update zusammenarbeit
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saschuta
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__pycache__/plotstyle.cpython-39.pyc
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__pycache__/utils_suseptibility.cpython-39.pyc
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@ -7,7 +7,7 @@ import numpy as np
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from utils_all_down import default_settings
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from utils_suseptibility import colors_overview
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from utils_suseptibility import default_figsize, NLI_scorename2,pearson_label, exclude_nans_for_corr, kernel_scatter, \
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plt_burst_modulation_hists, \
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scatter_with_marginals_colorcoded, \
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version_final
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from utils_all import update_cell_names, load_overview_susept, make_log_ticks, p_units_to_show, save_visualization, setting_overview_score
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from scipy import stats
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@ -114,7 +114,7 @@ def data_overview3():
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#ax_here = []
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#axd = plt.subplot(grid_lower_lower[0, c])
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# embed()
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#kernel_histogram(axk, colors[str(cell_type_here)], np.array(x_axis), norm=True, step=0.03, alpha=0.5)
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#embed()
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@ -166,11 +166,12 @@ def data_overview3():
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xlimk = None
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labelpad = 0.5#-1
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cmap, _, y_axis = plt_burst_modulation_hists(axx, axy, var_item_names[v], axs, cell_type_here, x_axis[v],
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frame_file, scores_here[v], ymin=ymin, xmin=xmin,
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burst_fraction_reset=burst_corr_reset, var_item=var_type,
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max_x=max_x[v], xlim=xlimk, x_pos=1, labelpad = labelpad,
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burst_fraction=burst_fraction[c], ha='right')
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cmap, _, y_axis = scatter_with_marginals_colorcoded(var_item_names[v], axs, cell_type_here, x_axis[v],
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frame_file, scores_here[v], axy, axx, ymin=ymin,
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xmin=xmin, burst_fraction_reset=burst_corr_reset,
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var_item=var_type, labelpad=labelpad, max_x=max_x[v],
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xlim=xlimk, x_pos=1, burst_fraction=burst_fraction[c],
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ha='right')
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print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]])))
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print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]])))
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@ -410,7 +411,7 @@ def data_overview3():
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#set_ylim_same()
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#ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
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# embed()
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#counter += 1
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#embed()
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@ -448,7 +449,7 @@ def plt_specific_cells(axs, cell_type_here, cv_name, frame_file, score, marker =
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cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
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# ax = plt.subplot(grid[1, cv_n])
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# todo: hier nur noch die kleinste und größte Amplitude nehmen
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# embed()
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if not marker:
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axs.scatter(frame_file[cv_name].loc[cells_extra], frame_file[score].loc[cells_extra],
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s=9, facecolor="None", edgecolor='black', alpha=0.7, clip_on=False) # colors[str(cell_type_here)]
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@ -467,9 +468,9 @@ def plt_var_axis(ax_j, axls, axss,score_name, burst_fraction, cell_type_here, co
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axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
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counter += 1
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cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here, x_axis[v],
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frame_file, scores_here[v], var_item=var_type,
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burst_fraction=burst_fraction[v])
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cmap, _, y_axis = scatter_with_marginals_colorcoded(var_item_names[v], axs, cell_type_here, x_axis[v],
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frame_file, scores_here[v], axl, axk, var_item=var_type,
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burst_fraction=burst_fraction[v])
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axs.set_ylabel(score_name[v])
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axs.set_xlabel(x_axis_names[v])
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if v in [0, 1]:
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@ -536,11 +537,10 @@ def species_with_both_cells(grid0, cell_types,ax_j, axls, axss, colors, cv_name_
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if c in [0, 2]:
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axk.set_title(species)
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# embed()
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if len(frame_file) > 0:
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axs, x_axis = kernel_scatter(axl, cell_types, axk, axs, ax_j, c, cell_type_here, colors, cv_n, cv_name,
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frame_file, grid0, max_val,
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score, log = log)
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axs, x_axis = kernel_scatter(axl, axk, axs, c, cell_type_here, colors, cv_name,
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frame_file, score, log = log)
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if log:
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axl.set_yscale('log')
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axl.set_yticks_blank()
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@ -49,7 +49,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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plot_style()
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default_figsize(column=2, length=3.1) #.254.75 0.75
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grid = gridspec.GridSpec(2, 5, wspace=0.95, bottom=0.09,
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hspace=0.25, width_ratios = [1,0,1,1,1], left=0.09, right=0.93, top=0.9)
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hspace=0.25, width_ratios = [2,0,2,2,2], left=0.09, right=0.93, top=0.9)
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a = 0
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maxs = []
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@ -64,7 +64,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
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nr = '2'
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# embed()
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# cell_contrasts = ["2013-01-08-aa-invivo-1"]
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# cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
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# cells_triangl_contrast = 1
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@ -84,14 +84,15 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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#################################
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# data cells
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# embed()
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grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 0],
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hspace=hs)
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fr_print = False#True
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#ypos_x_modelanddata()
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nr = 1
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ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width,
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cbar_label=True, eod_metrice = eod_metrice, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
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cbar_label=True, fr_print = fr_print, eod_metrice = eod_metrice, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
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for ax_external in ax_data:
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# ax.set_ylabel(F2_xlabel())
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# remove_xticks(ax)
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@ -123,14 +124,47 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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# for trial in trials:#.009
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trial_nr = 1000000#1000000
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save_names = [
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'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',
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'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',
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'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',
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'calc_RAM_model-2__nfft_whole_power_1_afe_0.025_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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'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',
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'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(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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]
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save_names = [
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'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',
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'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',
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'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',
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'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',
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] #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
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##########
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# Erklärung
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# Ich habe hier 0.009 und nicht 0.25 weil das Modell einen Fehler hat
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# den Stimulus in den Daten habe ich überprüft der tatsächliche stimulus ist 2.3 Prozent
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# sollte aber 2.5 Prozent sein
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# Im Fall von 2.5 Prozent wäre das ein Fehler von 0.36 sonst von 0.39
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# Hier werde ich nun mit dem Fehler von 0.36 verfahren
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# das bedeutet aber das sich den Stimulus zwar mit 0.009 ins Modell reintue später für die
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# Susceptiblitätsberechnung sollte ich ihn aber um den Faktor 0.36 teilen
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bias_factor = 0.36
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save_names = [
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'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',
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'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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'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',
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'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(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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]
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#'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',
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#'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',
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@ -139,7 +173,8 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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tr_name = trial_nr/1000000
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if tr_name == 1:
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tr_name = 1#'$c=1\,\%$','$c=0\,\%$'
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cs = ['$c=1\,\%$','$c=0\,\%$']
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c = 2.5
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cs = ['$c=%.1f$' %(c)+'$\,\%$','$c=0\,\%$']
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titles = ['Model\n$N=11$', 'Model\n$N=%s $' % (tr_name) +'\,million',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million',
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@ -148,6 +183,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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# 'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ '
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ax_model = []
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for s, sav_name in enumerate(save_names):
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try:
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ax_external = plt.subplot(grid[nrs_s[s]])
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@ -167,13 +203,13 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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# full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev))
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# stack_final = get_axis_on_full_matrix(full_matrix, stack)
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# im = plt_RAM_perc(ax, perc, np.abs(stack))
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# embed()
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stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
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if len(stack)> 0:
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add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s],
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width, eod_metrice = eod_metrice, titles_plot=True,
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resize=True, nr = nr)
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resize=True,bias_factor = bias_factor, fr_print = fr_print, nr = nr)
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# if s in [1,3,5]:
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@ -202,14 +238,14 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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if len(cells) > 1:
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a += 1
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set_clim_same_here(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=95)
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set_clim_same(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())
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#################################################
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# Flowcharts
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var_types = ['', 'additiv_cv_adapt_factor_scaled']#'additiv_cv_adapt_factor_scaled',
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##additiv_cv_adapt_factor_scaled
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a_fes = [0.009, 0]#, 0.009
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a_fes = [c/100, 0]#, 0.009
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eod_fe = [750, 750]#, 750
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ylim = [-0.5, 0.5]
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c_sigs = [0, 0.9]#, 0.9
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@ -226,7 +262,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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eod_fr = model_params['EODf'] # .iloc[0]
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deltat = model_params.pop("deltat") # .iloc[0]
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v_offset = model_params.pop("v_offset") # .iloc[0]
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# embed()eod_fr = stack.eod_fr.iloc[0]
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eod_fr = stack.eod_fr.iloc[0]
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print(var_types[g] + ' a_fe ' + str(a_fes[g]))
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noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params(
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a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
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@ -235,7 +271,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
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grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1,
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subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.2)
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subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.05)
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# if (np.unique(frame.RAM_afe) != 0):grid_left[g]
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@ -291,11 +327,14 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
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ax_external = plt.subplot(grid_lowpass[0])
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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_intrinsic.set_yticks_delta(6)
|
||||
ax_external.set_yticks_delta(6)
|
||||
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)
|
||||
@ -305,7 +344,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
|
||||
|
||||
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)
|
||||
@ -334,7 +373,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
|
||||
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],
|
||||
@ -380,7 +419,7 @@ 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
|
||||
|
||||
40001
model_and_data___c_sig_0_a_fe_0.025.csv
Normal file
40001
model_and_data___c_sig_0_a_fe_0.025.csv
Normal file
File diff suppressed because it is too large
Load Diff
40001
model_and_data___c_sig_0_a_fe_2.5.csv
Normal file
40001
model_and_data___c_sig_0_a_fe_2.5.csv
Normal file
File diff suppressed because it is too large
Load Diff
243
model_and_data__spikes_c_sig_0_a_fe_0.025.csv
Normal file
243
model_and_data__spikes_c_sig_0_a_fe_0.025.csv
Normal file
@ -0,0 +1,243 @@
|
||||
,spikes
|
||||
0,0.0091
|
||||
1,0.01665
|
||||
2,0.02315
|
||||
3,0.0287
|
||||
4,0.03725
|
||||
5,0.046900000000000004
|
||||
6,0.0631
|
||||
7,0.07055
|
||||
8,0.07385
|
||||
9,0.08025
|
||||
10,0.08575
|
||||
11,0.09335
|
||||
12,0.1051
|
||||
13,0.11585000000000001
|
||||
14,0.1245
|
||||
15,0.13305
|
||||
16,0.1374
|
||||
17,0.1482
|
||||
18,0.1525
|
||||
19,0.16870000000000002
|
||||
20,0.17725000000000002
|
||||
21,0.1826
|
||||
22,0.1903
|
||||
23,0.19775
|
||||
24,0.2031
|
||||
25,0.21705000000000002
|
||||
26,0.22360000000000002
|
||||
27,0.23225
|
||||
28,0.23875000000000002
|
||||
29,0.2462
|
||||
30,0.25365
|
||||
31,0.2591
|
||||
32,0.2721
|
||||
33,0.2795
|
||||
34,0.2861
|
||||
35,0.29475
|
||||
36,0.3022
|
||||
37,0.3118
|
||||
38,0.31825000000000003
|
||||
39,0.33025000000000004
|
||||
40,0.342
|
||||
41,0.3463
|
||||
42,0.3529
|
||||
43,0.3668
|
||||
44,0.371
|
||||
45,0.37865000000000004
|
||||
46,0.38955
|
||||
47,0.39495
|
||||
48,0.40555
|
||||
49,0.41305000000000003
|
||||
50,0.42400000000000004
|
||||
51,0.4293
|
||||
52,0.438
|
||||
53,0.44225000000000003
|
||||
54,0.453
|
||||
55,0.4627
|
||||
56,0.4746
|
||||
57,0.47985
|
||||
58,0.48425
|
||||
59,0.49175
|
||||
60,0.5004000000000001
|
||||
61,0.5133
|
||||
62,0.5176000000000001
|
||||
63,0.52295
|
||||
64,0.536
|
||||
65,0.54035
|
||||
66,0.5466500000000001
|
||||
67,0.55545
|
||||
68,0.5704
|
||||
69,0.5800500000000001
|
||||
70,0.58975
|
||||
71,0.5961500000000001
|
||||
72,0.60265
|
||||
73,0.6103500000000001
|
||||
74,0.6189
|
||||
75,0.6264000000000001
|
||||
76,0.635
|
||||
77,0.6404000000000001
|
||||
78,0.64895
|
||||
79,0.66515
|
||||
80,0.6695
|
||||
81,0.6738000000000001
|
||||
82,0.68665
|
||||
83,0.69425
|
||||
84,0.70065
|
||||
85,0.7062
|
||||
86,0.7202000000000001
|
||||
87,0.73285
|
||||
88,0.7372500000000001
|
||||
89,0.7416
|
||||
90,0.7512500000000001
|
||||
91,0.75575
|
||||
92,0.7675000000000001
|
||||
93,0.7804500000000001
|
||||
94,0.7867500000000001
|
||||
95,0.79545
|
||||
96,0.8041
|
||||
97,0.81915
|
||||
98,0.8246
|
||||
99,0.8321000000000001
|
||||
100,0.8376
|
||||
101,0.84285
|
||||
102,0.8460500000000001
|
||||
103,0.859
|
||||
104,0.86865
|
||||
105,0.8786
|
||||
106,0.8912500000000001
|
||||
107,0.8979
|
||||
108,0.9043
|
||||
109,0.9118
|
||||
110,0.91605
|
||||
111,0.92145
|
||||
112,0.9356500000000001
|
||||
113,0.9409500000000001
|
||||
114,0.9527
|
||||
115,0.96345
|
||||
116,0.9667
|
||||
117,0.9807
|
||||
118,0.9882000000000001
|
||||
119,0.99595
|
||||
120,1.0023
|
||||
121,1.0098500000000001
|
||||
122,1.01635
|
||||
123,1.026
|
||||
124,1.0379
|
||||
125,1.0463500000000001
|
||||
126,1.05175
|
||||
127,1.06255
|
||||
128,1.0712000000000002
|
||||
129,1.07555
|
||||
130,1.0842
|
||||
131,1.09165
|
||||
132,1.096
|
||||
133,1.1099
|
||||
134,1.11755
|
||||
135,1.12405
|
||||
136,1.1347
|
||||
137,1.14765
|
||||
138,1.1509
|
||||
139,1.15845
|
||||
140,1.17455
|
||||
141,1.18005
|
||||
142,1.18755
|
||||
143,1.1918
|
||||
144,1.1983000000000001
|
||||
145,1.21005
|
||||
146,1.2197500000000001
|
||||
147,1.2273
|
||||
148,1.23715
|
||||
149,1.2457
|
||||
150,1.2511
|
||||
151,1.2576
|
||||
152,1.2694
|
||||
153,1.2780500000000001
|
||||
154,1.28545
|
||||
155,1.2941500000000001
|
||||
156,1.2996
|
||||
157,1.30725
|
||||
158,1.3124500000000001
|
||||
159,1.3190000000000002
|
||||
160,1.3318
|
||||
161,1.3447
|
||||
162,1.3512
|
||||
163,1.3598000000000001
|
||||
164,1.36955
|
||||
165,1.3751
|
||||
166,1.38135
|
||||
167,1.3922
|
||||
168,1.39765
|
||||
169,1.40735
|
||||
170,1.4128
|
||||
171,1.42025
|
||||
172,1.4288500000000002
|
||||
173,1.4353
|
||||
174,1.44395
|
||||
175,1.45365
|
||||
176,1.4622000000000002
|
||||
177,1.47295
|
||||
178,1.48045
|
||||
179,1.487
|
||||
180,1.4998500000000001
|
||||
181,1.5052
|
||||
182,1.51295
|
||||
183,1.51815
|
||||
184,1.5322500000000001
|
||||
185,1.53655
|
||||
186,1.5409000000000002
|
||||
187,1.5537
|
||||
188,1.5645
|
||||
189,1.5732000000000002
|
||||
190,1.57955
|
||||
191,1.58925
|
||||
192,1.59695
|
||||
193,1.60535
|
||||
194,1.6108500000000001
|
||||
195,1.6216000000000002
|
||||
196,1.6269
|
||||
197,1.6356000000000002
|
||||
198,1.6464
|
||||
199,1.6528500000000002
|
||||
200,1.6657000000000002
|
||||
201,1.6701000000000001
|
||||
202,1.67655
|
||||
203,1.6852
|
||||
204,1.6938
|
||||
205,1.70235
|
||||
206,1.7088
|
||||
207,1.72275
|
||||
208,1.7334500000000002
|
||||
209,1.7379
|
||||
210,1.7432500000000002
|
||||
211,1.7509000000000001
|
||||
212,1.7628000000000001
|
||||
213,1.77025
|
||||
214,1.7821
|
||||
215,1.78855
|
||||
216,1.79495
|
||||
217,1.80465
|
||||
218,1.8166
|
||||
219,1.82305
|
||||
220,1.83275
|
||||
221,1.83925
|
||||
222,1.84345
|
||||
223,1.8467
|
||||
224,1.8597000000000001
|
||||
225,1.8682
|
||||
226,1.8780000000000001
|
||||
227,1.89185
|
||||
228,1.89735
|
||||
229,1.90805
|
||||
230,1.91235
|
||||
231,1.9167
|
||||
232,1.9220000000000002
|
||||
233,1.9297000000000002
|
||||
234,1.94255
|
||||
235,1.9532500000000002
|
||||
236,1.963
|
||||
237,1.9672500000000002
|
||||
238,1.98035
|
||||
239,1.9878
|
||||
240,1.9932
|
||||
241,1.99855
|
||||
|
1173
model_and_data__spikes_c_sig_0_a_fe_2.5.csv
Normal file
1173
model_and_data__spikes_c_sig_0_a_fe_2.5.csv
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
Binary file not shown.
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
BIN
model_full.pdf
BIN
model_full.pdf
Binary file not shown.
BIN
model_full.png
BIN
model_full.png
Binary file not shown.
