#from utils_suseptibility import default_settings #from plt_RAM import model_and_data_isi, model_cells #from utils_suseptibility import model_and_data, remove_yticks #from utils_suseptibility import * #from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \ # set_ylabel_arrow, \ # xpos_y_modelanddata #from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \ # overlap_cells, \ # plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim from utils_suseptibility import *#model_and_data #from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2 def table_printen(table): print(table.keys()) for l in range(len(table)): list_here = np.array(table.iloc[l]) l1 = "& ".join(list_here) print(l1) def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False, contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'], internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1], dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300], label=r'$\frac{1}{mV^2S}$'): # ['eRAM'] # plot_style()#['_RAMscaled']'_RAMscaled' 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 rep = 1000000 # 500000#0 repeats = [20, rep] # 250000 aa = 0 good_data, remaining = overlap_cells() cells_all = [good_data[0]] plot_style() default_figsize(column=2, length=3.1) #.254.75 0.75 grid = gridspec.GridSpec(2, 5, wspace=0.95, bottom=0.09, hspace=0.25, width_ratios = [1,0,1,1,1], left=0.09, right=0.93, top=0.9) a = 0 maxs = [] mins = [] mats = [] 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' # embed() # 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 # print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs) fig = plt.figure() hs = 0.45 ################################# # data cells # embed() grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 0], hspace=hs) #ypos_x_modelanddata() nr = 1 ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width, cbar_label=True, eod_metrice = eod_metrice, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True) for ax_external in ax_data: # ax.set_ylabel(F2_xlabel()) # remove_xticks(ax) ax_external.set_xticks_delta(100) set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87) #embed() set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata()) # ax.text(-0.42, 0.87, F2_xlabel(), ha='center', va='center', # transform=ax.transAxes, rotation = 90) # ax.text(1.66, 0.5, nonlin_title(), rotation=90, ha='center', va='center', # transform=ax.transAxes) ax_external.arrow_spines('lb') #embed() #plt.show() ################################## # model part trial_nr = 500000 cell = '2013-01-08-aa-invivo-1' cell = '2012-07-03-ak-invivo-1' print('cell'+str(cell)) cells_given = [cell] save_name_rev = load_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_0.009_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_500000_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_500000_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 titles = ['Model\n$N=11$ \n $c=1\,\%$', 'Model\n$N=%s $' % (tr_name) +'\,million \n $c=1\,\%$', 'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=0\,\%$', 'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million \n $c=0\,\%$', ]#% #'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=1\,\%$', # 'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ ' ax_model = [] for s, sav_name in enumerate(save_names): try: ax_external = plt.subplot(grid[nrs_s[s]]) except: print('vers something') embed() ax_model.append(ax_external) save_name = load_folder_name('calc_model') + '/' + sav_name cell_add, cells_save = find_cell_add(cells_given) perc = 'perc' path = save_name + '.pkl' # '../'+ # stack = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name) # full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev)) # stack_final = get_axis_on_full_matrix(full_matrix, stack) # im = plt_RAM_perc(ax, perc, np.abs(stack)) # embed() stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add) if len(stack)> 0: add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s], width, eod_metrice = eod_metrice, titles_plot=True, resize=True, nr = nr) # if s in [1,3,5]: ims.append(im) mats.append(stack_plot) maxs.append(np.max(np.array(stack_plot))) mins.append(np.min(np.array(stack_plot))) col = 2 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) remove_yticks(ax_external) else: set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87) 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) if len(cells) > 1: a += 1 set_clim_same_here(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=95) ################################################# # Flowcharts var_types = ['', 'additiv_cv_adapt_factor_scaled']#'additiv_cv_adapt_factor_scaled', ##additiv_cv_adapt_factor_scaled a_fes = [0.009, 0]#, 0.009 eod_fe = [750, 750]#, 750 ylim = [-0.5, 0.5] c_sigs = [0, 0.9]#, 0.9 grid_left = [[], grid[1, 0]]#, grid[2, 0] ax_ams = [] for g, grid_here in enumerate([grid[0, 2], grid[1, 2]]):#, grid[2, 0] grid_lowpass = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=grid_here, hspace=0.3, height_ratios=[1, 1,1, 0.1]) models = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core') model_params = models[models['cell'] == '2012-07-03-ak-invivo-1'].iloc[0] cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen eod_fr = model_params['EODf'] # .iloc[0] deltat = model_params.pop("deltat") # .iloc[0] v_offset = model_params.pop("v_offset") # .iloc[0] # embed()eod_fr = stack.eod_fr.iloc[0] print(var_types[g] + ' a_fe ' + str(a_fes[g])) noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params( a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types, eod_fe=eod_fe) if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1): grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.2) # if (np.unique(frame.RAM_afe) != 0):grid_left[g] ax_external = plt_time_arrays('red', grid_lowpass2, 1, (frame.RAM_afe)*100, time=time, nr=0) # if (np.unique(frame.RAM_noise) != 0): remove_xticks(ax_external) ax_intrinsic = plt_time_arrays('purple', grid_lowpass2, 1, (frame.RAM_noise)*100, time=time, nr=1) ax_intrinsic.