, power, nfft, a_fe, a_fr, var_type, cut_off1,trial_nrs) aa += 1 default_figsize(column=2, length=4.2) #5.25.5. 6.8 2+2.25+2.25 a = 0 maxs = [] mins = [] ims = [] perc05 = [] perc95 = [] iternames = [D_extraction_method, external_noise, internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal, c_noises, ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ] nr = '2' n = 2 b = 0 ax_lines = [] ax_psds = [] ax_diagonals = [] axss = [] resize_val = None for c, cell in enumerate(cells): axps = [] axpsl = [] #pss = [] lines = [] diags = [] pss = [] mats = [] a = 0 #fig = plt.figure(figsize=(12, 5.5)) fig = plt.figure() 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 # print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs) #072 grid0 = gridspec.GridSpec(4, 1, wspace=0.24, bottom=0.1, hspace=0.04, left=0.08, right=0.93, top=0.98, height_ratios = [1.1,1.3,1,5.8])#7.8 carrier, corrs_all, spikes_tr, spikes_tr_bef = save_spikes_burst_add( a_fe, a_fr, adapt_type, burst_corr, c_noise, c_sig, cell, cell_recording_save_name, cut_off1, cut_off2, dendrid, duration_noise, extract, fft_i, fft_o, formula, mimick, nfft, noise_added, power, ref_type, stim_type_afe, stim_type_noise, stimulus_length, stimulus_type, trans, trial_nrs, var_type, variant, trial_nrs_base=1, burst_corrs=burst_corrs) # embed() grid_stim = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0], wspace=0.34, hspace=0.3) ax_stim = plt.subplot(grid_stim[0]) model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core') model_params = model_cells[model_cells.cell == cell].iloc[0] cell = model_params.pop('cell') eod_fr = model_params['EODf'] deltat = model_params.pop("deltat") carrier = np.array(carrier['0']) time_s = np.arange(0, len(carrier)*deltat, deltat) carrier_RAM = np.random.normal(0, 1, size=len(time_s )) # *0.2 #sine = np.sin(2*np.pi*750)*(1+carrier_RAM*0.2) extracted_am, time_am = extract_am(carrier, time_s, norm=False) time_am = time_s * 1000 ax_stim.plot(time_am, extracted_am + 0.05, color='red') ax_stim.plot(time_am, carrier, color = 'grey', linewidth = '0.5') ax_stim.set_xlim(0, 200) ax_stim.show_spines('') grid_upper = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[1], wspace=0.34, hspace=0.3) ax_spikes = plt.subplot(grid_upper[0]) #tags.append(ax_spikes) #axss = plt.subplot(grid_data[0]) #todo: das modell hier einmal machen für eine variante alpha = 1/(len(burst_corrs)+1) #ax_spikes = plt.subplot(1,1,1) colors = colors_overview() for i in range(len(corrs_all)): ax_spikes.eventplot(np.array(corrs_all[i]) * 1000, lineoffsets = len(corrs_all)-i, linelengths = 0.85, alpha =alphafunc_burstadd( alpha, i), color=colors[' P-unit']) #ax_upper.eventplot(spikes_tr_bef, color='red') ax_spikes.set_xlim(0, 200) ax_spikes.xscalebar(0.9, -0.02, 20, 'ms', va='right', ha='bottom') #ax_diagonal.xscalebar(1, 0.5, 20, 'dB', va='center', ha='right') ax_spikes.show_spines('') #ax_spikes.set_yticks([]) ################################## grid_lower = gridspec.GridSpecFromSubplotSpec(row,col,grid0[3], wspace=0.25, hspace=0.3) labelpad = 0 for r, trials_stim in enumerate(repeats): for b, burst_corr in enumerate(burst_corrs): save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula, stim_type_noise, mimick, variant, trials_stim, power, stimulus_type, cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o, Hz=spikes_unit, mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe, extract=extract, noise_added=noise_added, c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type, var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr, trials_nr=trial_nrs, trans=trans, zeros='ones') path = save_name + '.pkl' # '../'+ print(save_name) print(a) grid_data = grid_matrices(a, grid_lower) #grid1 = gridspec.GridSpecFromSubplotSpec(1, 2, grid[c], hspace=0) ##########596##################### #frame = frame.sort_values(by='cv') #fr = frame[frame.cell == cell].fr # np.array(model_cells['cell']) #cv = frame[frame.cell == cell].cv model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core') model_params = model_cells[model_cells.cell == cell].iloc[0] eod_fr = model_params['EODf'] #if cont_cell: # cont_cell #grid_p_i = gridspec.GridSpecFromSubplotSpec(1, 2, grid_data[1]) #axp = plt.subplot(grid_p_i[0]) #axp.set_xlim(0,600) sampling_calc = 40000 spikes_mat = [[]]*len(spikes_tr) p_arrays = [] nfft_here = 2**16 diffs = [] f_array = [] #embed() #spikes_tr = np.array(spikes_tr) for s, sp in enumerate(corrs_all[b]): if len(sp)>0: #try: np.isnan(sp[-1]) if np.isnan(sp[-1]): sp = sp[~np.isnan(sp)] spikes_mat[s] = cr_spikes_mat(np.array(sp), sampling_rate=sampling_calc, length=int(sampling_calc * np.array(sp[-1]))) #embed() p_ar, f_array = ml.psd(spikes_mat[s] - np.mean(spikes_mat[s]), Fs=sampling_calc, NFFT=nfft_here, noverlap=nfft_here / 2) p_arrays.append(p_ar) diffs.extend(np.diff(sp)) ps = np.mean(p_arrays, axis = 0)[f_array<600] fss = f_array[f_array<600] #axi, axs = hist_part(grid1, cell_type, burst_corrs[b], colors, cell, spikes, eod_fr, ) ############################### # square axs = plt.subplot(grid_data[0]) axss.append(axs) model = load_model_susept(path, cells, save_name) #if cont:, if len(model) > 0: titles = '' model_show = model[ (model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)] #embed() new_keys = model_show.index.unique() # [0:490] # np.abs( try: stack_plot = model_show[list(map(str, new_keys))] except: stack_plot = model_show[new_keys] stack_plot = stack_plot.iloc[np.arange(0, len(new_keys), 1)] stack_plot.columns = list(map(float, stack_plot.columns)) axs.set_xlim(0, 300) axs.set_ylim(0, 300) axs.set_xticks_delta(100) axs.set_yticks_delta(100) axs.set_aspect('equal') model_params = model_cells[model_cells['cell'] == cell] if len(model_show) > 0: noise_strength = model_params.noise_strength.iloc[0] # **2/2 stack_plot = RAM_norm(stack_plot, trials_stim,model_show=model_show) if many == True: titles = titles + ' Ef=' + str(int(model_params.EODf.iloc[0])) color = title_color(cell) #embed() stack_plot, add_nonlin_title, resize_val = rescale_colorbar_and_values(stack_plot, resize_val = resize_val) #cbar[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=100 fr = int(np.round(model_show.fr.iloc[0])) #burst_corr + axs.text(1, 1.15, titles + ' $f_{Base}=%s$' %(int(np.round(model_show.fr_stim.iloc[0]))) + '\,Hz \n'+ r'$\rm{CV}=%s$' %( np.round(model_show.cv_stim.iloc[0], 2)), ha = 'right', va = 'center', transform=axs.transAxes, alpha=alphafunc_burstadd( alpha, a), color=colors[' P-unit']) # color=color,fontsize=fs,+\n $cv_{B}$='$_{stim} + str(np.round(model_show.cv.iloc[0], 2)) + ' $fr_{B}$=' + str(fr) + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2) perc = '' # 'perc' im = plt_RAM_perc(axs, perc, stack_plot) pos = np.argmin(np.abs(stack_plot.index -fr/2)) # wenn _ am Anfang des Labels ist verändert das irgendwas, deswgen muss man das auswechseln! if burst_corr == '': burst_corr_name = 'no burst corr' else: burst_corr_name = burst_corr.replace('_', '-') ###################################### # psds lines.append(stack_plot.iloc[pos]) diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot)) diags.append(diagonals_prj_l) pss.append(ps) # embed() ########################################## ims.append(im) mats.append(stack_plot) maxs.append(np.max(np.array(stack_plot))) mins.append(np.min(np.array(stack_plot))) perc05.append(np.percentile(stack_plot, 