|
Before Width: | Height: | Size: 81 KiB After Width: | Height: | Size: 81 KiB |
164
model_full.py
164
model_full.py
@ -38,7 +38,7 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], save=True, end='al
|
||||
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_rev,stack_final_rev = load_stack_data_susept(cell, redo = True, save_name = version_final(), end = '_revQuadrant_')
|
||||
mat, stack = load_stack_data_susept(cell, save_name=version_final(), end = '')
|
||||
|
||||
new_keys, stack_plot = convert_csv_str_to_float(stack)
|
||||
@ -69,7 +69,7 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], save=True, end='al
|
||||
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)
|
||||
set_clim_same([im], mats=[abs_matrix], lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())
|
||||
|
||||
#clim = im.get_clim()
|
||||
#if clim[1]> 1000:
|
||||
@ -145,7 +145,7 @@ def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], save=True, end='al
|
||||
|
||||
diagonal = 'test_data_cell_2022-01-05-aa-invivo-1'
|
||||
diagonal = 'diagonal1'
|
||||
# embed()
|
||||
|
||||
freq1_ratio = combis[diagonal][0]
|
||||
freq2_ratio = combis[diagonal][1]
|
||||
diagonal = ''
|
||||
@ -212,7 +212,7 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
model_cells = pd.read_csv(load_folder_name('calc_model_core') + "/models_big_fit_d_right.csv")
|
||||
if len(cells) < 1:
|
||||
cells = model_cells.cell.loc[range(cell_start, len(model_cells))]
|
||||
# embed()
|
||||
|
||||
plot_style()
|
||||
fr = float('nan')
|
||||
for cell in cells:
|
||||
@ -264,12 +264,12 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
isi = np.diff(spike_adapted[0])
|
||||
cv0 = np.std(isi) / np.mean(isi)
|
||||
cv1 = np.std(frate) / np.mean(frate)
|
||||
# embed()
|
||||
|
||||
|
||||
fs = 11
|
||||
# for fff, freq2 in enumerate(freqs2):
|
||||
# freq2 = [freq2]
|
||||
# embed()
|
||||
|
||||
for ff, freq1 in enumerate(freqs1):
|
||||
freq1 = [freq1]
|
||||
freq2 = [freqs2[ff]]
|
||||
@ -287,7 +287,7 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
# print(cell )
|
||||
# f_corr = create_beat_corr(np.array([freq1[f1]]), np.array([eod_fr]))
|
||||
# create the second eod_fish1 array analogous to the eod_fish_r array
|
||||
# embed()
|
||||
|
||||
phaseshift_f1, phaseshift_f2 = get_phaseshifts(a_f1, a_f2, phase_right, phaseshift_fr)
|
||||
eod_fish1, time_fish_e = eod_fish_e_generation(time_array, a_f1, freq1, f1)
|
||||
eod_fish2, time_fish_j = eod_fish_e_generation(time_array, a_f2, freq2, f2)
|
||||
@ -299,7 +299,7 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
eod_fr,
|
||||
time_array, a_f1)
|
||||
|
||||
# embed()
|
||||
|
||||
stimulus_orig = stimulus * 1
|
||||
|
||||
# damping variants
|
||||
@ -308,24 +308,24 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
std_dump=0, max_dump=0, range_dump=0)
|
||||
|
||||
stimuli.append(stimulus)
|
||||
# embed()
|
||||
|
||||
cvs, adapt_output, baseline_after, _, rate_adapted[t], rate_baseline_before[t], \
|
||||
rate_baseline_after[t], spikes[t], \
|
||||
stimulus_altered[t], \
|
||||
v_dent_output[t], offset_new, v_mem_output[t], noise_final = simulate(cell, offset,
|
||||
stimulus, f1,
|
||||
stimulus,
|
||||
adaptation_yes_e=f1,
|
||||
**model_params)
|
||||
|
||||
#embed()
|
||||
stimulus_altered_output = np.mean(stimulus_altered, axis=0)
|
||||
# time_var2 = time.time()
|
||||
|
||||
# embed()
|
||||
|
||||
test_stimulus_stability = False
|
||||
# embed()
|
||||
|
||||
# time_model = time_var2 - time_var # 8
|
||||
|
||||
# embed()ax[0, ff]
|
||||
|
||||
spikes_mat = [[]] * len(spikes)
|
||||
pps = [[]] * len(spikes)
|
||||
@ -411,11 +411,11 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
|
||||
# pp_mean = np.log
|
||||
print(freqs_beat)
|
||||
print(labels)
|
||||
plt_peaks_several(labels, freqs_beat, pp_mean, 0,
|
||||
axp, pp_mean, colors, f, add_log=2.5,
|
||||
text_extra=True, ha='center', rel='rel', rot=0, several_peaks=True,
|
||||
exact=False, texts_left=texts_left, add_texts=add_texts,several_peaks_nr=several_peaks_nr,
|
||||
rots=[0, 0, 0, 0,0], ms=14, alphas = [alpha]*len(colors), perc=perc, log=log, clip_on=True) # True
|
||||
plt_peaks_several(freqs_beat, pp_mean, axp, pp_mean, f, labels, 0, colors, ha='center',
|
||||
add_texts=add_texts, texts_left=texts_left, add_log=2.5,
|
||||
rots=[0, 0, 0, 0, 0], several_peaks_nr=several_peaks_nr, exact=False,
|
||||
text_extra=True, perc_peaksize=perc, rel='rel', alphas=[alpha] * len(colors),
|
||||
ms=14, clip_on=True, several_peaks=True, log=log) # True
|
||||
|
||||
axp.plot(f, pp_mean, color='black', zorder=0) # 0.45
|
||||
axp.set_xlim(xlim)
|
||||
@ -452,7 +452,7 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
ps = []
|
||||
results_diff = pd.DataFrame()
|
||||
for g in range(len(DF2_frmult)):
|
||||
# embed()
|
||||
|
||||
freq2_ratio = DF2_frmult[g]
|
||||
freq1_ratio = DF1_frmult[g]
|
||||
#embed()
|
||||
@ -492,7 +492,7 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
model_params = model_cells[model_cells['cell'] == cell_here].iloc[0]
|
||||
# model_params = model_cells.iloc[cell_nr]
|
||||
|
||||
# embed()
|
||||
|
||||
eod_fr = model_params['EODf'] # .iloc[0]
|
||||
|
||||
offset = model_params.pop('v_offset')
|
||||
@ -548,7 +548,7 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
|
||||
|
||||
#fig = plt.figure(figsize=(11.5, 5.4))
|
||||
# embed()
|
||||
|
||||
for run in range(runs):
|
||||
print(run)
|
||||
t1 = time.time()
|
||||
@ -563,23 +563,28 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
power_here = 'sinz'
|
||||
|
||||
cvs, adapt_output, baseline_after_b, _, rate_adapted_b, rate_baseline_before_b, rate_baseline_after_b, \
|
||||
spikes_base[t], _, _, offset_new, _,noise_final = simulate(cell, offset, stimulus, f1,
|
||||
nr=n,
|
||||
spikes_base[t], _, _, offset_new, _,noise_final = simulate(cell, offset, stimulus,
|
||||
deltat=deltat,
|
||||
adaptation_variant=adapt_offset,
|
||||
adaptation_yes_j=f2,
|
||||
adaptation_yes_e=f1,
|
||||
adaptation_yes_t=t,
|
||||
adaptation_upper_tol=upper_tol,
|
||||
adaptation_lower_tol=lower_tol,
|
||||
power_variant=power_here,
|
||||
adapt_offset=adapt_offset,
|
||||
add=add, alpha=alpha,
|
||||
power_alpha=alpha,
|
||||
power_nr=n,
|
||||
reshuffle=reshuffled,
|
||||
lower_tol=lower_tol,
|
||||
upper_tol=upper_tol,
|
||||
v_exp=v_exp, exp_tau=exp_tau,
|
||||
dent_tau_change=dent_tau_change,
|
||||
alter_taus=constant_reduction,
|
||||
exponential=exponential,
|
||||
exponential_mult=1,
|
||||
exponential_plus=plus,
|
||||
exponential_slope=slope,
|
||||
sig_val=sig_val, j=f2,
|
||||
deltat=deltat, t=t,
|
||||
tau_change_choice=constant_reduction,
|
||||
tau_change_val=dent_tau_change,
|
||||
sigmoidal_mult=1,
|
||||
sigmoidal_plus=plus,
|
||||
sigmoidal_slope=slope,
|
||||
sigmoidal_add=add,
|
||||
sigmoidal_sigmoidal_val=sig_val,
|
||||
LIF_exponential=exponential,
|
||||
LIF_exponential_tau=exp_tau,
|
||||
LIF_expontential__v=v_exp,
|
||||
**model_params)
|
||||
|
||||
if t == 0:
|
||||
@ -617,8 +622,8 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
eod_fr + third_fr] # , eod_fr - two_third_fr, third_fr,two_third_fr,third_eodf, eod_fr - third_eodf,two_third_eodf, eod_fr - two_third_eodf, ]
|
||||
#embed()
|
||||
sampling_rate = 1 / deltat
|
||||
base_cut, mat_base, smoothed0, mat0 = find_base_fr2(sampling_rate, spikes_base, deltat,
|
||||
stimulus_length, time_array, dev=dev)
|
||||
base_cut, mat_base, smoothed0, mat0 = find_base_fr2(spikes_base, deltat,
|
||||
stimulus_length, dev=dev)
|
||||
#fr = np.mean(base_cut)
|
||||
frate, isis_diff = ISI_frequency(time_array, spikes_base[0], fill=0.0)
|
||||
isi = np.diff(spikes_base[0])
|
||||
@ -638,30 +643,35 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
t1 = time.time()
|
||||
phaseshift_f1, phaseshift_f2 = get_phaseshifts(a_f1, a_f2, phase_right, phaseshift_fr)
|
||||
eod_fish1, time_fish_e = eod_fish_e_generation(time_array, a_f1, freq1, f1,
|
||||
nfft_for_morph, phaseshift_f1,
|
||||
cell_recording, fish_morph_harmonics_var,
|
||||
zeros, mimick, fish_emitter, sampling,
|
||||
stimulus_length, thistype='emitter')
|
||||
phaseshift_f1, sampling,
|
||||
stimulus_length, nfft_for_morph,
|
||||
cell_recording,
|
||||
fish_morph_harmonics_var, zeros,
|
||||
mimick, fish_emitter,
|
||||
thistype='emitter')
|
||||
eod_fish2, time_fish_j = eod_fish_e_generation(time_array, a_f2, freq2, f2,
|
||||
nfft_for_morph, phaseshift_f2,
|
||||
cell_recording, fish_morph_harmonics_var,
|
||||
zeros, mimick, fish_jammer, sampling,
|
||||
stimulus_length, thistype='jammer')
|
||||
phaseshift_f2, sampling,
|
||||
stimulus_length, nfft_for_morph,
|
||||
cell_recording,
|
||||
fish_morph_harmonics_var, zeros,
|
||||
mimick, fish_jammer,
|
||||
thistype='jammer')
|
||||
|
||||
eod_stimulus = eod_fish1 + eod_fish2
|
||||
|
||||
v_mems, offset_new, mat01, mat02, mat012, smoothed01, smoothed02, smoothed012, stimulus_01, stimulus_02, stimulus_012, mat05_01, spikes_01, mat05_02, spikes_02, mat05_012, spikes_012 = get_arrays_for_three(
|
||||
cell, a_f2, a_f1,
|
||||
SAM, eod_stimulus, eod_fish_r, freq2, eod_fish1, eod_fish_r,
|
||||
eod_fish2, stimulus_length,
|
||||
baseline_with_wave_damping, baseline_without_wave,
|
||||
offset, model_params, n, variant, t, adapt_offset,
|
||||
upper_tol, lower_tol, dent_tau_change, constant_reduction,
|
||||
exponential, plus, slope, add,
|
||||
deltat, alpha, sig_val, v_exp, exp_tau, f2,
|
||||
trials_nr, time_array,
|
||||
f1, freq1, damping_type,
|
||||
gain, eod_fr, damping, us_name, dev=dev, reshuffle=reshuffled)
|
||||
cell, a_f2, a_f1, SAM, eod_stimulus, eod_fish_r, freq2, eod_fish1, eod_fish2,
|
||||
stimulus_length, offset, model_params, n, 'sinz', adapt_offset, add, deltat,
|
||||
f2, trials_nr, time_array, f1, freq1, eod_fr, reshuffle=reshuffled, dev=dev)
|
||||
#cell, a_f2, a_f1,
|
||||
#SAM, eod_stimulus, eod_fish_r, freq2, eod_fish1, eod_fish2, stimulus_length,
|
||||
#baseline_with_wave_damping, offset, model_params, n, variant, adapt_offset,
|
||||
#upper_tol, lower_tol, dent_tau_change, constant_reduction,
|
||||
#exponential, plus, slope, add,
|
||||
#deltat, alpha, sig_val, v_exp, exp_tau, f2,
|
||||
#trials_nr, time_array,
|
||||
#f1, freq1, damping_type,
|
||||
#gain, eod_fr, damping, us_name, dev=dev, reshuffle=reshuffled)
|
||||
if printing:
|
||||
print('Generation process' + str(time.time() - t1))
|
||||
|
||||
@ -683,21 +693,21 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
|
||||
##################################
|
||||
# power spectrum
|
||||
# embed()
|
||||
|
||||
if dev_spikes == 'original':
|
||||
#nfft = 2 ** 15
|
||||
# embed()
|
||||
|
||||
p0, p02, p01, p012, fs = calc_ps(nfft, [np.mean(mat012, axis=0)],
|
||||
[np.mean(mat01, axis=0)],
|
||||
[np.mean(mat02, axis=0)],
|
||||
[np.mean(mat0, axis=0)],
|
||||
test=False, sampling_rate=sampling_rate)
|
||||
sampling_rate=sampling_rate)
|
||||
|
||||
else:
|
||||
#nfft = 2 ** 15
|
||||
p0, p02, p01, p012, fs = calc_ps(nfft, smoothed012,
|
||||
smoothed01, smoothed02, smoothed0,
|
||||
test=False, sampling_rate=sampling_rate)
|
||||
sampling_rate=sampling_rate)
|
||||
|
||||
# pp_mean = np.log
|
||||
ps.append(p012)
|
||||
@ -719,7 +729,7 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
if log == 'log':
|
||||
#p012 = 10 * np.log10(ps[j] / np.max(ps))
|
||||
#embed()
|
||||
p012 = log_calc_psd(ax, 'green', [], log, ps[j], np.max(ps))
|
||||
p012 = log_calc_psd(log, ps[j], np.max(ps))
|
||||
else:
|
||||
p012 = ps[j]
|
||||
|
||||
@ -749,8 +759,8 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled',af_2 = 0.1, datapoints=
|
||||
'DF2_H2', '|DF1-DF2|', '|DF1+DF2|', 'baseline']
|
||||
marker = markers[j]#'DF1_H1','DF1_H4',
|
||||
#embed()
|
||||
plt_peaks_several(labels, freqs, p0_mean, 0, ax,
|
||||
p0_mean , colors, fs, emb = False, exact = False, marker = marker, log = log, perc = 0.08, add_log = 2.5, ms = ms, clip_on = clip_on)
|
||||
plt_peaks_several(freqs, p0_mean, ax, p0_mean, fs, labels, 0, colors, emb=False, marker=marker, add_log=2.5,
|
||||
exact=False, perc_peaksize=0.08, ms=ms, clip_on=clip_on, log=log)
|
||||
ax.set_xlim(0, 300)
|
||||
#ax.set_ylim(0 - 20,
|
||||
# np.max(np.max(ps)) + 70) # np.min(np.min(p0_means))
|
||||
@ -859,7 +869,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
|
||||
DF1_desired = df1 # [::-1]
|
||||
DF2_desired = df2 # [::-1]
|
||||
# embed()
|
||||
|
||||
|
||||
#######################################
|
||||
# ROC part
|
||||
@ -883,7 +893,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
choices = [[[0, 1, 2,3, 6]] * 6, [[0, 1, 2, 3, 4]] * 6]
|
||||
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)
|
||||
@ -905,14 +915,14 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
concat=True)
|
||||
# mt_sorted['m1, m2']
|
||||
|
||||
# embed()
|
||||
|
||||
# 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)
|
||||
@ -932,13 +942,13 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
##############################################################
|
||||
# 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,
|
||||
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
|
||||
# embed()
|
||||
|
||||
try:
|
||||
check_var_substract_method(spikes_pure)
|
||||
except:
|
||||
@ -953,7 +963,7 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
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)
|
||||
|
||||
###########################################
|
||||
@ -961,9 +971,9 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
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
|
||||
@ -1018,13 +1028,13 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
if printing:
|
||||
print(time.time() - t3)
|
||||
|
||||
# embed()
|
||||
|
||||
|
||||
##########################################
|
||||
# spike response
|
||||
array_chosen = 1
|
||||
if d == 0: #
|
||||
# embed()
|
||||
|
||||
# plot the psds
|
||||
|
||||
p_means_all = {}
|
||||
@ -1097,12 +1107,12 @@ def plt_data_full_model(c1, chose_score, detections, devs, dfs, end, grid, mult_
|
||||
return DF1_desired, DF2_desired, fr, eod_fr, arrays_len
|
||||
|
||||
|
||||
def load_stack_data_susept(cell, save_name, end = ''):
|
||||
def load_stack_data_susept(cell, save_name, end = '', redo = False):
|
||||
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')
|
||||
load_version='csv', redo = redo)
|
||||
file_names_exclude = get_file_names_exclude()
|
||||
stack_cell = stack_cell[~stack_cell['file_name'].isin(file_names_exclude)]
|
||||
# if len(stack_cell):
|
||||
|
||||
33556
model_full_05_012.csv
33556
model_full_05_012.csv
File diff suppressed because it is too large
Load Diff
35292
model_full_05_base_0.csv
35292
model_full_05_base_0.csv
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,91 +1,87 @@
|
||||
,1,0
|
||||
0,2.355000000535088,15.005000000250956
|
||||
1,15.530000000581655,16.480000000278604
|
||||
2,17.055000000255692,18.205000000357185
|
||||
3,19.93000000038666,21.730000000438675
|
||||
4,21.555000000262968,34.605000000787925
|
||||
5,36.78000000036337,37.55500000084322
|
||||
6,38.23000000011308,39.18000000071953
|
||||
7,39.75500000069661,52.90500000051435
|
||||
8,56.05500000001558,55.705000000720986
|
||||
9,57.55500000032117,57.255000000672965
|
||||
10,59.07999999999521,58.75500000006906
|
||||
11,62.28000000010144,75.10500000085341
|
||||
12,75.73000000047688,76.6050000002495
|
||||
13,77.20500000050453,78.2800000006817
|
||||
14,80.17999999992827,93.25500000073117
|
||||
15,82.00500000020912,94.70500000048088
|
||||
16,95.35500000038229,97.68000000081412
|
||||
17,98.2550000007912,99.43000000026115
|
||||
18,99.90500000003595,115.63000000028734
|
||||
19,116.15500000061803,117.13000000059293
|
||||
20,117.65500000001413,118.68000000054491
|
||||
21,119.30500000016838,133.830000000721
|
||||
22,123.98000000030225,135.2800000004707
|
||||
23,135.68000000032117,136.78000000077628
|
||||
24,137.23000000027315,139.93000000032663
|
||||
25,140.1550000000505,154.7800000008054
|
||||
26,141.8800000001291,156.55500000053036
|
||||
27,155.33000000050453,159.3050000001811
|
||||
28,158.23000000000394,172.9050000004052
|
||||
29,159.8300000005118,175.93000000038484
|
||||
30,176.23000000003304,177.45500000005887
|
||||
31,177.73000000033863,179.155000000769
|
||||
32,179.329999999937,195.230000000315
|
||||
33,182.38000000019457,196.73000000062058
|
||||
34,195.78000000001413,199.85500000080248
|
||||
35,197.40499999989044,213.43000000074863
|
||||
36,200.38000000022367,214.90500000077628
|
||||
37,202.3050000007068,217.88000000020003
|
||||
38,217.08000000035173,219.73000000075882
|
||||
39,218.55500000037938,235.85500000086068
|
||||
40,220.1300000006093,237.32999999997884
|
||||
41,236.35500000000394,238.8800000008403
|
||||
42,237.88000000058747,254.08000000066278
|
||||
43,239.48000000018584,255.58000000005887
|
||||
44,244.27999999989044,257.0550000000865
|
||||
45,255.88000000061658,260.1800000002684
|
||||
46,257.48000000021494,274.95500000082285
|
||||
47,260.38000000062385,276.5550000004212
|
||||
48,262.1300000000709,278.2800000004998
|
||||
49,277.0800000007519,293.0300000007763
|
||||
50,278.5300000005016,296.0050000002
|
||||
51,280.05500000017565,297.555000000152
|
||||
52,296.4550000006064,299.15500000065987
|
||||
53,297.9550000000025,314.280000000558
|
||||
54,299.5300000002324,315.75500000058565
|
||||
55,314.7300000000549,317.1800000000574
|
||||
56,316.28000000000685,321.7300000006206
|
||||
57,317.85500000023677,333.7050000000589
|
||||
58,320.73000000036774,335.20500000036446
|
||||
59,335.579999999937,338.15500000041976
|
||||
60,337.1550000001669,339.88000000049834
|
||||
61,338.6550000004725,353.20500000039357
|
||||
62,340.30500000062676,356.20500000009525
|
||||
63,356.5300000000214,357.70500000040084
|
||||
64,358.0799999999734,374.55500000037756
|
||||
65,359.68000000048124,377.0549999999774
|
||||
66,362.93000000023386,380.23000000071517
|
||||
67,376.1050000002804,393.70500000045905
|
||||
68,377.7800000007126,395.2300000001331
|
||||
69,380.63000000056564,398.1800000001884
|
||||
70,395.73000000018584,399.78000000069625
|
||||
71,398.6050000003168,415.080000000721
|
||||
72,400.2300000001931,417.6299999999672
|
||||
73,415.35500000009125,419.255000000753
|
||||
74,416.8300000001189,434.3550000003732
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||||
75,418.3300000004245,435.8050000001229
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||||
76,422.8050000001538,437.28000000015055
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||||
77,436.25500000052926,453.805000000152
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||||
78,437.7800000002033,455.3050000004576
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||||
79,440.70499999998066,458.23000000023495
|
||||
80,442.5300000002615,459.8050000004649
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||||
81,455.955000000359,473.4800000006133
|
||||
82,458.80500000021203,476.3050000001884
|
||||
83,460.43000000008834,477.9300000000647
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||||
84,476.8300000005191,479.53000000057256
|
||||
85,478.2800000002688,494.5550000002684
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||||
86,479.8550000004987,495.9800000006497
|
||||
87,484.7800000006835,497.380000000753
|
||||
88,495.0800000005991,
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||||
89,496.6300000005511,
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||||
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||||
0,5.360000000375287,3.6000000003509633,5.439999999890446
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||||
1,6.9100000003272655,5.150000000302942,7.064999999766755
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||||
2,28.034999999628774,6.5749999997747075,26.89000000007671
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||||
3,29.509999999656422,8.299999999853288,28.31500000045797
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||||
4,31.210000000366556,12.724999999936234,29.739999999929736
|
||||
5,34.28499999999257,29.175000000013355,34.21499999965907
|
||||
6,36.98499999999694,30.750000000243276,35.765000000520544
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||||
7,43.310000000285065,35.04999999984601,37.4149999997653
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||||
8,58.38499999962732,36.59999999979799,58.16500000035465
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||||
9,61.23500000038984,42.7999999996059,59.6400000003823
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||||
10,64.23500000009153,59.30000000023891,61.34000000018294
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||||
11,65.88500000024578,60.89999999983728,65.69000000034156
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||||
12,88.18499999987762,65.2499999999959,67.26499999966198
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||||
13,89.70999999955166,66.75000000030148,88.31499999994865
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||||
14,91.45999999990818,68.64999999959717,89.76499999969836
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15,94.73499999993874,89.34999999963064,91.39000000048416
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||||
16,97.10999999996784,90.89999999958262,94.7649999998075
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||||
17,118.16000000025451,95.32499999966556,97.59000000029208
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||||
18,119.73499999957494,96.8500000002491,117.14000000027318
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||||
19,121.30999999980486,98.49999999949385,118.63999999966927
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||||
20,124.36000000006243,119.37499999965394,120.06500000005053
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||||
21,126.01000000021668,120.90000000023747,123.1150000003081
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||||
22,127.68499999973938,124.00000000014143,125.96500000016113
|
||||
23,148.43500000032873,125.65000000029568,147.1899999996649
|
||||
24,150.06000000020504,127.14999999969177,148.66499999969255
|
||||
25,154.46000000001004,134.67499999967868,150.06499999979587
|
||||
26,156.03500000023996,149.6250000003597,154.69000000028336
|
||||
27,157.58500000019194,151.32500000016034,156.2149999999574
|
||||
28,178.48499999972046,155.5750000001167,174.49000000031538
|
||||
29,180.03499999967244,157.07499999951278,178.6650000003474
|
||||
30,184.48500000003332,163.14999999974998,180.1150000000971
|
||||
31,186.0349999999853,179.650000000383,181.7900000005293
|
||||
32,187.58499999993728,181.37499999955207,186.11500000040996
|
||||
33,208.58499999966807,185.57499999986203,187.84000000048854
|
||||
34,210.08499999997366,187.09999999953607,208.9400000004216
|
||||
35,212.38500000007843,189.05000000029713,210.28999999996904
|
||||
36,216.03499999973064,194.75000000000318,211.9400000001233
|
||||
37,217.58499999968262,209.77499999969905,216.1649999998017
|
||||
38,238.68499999961568,211.32499999965103,217.86500000051183
|
||||
39,240.21000000019922,215.77500000001191,238.83999999996468
|
||||
40,241.8849999997219,217.2750000003175,240.34000000027027
|
||||
41,246.05999999975393,223.27499999972088,241.91500000050019
|
||||
42,247.6099999997059,239.87499999964666,245.0399999997726
|
||||
43,268.76000000019485,241.62500000000318,246.54000000007818
|
||||
44,270.2849999998689,245.8499999996816,267.59000000036485
|
||||
45,271.88500000037675,247.37500000026512,269.0650000003925
|
||||
46,276.2100000002574,249.07500000006576,270.5150000001422
|
||||
47,277.6850000002851,269.89999999966994,274.98999999987154
|
||||
48,298.90999999978885,271.39999999997553,276.56500000010146
|
||||
49,300.35999999953856,275.79999999978054,297.61500000038814
|
||||
50,301.9600000000464,277.3499999997325,299.11499999978423
|
||||
51,305.010000000304,279.10000000008904,300.5400000001655
|
||||
52,306.56000000025597,300.0249999998955,303.86499999984244
|
||||
53,308.1349999995764,301.54999999956954,306.6150000004027
|
||||
54,329.00999999973646,306.02500000020837,308.3150000002033
|
||||
55,330.51000000004206,307.52499999960446,329.16500000008546
|
||||
56,335.1100000002516,309.24999999968304,330.61499999983516
|
||||
57,336.58500000027925,330.09999999956517,332.2399999997115
|
||||
58,338.1849999998776,331.6250000001487,335.4399999998177
|
||||
59,359.2350000001643,334.6999999997747,337.0150000000476
|
||||
60,360.8600000000406,336.3000000002826,359.1649999998308
|
||||
61,365.0099999997947,337.7500000000323,360.6650000001364
|
||||
62,366.55999999974665,344.0500000000425,362.31500000029064
|
||||
63,368.10999999969863,360.2750000003466,365.3649999996387
|
||||
64,374.3349999997845,361.8250000002986,366.9650000001466
|
||||
65,389.4599999996826,366.0999999996234,388.01500000043325
|
||||
66,392.159999999687,367.64999999957536,389.41500000053657
|
||||
67,395.2849999998689,373.89999999993915,390.8650000002863
|
||||
68,396.88500000037675,390.39999999966267,392.5150000004405
|
||||
69,398.4100000000508,394.85000000002356,396.7899999997653
|
||||
70,419.63499999955457,396.3749999996976,398.61500000004617
|
||||
71,422.2599999996346,397.8750000000032,418.2399999999516
|
||||
72,425.45999999974083,402.6250000000614,419.8649999998279
|
||||
73,426.91000000040003,420.72500000029277,421.3150000004871
|
||||
74,428.43500000007407,425.12500000009777,425.5899999998119
|
||||
75,435.960000000061,426.62499999949387,427.09000000011747
|
||||
76,450.8349999999082,428.07500000015307,449.4899999999516
|
||||
77,452.4100000001381,433.97500000026366,450.9649999999792
|
||||
78,455.51000000004206,435.9749999997616,452.66499999977987
|
||||
79,457.18499999956475,454.9499999997165,455.6899999997595
|
||||
80,458.68499999987034,456.4999999996685,457.215000000343
|
||||
81,480.8100000002851,457.97499999969614,479.56500000053074
|
||||
82,482.3849999996055,461.324999999651,481.06499999992684
|
||||
83,485.3850000002167,,482.91500000048563
|
||||
84,486.98499999981505,,
|
||||
85,488.60999999969135,,
|
||||
|
||||
|
@ -1,72 +1,71 @@
|
||||
,1,0
|
||||
0,0.09999999997489795,7.850000000416912
|
||||
1,8.849999999938518,16.800000000785076
|
||||
2,12.225000000171349,22.82500000046639
|
||||
3,21.175000000539512,28.925000000072032
|
||||
4,26.975000000447835,37.925000000086584
|
||||
5,36.25000000079126,45.37500000014916
|
||||
6,42.07500000006803,49.95000000008076
|
||||
7,48.150000000305226,59.0000000006512
|
||||
8,54.15000000061809,64.82500000083746
|
||||
9,63.10000000007676,75.42500000045038
|
||||
10,69.17500000031396,77.12500000025102
|
||||
11,75.22500000027321,87.60000000029322
|
||||
12,81.15000000066175,95.0000000007094
|
||||
13,93.05000000017571,101.05000000066866
|
||||
14,96.27500000055988,107.25000000047658
|
||||
15,105.12500000072578,115.97500000016225
|
||||
16,111.32500000053369,122.02500000012151
|
||||
17,120.22500000034597,131.02500000013606
|
||||
18,126.32499999995161,137.02500000044893
|
||||
19,133.9500000001408,143.07500000040818
|
||||
20,136.77500000062537,149.15000000064538
|
||||
21,145.72500000008404,158.32500000078653
|
||||
22,156.09999999992397,167.1000000001186
|
||||
23,162.17500000016116,173.15000000007785
|
||||
