from utils_suseptibility import * def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], save=True, end='all', chose_score='mean_nrs', detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm',ylim = [-1.25, 1.25], dfs = ['m1', 'm2']): plot_style() default_figsize(column=2, length=2.3) grid = gridspec.GridSpec(1, 3, wspace=0.6, bottom = 0.1, width_ratios = [2,1,1], hspace=0.15, top=0.95, left=0.075, right=0.87) axes = [] ################################## # model part ls = '--' lw = 0.5 ax = plt.subplot(grid[0]) axes.append(ax) perc,im,stack_final = plt_model_big(ax, ls = ls, lw = 0.5) fr_waves = 139 fr_noise = 120 f1 = 33 f2 = 139 #embed() ax.plot(fr_noise * f1/fr_waves, fr_noise*f2/fr_waves, 'o', ms = 5, markeredgecolor = 'orange', markerfacecolor="None") ax.plot(-fr_noise * f1 / fr_waves, fr_noise * f2 / fr_waves, 'o', ms = 5, markeredgecolor='pink', markerfacecolor="None") # if len(cbar) > 0: ############################### # data part data_extra = False if data_extra: ax = plt.subplot(grid[0]) axes.append(ax) cell = '2012-07-03-ak-invivo-1' mat_rev,stack_final_rev = load_stack_data_susept(cell, save_name = version_final(), end = '_revQuadrant_') mat, stack = load_stack_data_susept(cell, save_name=version_final(), end = '') #embed() #try: full_matrix = create_full_matrix2(np.array(mat),np.array(mat_rev)) #except: # print('full matrix something') # embed() stack_final = get_axis_on_full_matrix(full_matrix, mat) abs_matrix = np.abs(stack_final) #embed() #if np. abs_matrix, add_nonlin_title, resize_val = rescale_colorbar_and_values(abs_matrix) ax.axhline(0, color = 'white', linestyle = ls, linewidth = lw) ax.axvline(0, color='white', linestyle = ls, linewidth = lw) im = plt_RAM_perc(ax, perc, abs_matrix) cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01) set_clim_same_here([im], mats=[abs_matrix], lim_type='up', nr_clim='perc', clims='', percnr=95) #clim = im.get_clim() #if clim[1]> 1000: #todo: change clim values with different Hz values #embed() cbar.set_label(nonlin_title(add_nonlin_title = ' ['+add_nonlin_title), rotation=90, labelpad=8) set_ylabel_arrow(ax, xpos = -0.07, ypos = 0.97) set_xlabel_arrow(ax, xpos=1, ypos=-0.07) ''' eod_fr, stack_spikes = plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp, title, width)''' cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01) ################# # power spectra data # mean_type = '_MeanTrialsIndexPhaseSort_Min0.25sExcluded_' extract = '' datasets, data_dir = find_all_dir_cells() cells = ['2022-01-28-ah-invivo-1'] # , '2022-01-28-af-invivo-1', '2022-01-28-ab-invivo-1', # '2022-01-27-ab-invivo-1', ] # ,'2022-01-28-ah-invivo-1', '2022-01-28-af-invivo-1', ] append_others = 'apend_others' # '#'apend_others'#'apend_others'#'apend_others'##'apend_others' autodefine = '_DFdesired_' autodefine = 'triangle_diagonal_fr' # ['triangle_fr', 'triangle_diagonal_fr', 'triangle_df2_fr','triangle_df2_eodf''triangle_df1_eodf', ] # ,'triangle_df2_fr''triangle_df1_fr','_triangle_diagonal__fr',] DF2_desired = [-33] DF1_desired = [133] autodefine = '_dfchosen_closest_' autodefine = '_dfchosen_closest_first_' cells = ['2021-08-03-ac-invivo-1'] ##'2021-08-03-ad-invivo-1',,[10, ][5 ] # c1s = [10] # 1, 10, # c2s = [10] minsetting = 'Min0.25sExcluded' c2 = 10 # detections = ['MeanTrialsIndexPhaseSort'] # ['AllTrialsIndex'] # ,'MeanTrialsIndexPhaseSort''DetectionAnalysis''_MeanTrialsPhaseSort' # detections = ['AllTrialsIndex'] # ['_MeanTrialsIndexPhaseSort_Min0.25sExcluded_extended_eod_loc_synch'] extend_trials = '' # 'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'# ok kein Plan was das hier ist # phase_sorting = ''#'PhaseSort' eodftype = '_psdEOD_' concat = '' # 'TrialsConcat' indices = ['_allindices_'] chirps = [ ''] # '_ChirpsDelete3_',,'_ChirpsDelete3_'','','',''#'_ChirpsDelete3_'#''#'_ChirpsDelete3_'#'#'_ChirpsDelete2_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsCache_' extract = '' # '_globalmax_' devs_savename = ['original', '05'] # ['05']##################### # control = pd.read_pickle( # load_folder_name( # 'calc_model') + '/modell_all_cell_no_sinz3_afe0.1__afr1__afj0.1__length1.5_adaptoffsetallall2___stepefish' + step + 'Hz_ratecorrrisidual35__modelbigfit_nfft4096.pkl') if len(cells) < 1: data_dir, cells = load_cells_three(end, data_dir=data_dir, datasets=datasets) cells, p_units_cells, pyramidals = restrict_cell_type(cells, 'p-units') # default_settings(fs=8) start = 'min' # cells = ['2021-08-03-ac-invivo-1'] tag_cells = [] for c, cell in enumerate(cells): counter_pic = 0 contrasts = [c2] tag_cell = [] for c, contrast in enumerate(contrasts): contrast_small = 'c2' contrast_big = 'c1' contrasts1 = [c1] for contrast1 in contrasts1: for devname_orig in devs: datapoints = [1000] for d in datapoints: ################################ # prepare DF1 desired # chose_score = 'auci02_012-auci_base_01' # hier muss das halt stimmen mit der auswahl # hier wollen wir eigntlich kein autodefine # sondern wir wollen so ein diagonal ding haben divergnce, fr, pivot_chosen, max_val, max_x, max_y, mult, DF1_desired, DF2_desired, min_y, min_x, min_val, diff_cut = chose_mat_max_value( DF1_desired, DF2_desired, '', mult_type, eodftype, indices, cell, contrast_small, contrast_big, contrast1, dfs, start, devname_orig, contrast, autodefine=autodefine, cut_matrix='cut', chose_score=chose_score) # chose_score = 'auci02_012-auci_base_01' DF1_desired = DF1_desired # [::-1] DF2_desired = DF2_desired # [::-1] # embed() ####################################### # ROC part # fr, celltype = get_fr_from_info(cell, data_dir[c]) version_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not() b = load_b_public(c, cell, data_dir) mt_sorted = predefine_grouping_frame(b, eodftype=eodftype, cell_name=cell) counter_waves = 0 mt_sorted = mt_sorted[(mt_sorted['c2'] == c2) & (mt_sorted['c1'] == c1)] for gg in range(len(DF1_desired)): # embed() # try: grid0 = gridspec.GridSpecFromSubplotSpec(len(DF1_desired), 1, wspace=0.15, hspace=0.35, subplot_spec=grid[1]) t3 = time.time() # except: # print('time thing') # embed() ax_w = [] ################### # all trials in one 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, concat=True) # except: # print('grouped thing') # embed() ################### # groups sorted by repro tag # todo: evnetuell die tuples gleich hier umspeichern vom csv '' # embed() grouped = mt_sorted.groupby( ['c1', 'c2', 'm1, m2', 'repro_tag_id'], as_index=False) grouped_orig = chose_certain_group(DF1_desired[gg], DF2_desired[gg], grouped, several=True) gr_trials = len(grouped_orig) ################### groups_variants = [grouped_mean] group_mean = [grouped_orig[0][0], grouped_mean] for d, detection in enumerate(detections): mean_type = '_' + detection # + '_' + minsetting + '_' + extend_trials + concat ############################################################## # load plotting arrays arrays, arrays_original, spikes_pure = save_arrays_susept( data_dir, cell, c, b, 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 # embed() 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 xlim = [0, 100] # plt.savefig(r'C:\Users\alexi\OneDrive - bwedu\Präsentations\latex\experimental_protocol.pdf') # embed() fr_end = divergence_title_add_on(group_mean, fr[gg], autodefine) ########################################### stimulus_length = 0.3 deltat = 1 / 40000 eodf = np.mean(group_mean[1].eodf) eod_fr = eodf # embed() a_fr = 1 # embed() eod_fe = eodf + np.mean( group_mean[1].DF2) # data.eodf.iloc[0] + 10 # cell_model.eode.iloc[0] a_fe = group_mean[0][1] / 100 eod_fj = eodf + np.mean( group_mean[1].DF1) # data.eodf.iloc[0] + 50 # cell_model.eodj.iloc[0] a_fj = group_mean[0][0] / 100 variant_cell = 'no' # 'receiver_emitter_jammer' print('f0' + str(eod_fr)) print('f1'+str(eod_fe)) print('f2' + str(eod_fj)) eod_fish_j, time_array, time_fish_r, eod_fish_r, time_fish_e, eod_fish_e, time_fish_sam, eod_fish_sam, stimulus_am, stimulus_sam = extract_waves( variant_cell, '', stimulus_length, deltat, eod_fr, a_fr, a_fe, [eod_fe], 0, eod_fj, a_fj) jammer_name = 'female' cocktail_names = False if cocktail_names: titles = ['receiver ', '+' + 'intruder ', '+' + jammer_name, '+' + jammer_name + '+intruder', []] ##'receiver + ' + 'receiver + receiver else: titles = title_motivation() gs = [0, 1, 2, 3, 4] waves_presents = [['receiver', '', '', 'all'], ['receiver', 'emitter', '', 'all'], ['receiver', '', 'jammer', 'all'], ['receiver', 'emitter', 'jammer', 'all'], ] # ['', '', '', ''],['receiver', '', '', 'all'], # ['receiver', '', 'jammer', 'all'], # ['receiver', 'emitter', '', 'all'],'receiver', 'emitter', 'jammer', symbols = [''] # '$+$', '$-$', '$-$', '$=$', symbols = ['', '', '', '', ''] time_array = time_array * 1000 color0 = 'green' color0_burst = 'darkgreen' color01 = 'green' color02 = 'red' color012 = 'orange' color01_2 = 'purple' colors_am = ['black', 'black', 'black', 'black'] # color01, color02, color012] extracted = [False, True, True, True] extracted2 = [False, False, False, False] printing = True if printing: print(time.time() - t3) # embed() ########################################## # spike response array_chosen = 1 if d == 0: # # embed() # plot the psds ax00 = plt.subplot(grid0[gg]) p_means_all = {} names = ['0', '02', '01', '012'] for j in range(len(arrays)): ######################################## # get the corresponding psds # hier kann man aussuchen welches power spektrum machen haben will f, nfft = get_psds_ROC(array_chosen, arrays, arrays_original, j, mean_type, names, p_means_all) #ax_as.append(ax_a) ps = {} p_means = {} ax_ps = [] color012_minus = 'purple', colors_p = [color0, color02, color01, color012, color02, color01, color012_minus, color0_burst, color0_burst, color0, color0] ax00, fr_isi = plt_psds_ROC(arrays, ax00, ax_ps, cell, colors_p, f, grid0, group_mean, nfft, p_means, p_means_all,ps, 4, spikes_pure,time_array) # arrays, ax00, ax_ps, cell, colors_p, f, grid0, group_mean, nfft, p_means, p_means_all, ps, row,spikes_pure, time_array, axes = [] axes.append(ax_w) ################# # power spectra model #print('finished model_full') fig = plt.gcf() #axes = plt.gca() #fig.tag(axes[::-1], xoffs=-4.5, yoffs=0.4) # ax_ams[3], save_visualization() def load_stack_data_susept(cell, save_name, end = ''): load_name = load_folder_name('calc_RAM') + '/' + save_name+end add = '_cell' + cell +end# str(f) # + '_amp_' + str(amp) #embed() stack_cell = load_data_susept(load_name + '_' + cell + '.pkl', load_name + '_' + cell, add=add, load_version='csv') file_names_exclude = get_file_names_exclude() stack_cell = stack_cell[~stack_cell['file_name'].isin(file_names_exclude)] # if len(stack_cell): file_names = stack_cell.file_name.unique() #embed() file_names = exclude_file_name_short(file_names) cut_off_nr = get_cutoffs_nr(file_names) try: maxs = list(map(float, cut_off_nr)) except: embed() file_names = file_names[np.argmax(maxs)] #embed() stack_file = stack_cell[stack_cell['file_name'] == file_names] amps = [np.min(stack_file.amp.unique())] amps = restrict_punits(cell, amps) amp = np.min(amps)#[0] # for amp in amps: stack_amps = stack_file[stack_file['amp'] == amp] lengths = stack_amps.stimulus_length.unique() trial_nr_double = stack_amps.trial_nr.unique() trial_nr = np.max(trial_nr_double) stack_final = stack_amps[ (stack_amps['stimulus_length'] == np.max(lengths)) & (stack_amps.trial_nr == trial_nr)] mat, new_keys = get_mat_susept(stack_final) return mat,stack_final if __name__ == '__main__': model_full()