5)) perc95.append(np.percentile(stack_plot, 95)) plt_triangle(axs, model_show.fr.iloc[0], np.round(model_show.fr_stim.iloc[0]), model_show.eod_fr.iloc[0], 300) #plt_peaks_several(['',''], [model_show.fr.iloc[0],np.round(model_show.fr_stim.iloc[0])],ps,0,axp,ps,['brown','red'], fss) axs.set_aspect('equal') if a in np.arange(col - 1, 100, col): cbar = colorbar_outside(axs, im, fig, add=0, width=width) cbar[0].set_label(nonlin_title(' ['+add_nonlin_title))#, labelpad=100 #if b > col-1: # axs.set_xlabel(F1_xlabel(), labelpad=20) #axs.text(1.05, -0.35, F1_xlabel(), ha='center', va='center', # transform=axs.transAxes) set_xlabel_arrow(axs, ypos=-0.28) #else: # remove_xticks(axs) #axs.set_ylabel(F2_xlabel()) #if a in np.arange(0, 10, 1) * col: if b in np.arange(0, 100,col): #axs.set_ylabel(F2_xlabel()) axs.text(-0.35, 0.97, F2_xlabel(), ha='center', va='center', transform=axs.transAxes, rotation=90) else: remove_yticks(axs) axs.arrow_spines('lb') #else: # remove_tick_ymarks(axs) else: print('no model there') embed() axs.set_title(var_type[7::] + ' \n' + burst_corr, fontsize=fs, color=color) # + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2) ##################################################### #################################################### # plt psds save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula, stim_type_noise, mimick, variant, trials_stim, power, stimulus_type, cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o, Hz=spikes_unit, mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe, extract=extract, noise_added=noise_added, c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type, var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr, trials_nr=trial_nrs, trans=trans, zeros='ones') path = save_name + '.pkl' # '../'+ grid_data = grid_matrices(a, grid_lower) #grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[b], # hspace=1, height_ratios = [1.4,1]) #1.2 height_ratios=[1,1,1, 6], grid_pds = gridspec.GridSpecFromSubplotSpec(1, 1, grid_data[1], wspace=0.34, hspace = 0.8)#1.5 #ax_psd = plt.subplot(grid_pds[0]) #ax_psds.append(ax_psd) ax_diagonal = plt.subplot(grid_pds[0]) ax_diagonals.append(ax_diagonal) line = False if line: ax_line = plt.subplot(grid_pds[2]) ax_lines.append(ax_line) colors = colors_overview() psd_all = False if psd_all: psd_plot = False if psd_plot: ############################## # psd max_val = pss[a] # np.array(diagonals_prj_l) loag_val = 10 * np.log10(max_val / np.max(pss)) # / np.max(max_val) ax_psd.plot(fss[fss < np.max(stack_plot.columns)], loag_val[fss < np.max(stack_plot.columns)], zorder=100 - a, alpha=alphafunc_burstadd( alpha, a), color=colors[' P-unit']) else: model = load_model_susept(path, cells, save_name) #frame = pd.read_pickle('../calc_model/noise2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_10000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV.pkl') # if cont:, if len(model) > 0: titles = '' model_show = model[ (model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)] try: model_show['io_cross'] = model_show['io_cross'].astype('complex') except: print('io cross something') embed() vals = np.abs(np.array(model_show['io_cross']))/model_show['isf_psd'] #embed() ax_psd.plot(stack_plot.columns, vals, alpha=alphafunc_burstadd( alpha, a), color=colors[' P-unit'])#[fss < np.max(stack_plot.columns)] arrow = False if arrow: if b == 3: set_xlabel_arrow(tranfer_xlabel(),xpos=1.15, ypos=-0.35) #ax_psd.set_xlabel(tranfer_xlabel(), labelpad = labelpad) else: set_xlabel_arrow('', xpos=1.15, ypos=-0.35) ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha = 'center', transform=ax_psd.transAxes) if b == 0: ax_psd.set_ylabel(trasnfer_ylabel()) else: remove_yticks(ax_psd) #vals = np.abs(vals) / (powers / counter) #if 'osf' in model_show.keys(): # plt_transferfunction(alpha, ax_psd, color, model_show, eod_fr=eod_fr) ####################################### # line throuhg if line: max_val = np.array(lines[a]) # stack_plot.iloc[pos] loag_val = 10 * np.log10(max_val / np.max(lines)) # / np.max(max_val) ax_line.plot(np.array(stack_plot.columns), loag_val, label=burst_corr_name, zorder=100 - b, alpha=alphafunc_burstadd( alpha, a), color=colors[' P-unit']) colors = colors_overview() # diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot)) axis_d = axis_projection(stack_plot, axis='') if psd_all: ax_psd.show_spines('lb') ax_psd.set_xlim(0, axis_d[-1]) a += 1 alpha = 1 / (len(burst_corrs) + 1) a = 0 for b, burst_corr in enumerate(burst_corrs): ####################################### # diagonal axis_d = axis_projection(stack_plot, axis='') max_val = np.array(diags[b]) loag_val = 10 * np.log10(max_val / np.max(diags)) # / np.max(max_val) colors = colors_overview() print(np.max(loag_val)) ax_diagonal = ax_diagonals[b] ax_diagonal.plot(axis_d, loag_val, label=burst_corr_name, zorder=100 - a, alpha=alphafunc_burstadd( alpha, a), color=colors[' P-unit']) a += 1 ax_diagonal.set_xlim(0, axis_d[-1]) #remove_xticks(ax_psd) #ax_psd.set_ylim(-12, 0) ax_diagonal.set_ylim(-14, 0) scalebar = False if scalebar: if b == 0: ax_diagonal.text(-0.2, 0.5, chi_name(), ha='center', va='center', rotation=90, transform=ax_diagonal.transAxes) # diagonal_xlabel()) if b == 3:#a == len(lines) - 1: ax_diagonal.show_spines('b') ax_diagonal.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right') # ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right') #ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right') #ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center', # transform=ax_diagonal.transAxes, rotation=90) #ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center', # transform=ax_psd.transAxes, rotation=90) if line: ax_line.text(1.25, 0.5, 'Line', ha='center', va='center', transform=ax_line.transAxes, rotation=90) ax_line.show_spines('b') else: if b == 0: ax_diagonal.set_ylabel('dB', va='center', ha='right') else: remove_yticks(ax_diagonal) # ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right') # ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right') # ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center', # transform=ax_diagonal.transAxes, rotation=90) # ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center', # transform=ax_psd.transAxes, rotation=90) #if line: # ax_line.text(1.25, 0.5, 'Line', ha='center', va='center', # transform=ax_line.transAxes, rotation=90) # ax_line.show_spines('b') # else: if line: ax_line.set_xlim(0, axis_d[-1]) ax_line.set_ylim(-25, 0) remove_xticks(ax_diagonal) else: arrow = False if arrow: if b == 3: set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35) else: set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35) #ax_diagonal.text(0.45, -0.6,diagonal_xlabel(), va='center', ha='center', transform=ax_diagonal.transAxes) #ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha='center', transform=ax_psd.transAxes) ax_diagonal.set_xlabel(diagonal_xlabel(), labelpad = labelpad) for a in axps: a.set_ylim(0,np.max(axpsl)) #set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95) #set_same_clim_perc(ims, perc05, perc95) set_clim_same_here(ims, nr_clim='perc',mats = mats, percnr = 85,perc05=perc05, perc95=perc95, lim_type='up') if