24,169.9000000005526,179.2000000000371
|
||||
25,174.22500000043328,185.15000000070359
|
||||
26,180.30000000067048,191.5250000006381
|
||||
27,189.25000000012915,203.07500000080836
|
||||
28,195.27500000071996,209.10000000048967
|
||||
29,203.0000000002019,216.67500000012296
|
||||
30,210.7499999999618,224.1000000008171
|
||||
31,219.27500000015243,230.02500000029613
|
||||
32,225.57500000016262,233.3000000003267
|
||||
33,234.27500000047985,242.2750000000633
|
||||
34,240.30000000016116,249.8500000006061
|
||||
35,246.32500000075197,255.72500000043874
|
||||
36,252.4250000003576,266.25000000012733
|
||||
37,258.5000000005948,275.22500000077343
|
||||
38,267.4750000003314,278.2250000004751
|
||||
39,276.475000000346,284.3000000007123
|
||||
40,282.5749999999516,290.4750000002423
|
||||
41,288.4250000004158,297.8250000001026
|
||||
42,294.5500000002994,308.32500000042273
|
||||
43,303.3499999999094,311.5500000008069
|
||||
44,306.45000000072287,317.225000000235
|
||||
45,315.5500000000302,329.4250000003558
|
||||
46,324.5750000003227,334.0000000002874
|
||||
47,330.7000000002063,339.8500000007516
|
||||
48,338.1750000005468,344.4250000006832
|
||||
49,344.05000000037944,354.9500000003718
|
||||
50,351.52500000071996,362.4250000007123
|
||||
51,357.6250000003256,371.375000000171
|
||||
52,366.4750000004915,376.0750000005828
|
||||
53,368.2750000004944,383.40000000016516
|
||||
54,378.6250000000564,389.6750000008069
|
||||
55,387.6500000003489,397.00000000038926
|
||||
56,393.6750000000302,407.47500000043146
|
||||
57,399.7500000002674,410.65000000025975
|
||||
58,411.4999999999327,419.45000000077926
|
||||
59,413.2250000000113,422.8250000001026
|
||||
60,423.5500000002048,433.05000000009386
|
||||
61,426.6750000003867,434.95000000029904
|
||||
62,435.6250000007549,446.5000000004693
|
||||
63,446.20000000008986,449.7000000005755
|
||||
64,451.200000000199,458.57500000010987
|
||||
65,459.6500000004653,464.7250000002714
|
||||
66,462.75000000036925,473.45000000086657
|
||||
67,471.8500000005861,479.6500000006745
|
||||
68,479.5000000001437,485.7250000000022
|
||||
69,485.4000000002543,494.85000000049695
|
||||
70,492.7500000001146,
|
||||
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|
||||
0,4.374999999527063,0.8000000000265572,3.375000000005457
|
||||
1,12.3249999996915,9.94999999988977,9.649999999737702
|
||||
2,19.900000000234286,15.899999999646752,17.150000000356158
|
||||
3,25.649999999586726,22.12499999973261,24.5250000004944
|
||||
4,31.525000000328873,31.100000000378714,33.44999999967513
|
||||
5,40.77500000039436,38.499999999885404,39.524999999912325
|
||||
6,46.55000000002474,44.67500000032487,48.62500000012915
|
||||
7,52.64999999963038,50.700000000006185,53.24999999970714
|
||||
8,61.674999999922875,58.0000000002201,62.050000000226646
|
||||
9,72.19999999961146,65.67500000005566,72.6750000001175
|
||||
10,79.62500000030559,73.2250000003205,78.69999999979882
|
||||
11,85.72499999991123,82.09999999985484,87.6000000005206
|
||||
12,94.62499999972351,88.25000000001637,90.75000000007094
|
||||
13,99.79999999995925,95.87500000020555,96.7500000003838
|
||||
14,106.87500000040018,104.67499999981555,108.67500000017571
|
||||
15,115.67500000001019,106.3749999996162,111.65000000050895
|
||||
16,117.57500000021537,115.27500000033797,120.67499999989195
|
||||
17,127.87500000013097,121.27499999974134,126.7249999998512
|
||||
18,133.74999999996362,130.59999999973115,132.8000000000884
|
||||
19,142.74999999997817,136.39999999963948,138.95000000024993
|
||||
20,146.09999999993306,142.549999999801,147.87500000034015
|
||||
21,156.45000000040454,149.77500000009059,155.50000000052933
|
||||
22,160.874999999578,160.600000000386,161.4999999999327
|
||||
23,171.62499999994907,169.37499999971806,169.09999999984393
|
||||
24,177.47500000041327,172.60000000010223,179.42500000003747
|
||||
25,182.24999999983993,181.42499999999018,183.97499999969114
|
||||
26,188.17500000022847,186.22499999969477,191.74999999972897
|
||||
27,197.1000000003187,193.44999999998436,198.97500000001855
|
||||
28,206.0750000000553,202.40000000035252,207.9750000000331
|
||||
29,212.12500000001455,208.5250000002361,211.04999999965912
|
||||
30,218.10000000004948,215.97500000029868,223.0749999996533
|
||||
31,230.0999999997657,222.32499999995525,229.00000000004184
|
||||
32,233.17500000030122,231.07499999991887,235.0500000000011
|
||||
33,242.1249999997599,235.7249999997748,241.0749999996824
|
||||
34,245.2499999999418,241.72500000008768,250.07499999969696
|
||||
35,254.37499999952706,250.599999999622,256.32500000006075
|
||||
36,263.1499999997686,253.92500000020846,265.2000000005046
|
||||
37,264.9250000004031,265.800000000354,271.2250000001859
|
||||
38,275.3999999995358,272.0750000000862,280.17499999964457
|
||||
39,279.9750000003769,277.8499999997166,287.900000000036
|
||||
40,291.7749999996886,285.3749999997035,296.699999999646
|
||||
41,293.4750000003987,290.15000000003965,298.3250000004318
|
||||
42,299.4749999998021,296.0249999998723,307.3250000004464
|
||||
43,308.324999999968,306.7750000002434,313.27500000020336
|
||||
44,315.87500000023283,316.850000000386,322.4999999999909
|
||||
45,323.4499999998661,320.1000000001386,331.3999999998032
|
||||
46,329.47499999954744,328.9500000003045,337.425000000394
|
||||
47,335.62499999970896,335.09999999955653,343.47500000035325
|
||||
48,346.3000000001557,344.124999999849,352.6500000004944
|
||||
49,350.8249999995314,352.949999999737,358.4250000001248
|
||||
50,359.69999999997526,360.69999999949687,361.70000000015534
|
||||
51,367.2500000002401,365.19999999950414,372.12499999964166
|
||||
52,371.6500000000451,374.2750000003525,379.70000000018445
|
||||
53,382.09999999980937,381.8000000003394,387.34999999974207
|
||||
54,386.55000000017026,389.0750000002754,394.62499999967804
|
||||
55,392.57499999985157,395.17499999988104,405.05000000007385
|
||||
56,398.92499999950815,404.1250000002492,406.5749999997479
|
||||
57,409.1500000004089,413.17499999991014,412.7249999999094
|
||||
58,415.29999999966094,419.175000000223,418.82500000042455
|
||||
59,422.82499999964784,426.77500000013424,427.67499999968095
|
||||
60,431.775000000016,435.6749999999465,436.92499999974643
|
||||
61,437.8999999998996,437.4000000000251,445.97500000031687
|
||||
62,445.34999999996217,447.8750000000673,454.7749999999269
|
||||
63,452.8250000003027,455.37499999977626,461.29999999971005
|
||||
64,461.9750000001659,461.67499999978645,468.4500000000753
|
||||
65,470.97500000018044,470.34999999982574,475.87499999985994
|
||||
66,475.4499999999098,476.37499999950705,482.0999999999458
|
||||
67,481.4249999999447,482.32500000017353,489.72500000013497
|
||||
68,487.5499999998283,488.52499999998145,496.95000000042455
|
||||
69,497.90000000029977,496.09999999961474,
|
||||
|
||||
|
@ -1,76 +1,79 @@
|
||||
,0,1
|
||||
0,3.7000000006628397,0.8999999999021384
|
||||
1,6.925000000137516,8.125000000191722
|
||||
2,14.3250000005537,12.849999999971988
|
||||
3,20.250000000032742,18.875000000562796
|
||||
4,26.27500000062355,26.17500000077671
|
||||
5,36.87500000023647,30.800000000354704
|
||||
6,42.650000000776345,38.575000000392535
|
||||
7,44.35000000057698,48.825000000661746
|
||||
8,54.70000000013897,54.89999999998945
|
||||
9,60.825000000022555,60.75000000045365
|
||||
10,68.10000000086802,66.82500000069085
|
||||
11,73.05000000042128,73.17500000034742
|
||||
12,80.17500000050859,80.35000000008112
|
||||
13,84.95000000084474,86.37500000067193
|
||||
14,92.17500000022483,96.87500000008258
|
||||
15,100.00000000081855,102.8500000001175
|
||||
16,108.92499999999927,109.1000000004813
|
||||
17,115.05000000079235,116.37500000041727
|
||||
18,122.37500000037471,118.07500000021791
|
||||
19,130.07500000048822,128.37500000013353
|
||||
20,134.30000000016662,134.65000000077526
|
||||
21,140.30000000047949,140.4500000006836
|
||||
22,146.25000000023647,150.95000000009424
|
||||
23,152.37500000012005,152.92500000022375
|
||||
24,162.8250000007938,163.05000000001274
|
||||
25,164.57500000024083,170.57499999999965
|
||||
26,170.4000000004271,175.10000000028487
|
||||
27,176.40000000073996,182.5250000000695
|
||||
28,186.85000000050422,193.3000000007185
|
||||
29,188.60000000086075,199.00000000042456
|
||||
30,199.00000000006912,205.07500000066176
|
||||
31,205.02500000065993,211.14999999998946
|
||||
32,212.4750000007225,218.65000000060792
|
||||
33,218.42500000047949,224.70000000056717
|
||||
34,224.65000000056534,230.87500000009715
|
||||
35,234.95000000048094,241.10000000008841
|
||||
36,241.0000000004402,242.87500000072288
|
||||
37,248.42500000022483,253.1250000000826
|
||||
38,253.02500000043437,259.1500000006734
|
||||
39,259.2250000002423,265.1250000007083
|
||||
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74,494.9250000004213,
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48,300.17499999990866,314.2250000000786,307.74999999967224
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49,307.57500000032485,322.82500000019354,315.325000000215
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50,315.19999999960453,328.84999999987485,322.7249999997217
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51,321.3250000003976,330.5000000000291,330.3499999999109
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53,337.60000000034813,345.7250000001295,337.97500000010007
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54,339.30000000014877,352.8500000002168,351.5500000000462
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55,346.7749999995798,359.0750000003027,353.0999999999982
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56,352.87500000009493,368.02499999976135,360.47500000013645
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57,360.34999999952595,369.8500000000422,367.8500000002747
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58,367.7249999996642,381.67499999963184,374.07500000036055
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59,375.3249999995754,383.20000000021537,377.0499999997843
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61,390.4250000001051,398.0249999995067,390.8749999997814
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62,397.8249999996118,404.1749999996682,397.92499999994436
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63,399.6249999996147,406.04999999959546,405.9249999997552
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64,405.4750000000789,413.1250000000364,413.15000000004477
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65,407.3750000002841,420.7249999999476,420.525000000183
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66,419.075000000303,428.14999999973224,428.0500000001699
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67,427.94999999983736,435.6999999999971,434.24999999997783
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68,429.649999999638,437.4500000003536,435.9000000001321
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69,435.7249999998752,449.39999999951397,443.2499999999924
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70,443.1499999996598,456.9000000001324,450.8000000002572
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71,450.774999999849,458.34999999988213,458.1249999998396
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72,458.02499999950703,465.82500000022264,465.75000000002876
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73,465.5749999997719,473.2750000002852,473.20000000009134
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74,473.15000000031466,480.92499999984284,480.7749999997246
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75,479.4250000000469,488.27499999970314,482.6000000000055
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76,488.1000000000862,494.4500000001426,488.3999999999138
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77,489.87499999981117,,
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||||
|
||||
|
@ -1,90 +1,88 @@
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||||
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||||
0,2.21500000002689,0.6200000005089019
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1,12.515000000851984,13.89500000075774
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2,14.09000000017241,15.445000000709719
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3,15.66500000040233,17.020000000030144
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4,18.79000000058423,32.02000000035756
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5,32.16500000012584,33.52000000066315
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6,33.690000000709375,35.02000000005925
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7,36.59000000020879,37.970000000114545
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8,51.59000000053621,53.070000000644235
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9,53.16500000076613,54.57000000004033
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10,54.715000000718106,56.11999999999231
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11,57.61500000021752,59.34500000037648
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12,72.7650000003936,73.09500000044923
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13,75.56500000060024,75.54500000040267
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14,77.3899999999716,77.21999999992536
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15,92.36500000002107,92.42000000065732
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16,93.84000000004872,95.02000000045942
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17,96.6650000005333,96.64500000033573
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18,111.69000000022916,98.47000000061658
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19,113.29000000073702,113.19500000061512
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20,114.81500000041106,114.69500000001122
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21,117.76500000046636,116.29500000051908
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22,132.81500000044016,119.67000000075191
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23,134.31500000074575,134.170000000068
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24,135.86500000069773,135.72000000001998
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25,150.99000000059587,137.37000000017423
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26,153.6650000003223,152.3950000007796
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27,155.36500000012293,154.39500000027755
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28,157.16500000012584,156.72000000066026
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29,173.26500000085926,158.44500000073884
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30,174.7900000005333,173.42000000078832
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31,176.53999999998032,174.9700000007403
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32,192.8400000002088,176.52000000069228
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33,194.34000000051438,192.7950000006428
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34,195.81500000054203,194.2950000000389
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35,197.81500000004,195.87000000026882
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36,212.79000000008946,197.5950000003474
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37,215.31499999996723,212.44499999991666
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38,217.01500000067736,215.22000000075485
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45,256.26500000048816,256.07000000016404
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46,271.290000000184,258.0200000000156
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47,273.91500000026406,272.44500000031684
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48,275.6650000006206,274.0449999999152
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49,279.99000000050125,275.5950000007767
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50,293.5650000004474,278.42000000035176
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51,295.0150000001971,293.4200000006792
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52,296.6900000006293,294.97000000063116
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53,312.96500000057983,296.52000000058314
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54,314.4900000002539,311.8449999999763
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55,316.09000000076173,314.39500000013203
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56,317.76500000028443,316.0700000005642
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57,332.6650000004096,320.77000000006655
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58,334.16500000071517,332.5950000005657
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59,335.7900000005915,334.37000000029064
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60,340.0400000005478,338.5200000000447
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61,353.615000000494,353.4950000000942
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62,355.11500000079957,355.0200000006777
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63,356.9150000008025,356.59499999999815
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64,373.09000000055073,359.77000000073593
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65,374.61500000022477,373.14500000027755
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66,376.1900000004547,374.6950000002295
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67,391.61500000005014,376.5200000005104
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68,394.16500000020585,392.59500000005636
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69,395.8650000000065,394.1950000005642
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70,397.41500000086796,395.76999999988465
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71,412.3650000006395,400.39500000037214
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72,415.1400000005682,413.6200000000651
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73,416.79000000072244,415.195000000295
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74,433.16499999996574,416.8200000001713
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75,434.7150000008272,431.8700000001451
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76,436.2650000007792,433.44500000037505
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77,438.1150000004285,436.1950000000258
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78,452.865000000705,437.9450000003823
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79,454.34000000073263,452.7949999999516
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80,456.0150000002553,454.2950000002572
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81,472.3900000004081,455.79500000056277
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82,473.86500000043577,458.84499999991084
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83,475.3900000001098,473.8700000005162
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84,478.31500000079666,475.4200000004682
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85,493.2650000005682,476.9950000006981
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86,494.8400000007981,492.12000000059624
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||||
87,496.4400000003965,494.72000000039833
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||||
88,499.44000000009817,496.4450000004769
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||||
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||||
0,18.92999999987338,2.57499999995375,20.395000000194614
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||||
1,20.47999999982536,20.775000000387397,21.99499999979298
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2,22.05500000005528,23.249999999709278,23.595000000300843
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3,24.930000000186247,25.04999999971219,26.494999999800257
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4,26.655000000264828,28.474999999591407,29.519999999779884
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5,29.77999999953723,32.59999999997703,50.52000000042017
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6,49.204999999947596,50.474999999525934,52.120000000018536
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7,52.330000000129495,52.374999999731116,55.09500000035177
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8,55.07999999978025,55.100000000013424,56.645000000303746
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9,56.97999999998543,58.12499999999305,59.64500000000543
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10,61.27999999958816,80.59999999975149,80.6950000002921
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11,80.73000000027648,82.17499999998141,83.4700000002208
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12,82.3049999995969,85.1250000000367,85.14499999974349
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13,85.15500000035942,86.70000000026663,86.67000000032702
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14,86.85500000016006,88.57500000019387,89.56999999982644
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15,89.80500000021536,110.59999999949683,110.6699999997595
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16,110.90500000014842,112.24999999965108,112.31999999991375
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17,112.50499999974679,113.89999999980533,113.89500000014367
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18,115.20499999975115,116.65000000036558,116.7449999999967
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19,116.88000000018334,119.67500000034521,119.72000000032995
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20,118.45500000041326,140.77500000027825,140.74500000033868
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21,139.35499999994178,142.44999999980095,142.27000000001271
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22,142.30499999999708,143.95000000010654,143.84500000024263
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23,145.3299999999767,146.77499999968163,146.69500000009566
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24,147.0050000004089,149.67500000009053,149.844999999646
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25,150.22999999988357,152.99999999976748,171.04499999978134
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26,171.2549999998923,173.69999999980095,173.7950000003416
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27,173.90500000025028,175.3000000003088,175.369999999662
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28,175.50499999984865,176.92500000018512,177.16999999966492
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29,177.25500000020517,179.87500000024042,179.92000000022517
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30,181.52999999952996,183.24999999956376,200.9949999998803
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31,202.47999999961436,202.3750000002768,202.59500000038815
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32,204.0050000001979,203.97499999987517,205.41999999996324
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33,205.60499999979626,205.80000000015602,207.09500000039543
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34,207.40499999979917,208.47499999988244,209.99499999989484
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35,211.7050000003114,213.12499999973838,231.01999999990358
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38,235.6049999995416,235.75000000025497,237.37000000046964
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39,237.30500000025174,239.9499999996554,241.77000000027465
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40,238.9049999998501,241.89999999950697,261.22000000005346
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41,261.22999999975985,261.249999999993,262.89500000048565
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42,262.73000000006544,264.2000000000483,265.8699999999094
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43,265.6050000001964,267.04999999990133,267.6699999999123
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44,267.2300000000727,268.89999999955063,270.3200000002703
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45,268.9049999995954,290.09999999968596,291.1700000001524
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46,290.23000000021096,292.92500000017054,292.7200000001044
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47,292.70499999953284,295.7250000003772,294.2950000003343
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48,294.405000000243,297.3500000002535,297.26999999975806
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49,297.50500000014694,300.3000000003088,299.12000000031685
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50,299.42999999972056,321.52499999981256,321.39499999967074
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51,320.1050000003856,324.39999999994353,323.1199999997493
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52,322.92999999996067,326.0750000003757,325.79500000038524
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53,325.854999999738,329.0249999995215,327.5200000004638
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54,327.55499999953867,330.6749999996758,330.3450000000389
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55,329.22999999997086,351.67500000031606,351.3950000003256
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56,332.35500000015276,354.27500000011815,352.99499999992395
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57,352.95499999998395,355.94999999964085,354.6450000000782
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58,354.5549999995823,358.95000000025203,357.4200000000069
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60,359.07999999986754,381.47499999965686,381.64500000012185
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63,384.5799999996056,387.5249999996161,387.6200000001568
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64,386.28000000031574,390.6000000001516,390.5199999996562
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68,414.7799999997555,417.57499999991734,417.5449999999778
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75,449.2299999998617,471.7749999994997,453.8950000002892
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78,473.42999999969874,477.99999999958555,476.4700000002499
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||||
79,476.37999999975403,481.00000000019674,478.12000000040416
|
||||
80,478.00499999963034,,481.07000000045946
|
||||
81,482.45499999999123,,501.89500000006365
|
||||
82,500.6549999995153,,
|
||||
83,503.4799999999999,,
|
||||
84,505.35499999992714,,
|
||||
85,508.1550000001338,,
|
||||
86,509.88000000021236,,
|
||||
|
||||
|
BIN
motivation.pdf
BIN
motivation.pdf
Binary file not shown.