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=ax_intrinsic.transAxes) ax_intrinsic.show_spines('l') ax_external.show_spines('l') # ax_ams.append(axt_p2) #color_timeseries = 'black' #axt_p2.set_xlabel('Time [ms]') #axt_p2.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=axt_p2.transAxes) #ax_ams.append(axt_p2) vers = 'all' elif (len(np.unique(frame.RAM_afe)) > 1): color_timeseries = 'red' nr_plot = 0 print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise))) try: ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, (frame.RAM_afe + frame.RAM_noise)*100, deltat, eod_fr, color1=color_timeseries, lw=1, extract=False) except: print('add up thing') embed() ax_external.show_spines('l') ax_intrinsic = plt.subplot(grid_lowpass[1]) ax_intrinsic.show_spines('l') ax_intrinsic.axhline(0, color='black', lw=0.5) ax_intrinsic.axhline(0, color='purple', lw=0.5) remove_xticks(ax_external) remove_xticks(ax_intrinsic) join_x([ax_intrinsic, ax_external]) join_y([ax_intrinsic, ax_external]) vers = 'first' elif (len(np.unique(frame.RAM_noise)) > 1): color_timeseries = 'purple' nr_plot = 1 print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise))) try: ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, (frame.RAM_afe + frame.RAM_noise)*100, deltat, eod_fr, color1=color_timeseries, lw=1, extract=False) except: print('add up thing') embed() ax_external = plt.subplot(grid_lowpass[0]) ax_external.show_spines('l') ax_intrinsic.show_spines('l') ax_external.axhline(0, color='black', lw=0.5) ax_external.axhline(0, color='red', lw=0.5) join_x([ax_intrinsic,ax_external]) join_y([ax_intrinsic, ax_external]) vers = 'second' ax_external.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_external.transAxes) ax_intrinsic.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_intrinsic.transAxes) remove_xticks(ax_intrinsic) # if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1): ax_external.set_xlabel('') # remove_yticks(ax) ax_ams.append(ax_external) remove_xticks(ax_external) # embed() ax_n, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[2]], time, noise_final_c, deltat, eod_fr, extract=False, color1='grey', lw=1) remove_yticks(ax_n) if g == 1: ax_n.set_xlabel('Time [ms]', labelpad = -0.5) else: remove_xticks(ax_n) ax_n.set_ylim(ylim) if vers == 'first': ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes) ax_n.text(start_pos_modeldata(), 1.1, noise_component_name(), ha='right', color='gray', transform=ax_n.transAxes) elif vers == 'second': ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes) ax_intrinsic.text(start_pos_modeldata(), 1.1, signal_component_name(), ha='right', color='purple', transform=ax_intrinsic.transAxes) ax_n.text(start_pos_modeldata(), 0.9, noise_component_name(), ha='right', color='gray', transform=ax_n.transAxes) else: ax_n.text(start_pos_modeldata(), 0.9, noise_component_name(), ha='right', color='gray', transform=ax_n.transAxes) ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes) ax_intrinsic.text(start_pos_modeldata(), 1.1, signal_component_name(), ha='right', color='purple', transform=ax_intrinsic.transAxes) #embed() set_same_ylim(ax_ams, up='up') # embed() 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], #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([axes[0:3]], xoffs=-3, yoffs=1.6) # ax_ams[3], #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) #, 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) def start_pos_modeldata(): return 1.03 def signal_component_name(): return r'$\xi_{signal}$'#signal noise' def noise_component_name():#$\xi_{noise}$noise_name = return r'$\xi_{noise}$'#'Noise component'#'intrinsic noise' def ypos_x_modelanddata(): return -0.45 if __name__ == '__main__': model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core') cells = model.cell.unique() # embed() params = {'cells': cells} show = True # if show == False: # low CV: cells = ['2012-07-03-ak-invivo-1'] plot_style() default_settings(lw=0.5, column=2, length=3.35) #8.5 redo = False D_extraction_method = ['additiv_cv_adapt_factor_scaled'] # D_extraction_method = ['additiv_visual_d_4_scaled'] ########################## # hier printen wir die table Werte zum kopieren in den Text path = 'print_table_suscept-model_params_suscept_table.csv' if os.path.exists(path): table = pd.read_csv(path) table_printen(table) path = 'print_table_all-model_params_suscept_table.csv' if os.path.exists(path): table = pd.read_csv() print('model big') table_printen(table) #embed() ########################## #embed() model_and_data2(eod_metrice = False, width=0.005, show=show, D_extraction_method=D_extraction_method, label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'