line: ax_lines[0].get_shared_y_axes().join(*ax_lines) ax_diagonals[0].get_shared_y_axes().join(*ax_diagonals) #ax_psds[0].get_shared_y_axes().join(*ax_psds)ax_psds, tags = np.transpose([axss])#,ax_diagonals, xoffs = -3.1#2.9 fig.tag(ax_stim, xoffs=xoffs) fig.tag(ax_spikes,xoffs=xoffs)#,-4.5 tag2(fig,axss,xoffs=xoffs, yoffs = 1.9)#.35#, -0.2 save_visualization(pdf=True) def grid_matrices(a, grid_lower): grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[a], hspace=0.5, height_ratios=[1.7, 1]) # height_ratios=[1,1,1, 6], return grid_data def alphafunc_burstadd(alpha, i): return 1 - alpha * i def set_same_clim_perc(ims, perc05, perc95): for i, im in enumerate(ims): im.set_clim(np.min(perc05), np.max(perc95)) def set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95): if 'all' in clims: remainer = clims.replace('all', '') if remainer == '': remainer = 0 else: remainer = int(remainer) # embed() # remainer = 0 # set_clim_same_here() for i, im in enumerate(ims): if i < remainer: # im.set_clim(np.min(np.min(mins)) * nr_clim, np.max(np.max(maxs) / nr_clim)) im.set_clim(perc05[i], perc95[i]) else: if nr_clim == 'perc': im.set_clim(perc05[i], perc95[i]) else: im.set_clim(np.min(perc05), np.max(perc95)) # im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim) else: for i, im in enumerate(ims): if nr_clim == 'perc': im.set_clim(perc05[i], perc95[i]) else: im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim) ##################################################### if __name__ == '__main__': ################################################## # compare all scales with 0.2, 0.25, 0.3, 0.35 model = resave_small_files("models_big_fit_d_right.csv", load_folder = 'calc_model_core') #e#mbed() cells = model.cell.unique() cells_m = np.array(model.cell) cells = [] cells = ['2018-05-08-af-invivo-1', '2018-05-08-ad-invivo-1', '2012-12-20-ae-invivo-1', '2015-01-20-af-invivo-1', '2012-07-12-ap-invivo-1', '2018-05-08-ae-invivo-1', '2014-12-11-aa-invivo-1', '2015-01-15-ab-invivo-1', '2018-06-25-ad-invivo-1', '2011-10-25-ad-invivo-1', '2018-05-08-ad-invivo-1', '2018-05-08-ab-invivo-1', '2014-06-06-ag-invivo-1', '2014-06-06-ac-invivo-1', '2012-04-20-ak-invivo-1', '2018-05-08-aa-invivo-1'] cells.extend(cells_m) cells = ["2013-01-08-aa-invivo-1"] #cells = ['2012-04-20-ak-invivo-1'] burst_corrs = ['', '_burst_added1_','_burst_added2_','_burst_added3_',]#'_burst_added2only_',]#'_burst_added2_', params = {'burst_corrs': burst_corrs} ######## # das mit den niedriegeren CVs machen redo = True show = True repeats = [1000000] #250000 250000**params, #repeats = [10000] D_extraction_method = ['additiv_cv_adapt_factor_scaled0.2','additiv_cv_adapt_factor_scaled0.25', 'additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35',] D_extraction_method = ['additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35', 'additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',] D_extraction_method = ['additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',] D_extraction_method = ['additiv_cv_adapt_factor_scaled', ]#'additiv_visual_d_4_scaled', #cells = ['2018-05-08-ad-invivo-1','2018-05-08-af-invivo-1','2018-06-25-ad-invivo-1'] #todo:hier das mit dem psd machen #embed()#'perc' plt_squares_bursts_single_psd2(row = 1, level_extraction=[''], col = 4, cells = cells, burst_corrs=burst_corrs, D_extraction_method=D_extraction_method, nr_clim=10, many=False,internal_noise=['RAM'], external_noise=['RAM'], width=0.005, HZ50=False, fs=7, hs=0.8, redo=redo, var_items=[k for k in params], clims='all0', repeats=repeats, show=show, label=r'$\frac{1}{S}$')#r'$\frac{1}{mV^2S}$' #embed()