BIN
motivation.png
BIN
motivation.png
Binary file not shown.
|
Before Width: | Height: | Size: 47 KiB After Width: | Height: | Size: 50 KiB |
@ -126,7 +126,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
mt_sorted = mt_sorted[(mt_sorted['c2'] == c2) & (mt_sorted['c1'] == c1)]
|
||||
for gg in range(len(DF1_desired)):
|
||||
|
||||
# embed()
|
||||
|
||||
# try:
|
||||
t3 = time.time()
|
||||
# except:
|
||||
@ -150,7 +150,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
###################
|
||||
# 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)
|
||||
@ -170,13 +170,13 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
##############################################################
|
||||
# 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, base_several = True, dev_desired = dev_desired)
|
||||
data_dir, cell, c, chirps, devs, extract, group_mean, mean_type, plot_group=0,
|
||||
rocextra=False, sorted_on=sorted_on, dev_desired = dev_desired)
|
||||
####################################################
|
||||
|
||||
####################################################
|
||||
# hier checken wir ob für diesen einen Punkt das funkioniert mit der standardabweichung
|
||||
# embed()
|
||||
|
||||
try:
|
||||
check_var_substract_method(spikes_pure)
|
||||
except:
|
||||
@ -200,7 +200,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
|
||||
coh = False
|
||||
if coh:
|
||||
ax_w, d, data_dir, devs = plt_coherences(ax_w, d, data_dir, devs, grid)
|
||||
ax_w, d, data_dir, devs = plt_coherences(ax_w, d, devs, grid)
|
||||
|
||||
# ax_cohs = plt.subplot(grid[0,1])
|
||||
|
||||
@ -216,9 +216,9 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
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
|
||||
@ -264,11 +264,10 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
extracted2 = [False, False, False, False]
|
||||
for i in range(len(waves_presents)):
|
||||
ax = plot_shemes4(eod_fish_r, eod_fish_e, eod_fish_j, grid0, time_array,
|
||||
g=gs[i], title_top=True, add=0.1, eod_fr=eod_fr,
|
||||
g=gs[i], title_top=True, eod_fr=eod_fr,
|
||||
waves_present=waves_presents[i], ylim=ylim,
|
||||
xlim=xlim, jammer_label='f2', color_am=colors_am[i],
|
||||
emitter_label='f1',color_am2 = color01_2, receiver_label='f0',
|
||||
extracted=extracted[i],extracted2=extracted2[i],
|
||||
xlim=xlim, color_am=colors_am[i],
|
||||
color_am2 = color01_2, extracted=extracted[i], extracted2=extracted2[i],
|
||||
title=titles[i]) # 'intruder','receiver'#jammer_name
|
||||
|
||||
ax_w.append(ax)
|
||||
@ -285,7 +284,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
if printing:
|
||||
print(time.time() - t3)
|
||||
|
||||
# embed()
|
||||
|
||||
|
||||
##########################################
|
||||
# spike response
|
||||
@ -302,7 +301,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
frs.append(fr)
|
||||
fr = np.mean(frs)
|
||||
#embed()
|
||||
# embed()
|
||||
|
||||
base_several = False
|
||||
if base_several:
|
||||
spikes_new = []
|
||||
@ -335,15 +334,11 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
|
||||
[[smoothed_base], arrays[2], arrays[1], arrays[3]],
|
||||
[[mat_base], arrays_original[2], arrays_original[1],
|
||||
arrays_original[3]], spikes_pure, fr, cell, grid0, chirps, extract,
|
||||
mean_type,
|
||||
group_mean, b, devs,
|
||||
xlim=xlim, row=1 + d * 3,
|
||||
arrays_original[3]], spikes_pure, cell, grid0, mean_type,
|
||||
group_mean, xlim=xlim, row=1 + d * 3,
|
||||
array_chosen=array_chosen,
|
||||
color0_burst=color0_burst, mean_types=[mean_type],
|
||||
color01=color01, color02=color02,ylim_log=(-15, 3),
|
||||
color012=color012,color012_minus = color01_2,color0=color0,
|
||||
color01_2=color01_2)
|
||||
color0_burst=color0_burst, color01=color01, color02=color02,ylim_log=(-15, 3),
|
||||
color012=color012,color012_minus = color01_2,color0=color0)
|
||||
|
||||
##########################################################################
|
||||
|
||||
@ -352,13 +347,13 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
|
||||
|
||||
# save_all(individual_tag, show, counter_contrast=0, savename='')
|
||||
# print('individual_tag')
|
||||
# embed()
|
||||
|
||||
axes = []
|
||||
axes.append(ax_w)
|
||||
# axes.extend(np.transpose(ax_as))
|
||||
# axes.append(np.transpose(ax_ps))
|
||||
# np.transpose(axes)
|
||||
# embed()
|
||||
|
||||
#fig.tag(ax_w[0:3], xoffs=-2.3, yoffs=1.7)
|
||||
#fig.tag(ax_w[3::], xoffs=-1.9, yoffs=1.4)
|
||||
fig.tag(ax_w, xoffs=-1.9, yoffs=1.4)
|
||||
|
||||
Binary file not shown.
BIN
motivation_stim.pdf
Normal file
BIN
motivation_stim.pdf
Normal file
Binary file not shown.
BIN
motivation_stim.png
Normal file
BIN
motivation_stim.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 50 KiB |
371
motivation_stim.py
Normal file
371
motivation_stim.py
Normal file
@ -0,0 +1,371 @@
|
||||
#from matplotlib import gridspec as gridspec, pyplot as plt
|
||||
|
||||
#from plotstyle import plot_style
|
||||
#from utils_all import chose_mat_max_value, default_settings, find_code_vs_not, save_visualization
|
||||
#from utils_susept import check_var_substract_method, chose_certain_group, divergence_title_add_on, extract_waves, \
|
||||
# find_all_dir_cells, \
|
||||
# load_b_public, \
|
||||
# load_cells_three, \
|
||||
# plot_arrays_ROC_psd_single3, plot_shemes4, plt_coherences, predefine_grouping_frame, \
|
||||
# restrict_cell_type, save_arrays_susept
|
||||
from utils_suseptibility import *
|
||||
|
||||
|
||||
#sys.path.insert(0, '..')
|
||||
#from utils_suseptibility import motivation_small
|
||||
|
||||
#from utils import divergence_title_add_on,chose_certain_group,predefine_grouping_frame,check_nix_fish,find_all_dir_cells,load_cells_three,restrict_cell_type,plot_shemes2,plot_arrays_ROC_psd_single
|
||||
|
||||
|
||||
|
||||
def motivation_all_small_stim(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1', 'm2'], mult_type='_multsorted2_', top=0.94, devs=['2'],
|
||||
figsize=None, redo=False, save=True, end='0', cut_matrix='malefemale', chose_score='mean_nrs',
|
||||
a_fr=1, restrict='modulation', adapt='adaptoffsetallall2', step=str(30),
|
||||
detections=['AllTrialsIndex'], variant='no', sorted_on='LocalReconst0.2Norm'):
|
||||
autodefines = [
|
||||
'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',]
|
||||
cells = ['2021-08-03-ac-invivo-1'] ##'2021-08-03-ad-invivo-1',,[10, ][5 ]
|
||||
c1s = [10] # 1, 10,
|
||||
c2s = [10]
|
||||
|
||||
plot_style()
|
||||
default_figsize(column=2, length=3.3) #6.7 ts=12, ls=12, fs=12
|
||||
show = True
|
||||
DF2_desired = [0.8]
|
||||
DF1_desired = [0.87]
|
||||
|
||||
DF2_desired = [-0.23]
|
||||
DF1_desired = [0.94]
|
||||
|
||||
# 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
|
||||
|
||||
extra_f_calculatoin = False
|
||||
if extra_f_calculatoin:
|
||||
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 = [1.2]#DF1_desired # [::-1]
|
||||
DF2_desired = [0.95]#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)):
|
||||
|
||||
|
||||
# try:
|
||||
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 ''
|
||||
|
||||
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, chirps, devs, extract, group_mean, mean_type, plot_group=0,
|
||||
rocextra=False, sorted_on=sorted_on, dev_desired = dev_desired)
|
||||
####################################################
|
||||
|
||||
####################################################
|
||||
# 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)
|
||||
if figsize:
|
||||
fig = plt.figure(figsize=figsize)
|
||||
else:
|
||||
fig = plt.figure()
|
||||
grid = gridspec.GridSpec(1, 1, wspace=0.7, hspace=0.35, left=0.055, top=top,
|
||||
bottom=0.15,
|
||||
right=0.935) # height_ratios=[1, 2], height_ratios = [1,6]bottom=0.25, top=0.8,
|
||||
hr = [1, 0.35, 1.2, 0, 3, ] # 1
|
||||
##########################################################################
|
||||
# several coherence plot
|
||||
|
||||
# frame_psd = pd.read_pickle(load_folder_name('calc_RAM')+'/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
|
||||
# frame_psd = pd.read_pickle(load_folder_name('calc_RAM') + '/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
|
||||
|
||||
coh = False
|
||||
if coh:
|
||||
ax_w, d, data_dir, devs = plt_coherences(ax_w, d, devs, grid)
|
||||
|
||||
# ax_cohs = plt.subplot(grid[0,1])
|
||||
|
||||
##########################################################################
|
||||
# part with the power spectra
|
||||
grid0 = gridspec.GridSpecFromSubplotSpec(5, 4, wspace=0.15, hspace=0.35,
|
||||
subplot_spec=grid[:, :],
|
||||
height_ratios=hr)
|
||||
|
||||
xlim = [0, 100]
|
||||
###########################################
|
||||
stimulus_length = 0.3
|
||||
deltat = 1 / 40000
|
||||
eodf = np.mean(group_mean[1].eodf)
|
||||
eod_fr = eodf
|
||||
|
||||
a_fr = 1
|
||||
|
||||
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
|
||||
|
||||
color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
|
||||
colors_am = ['black', 'black', 'black', 'black'] # color01, color02, color012]
|
||||
extracted = [False, True, True, True]
|
||||
extracted2 = [False, False, False, False]
|
||||
for i in range(len(waves_presents)):
|
||||
ax = plot_shemes4(eod_fish_r, eod_fish_e, eod_fish_j, grid0, time_array,
|
||||
g=gs[i], title_top=True, eod_fr=eod_fr,
|
||||
waves_present=waves_presents[i], ylim=ylim,
|
||||
xlim=xlim, color_am=colors_am[i],
|
||||
color_am2 = color01_2, extracted=extracted[i], extracted2=extracted2[i],
|
||||
title=titles[i]) # 'intruder','receiver'#jammer_name
|
||||
|
||||
ax_w.append(ax)
|
||||
if ax != []:
|
||||
ax.text(1.1, 0.45, symbols[i], fontsize=35, transform=ax.transAxes)
|
||||
bar = False
|
||||
if bar:
|
||||
if i == 0:
|
||||
ax.plot([0, 20], [ylim[0] + 0.01, ylim[0] + 0.01], color='black')
|
||||
ax.text(0, -0.16, '20 ms', va='center', fontsize=10,
|
||||
transform=ax.transAxes)
|
||||
|
||||
printing = True
|
||||
if printing:
|
||||
print(time.time() - t3)
|
||||
|
||||
|
||||
|
||||
##########################################
|
||||
# spike response
|
||||
array_chosen = 1
|
||||
if d == 0: #
|
||||
|
||||
#embed()
|
||||
|
||||
frs = []
|
||||
for i in range(len(spikes_pure['base_0'])):
|
||||
#duration = spikes_pure['base_0'][i][-1] / 1000
|
||||
duration = 0.5
|
||||
fr = len(spikes_pure['base_0'][i])/duration
|
||||
frs.append(fr)
|
||||
fr = np.mean(frs)
|
||||
#embed()
|
||||
|
||||
base_several = False
|
||||
if base_several:
|
||||
spikes_new = []
|
||||
for i in range(len(spikes_pure['base_0'])):
|
||||
duration = 100
|
||||
duration_full = 101#501
|
||||
dur = np.arange(0, duration_full, duration)
|
||||
for d_nr in range(len(dur) - 1):
|
||||
#embed()
|
||||
spikes_new.append(np.array(spikes_pure['base_0'][i][
|
||||
(spikes_pure['base_0'][i] > dur[d_nr]) & (
|
||||
spikes_pure['base_0'][i] < dur[
|
||||
d_nr + 1])])/1000-dur[d_nr]/1000)
|
||||
# spikes_pure['base_0'] = spikes_new
|
||||
|
||||
sampling_rate = 1/np.diff(time_array)
|
||||
sampling_rate = int(sampling_rate[0]*1000)
|
||||
spikes_mats = []
|
||||
smoothed05 = []
|
||||
for i in range(len(spikes_new)):
|
||||
spikes_mat = cr_spikes_mat(spikes_new[i], sampling_rate, int(sampling_rate*duration/1000))
|
||||
spikes_mats.append(spikes_mat)
|
||||
smoothed05.append(gaussian_filter(spikes_mat, sigma=(float(dev_desired)/1000) * sampling_rate))
|
||||
smoothed_base = np.mean(smoothed05, axis=0)
|
||||
mat_base = np.mean(spikes_mats, axis=0)
|
||||
else:
|
||||
smoothed_base = arrays[0][0]
|
||||
mat_base = arrays_original[0][0]
|
||||
#embed()#arrays[0]v
|
||||
fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single4([[smoothed_base], arrays[2], arrays[1], arrays[3]],
|
||||
[[smoothed_base], arrays[2], arrays[1], arrays[3]],
|
||||
[[mat_base], arrays_original[2], arrays_original[1],
|
||||
arrays_original[3]], spikes_pure, cell, grid0, mean_type,
|
||||
group_mean, xlim=xlim, row=1 + d * 3,
|
||||
array_chosen=array_chosen,
|
||||
color0_burst=color0_burst, color01=color01, color02=color02,ylim_log=(-15, 3),
|
||||
color012=color012,color012_minus = color01_2,color0=color0)
|
||||
|
||||
##########################################################################
|
||||
|
||||
individual_tag = 'DF1' + str(DF1_desired[gg]) + 'DF2' + str(
|
||||
DF2_desired[gg]) + cell + '_c1_' + str(c1) + '_c2_' + str(c2) + mean_type
|
||||
|
||||
# save_all(individual_tag, show, counter_contrast=0, savename='')
|
||||
# print('individual_tag')
|
||||
|
||||
axes = []
|
||||
axes.append(ax_w)
|
||||
# axes.extend(np.transpose(ax_as))
|
||||
# axes.append(np.transpose(ax_ps))
|
||||
# np.transpose(axes)
|
||||
|
||||
#fig.tag(ax_w[0:3], xoffs=-2.3, yoffs=1.7)
|
||||
#fig.tag(ax_w[3::], xoffs=-1.9, yoffs=1.4)
|
||||
fig.tag(ax_w, xoffs=-1.9, yoffs=1.4)
|
||||
# ax_w, np.transpose(ax_as), ax_ps
|
||||
if save:
|
||||
save_visualization(individual_tag=individual_tag, show=show, pdf=True)
|
||||
# fig = plt.gcf()
|
||||
# fig.savefig
|
||||
# plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':#2.5
|
||||
motivation_all_small_stim(dev_desired = '1', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
|
||||
cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
|
||||
detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')#
|
||||
15506
motivation_stim_05_012.csv
Normal file
15506
motivation_stim_05_012.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_05_base_0.csv
Normal file
15506
motivation_stim_05_base_0.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_05_control_01.csv
Normal file
15506
motivation_stim_05_control_01.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_05_control_02.csv
Normal file
15506
motivation_stim_05_control_02.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_original_012.csv
Normal file
15506
motivation_stim_original_012.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_original_base_0.csv
Normal file
15506
motivation_stim_original_base_0.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_original_control_01.csv
Normal file
15506
motivation_stim_original_control_01.csv
Normal file
File diff suppressed because it is too large
Load Diff
15506
motivation_stim_original_control_02.csv
Normal file
15506
motivation_stim_original_control_02.csv
Normal file
File diff suppressed because it is too large
Load Diff
87
motivation_stim_spikes_012.csv
Normal file
87
motivation_stim_spikes_012.csv
Normal file
@ -0,0 +1,87 @@
|
||||
,0,1,2
|
||||
0,5.360000000375287,3.6000000003509633,5.439999999890446
|
||||
1,6.9100000003272655,5.150000000302942,7.064999999766755
|
||||
2,28.034999999628774,6.5749999997747075,26.89000000007671
|
||||
3,29.509999999656422,8.299999999853288,28.31500000045797
|
||||
4,31.210000000366556,12.724999999936234,29.739999999929736
|
||||
5,34.28499999999257,29.175000000013355,34.21499999965907
|
||||
6,36.98499999999694,30.750000000243276,35.765000000520544
|
||||
7,43.310000000285065,35.04999999984601,37.4149999997653
|
||||
8,58.38499999962732,36.59999999979799,58.16500000035465
|
||||
9,61.23500000038984,42.7999999996059,59.6400000003823
|
||||
10,64.23500000009153,59.30000000023891,61.34000000018294
|
||||
11,65.88500000024578,60.89999999983728,65.69000000034156
|
||||
12,88.18499999987762,65.2499999999959,67.26499999966198
|
||||
13,89.70999999955166,66.75000000030148,88.31499999994865
|
||||
14,91.45999999990818,68.64999999959717,89.76499999969836
|
||||
15,94.73499999993874,89.34999999963064,91.39000000048416
|
||||
16,97.10999999996784,90.89999999958262,94.7649999998075
|
||||
17,118.16000000025451,95.32499999966556,97.59000000029208
|
||||
18,119.73499999957494,96.8500000002491,117.14000000027318
|
||||
19,121.30999999980486,98.49999999949385,118.63999999966927
|
||||
20,124.36000000006243,119.37499999965394,120.06500000005053
|
||||
21,126.01000000021668,120.90000000023747,123.1150000003081
|
||||
22,127.68499999973938,124.00000000014143,125.96500000016113
|
||||
23,148.43500000032873,125.65000000029568,147.1899999996649
|
||||
24,150.06000000020504,127.14999999969177,148.66499999969255
|
||||
25,154.46000000001004,134.67499999967868,150.06499999979587
|
||||
26,156.03500000023996,149.6250000003597,154.69000000028336
|
||||
27,157.58500000019194,151.32500000016034,156.2149999999574
|
||||
28,178.48499999972046,155.5750000001167,174.49000000031538
|
||||
29,180.03499999967244,157.07499999951278,178.6650000003474
|
||||
30,184.48500000003332,163.14999999974998,180.1150000000971
|
||||
31,186.0349999999853,179.650000000383,181.7900000005293
|
||||
32,187.58499999993728,181.37499999955207,186.11500000040996
|
||||
33,208.58499999966807,185.57499999986203,187.84000000048854
|
||||
34,210.08499999997366,187.09999999953607,208.9400000004216
|
||||
35,212.38500000007843,189.05000000029713,210.28999999996904
|
||||
36,216.03499999973064,194.75000000000318,211.9400000001233
|
||||
37,217.58499999968262,209.77499999969905,216.1649999998017
|
||||
38,238.68499999961568,211.32499999965103,217.86500000051183
|
||||
39,240.21000000019922,215.77500000001191,238.83999999996468
|
||||
40,241.8849999997219,217.2750000003175,240.34000000027027
|
||||
41,246.05999999975393,223.27499999972088,241.91500000050019
|
||||
42,247.6099999997059,239.87499999964666,245.0399999997726
|
||||
43,268.76000000019485,241.62500000000318,246.54000000007818
|
||||
44,270.2849999998689,245.8499999996816,267.59000000036485
|
||||
45,271.88500000037675,247.37500000026512,269.0650000003925
|
||||
46,276.2100000002574,249.07500000006576,270.5150000001422
|
||||
47,277.6850000002851,269.89999999966994,274.98999999987154
|
||||
48,298.90999999978885,271.39999999997553,276.56500000010146
|
||||
49,300.35999999953856,275.79999999978054,297.61500000038814
|
||||
50,301.9600000000464,277.3499999997325,299.11499999978423
|
||||
51,305.010000000304,279.10000000008904,300.5400000001655
|
||||
52,306.56000000025597,300.0249999998955,303.86499999984244
|
||||
53,308.1349999995764,301.54999999956954,306.6150000004027
|
||||
54,329.00999999973646,306.02500000020837,308.3150000002033
|
||||
55,330.51000000004206,307.52499999960446,329.16500000008546
|
||||
56,335.1100000002516,309.24999999968304,330.61499999983516
|
||||
57,336.58500000027925,330.09999999956517,332.2399999997115
|
||||
58,338.1849999998776,331.6250000001487,335.4399999998177
|
||||
59,359.2350000001643,334.6999999997747,337.0150000000476
|
||||
60,360.8600000000406,336.3000000002826,359.1649999998308
|
||||
61,365.0099999997947,337.7500000000323,360.6650000001364
|
||||
62,366.55999999974665,344.0500000000425,362.31500000029064
|
||||
63,368.10999999969863,360.2750000003466,365.3649999996387
|
||||
64,374.3349999997845,361.8250000002986,366.9650000001466
|
||||
65,389.4599999996826,366.0999999996234,388.01500000043325
|
||||
66,392.159999999687,367.64999999957536,389.41500000053657
|
||||
67,395.2849999998689,373.89999999993915,390.8650000002863
|
||||
68,396.88500000037675,390.39999999966267,392.5150000004405
|
||||
69,398.4100000000508,394.85000000002356,396.7899999997653
|
||||
70,419.63499999955457,396.3749999996976,398.61500000004617
|
||||
71,422.2599999996346,397.8750000000032,418.2399999999516
|
||||
72,425.45999999974083,402.6250000000614,419.8649999998279
|
||||
73,426.91000000040003,420.72500000029277,421.3150000004871
|
||||
74,428.43500000007407,425.12500000009777,425.5899999998119
|
||||
75,435.960000000061,426.62499999949387,427.09000000011747
|
||||
76,450.8349999999082,428.07500000015307,449.4899999999516
|
||||
77,452.4100000001381,433.97500000026366,450.9649999999792
|
||||
78,455.51000000004206,435.9749999997616,452.66499999977987
|
||||
79,457.18499999956475,454.9499999997165,455.6899999997595
|
||||
80,458.68499999987034,456.4999999996685,457.215000000343
|
||||
81,480.8100000002851,457.97499999969614,479.56500000053074
|
||||
82,482.3849999996055,461.324999999651,481.06499999992684
|
||||
83,485.3850000002167,,482.91500000048563
|
||||
84,486.98499999981505,,
|
||||
85,488.60999999969135,,
|
||||
|
71
motivation_stim_spikes_base_0.csv
Normal file
71
motivation_stim_spikes_base_0.csv
Normal file
@ -0,0 +1,71 @@
|
||||
,0,1,2
|
||||
0,4.374999999527063,0.8000000000265572,3.375000000005457
|
||||
1,12.3249999996915,9.94999999988977,9.649999999737702
|
||||
2,19.900000000234286,15.899999999646752,17.150000000356158
|
||||
3,25.649999999586726,22.12499999973261,24.5250000004944
|
||||
4,31.525000000328873,31.100000000378714,33.44999999967513
|
||||
5,40.77500000039436,38.499999999885404,39.524999999912325
|
||||
6,46.55000000002474,44.67500000032487,48.62500000012915
|
||||
7,52.64999999963038,50.700000000006185,53.24999999970714
|
||||
8,61.674999999922875,58.0000000002201,62.050000000226646
|
||||
9,72.19999999961146,65.67500000005566,72.6750000001175
|
||||
10,79.62500000030559,73.2250000003205,78.69999999979882
|
||||
11,85.72499999991123,82.09999999985484,87.6000000005206
|
||||
12,94.62499999972351,88.25000000001637,90.75000000007094
|
||||
13,99.79999999995925,95.87500000020555,96.7500000003838
|
||||
14,106.87500000040018,104.67499999981555,108.67500000017571
|
||||
15,115.67500000001019,106.3749999996162,111.65000000050895
|
||||
16,117.57500000021537,115.27500000033797,120.67499999989195
|
||||
17,127.87500000013097,121.27499999974134,126.7249999998512
|
||||
18,133.74999999996362,130.59999999973115,132.8000000000884
|
||||
19,142.74999999997817,136.39999999963948,138.95000000024993
|
||||
20,146.09999999993306,142.549999999801,147.87500000034015
|
||||
21,156.45000000040454,149.77500000009059,155.50000000052933
|
||||
22,160.874999999578,160.600000000386,161.4999999999327
|
||||
23,171.62499999994907,169.37499999971806,169.09999999984393
|
||||
24,177.47500000041327,172.60000000010223,179.42500000003747
|
||||
25,182.24999999983993,181.42499999999018,183.97499999969114
|
||||
26,188.17500000022847,186.22499999969477,191.74999999972897
|
||||
27,197.1000000003187,193.44999999998436,198.97500000001855
|
||||
28,206.0750000000553,202.40000000035252,207.9750000000331
|
||||
29,212.12500000001455,208.5250000002361,211.04999999965912
|
||||
30,218.10000000004948,215.97500000029868,223.0749999996533
|
||||
31,230.0999999997657,222.32499999995525,229.00000000004184
|
||||
32,233.17500000030122,231.07499999991887,235.0500000000011
|
||||
33,242.1249999997599,235.7249999997748,241.0749999996824
|
||||
34,245.2499999999418,241.72500000008768,250.07499999969696
|
||||
35,254.37499999952706,250.599999999622,256.32500000006075
|
||||
36,263.1499999997686,253.92500000020846,265.2000000005046
|
||||
37,264.9250000004031,265.800000000354,271.2250000001859
|
||||
38,275.3999999995358,272.0750000000862,280.17499999964457
|
||||
39,279.9750000003769,277.8499999997166,287.900000000036
|
||||
40,291.7749999996886,285.3749999997035,296.699999999646
|
||||
41,293.4750000003987,290.15000000003965,298.3250000004318
|
||||
42,299.4749999998021,296.0249999998723,307.3250000004464
|
||||
43,308.324999999968,306.7750000002434,313.27500000020336
|
||||
44,315.87500000023283,316.850000000386,322.4999999999909
|
||||
45,323.4499999998661,320.1000000001386,331.3999999998032
|
||||
46,329.47499999954744,328.9500000003045,337.425000000394
|
||||
47,335.62499999970896,335.09999999955653,343.47500000035325
|
||||
48,346.3000000001557,344.124999999849,352.6500000004944
|
||||
49,350.8249999995314,352.949999999737,358.4250000001248
|
||||
50,359.69999999997526,360.69999999949687,361.70000000015534
|
||||
51,367.2500000002401,365.19999999950414,372.12499999964166
|
||||
52,371.6500000000451,374.2750000003525,379.70000000018445
|
||||
53,382.09999999980937,381.8000000003394,387.34999999974207
|
||||
54,386.55000000017026,389.0750000002754,394.62499999967804
|
||||
55,392.57499999985157,395.17499999988104,405.05000000007385
|
||||
56,398.92499999950815,404.1250000002492,406.5749999997479
|
||||
57,409.1500000004089,413.17499999991014,412.7249999999094
|
||||
58,415.29999999966094,419.175000000223,418.82500000042455
|
||||
59,422.82499999964784,426.77500000013424,427.67499999968095
|
||||
60,431.775000000016,435.6749999999465,436.92499999974643
|
||||
61,437.8999999998996,437.4000000000251,445.97500000031687
|
||||
62,445.34999999996217,447.8750000000673,454.7749999999269
|
||||
63,452.8250000003027,455.37499999977626,461.29999999971005
|
||||
64,461.9750000001659,461.67499999978645,468.4500000000753
|
||||
65,470.97500000018044,470.34999999982574,475.87499999985994
|
||||
66,475.4499999999098,476.37499999950705,482.0999999999458
|
||||
67,481.4249999999447,482.32500000017353,489.72500000013497
|
||||
68,487.5499999998283,488.52499999998145,496.95000000042455
|
||||
69,497.90000000029977,496.09999999961474,
|
||||
|
79
motivation_stim_spikes_control_01.csv
Normal file
79
motivation_stim_spikes_control_01.csv
Normal file
@ -0,0 +1,79 @@
|
||||
,1,0,2
|
||||
0,0.9749999995259713,5.37499999973079,6.675000000271757
|
||||
1,6.624999999585635,6.87500000003638,12.875000000079671
|
||||
2,14.17499999985048,13.200000000324508,14.525000000233922
|
||||
3,15.949999999575448,20.599999999831198,21.774999999891953
|
||||
4,21.650000000190992,22.149999999783176,29.40000000008113
|
||||
5,29.575000000077488,31.124999999519787,36.77500000021937
|
||||
6,38.50000000016771,37.04999999990832,43.17500000043183
|
||||
7,44.59999999977335,44.574999999895226,51.82500000019318
|
||||
8,51.899999999987266,52.05000000023574,53.574999999640205
|
||||
9,59.299999999493956,58.2750000003216,59.6499999998774
|
||||
10,61.15000000005275,67.00000000000728,67.12500000021791
|
||||
11,68.89999999981265,73.44999999986612,74.5249999997246
|
||||
12,80.52499999990724,80.87499999965075,81.89999999986284
|
||||
13,81.97499999965694,89.5250000003216,88.02499999974643
|
||||
14,89.42499999971952,91.34999999969295,89.74999999982501
|
||||
15,90.99999999994944,97.22499999952561,97.65000000034306
|
||||
16,97.37499999988395,103.5249999995358,104.60000000030377
|
||||
17,104.72499999974426,112.19999999957508,112.05000000036634
|
||||
18,112.05000000023611,118.37500000001455,113.8749999997377
|
||||
19,119.50000000029868,127.1999999999025,125.74999999988322
|
||||
20,121.24999999974571,133.37500000034197,127.2500000001888
|
||||
21,133.37499999994216,135.00000000021828,134.6000000000491
|
||||
22,136.19999999951725,142.39999999972497,136.40000000005202
|
||||
23,142.250000000386,148.44999999968422,149.77500000050313
|
||||
24,149.67500000017063,157.4000000000524,157.17500000000982
|
||||
25,151.34999999969332,164.9250000000393,158.9000000000884
|
||||
26,157.42499999993052,172.37500000010186,164.82500000047693
|
||||
27,166.47499999959146,179.87499999981083,172.19999999970568
|
||||
28,172.25000000013134,181.57499999961146,179.87500000045074
|
||||
29,179.7000000001939,187.64999999984866,187.3750000001597
|
||||
30,181.50000000019682,196.6499999998632,193.29999999963874
|
||||
31,194.77499999953616,202.52499999969586,194.97500000007093
|
||||
32,196.49999999961474,210.17500000016298,202.45000000041145
|
||||
33,202.324999999801,217.49999999974534,210.15000000052495
|
||||
34,208.5750000001648,225.09999999965657,217.40000000018298
|
||||
35,217.29999999985048,226.92499999993743,225.07500000001855
|
||||
36,224.775000000191,232.60000000027503,232.37500000023246
|
||||
37,226.77499999968896,240.37500000031287,234.1249999996795
|
||||
38,238.5499999996322,247.6249999999709,241.72500000050022
|
||||
39,240.17499999950851,255.3250000000844,247.5000000001306
|
||||
40,247.45000000035398,262.775000000147,253.8750000000651
|
||||
41,253.67499999953034,268.72499999990396,262.5249999998265
|
||||
42,262.42499999949393,270.34999999978027,270.22499999994
|
||||
43,268.62500000021134,277.75000000019645,277.5749999998003
|
||||
44,277.42499999982135,285.2999999995518,279.27500000051043
|
||||
45,279.124999999622,292.699999999968,292.5250000004813
|
||||
46,286.57499999968456,298.82499999985157,294.3000000002063
|
||||
47,292.6249999996438,300.50000000028376,300.22499999968534
|
||||
48,300.17499999990866,314.2250000000786,307.74999999967224
|
||||
49,307.57500000032485,322.82500000019354,315.325000000215
|
||||
50,315.19999999960453,328.84999999987485,322.7249999997217
|
||||
51,321.3250000003976,330.5000000000291,330.3499999999109
|
||||
52,329.0750000001575,337.8999999995358,336.4250000001481
|
||||
53,337.60000000034813,345.7250000001295,337.97500000010007
|
||||
54,339.30000000014877,352.8500000002168,351.5500000000462
|
||||
55,346.7749999995798,359.0750000003027,353.0999999999982
|
||||
56,352.87500000009493,368.02499999976135,360.47500000013645
|
||||
57,360.34999999952595,369.8500000000422,367.8500000002747
|
||||
58,367.7249999996642,381.67499999963184,374.07500000036055
|
||||
59,375.3249999995754,383.20000000021537,377.0499999997843
|
||||
60,382.79999999991594,389.3500000003769,383.1500000002994
|
||||
61,390.4250000001051,398.0249999995067,390.8749999997814
|
||||
62,397.8249999996118,404.1749999996682,397.92499999994436
|
||||
63,399.6249999996147,406.04999999959546,405.9249999997552
|
||||
64,405.4750000000789,413.1250000000364,413.15000000004477
|
||||
65,407.3750000002841,420.7249999999476,420.525000000183
|
||||
66,419.075000000303,428.14999999973224,428.0500000001699
|
||||
67,427.94999999983736,435.6999999999971,434.24999999997783
|
||||
68,429.649999999638,437.4500000003536,435.9000000001321
|
||||
69,435.7249999998752,449.39999999951397,443.2499999999924
|
||||
70,443.1499999996598,456.9000000001324,450.8000000002572
|
||||
71,450.774999999849,458.34999999988213,458.1249999998396
|
||||
72,458.02499999950703,465.82500000022264,465.75000000002876
|
||||
73,465.5749999997719,473.2750000002852,473.20000000009134
|
||||
74,473.15000000031466,480.92499999984284,480.7749999997246
|
||||
75,479.4250000000469,488.27499999970314,482.6000000000055
|
||||
76,488.1000000000862,494.4500000001426,488.3999999999138
|
||||
77,489.87499999981117,,
|
||||
|
88
motivation_stim_spikes_control_02.csv
Normal file
88
motivation_stim_spikes_control_02.csv
Normal file
@ -0,0 +1,88 @@
|
||||
,0,1,2
|
||||
0,18.92999999987338,2.57499999995375,20.395000000194614
|
||||
1,20.47999999982536,20.775000000387397,21.99499999979298
|
||||
2,22.05500000005528,23.249999999709278,23.595000000300843
|
||||
3,24.930000000186247,25.04999999971219,26.494999999800257
|
||||
4,26.655000000264828,28.474999999591407,29.519999999779884
|
||||
5,29.77999999953723,32.59999999997703,50.52000000042017
|
||||
6,49.204999999947596,50.474999999525934,52.120000000018536
|
||||
7,52.330000000129495,52.374999999731116,55.09500000035177
|
||||
8,55.07999999978025,55.100000000013424,56.645000000303746
|
||||
9,56.97999999998543,58.12499999999305,59.64500000000543
|
||||
10,61.27999999958816,80.59999999975149,80.6950000002921
|
||||
11,80.73000000027648,82.17499999998141,83.4700000002208
|
||||
12,82.3049999995969,85.1250000000367,85.14499999974349
|
||||
13,85.15500000035942,86.70000000026663,86.67000000032702
|
||||
14,86.85500000016006,88.57500000019387,89.56999999982644
|
||||
15,89.80500000021536,110.59999999949683,110.6699999997595
|
||||
16,110.90500000014842,112.24999999965108,112.31999999991375
|
||||
17,112.50499999974679,113.89999999980533,113.89500000014367
|
||||
18,115.20499999975115,116.65000000036558,116.7449999999967
|
||||
19,116.88000000018334,119.67500000034521,119.72000000032995
|
||||
20,118.45500000041326,140.77500000027825,140.74500000033868
|
||||
21,139.35499999994178,142.44999999980095,142.27000000001271
|
||||
22,142.30499999999708,143.95000000010654,143.84500000024263
|
||||
23,145.3299999999767,146.77499999968163,146.69500000009566
|
||||
24,147.0050000004089,149.67500000009053,149.844999999646
|
||||
25,150.22999999988357,152.99999999976748,171.04499999978134
|
||||
26,171.2549999998923,173.69999999980095,173.7950000003416
|
||||
27,173.90500000025028,175.3000000003088,175.369999999662
|
||||
28,175.50499999984865,176.92500000018512,177.16999999966492
|
||||
29,177.25500000020517,179.87500000024042,179.92000000022517
|
||||
30,181.52999999952996,183.24999999956376,200.9949999998803
|
||||
31,202.47999999961436,202.3750000002768,202.59500000038815
|
||||
32,204.0050000001979,203.97499999987517,205.41999999996324
|
||||
33,205.60499999979626,205.80000000015602,207.09500000039543
|
||||
34,207.40499999979917,208.47499999988244,209.99499999989484
|
||||
35,211.7050000003114,213.12499999973838,231.01999999990358
|
||||
36,229.9300000001135,231.15000000004542,232.67000000005783
|
||||
37,232.7050000000422,233.99999999989845,235.7200000003154
|
||||
38,235.6049999995416,235.75000000025497,237.37000000046964
|
||||
39,237.30500000025174,239.9499999996554,241.77000000027465
|
||||
40,238.9049999998501,241.89999999950697,261.22000000005346
|
||||
41,261.22999999975985,261.249999999993,262.89500000048565
|
||||
42,262.73000000006544,264.2000000000483,265.8699999999094
|
||||
43,265.6050000001964,267.04999999990133,267.6699999999123
|
||||
44,267.2300000000727,268.89999999955063,270.3200000002703
|
||||
45,268.9049999995954,290.09999999968596,291.1700000001524
|
||||
46,290.23000000021096,292.92500000017054,292.7200000001044
|
||||
47,292.70499999953284,295.7250000003772,294.2950000003343
|
||||
48,294.405000000243,297.3500000002535,297.26999999975806
|
||||
49,297.50500000014694,300.3000000003088,299.12000000031685
|
||||
50,299.42999999972056,321.52499999981256,321.39499999967074
|
||||
51,320.1050000003856,324.39999999994353,323.1199999997493
|
||||
52,322.92999999996067,326.0750000003757,325.79500000038524
|
||||
53,325.854999999738,329.0249999995215,327.5200000004638
|
||||
54,327.55499999953867,330.6749999996758,330.3450000000389
|
||||
55,329.22999999997086,351.67500000031606,351.3950000003256
|
||||
56,332.35500000015276,354.27500000011815,352.99499999992395
|
||||
57,352.95499999998395,355.94999999964085,354.6450000000782
|
||||
58,354.5549999995823,358.95000000025203,357.4200000000069
|
||||
59,356.12999999981224,361.09999999959865,359.1450000000855
|
||||
60,359.07999999986754,381.47499999965686,381.64500000012185
|
||||
61,362.20500000004944,383.09999999953317,383.14500000042744
|
||||
62,381.9799999998035,384.7499999996874,384.89499999987447
|
||||
63,384.5799999996056,387.5249999996161,387.6200000001568
|
||||
64,386.28000000031574,390.6000000001516,390.5199999996562
|
||||
65,389.13000000016876,411.5749999996045,392.47000000041726
|
||||
66,392.330000000275,413.100000000188,413.2200000000971
|
||||
67,413.1800000001571,414.69999999978637,414.69500000012476
|
||||
68,414.7799999997555,417.57499999991734,417.5449999999778
|
||||
69,416.2800000000611,420.6250000001749,419.27000000005637
|
||||
70,417.9799999998617,441.90000000023457,421.04499999978134
|
||||
71,422.22999999981806,444.6249999996074,441.7950000003707
|
||||
72,441.8800000000014,446.3000000000396,444.6199999999458
|
||||
73,443.380000000307,447.8249999997136,446.3700000003023
|
||||
74,446.23000000016003,450.84999999969324,448.14500000002727
|
||||
75,449.2299999998617,471.7749999994997,453.8950000002892
|
||||
76,451.10499999978896,473.4999999995783,471.8950000003183
|
||||
77,471.93000000030264,476.2999999997849,474.7949999998177
|
||||
78,473.42999999969874,477.99999999958555,476.4700000002499
|
||||
79,476.37999999975403,481.00000000019674,478.12000000040416
|
||||
80,478.00499999963034,,481.07000000045946
|
||||
81,482.45499999999123,,501.89500000006365
|
||||
82,500.6549999995153,,
|
||||
83,503.4799999999999,,
|
||||
84,505.35499999992714,,
|
||||
85,508.1550000001338,,
|
||||
86,509.88000000021236,,
|
||||
|
@ -22,7 +22,7 @@ from IPython import embed
|
||||
|
||||
def plot_style(ns=__main__):
|
||||
print('## added imports plotstyle.py version')
|
||||
# embed()
|
||||
|
||||
ns.palette = palettes['muted']
|
||||
|
||||
palette = ns.palette
|
||||
|
||||
@ -4457,7 +4457,7 @@ We collected weakly electric gymnotoid fish in the vicinity of Manaus, Amazonas,
|
||||
@Article{Meyer1982,
|
||||
Title = {Androgens alter the tuning of electroreceptors.},
|
||||
Author = {Meyer, J H and Zakon, H H},
|
||||
Journal = Science,
|
||||
Journal = {Science},
|
||||
Year = {1982},
|
||||
Pages = {635--637},
|
||||
Volume = {217}
|
||||
@ -4466,7 +4466,7 @@ We collected weakly electric gymnotoid fish in the vicinity of Manaus, Amazonas,
|
||||
@ARTICLE{Meyer1982Impedances,
|
||||
AUTHOR = {J. Harlan Meyer},
|
||||
TITLE = {Behavioral responses of weakly electric fish to complex impedances.},
|
||||
JOURNAL = JCompPhysiol,
|
||||
JOURNAL = {JCompPhysiol},
|
||||
YEAR = {1982},
|
||||
VOLUME = {145},
|
||||
PAGES = {459--470}
|
||||
@ -4477,7 +4477,7 @@ We collected weakly electric gymnotoid fish in the vicinity of Manaus, Amazonas,
|
||||
Author = {Meyer, J. Harlan
|
||||
and Leong, Margaret
|
||||
and Keller, Clifford H.},
|
||||
Journal = JCompPhysiolA,
|
||||
Journal = {JCompPhysiolA},
|
||||
Year = {1987},
|
||||
Number = {3},
|
||||
Pages = {385--394},
|
||||
|
||||
BIN
susceptibility1.dvi
Normal file
BIN
susceptibility1.dvi
Normal file
Binary file not shown.
@ -522,9 +522,11 @@ The P-unit responses can be partially explained by simple linear filters. The li
|
||||
|
||||
|
||||
\section*{Results}
|
||||
Theoretical work shows that leaky-integrate-and-fire (LIF) model neurons show a distinct pattern of nonlinear stimulus encoding when the model is driven by two cosine signals. In the context of the weakly electric fish, such a setting is part of the animal's everyday life as the sinusoidal electric-organ discharges (EODs) of neighboring animals interfere with the own field and each lead to sinusoidal amplitude modulations (AMs) that are called beats and envelopes \cite{Middleton2006, Savard2011,Stamper2012Envelope}. The p-type electroreceptor afferents of the tuberous electrosensory system, i.e. the P-units, respond to such AMs of the underlying EOD carrier and their time-dependent firing rate carries information about the stimulus' time-course. P-units are heterogeneous in their baseline firing properties \cite{Grewe2017, Hladnik2023} and differ with respect to their baseline firing rates, the sensitivity and their noisiness. We here explore the nonlinear mechanism in different cells that exhibit distinctly different levels of noise, i.e. differences in the coefficient of variation (CV) of the interspike intervals (ISI). Low-CV P-units have a less noisy, more regular, firing pattern whereas high-CV P-units show a less regular firing pattern in their baseline activity.
|
||||
Theoretical work \cite{Voronenko2017} shows that stochastic leaky integrate-and-fire (LIF) model neurons may show nonlinear stimulus encoding when the model is driven by two cosine signals with specific frequencies. In the context of weakly electric fish, such a setting is part of the animal's everyday life. The sinusoidal electric-organ discharges (EODs) of neighboring animals interfere and lead to amplitude modulations (AMs) that are called beats (two-fish interaction) and envelopes (multiple-fish interaction) \cite{Middleton2006, Savard2011,Stamper2012Envelope}. The p-type electroreceptor afferents of the tuberous electrosensory system, i.e. the P-units encode such AMs of the underlying EOD carrier in their time-dependent firing rates \cite{Bastian1981a,Walz2014}. We here explore nonlinear responses of different cells that exhibit distinctly different levels of output variability, quantified by the coefficient of variation (CV) of their interspike intervals (ISI). Low-CV P-units have a less noisy firing pattern that is closer to pacemaker firing, whereas high-CV P-units show a more irregular firing pattern that is closer to a Poisson process.
|
||||
%P-units are heterogeneous in their baseline firing properties \cite{Grewe2017, Hladnik2023} and differ with respect to their baseline firing rates, the sensitivity and their noisiness.
|
||||
|
||||
\subsection*{Low-CV P-units exhibit nonlinear interactions} %frequency combinations withappearing when the input frequencies are related to \fbase{} are
|
||||
|
||||
\subsection*{Nonlinear signal transmission in low-CV P-units} %frequency combinations withappearing when the input frequencies are related to \fbase{} are
|
||||
|
||||
Second-order susceptibility is expected to be especially pronounced for low-CV cells \cite{Voronenko2017}. P-units fire action potentials probabilistically phase-locked to the self-generated EOD. Skipping of EOD cycles leads to the characteristic multimodal ISI distribution with maxima at integer multiples of the EOD period (\subfigrefb{fig:cells_suscept}{A}). In this example the ISI distribution has a CV of 0.2 which can be considered low among P-units\cite{Hladnik2023}. Spectral analysis of the baseline activity shows two major peaks, the first is located at the baseline firing rate (\fbase), the second is located at the discharge frequency of the electric organ (\feod{}) and is flanked by two smaller peaks at $\feod \pm \fbase{}$ (\subfigref{fig:cells_suscept}{B}).
|
||||
|
||||
@ -550,13 +552,15 @@ Irrespective of the CV, neither cell shows the complete proposed structure of no
|
||||
}
|
||||
\end{figure*}
|
||||
|
||||
\subsection*{Internal noise hides parts of the nonlinearity structure}
|
||||
Traces of the proposed structure of second-order susceptibility are found in both ampullary and p-type electrosensory afferents. The nonlinerarity seems to depend on the CV, i.e. the level of intrinsic noise in the cells and in the next step we address whether the level of intrinsic masks the expected structures. One option to reduce the impact of this intrinsic noise is to average it out over many repetitions of the same stimulus. In the electrophysiological experiments we only have a limited recording duration and hence the number of stimulus repetitions is limited. To overcome these limitations we compared between an electrophysiologically recorded low-CV P-unit and its P-unit LIF model counterpart fitted to reproduce the P-unit behavior faithfully (see \figref{flowchart})\cite{Barayeu2023}. In the recording depicted in \subfigrefb{model_and_data}{A} the cell was stimulated with a weak RAM stimulus with the contrast of 1\,$\%$ (red, \subfigrefb{model_and_data}\,\panel[i]{B}). The across trial average ($\n{}=11$) shows the diagonal band in second-order susceptibility at \fsumb{} (yellow diagonal in pink edges) and an additional nonlinear band appeared at \fsumehalf{} (\subfigrefb{model_and_data}{A}). The matched model reproduces the same diagonal at \fsumb{} (\subfigrefb{model_and_data}\,\panel[ii]{B}) but not the diagonal at \fsumehalf. By increasing the number of trials in the simulation, the signal-to-noise ratio and thus the estimation of the nonlinearity structures can be improved. Still, even with 1 million repetitions, no changes are observable in the nonlinearity structures (\subfigrefb{model_and_data}\,\panel[iii]{B}). The increased trial count, however goes along with generally smaller second-order susceptibility values (\subfigrefb{model_and_data}\,\panel[iii]{B}). The decrease of the second-order susceptibility follows the relation $1/ \sqrt{N} $ (\figrefb{trialnr}).
|
||||
\subsection*{High level of total noise hides parts of the nonlinearity structure}
|
||||
Traces of the expected structure of second-order susceptibility are found in both ampullary and p-type electrosensory afferents. In the recordings shown above (\figrefb{fig:cells_suscept}, \figrefb{fig:ampullary}), the nonlinear response is strong whenever the two frequencies (\fone{}, \ftwo{}) fall onto the antidiagonal \fsumb{}, which is in line with theoretical expectations \cite{Voronenko2017}. However, a pronounced nonlinear response for frequencies with \foneb{} or \ftwob{}, although predicted by theory, cannot be observed. Here we investigate how these discrepancies can be understood.
|
||||
|
||||
%Note that this line doesn't appear in the susceptibility matrix of the model (\subfigrefb{model_and_data}{C})Each cell has an intrinsic noise level (\subfigrefb{model_and_data}{A}, bottom).
|
||||
%The signal component (purple) compensates for this total noise reduction, is not a weak signal anymore and
|
||||
Based on the Novikov-Furutsu Theorem \cite{Novikov1965, Furutsu1963} the intrinsic noise of a LIF model can be split up into several independent noise processes with the same correlation function. We make use of this and split the intrinsic noise $\xi$ into two parts: 90\% are then treated as signal ($\xi_{signal}$) while the remaining 10\% are treated as noise ($\xi_{noise}$, see methods for details). With this, the signal-to-noise ratio in the simulation can be arbitrarily varied and the combination of many repetitions and noise-split indeed reveals the triangular shape shown theoretically and for LIF models without carrier\cite{Voronenko2017}(\subfigrefb{model_and_data}\,\panel[iii]{C}).
|
||||
In the electrophysiological experiments we only have a limited number of trials and this insufficient averaging may occlude the full nonlinear structure. This limitation can be overcome by using a computational model for the P-unit, a stochastic leaky integrate-and-fire model with adaptation current and dendritic preprocessing, with parameters fitted to the experimentally recorded P-unit (\figrefb{flowchart}) \cite{Barayeu2023}. The model faithfully reproduces the second-order susceptibility of a low-CV cell estimated from the same low number of repetitions ($\n{}=11$, compare \panel{A} and \panel[ii]{B} in \figrefb{model_and_data}). In the model we can increase the number of repetitions substantially but still do not observe the full nonlinear structure ($\n{}=10^6$, \subfigrefb{model_and_data}\,\panel[iii]{B}). A possible reason for this could be that by applying a broadband stimulus the effective input-noise level is increased and this may linearize the signal
|
||||
transmission \cite{Longtin1993, Chialvo1997, Roddey2000, Voronenko2017}. Assuming that the intrinsic noise level in this P-unit is small enough, the full nonlinear structure should appear in the limit of weak AMs. Again, this cannot be done experimentally, because the problem of insufficient averaging becomes even more severe for weak AMs (low contrast). In the model, however, we know the time course of the intrinsic noise and can use this knowledge to determine the susceptibilities by input-output correlations via the Furutsu-Novikov theorem \cite{Furutsu1963, Novikov1965}. This theorem, in its simplest form, states that the cross-spectrum $S_{x\eta}(\omega)$ of a Gaussian noise $\eta(t)$ driving a nonlinear system and the system's output $x(t)$ is proportional to the linear susceptibility $S_{x\eta}(\omega)=\chi(\omega)S_{\eta\eta}(\omega)$. Here $\chi(\omega)$ characterizes the linear response to an infinitely weak signal $\varepsilon s(t)$ in the presence of the background noise $\eta(t)$. Likewise, the nonlinear susceptibility can be determined in an analogous fashion from higher-order input-output cross-spectra (see methods \eqref{eq:crosshigh}, \eqref{eq:susceptibility}) \cite{Egerland2020}. In line with an alternative derivation of the Furutsu-Novikov theorem \cite{Lindner2022}, we can split the total noise and consider a fraction of it as stimulus. This allows to calculate the susceptibility from the cross-spectrum between the output and this stimulus fraction of the noise. Adapting this approach to our P-unit model (see methods), we replace the intrinsic noise by an approximately equivalent RAM stimulus $s_\xi(t)$ and a weak remaining intrinsic noise ($\sqrt{2D \, c_{noise}} \cdot \xi(t)$, with $c_\text{noise} = 0.1$, see methods \eqref{eq:ram_split}, \eqref{eq:Noise_split_intrinsic}, \eqref{eq:Noise_split_intrinsic_dendrite}, \subfigrefb{model_and_data}\,\panel[i]{C}). We tune the amplitude / standard deviation of the RAM stimulus $s_\xi(t)$ such that the output firing rate and variability (CV) are the same as in the baseline activity (i.e. full intrinsic noise $\sqrt{2D}\xi(t)$ in the voltage equation but no RAM) and compute the second- and third-order cross-spectra between the RAM part of the noise $s_\xi(t)$ and the output spike train. This procedure has two consequences: (i) by means of the cross-spectrum between the output and $s_\xi(t)$, which is a large fraction of the noise, the signal-to-noise ratio of the measured susceptibilities is drastically improved; (ii) the total noise in the system has been reduced (by what was before the external RAM stimulus $s(t)$), which makes the system more nonlinear. For both reasons we now see the expected nonlinear features in the second-order susceptibility for a sufficient number of trials (\subfigrefb{model_and_data}\,\panel[iii]{B}), but not for a number of trials comparable to the experiment (\subfigrefb{model_and_data}\,\panel[ii]{B}). In addition to the strong response for \fsumb{}, we now also observe pronounced nonlinear responses at \foneb{} and \ftwob{} (vertical and horizontal lines, \subfigrefb{model_and_data}\,\panel[iii]{C}). Note, that the increased number of repetitions goes along with a substantial reduction of second-order susceptibility values (\subfigrefb{model_and_data}\,\panel[iii]{B}), that saturate in its peak values for $N>10^5$ (\figrefb{trialnr}). This demonstrates the limited reliability of the statistics that is based on 11 trials. However, we would like to point out that already the limited number of trials as used in the experiments reveals key features of the nonlinear response.
|
||||
|
||||
|
||||
%Note that this line doesn't appear in the susceptibility matrix of the model (\subfigrefb{model_and_data}{C})Each cell has an intrinsic noise level (\subfigrefb{model_and_data}{A}, bottom).
|
||||
%The signal component (purple) compensates for this total noise reduction, is not a weak signal anymore
|
||||
%Adding an additional, independent, RAM stimulus to the simulation does not heavily influence the qualitative observations but the nonlinearity becomes weaker (compare \subfigrefb{model_and_data}\,\panel[iii]{C} and \panel[iv]{C})
|
||||
|
||||
% Based on this a weak RAM signal as the input to the P-unit model (red in \subfigrefb{model_and_data}\,\panel[i]{A}) can be approximated by a model where no RAM stimulus is present (red) but instead the total noise of the model is split up into a reduced intrinsic noise component (gray) and a signal component (purple), maintaining the CV and \fbase{} as during baseline (\subfigrefb{model_and_data}\,\panel[i]{B}, see methods section \ref{intrinsicsplit_methods} for more details). This signal component (purple) can be used for the calculation of the second-order susceptibility. With the reduced noise component the signal-to-noise ratio increases and the number of stimulus realizations can be reduced. This noise split cannot be applied in experimentally measured cells. If this noise split is applied in the model with $\n{}=11$ stimulus realizations the nonlinearity at \fsumb{} is still present (\subfigrefb{model_and_data}\,\panel[iii]{B}). If instead, the RAM stimulus is drawn 1 million times the diagonal nonlinearity band is complemented by vertical and horizontal lines appearing at \foneb{} and \ftwob{} (\subfigrefb{model_and_data}\,\panel[iv]{B}). These nonlinear structures correspond to the ones observed in previous works \cite{Voronenko2017}. If now a weak RAM stimulus is added (\subfigrefb{model_and_data}\,\panel[i]{C}, red), simultaneously the noise is split up into a noise and signal component (gray and purple) and the calculation is performed on the sum of the signal component and the RAM (red plus purple) only the diagonal band is present with $\n{}=11$ (\subfigrefb{model_and_data}\,\panel[iii]{C}) but also with 1\,million stimulus realizations (\subfigrefb{model_and_data}\,\panel[iv]{C}).
|
||||
@ -684,7 +688,11 @@ The baseline firing rate \fbase{} was calculated as the number of spikes divided
|
||||
|
||||
\paragraph{White noise analysis} \label{response_modulation}
|
||||
In the stimulus driven case, the neuronal activity of the recorded cell is modulated around the average firing rate that is similar to \fbase{} and in that way encodes the time-course of the stimulus.
|
||||
The time-dependent response of the neuron was estimated from the spiking activity $x_k(t) = \sum_i\delta(t-t_{k,i})$ recorded for each stimulus presentation, $k$, by kernel convolution with a Gaussian kernel
|
||||
The time-dependent response of the neuron was estimated from the spiking activity
|
||||
\begin{equation}\label{eq:spikes}
|
||||
x_k(t) = \sum_i\delta(t-t_{k,i})
|
||||
\end{equation}
|
||||
recorded for each stimulus presentation, $k$, by kernel convolution with a Gaussian kernel
|
||||
|
||||
\begin{equation}
|
||||
K(t) = \scriptstyle \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{t^2}{2\sigma^2}}
|
||||
@ -699,42 +707,42 @@ where $*$ denotes the convolution. $r(t)$ is then calculated as the across-trial
|
||||
r(t) = \left\langle r_k(t) \right\rangle _k.
|
||||
\end{equation}
|
||||
|
||||
To quantify how strongly the neuron is driven by the stimulus we quantified the response modulation as the standard deviation $\sigma_{M} = \sqrt{\langle (r(t)-\langle r(t) \rangle)^2\rangle}$.
|
||||
To quantify how strongly the neuron is driven by the stimulus we quantified the response modulation as the standard deviation $\sigma_{M} = \sqrt{\langle (r(t)-\langle r(t) \rangle_t)^2\rangle_t}$, where $\langle \cdot \rangle_t$ indicates averaging over time.
|
||||
|
||||
\paragraph{Spectral analysis}\label{susceptibility_methods}
|
||||
The neuron is driven by the stimulus and thus the neuronal response $r(t)$ depends on the stimulus $s(t)$. To investigate the relation between stimulus and response we calculated the first- and second-order susceptibility of the neuron to the stimulus in the frequency domain. The Fourier transforms of $s(t)$ and $r(t)$ are denoted as $\tilde s(\omega)$ and $\tilde r(\omega)$ and were calculated according to $\tilde x(\omega) = \int_{0}^{T} \, x(t) \cdot e^{- i \omega t}\,dt$, with $T$ being the signal duration. Stimuli had a duration of 10\,s and spectra of stimulus and response were calculated in separate segments of 0.5\,s with no overlap resulting in a spectral resolution of 2\,Hz. $r(t)$ was estimated by kernel convolution with a box kernel that had a width matching the sampling interval to preserve temporal accuracy as far as possible.
|
||||
The neuron is driven by the stimulus and thus the spiking response $x(t)$ (\eqref{eq:spikes}) depends on the stimulus $s(t)$. To investigate the relation between stimulus and response we calculated the first- and second-order susceptibility of the neuron to the stimulus in the frequency domain. The Fourier transforms of $s(t)$ and $x(t)$ are denoted as $\tilde s(\omega)$ and $\tilde x(\omega)$ and were calculated according to $\tilde x(\omega) = \int_{0}^{T} \, x(t) \cdot e^{- i \omega t}\,dt$, with $T$ being the signal duration. Stimuli had a duration of 10\,s and spectra of stimulus and response were calculated in separate segments of 0.5\,s with no overlap resulting in a spectral resolution of 2\,Hz.
|
||||
|
||||
The power spectrum was calculated as
|
||||
The power spectrum of the stimulus $s(t)$ was calculated as
|
||||
\begin{equation}
|
||||
\label{powereq}
|
||||
\begin{split}
|
||||
S_{ss}(\omega) = \frac{\langle \tilde s(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
\end{split}
|
||||
\end{equation}
|
||||
with $\tilde s^* $ being the complex conjugate and $\langle ... \rangle$ denoting averaging over the segments. The cross-spectrum $S_{rs}(\omega)$ was calculated according to
|
||||
with $\tilde s^* $ being the complex conjugate and $\langle ... \rangle$ denoting averaging over the segments. The power spectrum of the spike trains $S_{xx}$ was calculated accordingly. The cross-spectrum $S_{xs}(\omega)$ between stimulus and evoked spike trains was calculated according to
|
||||
\begin{equation}
|
||||
\label{cross}
|
||||
\begin{split}
|
||||
S_{rs}(\omega) = \frac{\langle \tilde r(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
S_{xs}(\omega) = \frac{\langle \tilde x(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
\end{split}
|
||||
\end{equation}
|
||||
From $S_{rs}(\omega)$ and $ S_{ss}(\omega)$ we calculated the linear susceptibility (transfer function) as
|
||||
From $S_{xs}(\omega)$ and $ S_{ss}(\omega)$ we calculated the linear susceptibility (transfer function) as
|
||||
\begin{equation}
|
||||
\label{linearencoding_methods}
|
||||
\begin{split}
|
||||
\chi_{1}(\omega) = \frac{S_{rs}(\omega) }{S_{ss}(\omega) }
|
||||
\chi_{1}(\omega) = \frac{S_{xs}(\omega) }{S_{ss}(\omega) }
|
||||
\end{split}
|
||||
\end{equation}
|
||||
The second-order cross-spectrum that depends on the two frequencies $\omega_1$ and $\omega_2$ was calculated according to
|
||||
\begin{equation}
|
||||
\label{eq:crosshigh}
|
||||
S_{rss} (\omega_{1},\omega_{2}) = \frac{\langle \tilde r (\omega_{1}+\omega_{2}) \tilde s^* (\omega_{1})\tilde s^* (\omega_{2}) \rangle}{T}
|
||||
S_{xss} (\omega_{1},\omega_{2}) = \frac{\langle \tilde x (\omega_{1}+\omega_{2}) \tilde s^* (\omega_{1})\tilde s^* (\omega_{2}) \rangle}{T}
|
||||
\end{equation}
|
||||
The second-order susceptibility was calculated by dividing the higher-order cross-spectrum by the spectral power at the respective frequencies.
|
||||
\begin{equation}
|
||||
\label{eq:susceptibility}
|
||||
%\begin{split}
|
||||
\chi_{2}(\omega_{1}, \omega_{2}) = \frac{S_{rss} (\omega_{1},\omega_{2})}{2S_{ss} (\omega_{1}) S_{ss} (\omega_{2})}
|
||||
\chi_{2}(\omega_{1}, \omega_{2}) = \frac{S_{xss} (\omega_{1},\omega_{2})}{2S_{ss} (\omega_{1}) S_{ss} (\omega_{2})}
|
||||
%\end{split}
|
||||
\end{equation}
|
||||
% Applying the Fourier transform this can be rewritten resulting in:
|
||||
@ -750,7 +758,7 @@ The absolute value of a second-order susceptibility matrix is visualized in \fig
|
||||
We expect to see nonlinear susceptibility when $\omega_1 + \omega_2 = \fbase{}$. To characterize this we calculated the peakedness of the nonlinearity (PNL) as
|
||||
\begin{equation}
|
||||
\label{eq:nli_equation}
|
||||
\nli{} = \frac{ D(\max(\fbase{}-5\,\rm{Hz} \leq f \leq \fbase{}+5\,\rm{Hz}))}{\mathrm{med}(D(f))}
|
||||
\nli{} = \frac{ \max D(\fbase{}-5\,\rm{Hz} \leq f \leq \fbase{}+5\,\rm{Hz})}{\mathrm{med}(D(f))}
|
||||
\end{equation}
|
||||
For this index, the second-order susceptibility matrix was projected onto the diagonal $D(f)$, by taking the mean of the anti-diagonals. The peakedness at the frequency $\fbase{}$ in $D(f)$ was quantified by finding the maximum of $D(f)$ in the range $\fbase{} \pm 5$\,Hz (\subfigrefb{fig:cells_suscept}{G}) and dividing it by the median of $D(f)$.
|
||||
|
||||
@ -829,9 +837,9 @@ The model neurons were driven with similar stimuli as the real neurons in the or
|
||||
The random amplitude modulation (RAM) input to the model was created by drawing random amplitude and phases from Gaussian distributions for each frequency component in the range 0--300 Hz. An inverse Fourier transform was applied to get the final amplitude RAM time-course. The input to the model was then
|
||||
\begin{equation}
|
||||
\label{eq:ram_equation}
|
||||
x(t) = (1+ RAM(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
y(t) = (1+ s(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
\end{equation}
|
||||
|
||||
\note{fix stimulus x and y notation and RAM}
|
||||
From each simulation run, the first second was discarded and the analysis was based on the last second of the data. The resulting spectra thus have a spectral resolution of 1\,Hz.
|
||||
% \subsection{Second-order susceptibility analysis of the model}
|
||||
% %\subsubsection{Model second-order nonlinearity}
|
||||
@ -839,24 +847,25 @@ From each simulation run, the first second was discarded and the analysis was ba
|
||||
% The second-order susceptibility in the model was calculated with \Eqnref{eq:susceptibility}, resulting in matrices as in \figrefb{model_and_data} and \figrefb{fig:model_full}. For this, the model neuron was presented the input $x(t)$ for 2\,s, with the first second being dismissed as the transient. The second-order susceptibility calculation was performed on the last second, resulting in a frequency resolution of 1\,Hz.
|
||||
|
||||
\subsection*{Model noise split into a noise and a stimulus component}\label{intrinsicsplit_methods}
|
||||
According to the Novikov-Furutsu Theorem \cite{Novikov1965, Furutsu1963} the total noise of a LIF model ($\xi$) can be split up into several independent noise processes with the same correlation function. Here we split the internal noise into two parts: (i) One part is treated as a driving input signal $\xi_{signal} = \sqrt{\rho \, 2D \,c_{signal}} \cdot \xi(t) $ and used to calculate the cross-spectra in \Eqnref{eq:crosshigh} and (ii) the remaining noise $\xi_{noise} = \sqrt{2D \, c_{noise}} \cdot \xi(t)$ that is treated as pure noise. In this way the effective signal-to-noise ratio can be increased while maintaining the total noise in the system. $\rho$ a scaling factor that compensates (see below) for the signal transformations the amplitude modulation stimulus undergoes in the model, i.e. the threshold and the dendritic lowpass. In our case the model has a carrier (the fish's self-generated EOD) and we thus want to drive the model with an amplitude modulation stimulus
|
||||
According to the Novikov-Furutsu Theorem \cite{Novikov1965, Furutsu1963} the total noise of a LIF model ($\xi$) can be split up into several independent noise processes with the same correlation function. Here we split the internal noise into two parts: (i) One part is treated as a driving input signal $s_\xi(t)$ \note{hier umschreiben das xi muss ein RAM sein} and used to calculate the cross-spectra in \Eqnref{eq:crosshigh} and (ii) the remaining noise $\sqrt{2D \, c_{noise}} \cdot \xi(t)$ that is treated as pure noise. In this way the effective signal-to-noise ratio can be increased while maintaining the total noise in the system. $\rho$ a scaling factor that compensates (see below) for the signal transformations the amplitude modulation stimulus undergoes in the model, i.e. the threshold and the dendritic lowpass. In our case the model has a carrier (the fish's self-generated EOD) and we thus want to drive the model with an amplitude modulation stimulus
|
||||
%\sqrt{\rho \, 2D \,c_{signal}} \cdot \xi(t)
|
||||
|
||||
%(1-c_{signal})\cdot\xi$c_{noise} = 1-c_{signal}$
|
||||
%c_{signal} \cdot \xi
|
||||
\begin{equation}
|
||||
\label{eq:ram_split}
|
||||
x(t) = (1+ \xi_{signal}) \cdot \cos(2\pi f_{EOD} t)
|
||||
y(t) = (1+ s_\xi(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
\end{equation}
|
||||
|
||||
\begin{equation}
|
||||
\label{eq:Noise_split_intrinsic_dendrite}
|
||||
\tau_{d} \frac{d V_{d}}{d t} = -V_{d}+ \lfloor x(t) \rfloor_{0}
|
||||
\tau_{d} \frac{d V_{d}}{d t} = -V_{d}+ \lfloor y(t) \rfloor_{0}
|
||||
\end{equation}
|
||||
|
||||
|
||||
\begin{equation}
|
||||
\label{eq:Noise_split_intrinsic}
|
||||
\tau_{m} \frac{d V_{m}}{d t} = - V_{m} + \mu + \alpha V_{d} - A + \xi_{noise}
|
||||
\tau_{m} \frac{d V_{m}}{d t} = - V_{m} + \mu + \alpha V_{d} - A + \sqrt{2D \, c_{noise}} \cdot \xi(t)
|
||||
\end{equation}
|
||||
% das stimmt so, das c kommt unter die Wurzel!
|
||||
|
||||
@ -907,7 +916,7 @@ A big portion of the total noise was assigned to the signal component ($c_{signa
|
||||
|
||||
\section*{Supporting information}
|
||||
|
||||
\%subsection*{High-CV P-units do not exhibit increased nonlinear interactions at \fsum}
|
||||
%\subsection*{High-CV P-units do not exhibit increased nonlinear interactions at \fsum}
|
||||
|
||||
|
||||
|
||||
|
||||
@ -89,6 +89,9 @@
|
||||
\usepackage{nameref}%,hyperref
|
||||
\usepackage[breaklinks=true,colorlinks=true,citecolor=blue!30!black,urlcolor=blue!30!black,linkcolor=blue!30!black]{hyperref}
|
||||
|
||||
% \usepackage{natbib}%sort,comma,,round
|
||||
% \setcitestyle{super,comma,sort&compress}%authoryear,
|
||||
|
||||
% line numbers
|
||||
\usepackage[right]{lineno}
|
||||
|
||||
@ -138,8 +141,8 @@
|
||||
\renewcommand{\figurename}{Fig}
|
||||
|
||||
% Use the PLoS provided BiBTeX style
|
||||
\bibliographystyle{plos2015}
|
||||
|
||||
%\bibliographystyle{plos2015}
|
||||
\bibliographystyle{apalike}%alpha}%}%alpha}%apalike}
|
||||
% Remove brackets from numbering in List of References
|
||||
\makeatletter
|
||||
\renewcommand{\@biblabel}[1]{\quad#1.}
|
||||
@ -425,12 +428,12 @@
|
||||
\\
|
||||
Alexandra Barayeu\textsuperscript{1},
|
||||
Maria Schlungbaum\textsuperscript{2,3},
|
||||
Benjamin Lindner\textsubscript{2,3},
|
||||
Benjamin Lindner\textsuperscript{2,3},
|
||||
Jan Benda\textsuperscript{1, 4}
|
||||
Jan Grewe\textsuperscript{1, *}
|
||||
\\
|
||||
\bigskip
|
||||
\textbf{1} Institute for Neurobiology, Eberhardt Karls Universit\"at T\"ubingen, 72076 T\"ubingen, Germany\\
|
||||
\textbf{1} Institute for Neurobiology, Eberhard Karls Universit\"at T\"ubingen, 72076 T\"ubingen, Germany\\
|
||||
\textbf{2} Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany\\
|
||||
\textbf{3} Department of Physics, Humboldt University Berlin, Berlin, Germany\\
|
||||
\textbf{4} Bernstein Center for Computational Neuroscience Tübingen, Tübingen, 72076, Germany\\
|
||||
@ -519,9 +522,11 @@ The P-unit responses can be partially explained by simple linear filters. The li
|
||||
|
||||
|
||||
\section*{Results}
|
||||
Theoretical work shows that leaky-integrate-and-fire (LIF) model neurons show a distinct pattern of nonlinear stimulus encoding when the model is driven by two cosine signals. In the context of the weakly electric fish, such a setting is part of the animal's everyday life as the sinusoidal electric-organ discharges (EODs) of neighboring animals interfere with the own field and each lead to sinusoidal amplitude modulations (AMs) that are called beats and envelopes \cite{Middleton2006, Savard2011,Stamper2012Envelope}. The p-type electroreceptor afferents of the tuberous electrosensory system, i.e. the P-units, respond to such AMs of the underlying EOD carrier and their time-dependent firing rate carries information about the stimulus' time-course. P-units are heterogeneous in their baseline firing properties \cite{Grewe2017, Hladnik2023} and differ with respect to their baseline firing rates, the sensitivity and their noisiness. We here explore the nonlinear mechanism in different cells that exhibit distinctly different levels of noise, i.e. differences in the coefficient of variation (CV) of the interspike intervals (ISI). Low-CV P-units have a less noisy, more regular, firing pattern whereas high-CV P-units show a less regular firing pattern in their baseline activity.
|
||||
Theoretical work \cite{Voronenko2017} shows that stochastic leaky integrate-and-fire (LIF) model neurons may show nonlinear stimulus encoding when the model is driven by two cosine signals with specific frequencies. In the context of weakly electric fish, such a setting is part of the animal's everyday life. The sinusoidal electric-organ discharges (EODs) of neighboring animals interfere and lead to amplitude modulations (AMs) that are called beats (two-fish interaction) and envelopes (multiple-fish interaction) \cite{Middleton2006, Savard2011,Stamper2012Envelope}. The p-type electroreceptor afferents of the tuberous electrosensory system, i.e. the P-units encode such AMs of the underlying EOD carrier in their time-dependent firing rates \cite{Bastian1981a,Walz2014}. We here explore nonlinear responses of different cells that exhibit distinctly different levels of output variability, quantified by the coefficient of variation (CV) of their interspike intervals (ISI). Low-CV P-units have a less noisy firing pattern that is closer to pacemaker firing, whereas high-CV P-units show a more irregular firing pattern that is closer to a Poisson process.
|
||||
%P-units are heterogeneous in their baseline firing properties \cite{Grewe2017, Hladnik2023} and differ with respect to their baseline firing rates, the sensitivity and their noisiness.
|
||||
|
||||
|
||||
\subsection*{Low-CV P-units exhibit nonlinear interactions} %frequency combinations withappearing when the input frequencies are related to \fbase{} are
|
||||
\subsection*{Nonlinear signal transmission in low-CV P-units} %frequency combinations withappearing when the input frequencies are related to \fbase{} are
|
||||
|
||||
Second-order susceptibility is expected to be especially pronounced for low-CV cells \cite{Voronenko2017}. P-units fire action potentials probabilistically phase-locked to the self-generated EOD. Skipping of EOD cycles leads to the characteristic multimodal ISI distribution with maxima at integer multiples of the EOD period (\subfigrefb{fig:cells_suscept}{A}). In this example the ISI distribution has a CV of 0.2 which can be considered low among P-units\cite{Hladnik2023}. Spectral analysis of the baseline activity shows two major peaks, the first is located at the baseline firing rate (\fbase), the second is located at the discharge frequency of the electric organ (\feod{}) and is flanked by two smaller peaks at $\feod \pm \fbase{}$ (\subfigref{fig:cells_suscept}{B}).
|
||||
|
||||
@ -547,8 +552,13 @@ Irrespective of the CV, neither cell shows the complete proposed structure of no
|
||||
}
|
||||
\end{figure*}
|
||||
|
||||
\subsection*{Internal noise hides parts of the nonlinearity structure}
|
||||
Traces of the proposed structure of second-order susceptibility are found in both ampullary and p-type electrosensory afferents. The nonlinerarity seems to depend on the CV, i.e. the level of intrinsic noise in the cells and in the next step we address whether the level of intrinsic masks the expected structures. One option to reduce the impact of this intrinsic noise is to average it out over many repetitions of the same stimulus. In the electrophysiological experiments we only have a limited recording duration and hence the number of stimulus repetitions is limited. To overcome these limitations we compared between an electrophysiologically recorded low-CV P-unit and its P-unit LIF model counterpart fitted to reproduce the P-unit behavior faithfully (see \figref{flowchart})\cite{Barayeu2023}. In the recording depicted in \subfigrefb{model_and_data}{A} the cell was stimulated with a weak RAM stimulus with the contrast of 1\,$\%$ (red, \subfigrefb{model_and_data}\,\panel[i]{B}). The across trial average ($\n{}=11$) shows the diagonal band in second-order susceptibility at \fsumb{} (yellow diagonal in pink edges) and an additional nonlinear band appeared at \fsumehalf{} (\subfigrefb{model_and_data}{A}). The matched model reproduces the same diagonal at \fsumb{} (\subfigrefb{model_and_data}\,\panel[ii]{B}) but not the diagonal at \fsumehalf. By increasing the number of trials in the simulation, the signal-to-noise ratio and thus the estimation of the nonlinearity structures can be improved. Still, even with 1 million repetitions, no changes are observable in the nonlinearity structures (\subfigrefb{model_and_data}\,\panel[iii]{B}). The increased trial count, however goes along with generally smaller second-order susceptibility values (\subfigrefb{model_and_data}\,\panel[iii]{B}). The decrease of the second-order susceptibility follows the relation $1/ \sqrt{N} $ (\figrefb{trialnr}).
|
||||
\subsection*{High level of total noise hides parts of the nonlinearity structure}
|
||||
Traces of the expected structure of second-order susceptibility are found in both ampullary and p-type electrosensory afferents. In the recordings shown above (\figrefb{fig:cells_suscept}, \figrefb{fig:ampullary}), the nonlinear response is strong whenever the two frequencies (\fone{}, \ftwo{}) fall onto the antidiagonal \fsumb{}, which is in line with theoretical expectations \cite{Voronenko2017}. However, a pronounced nonlinear response for frequencies with \foneb{} or \ftwob{}, although predicted by theory, cannot be observed. Here we investigate how these discrepancies can be understood.
|
||||
|
||||
In the electrophysiological experiments we only have a limited number of trials and this insufficient averaging may occlude the full nonlinear structure. This limitation can be overcome by using a computational model for the P-unit, a stochastic leaky integrate-and-fire model with adaptation current and dendritic preprocessing, with parameters fitted to the experimentally recorded P-unit (\figrefb{flowchart}) \cite{Barayeu2023}. The model faithfully reproduces the second-order susceptibility of a low-CV cell estimated from the same low number of repetitions ($\n{}=11$, compare \panel{A} and \panel[ii]{B} in \figrefb{model_and_data}). In the model we can increase the number of repetitions substantially but still do not observe the full nonlinear structure ($\n{}=10^6$, \subfigrefb{model_and_data}\,\panel[iii]{B}). A possible reason for this could be that by applying a broadband stimulus the effective input-noise level is increased and this may linearize the signal
|
||||
transmission \cite{Longtin1993, Chialvo1997, Roddey2000, Voronenko2017}. Assuming that the intrinsic noise level in this P-unit is small enough, the full nonlinear structure should appear in the limit of weak AMs. Again, this cannot be done experimentally, because the problem of insufficient averaging becomes even more severe for weak AMs (low contrast). In the model, however, we know the time course of the intrinsic noise and can use this knowledge to determine the susceptibilities by input-output correlations via the Furutsu-Novikov theorem \cite{Furutsu1963, Novikov1965}. This theorem, in its simplest form, states that the cross-spectrum $S_{x\eta}(\omega)$ of a Gaussian noise $\eta(t)$ driving a nonlinear system and the system's output $x(t)$ is proportional to the linear susceptibility $S_{x\eta}(\omega)=\chi(\omega)S_{\eta\eta}(\omega)$. Here $\chi(\omega)$ characterizes the linear response to an infinitely weak signal $\varepsilon s(t)$ in the presence of the background noise $\eta(t)$. Likewise, the nonlinear susceptibility can be determined in an analogous fashion from higher-order input-output cross-spectra (see methods \eqref{eq:crosshigh}, \eqref{eq:susceptibility}) \cite{Egerland2020}. In line with an alternative derivation of the Furutsu-Novikov theorem \cite{Lindner2022}, we can split the total noise and consider a fraction of it as stimulus. This allows to calculate the susceptibility from the cross-spectrum between the output and this stimulus fraction of the noise. Adapting this approach to our P-unit model (see methods), we replace the intrinsic noise by an approximately equivalent RAM stimulus $s_\xi(t)$ and a weak remaining intrinsic noise ($\sqrt{2D \, c_{noise}} \cdot \xi(t)$, with $c_\text{noise} = 0.1$, see methods \eqref{eq:ram_split}, \eqref{eq:Noise_split_intrinsic}, \eqref{eq:Noise_split_intrinsic_dendrite}, \subfigrefb{model_and_data}\,\panel[i]{C}). We tune the amplitude / standard deviation of the RAM stimulus $s_\xi(t)$ such that the output firing rate and variability (CV) are the same as in the baseline activity (i.e. full intrinsic noise $\sqrt{2D}\xi(t)$ in the voltage equation but no RAM) and compute the second- and third-order cross-spectra between the RAM part of the noise $s_\xi(t)$ and the output spike train. This procedure has two consequences: (i) by means of the cross-spectrum between the output and $s_\xi(t)$, which is a large fraction of the noise, the signal-to-noise ratio of the measured susceptibilities is drastically improved; (ii) the total noise in the system has been reduced (by what was before the external RAM stimulus $s(t)$), which makes the system more nonlinear. For both reasons we now see the expected nonlinear features in the second-order susceptibility for a sufficient number of trials (\subfigrefb{model_and_data}\,\panel[iii]{B}), but not for a number of trials comparable to the experiment (\subfigrefb{model_and_data}\,\panel[ii]{B}). In addition to the strong response for \fsumb{}, we now also observe pronounced nonlinear responses at \foneb{} and \ftwob{} (vertical and horizontal lines, \subfigrefb{model_and_data}\,\panel[iii]{C}). Note, that the increased number of repetitions goes along with a substantial reduction of second-order susceptibility values (\subfigrefb{model_and_data}\,\panel[iii]{B}), that saturate in its peak values for $N>10^5$ (\figrefb{trialnr}). This demonstrates the limited reliability of the statistics that is based on 11 trials. However, we would like to point out that already the limited number of trials as used in the experiments
|
||||
|
||||
The nonlinerarity seems to depend on the CV, which is presumably related to the level of intrinsic noise in the cells. In the next step we address whether the level of intrinsic masks the expected structures. One option to reduce the impact of this intrinsic noise is to average it out over many repetitions of the same stimulus.
|
||||
|
||||
%Note that this line doesn't appear in the susceptibility matrix of the model (\subfigrefb{model_and_data}{C})Each cell has an intrinsic noise level (\subfigrefb{model_and_data}{A}, bottom).
|
||||
%The signal component (purple) compensates for this total noise reduction, is not a weak signal anymore and
|
||||
@ -681,7 +691,11 @@ The baseline firing rate \fbase{} was calculated as the number of spikes divided
|
||||
|
||||
\paragraph{White noise analysis} \label{response_modulation}
|
||||
In the stimulus driven case, the neuronal activity of the recorded cell is modulated around the average firing rate that is similar to \fbase{} and in that way encodes the time-course of the stimulus.
|
||||
The time-dependent response of the neuron was estimated from the spiking activity $x_k(t) = \sum_i\delta(t-t_{k,i})$ recorded for each stimulus presentation, $k$, by kernel convolution with a Gaussian kernel
|
||||
The time-dependent response of the neuron was estimated from the spiking activity
|
||||
\begin{equation}\label{eq:spikes}
|
||||
x_k(t) = \sum_i\delta(t-t_{k,i})
|
||||
\end{equation}
|
||||
recorded for each stimulus presentation, $k$, by kernel convolution with a Gaussian kernel
|
||||
|
||||
\begin{equation}
|
||||
K(t) = \scriptstyle \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{t^2}{2\sigma^2}}
|
||||
@ -696,42 +710,42 @@ where $*$ denotes the convolution. $r(t)$ is then calculated as the across-trial
|
||||
r(t) = \left\langle r_k(t) \right\rangle _k.
|
||||
\end{equation}
|
||||
|
||||
To quantify how strongly the neuron is driven by the stimulus we quantified the response modulation as the standard deviation $\sigma_{M} = \sqrt{\langle (r(t)-\langle r(t) \rangle)^2\rangle}$.
|
||||
To quantify how strongly the neuron is driven by the stimulus we quantified the response modulation as the standard deviation $\sigma_{M} = \sqrt{\langle (r(t)-\langle r(t) \rangle_t)^2\rangle_t}$, where $\langle \cdot \rangle_t$ indicates averaging over time.
|
||||
|
||||
\paragraph{Spectral analysis}\label{susceptibility_methods}
|
||||
The neuron is driven by the stimulus and thus the neuronal response $r(t)$ depends on the stimulus $s(t)$. To investigate the relation between stimulus and response we calculated the first- and second-order susceptibility of the neuron to the stimulus in the frequency domain. The Fourier transforms of $s(t)$ and $r(t)$ are denoted as $\tilde s(\omega)$ and $\tilde r(\omega)$ and were calculated according to $\tilde x(\omega) = \int_{0}^{T} \, x(t) \cdot e^{- i \omega t}\,dt$, with $T$ being the signal duration. Stimuli had a duration of 10\,s and spectra of stimulus and response were calculated in separate segments of 0.5\,s with no overlap resulting in a spectral resolution of 2\,Hz. $r(t)$ was estimated by kernel convolution with a box kernel that had a width matching the sampling interval to preserve temporal accuracy as far as possible.
|
||||
The neuron is driven by the stimulus and thus the spiking response $x(t)$ (\eqref{eq:spikes}) depends on the stimulus $s(t)$. To investigate the relation between stimulus and response we calculated the first- and second-order susceptibility of the neuron to the stimulus in the frequency domain. The Fourier transforms of $s(t)$ and $x(t)$ are denoted as $\tilde s(\omega)$ and $\tilde x(\omega)$ and were calculated according to $\tilde x(\omega) = \int_{0}^{T} \, x(t) \cdot e^{- i \omega t}\,dt$, with $T$ being the signal duration. Stimuli had a duration of 10\,s and spectra of stimulus and response were calculated in separate segments of 0.5\,s with no overlap resulting in a spectral resolution of 2\,Hz.
|
||||
|
||||
The power spectrum was calculated as
|
||||
The power spectrum of the stimulus $s(t)$ was calculated as
|
||||
\begin{equation}
|
||||
\label{powereq}
|
||||
\begin{split}
|
||||
S_{ss}(\omega) = \frac{\langle \tilde s(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
\end{split}
|
||||
\end{equation}
|
||||
with $\tilde s^* $ being the complex conjugate and $\langle ... \rangle$ denoting averaging over the segments. The cross-spectrum $S_{rs}(\omega)$ was calculated according to
|
||||
with $\tilde s^* $ being the complex conjugate and $\langle ... \rangle$ denoting averaging over the segments. The power spectrum of the spike trains $S_{xx}$ was calculated accordingly. The cross-spectrum $S_{xs}(\omega)$ between stimulus and evoked spike trains was calculated according to
|
||||
\begin{equation}
|
||||
\label{cross}
|
||||
\begin{split}
|
||||
S_{rs}(\omega) = \frac{\langle \tilde r(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
S_{xs}(\omega) = \frac{\langle \tilde x(\omega) \tilde s^* (\omega)\rangle}{T}
|
||||
\end{split}
|
||||
\end{equation}
|
||||
From $S_{rs}(\omega)$ and $ S_{ss}(\omega)$ we calculated the linear susceptibility (transfer function) as
|
||||
From $S_{xs}(\omega)$ and $ S_{ss}(\omega)$ we calculated the linear susceptibility (transfer function) as
|
||||
\begin{equation}
|
||||
\label{linearencoding_methods}
|
||||
\begin{split}
|
||||
\chi_{1}(\omega) = \frac{S_{rs}(\omega) }{S_{ss}(\omega) }
|
||||
\chi_{1}(\omega) = \frac{S_{xs}(\omega) }{S_{ss}(\omega) }
|
||||
\end{split}
|
||||
\end{equation}
|
||||
The second-order cross-spectrum that depends on the two frequencies $\omega_1$ and $\omega_2$ was calculated according to
|
||||
\begin{equation}
|
||||
\label{eq:crosshigh}
|
||||
S_{rss} (\omega_{1},\omega_{2}) = \frac{\langle \tilde r (\omega_{1}+\omega_{2}) \tilde s^* (\omega_{1})\tilde s^* (\omega_{2}) \rangle}{T}
|
||||
S_{xss} (\omega_{1},\omega_{2}) = \frac{\langle \tilde x (\omega_{1}+\omega_{2}) \tilde s^* (\omega_{1})\tilde s^* (\omega_{2}) \rangle}{T}
|
||||
\end{equation}
|
||||
The second-order susceptibility was calculated by dividing the higher-order cross-spectrum by the spectral power at the respective frequencies.
|
||||
\begin{equation}
|
||||
\label{eq:susceptibility}
|
||||
%\begin{split}
|
||||
\chi_{2}(\omega_{1}, \omega_{2}) = \frac{S_{rss} (\omega_{1},\omega_{2})}{2S_{ss} (\omega_{1}) S_{ss} (\omega_{2})}
|
||||
\chi_{2}(\omega_{1}, \omega_{2}) = \frac{S_{xss} (\omega_{1},\omega_{2})}{2S_{ss} (\omega_{1}) S_{ss} (\omega_{2})}
|
||||
%\end{split}
|
||||
\end{equation}
|
||||
% Applying the Fourier transform this can be rewritten resulting in:
|
||||
@ -747,7 +761,7 @@ The absolute value of a second-order susceptibility matrix is visualized in \fig
|
||||
We expect to see nonlinear susceptibility when $\omega_1 + \omega_2 = \fbase{}$. To characterize this we calculated the peakedness of the nonlinearity (PNL) as
|
||||
\begin{equation}
|
||||
\label{eq:nli_equation}
|
||||
\nli{} = \frac{ D(\max(\fbase{}-5\,\rm{Hz} \leq f \leq \fbase{}+5\,\rm{Hz}))}{\mathrm{med}(D(f))}
|
||||
\nli{} = \frac{ \max D(\fbase{}-5\,\rm{Hz} \leq f \leq \fbase{}+5\,\rm{Hz})}{\mathrm{med}(D(f))}
|
||||
\end{equation}
|
||||
For this index, the second-order susceptibility matrix was projected onto the diagonal $D(f)$, by taking the mean of the anti-diagonals. The peakedness at the frequency $\fbase{}$ in $D(f)$ was quantified by finding the maximum of $D(f)$ in the range $\fbase{} \pm 5$\,Hz (\subfigrefb{fig:cells_suscept}{G}) and dividing it by the median of $D(f)$.
|
||||
|
||||
@ -826,9 +840,9 @@ The model neurons were driven with similar stimuli as the real neurons in the or
|
||||
The random amplitude modulation (RAM) input to the model was created by drawing random amplitude and phases from Gaussian distributions for each frequency component in the range 0--300 Hz. An inverse Fourier transform was applied to get the final amplitude RAM time-course. The input to the model was then
|
||||
\begin{equation}
|
||||
\label{eq:ram_equation}
|
||||
x(t) = (1+ RAM(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
y(t) = (1+ s(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
\end{equation}
|
||||
|
||||
\note{fix stimulus x and y notation and RAM}
|
||||
From each simulation run, the first second was discarded and the analysis was based on the last second of the data. The resulting spectra thus have a spectral resolution of 1\,Hz.
|
||||
% \subsection{Second-order susceptibility analysis of the model}
|
||||
% %\subsubsection{Model second-order nonlinearity}
|
||||
@ -836,24 +850,25 @@ From each simulation run, the first second was discarded and the analysis was ba
|
||||
% The second-order susceptibility in the model was calculated with \Eqnref{eq:susceptibility}, resulting in matrices as in \figrefb{model_and_data} and \figrefb{fig:model_full}. For this, the model neuron was presented the input $x(t)$ for 2\,s, with the first second being dismissed as the transient. The second-order susceptibility calculation was performed on the last second, resulting in a frequency resolution of 1\,Hz.
|
||||
|
||||
\subsection*{Model noise split into a noise and a stimulus component}\label{intrinsicsplit_methods}
|
||||
According to the Novikov-Furutsu Theorem \cite{Novikov1965, Furutsu1963} the total noise of a LIF model ($\xi$) can be split up into several independent noise processes with the same correlation function. Here we split the internal noise into two parts: (i) One part is treated as a driving input signal $\xi_{signal} = \sqrt{\rho \, 2D \,c_{signal}} \cdot \xi(t) $ and used to calculate the cross-spectra in \Eqnref{eq:crosshigh} and (ii) the remaining noise $\xi_{noise} = \sqrt{2D \, c_{noise}} \cdot \xi(t)$ that is treated as pure noise. In this way the effective signal-to-noise ratio can be increased while maintaining the total noise in the system. $\rho$ a scaling factor that compensates (see below) for the signal transformations the amplitude modulation stimulus undergoes in the model, i.e. the threshold and the dendritic lowpass. In our case the model has a carrier (the fish's self-generated EOD) and we thus want to drive the model with an amplitude modulation stimulus
|
||||
According to the Novikov-Furutsu Theorem \cite{Novikov1965, Furutsu1963} the total noise of a LIF model ($\xi$) can be split up into several independent noise processes with the same correlation function. Here we split the internal noise into two parts: (i) One part is treated as a driving input signal $s_\xi(t)$ \note{hier umschreiben das xi muss ein RAM sein} and used to calculate the cross-spectra in \Eqnref{eq:crosshigh} and (ii) the remaining noise $\sqrt{2D \, c_{noise}} \cdot \xi(t)$ that is treated as pure noise. In this way the effective signal-to-noise ratio can be increased while maintaining the total noise in the system. $\rho$ a scaling factor that compensates (see below) for the signal transformations the amplitude modulation stimulus undergoes in the model, i.e. the threshold and the dendritic lowpass. In our case the model has a carrier (the fish's self-generated EOD) and we thus want to drive the model with an amplitude modulation stimulus
|
||||
%\sqrt{\rho \, 2D \,c_{signal}} \cdot \xi(t)
|
||||
|
||||
%(1-c_{signal})\cdot\xi$c_{noise} = 1-c_{signal}$
|
||||
%c_{signal} \cdot \xi
|
||||
\begin{equation}
|
||||
\label{eq:ram_split}
|
||||
x(t) = (1+ \xi_{signal}) \cdot \cos(2\pi f_{EOD} t)
|
||||
y(t) = (1+ s_\xi(t)) \cdot \cos(2\pi f_{EOD} t)
|
||||
\end{equation}
|
||||
|
||||
\begin{equation}
|
||||
\label{eq:Noise_split_intrinsic_dendrite}
|
||||
\tau_{d} \frac{d V_{d}}{d t} = -V_{d}+ \lfloor x(t) \rfloor_{0}
|
||||
\tau_{d} \frac{d V_{d}}{d t} = -V_{d}+ \lfloor y(t) \rfloor_{0}
|
||||
\end{equation}
|
||||
|
||||
|
||||
\begin{equation}
|
||||
\label{eq:Noise_split_intrinsic}
|
||||
\tau_{m} \frac{d V_{m}}{d t} = - V_{m} + \mu + \alpha V_{d} - A + \xi_{noise}
|
||||
\tau_{m} \frac{d V_{m}}{d t} = - V_{m} + \mu + \alpha V_{d} - A + \sqrt{2D \, c_{noise}} \cdot \xi(t)
|
||||
\end{equation}
|
||||
% das stimmt so, das c kommt unter die Wurzel!
|
||||
|
||||
@ -895,13 +910,16 @@ A big portion of the total noise was assigned to the signal component ($c_{signa
|
||||
% here. See http://journals.plos.org/plosone/s/latex for
|
||||
% step-by-step instructions.
|
||||
%
|
||||
\bibliography{references}
|
||||
|
||||
%\bibliographystyle{apalike}%alpha}%}%alpha}%apalike}
|
||||
\bibliography{journalsabbrv,references}
|
||||
% \bibliographystyle{apalike} %or any other style you like
|
||||
%\bibliography{references}
|
||||
%\bibliography{journalsabbrv,references}
|
||||
\newpage
|
||||
|
||||
\section*{Supporting information}
|
||||
|
||||
\%subsection*{High-CV P-units do not exhibit increased nonlinear interactions at \fsum}
|
||||
%\subsection*{High-CV P-units do not exhibit increased nonlinear interactions at \fsum}
|
||||
|
||||
|
||||
|
||||
@ -924,12 +942,12 @@ CVs in P-units can range up to 1.5 \cite{Grewe2017, Hladnik2023}. We show the sa
|
||||
}
|
||||
\end{figure*}
|
||||
|
||||
\bibliographystyle{iscience}
|
||||
% \bibliographystyle{iscience}
|
||||
%\bibliographystyle{apalike}%alpha}%}%alpha}%apalike}
|
||||
%\bibliographystyle{elsarticle-num-names}%elsarticle-num-names}
|
||||
%\ExecuteBibliographyOptions{sorting=nty}
|
||||
%\bibliographystyle{authordate2}
|
||||
\bibliography{journalsabbrv,references}
|
||||
%\bibliography{journalsabbrv,references}
|
||||
%\begin{thebibliography}{00}
|
||||
|
||||
\end{document}
|
||||
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67
trialnr.py
67
trialnr.py
@ -64,7 +64,7 @@ def trialnr(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells
|
||||
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
|
||||
@ -139,12 +139,12 @@ def trialnr(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells
|
||||
if len(stack)> 0:
|
||||
model_show, stack_plot, stack_plot_wo_norm = get_stack(cell, stack)
|
||||
stacks.append(stack_plot)
|
||||
perc95.append(np.percentile(stack_plot,95))
|
||||
perc05.append(np.percentile(stack_plot, 5))
|
||||
perc95.append(np.percentile(stack_plot,99.9))
|
||||
perc05.append(np.percentile(stack_plot, 0))
|
||||
median.append(np.percentile(stack_plot, 50))
|
||||
stacks_wo_norm.append(stack_plot_wo_norm)
|
||||
perc95_wo_norm.append(np.percentile(stack_plot_wo_norm,95))
|
||||
perc05_wo_norm.append(np.percentile(stack_plot_wo_norm, 5))
|
||||
perc95_wo_norm.append(np.percentile(stack_plot_wo_norm,99.9))
|
||||
perc05_wo_norm.append(np.percentile(stack_plot_wo_norm, 0))
|
||||
median_wo_norm.append(np.percentile(stack_plot_wo_norm, 50))
|
||||
|
||||
else:
|
||||
@ -158,18 +158,53 @@ def trialnr(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells
|
||||
perc05_wo_norm.append(float('nan'))
|
||||
median_wo_norm.append(float('nan'))
|
||||
|
||||
ax = plt.subplot(1,1,1)
|
||||
ax.plot(trial_nrs_here, perc05, color = 'grey')
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ax.plot(trial_nrs_here, perc95, color = 'grey')
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||||
ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
ax.fill_between(trial_nrs_here, perc05, perc95, color='grey')
|
||||
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
fig, ax = plt.subplots(2,2)
|
||||
#ax.plot(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[0,0].plot(trial_nrs_here, perc95, color = 'grey')
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[0,0].scatter(trial_nrs_here, perc95, color = 'black')
|
||||
|
||||
ax.set_xlabel('Trials [$N$]')
|
||||
ax.set_ylabel('$\chi_{2}$\,[Hz]')
|
||||
ax[0,1].plot(trial_nrs_here, perc95_wo_norm / trial_nrs_here, color = 'grey')
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[0,1].scatter(trial_nrs_here, perc95_wo_norm / trial_nrs_here, color = 'black')
|
||||
|
||||
##################################
|
||||
ax[1,0].plot(trial_nrs_here, median, color = 'grey')
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[1,0].scatter(trial_nrs_here, median, color = 'black')
|
||||
|
||||
ax[1,1].plot(trial_nrs_here, median_wo_norm / trial_nrs_here, color = 'grey')
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[1,1].scatter(trial_nrs_here, median_wo_norm / trial_nrs_here, color = 'black')
|
||||
#ax[2].plot(trial_nrs_here, perc95, color = 'grey')
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
#ax[2].scatter(trial_nrs_here, perc95, color = 'black')
|
||||
#ax.scatter(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.fill_between(trial_nrs_here, perc05, perc95, color='grey')
|
||||
|
||||
ax[0,0].set_xscale('log')
|
||||
ax[0,0].set_yscale('log')
|
||||
ax[0,1].set_xscale('log')
|
||||
ax[0,1].set_yscale('log')
|
||||
ax[1,0].set_xscale('log')
|
||||
ax[1,0].set_yscale('log')
|
||||
ax[1,1].set_xscale('log')
|
||||
ax[1,1].set_yscale('log')
|
||||
|
||||
ax[0,0].set_xlabel('Trials [$N$]')
|
||||
ax[0,0].set_ylabel('$\chi_{2}$\,[Hz]')
|
||||
ax[1,0].set_xlabel('Trials [$N$]')
|
||||
ax[1,0].set_ylabel('$\chi_{2}$\,[Hz]')
|
||||
|
||||
ax[0,1].set_xlabel('Trials [$N$]')
|
||||
ax[0,1].set_ylabel('$cross$\,[Hz]')
|
||||
ax[1,1].set_xlabel('Trials [$N$]')
|
||||
ax[1,1].set_ylabel('$cross$\,[Hz]')
|
||||
''' ax = plt.subplot(1,3,2)
|
||||
ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
|
||||
ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
|
||||
@ -228,7 +263,7 @@ 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
|
||||
|
||||
2249
utils_all_down.py
2249
utils_all_down.py
File diff suppressed because it is too large
Load Diff
53283
utils_paper.py
53283
utils_paper.py
File diff suppressed because it is too large
Load Diff
@ -2,7 +2,7 @@ import os
|
||||
|
||||
try:
|
||||
from numba import jit
|
||||
# embed()
|
||||
|
||||
except ImportError:
|
||||
def jit(nopython):
|
||||
def decorator_jit(func):
|
||||
@ -90,7 +90,7 @@ if cont_other_dir == False:
|
||||
if ('code' not in inspect.stack()[-1][1]) | (not (('alex' in os.getlogin()) | ('rudnaya' in os.getlogin()))):
|
||||
version = 'public' # für alle sollte version public sein!
|
||||
|
||||
# embed()
|
||||
|
||||
# this we do only in the develop mode
|
||||
if version == 'develop': #
|
||||
copy = True
|
||||
@ -177,7 +177,7 @@ def plt_scatter_four(grid, frame, cell_types, cell_type_type, annotate, colors):
|
||||
# frame_all = frame[(frame[cell_type_type] == cell_type)]
|
||||
frame_g = frame[
|
||||
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
|
||||
# embed()
|
||||
|
||||
plt_cv_fr(annotate, ax2, add[1], frame_g, colors, cell_type_it)
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user