pictures
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@ -29,7 +29,7 @@ def data_overview3():
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row = 2 # sharex=True,
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plot_style()
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default_figsize(column=2, length=4.2) #65.5
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default_figsize(column=2, length=6.2) #65.5
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#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
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var_it = 'Response Modulation [Hz]'
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var_it2 = ''
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@ -45,8 +45,8 @@ def data_overview3():
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right = 0.85
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ws = 0.75
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print(right)
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grid0 = gridspec.GridSpec(2, 2, wspace=ws, bottom=0.13,
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hspace=0.3, left=0.1, right=right, top=0.95)
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grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.13,
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hspace=0.45, left=0.1, right=right, top=0.95)
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###################################
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###############################
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@ -86,9 +86,7 @@ def data_overview3():
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scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
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] # + '_diagonal_proj'
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max_xs = [[5,5,[]],[[],[],[]]]
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for c, cell_type_here in enumerate(cell_types):
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frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
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@ -111,17 +109,18 @@ def data_overview3():
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colorbar = False
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#if colorbar:
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x_axis = ['cv_base','response_modulation']#,'fr_base']#
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var_item_names = [var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
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var_types = ['response_modulation','']#,'']#'response_modulation'
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x_axis_names = ['CV','Response Modulation [Hz]']#$_{Base}$,'Fr$_{Base}$',]
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x_axis = ['cv_base','cv_stim','response_modulation']#,'fr_base']#
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var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
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var_types = ['response_modulation','response_modulation','']#,'']#'response_modulation'
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max_x = max_xs[c]
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x_axis_names = ['CV$_{Base}$','CV$_{stim}$','Response Modulation [Hz]']#$_{Base}$,'Fr$_{Base}$',]
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#score = scores[0]
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score_n = ['Perc99/Med', 'Perc99/Med']
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score_n = ['Perc99/Med', 'Perc99/Med', 'Perc99/Med']
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score = scores[c]
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scores_here = [score,score]#,score]
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scores_here = [score,score,score]#,score]
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score_name = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr']#,'max(diag5Hz)/med_diagonal_proj_fr']#'Perc99/Med'
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score_name = ['Fr/Med', 'Fr/Med']#'Fr/Med'] # 'Perc99/Med'
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score_name = [NLI_scorename(), NLI_scorename()]#NLI_scorename()] # 'Fr/Med''Perc99/Med'
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score_name = [NLI_scorename(), NLI_scorename(), NLI_scorename()]#NLI_scorename()] # 'Fr/Med''Perc99/Med'
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ax_j = []
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axls = []
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axss = []
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@ -132,6 +131,7 @@ def data_overview3():
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log = False
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marker = ['o']
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for v, var_type in enumerate(var_types):
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# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
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@ -149,9 +149,11 @@ def data_overview3():
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else:
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xlimk = None
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cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
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x_axis[v], frame_file, max_val, scores_here[v],
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burst_fraction=burst_fraction[c],xlim = xlimk, ha = 'right', x_pos = 1, xmin = xmin, ymin = ymin, burst_fraction_reset = burst_corr_reset,var_item=var_type)
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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], 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,
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burst_fraction=burst_fraction[c], ha='right')
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if v == 0:
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colors = colors_overview()
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@ -161,10 +163,12 @@ def data_overview3():
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axl.show_spines('')
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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 (' P-unit' in cell_type_here) & (x_axis[v] == 'cv_base' ):
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axs.set_xlim(xlimk)
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axk.set_xlim(xlimk)
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#embed()
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extra_lim = False
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if extra_lim:
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if (' P-unit' in cell_type_here) & ('cv' in x_axis[v]):
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axs.set_xlim(xlimk)
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axk.set_xlim(xlimk)
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#embed()
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#remove_yticks(axl)
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if log:
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@ -174,7 +178,7 @@ def data_overview3():
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axl.minorticks_off()
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axl.set_yticks_blank()
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plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, scores_here[v])
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plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, scores_here[v], marker = ['o',"s"])
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tags.append(axk)
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counter += 1
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#plt.show()
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@ -400,7 +404,7 @@ def start_name(cell_type_here, species):
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return species[0:7] + ' ' + cell_type_here[0:7]
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def plt_specific_cells(axs, cell_type_here, cv_name, frame_file, score):
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def plt_specific_cells(axs, cell_type_here, cv_name, frame_file, score, marker = []):
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######################################################
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# hier kommen die kontrast Punkte dazu
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# für die Zellen spielt Burst correctin ja keine Rolle
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@ -414,9 +418,16 @@ def plt_specific_cells(axs, cell_type_here, cv_name, frame_file, score):
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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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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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#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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else:
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#embed()
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axs.scatter(frame_file[cv_name].loc[cells_extra][0:2], frame_file[score].loc[cells_extra][0:2],
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s=9, facecolor="None", marker = marker[1], edgecolor='black', alpha=0.7, clip_on=False) # colors[str(cell_type_here)]
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axs.scatter(frame_file[cv_name].loc[cells_extra][2:4], frame_file[score].loc[cells_extra][2:4],
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s=9, facecolor="None", marker = marker[0], edgecolor='black', alpha=0.7, clip_on=False) # colors[str(cell_type_here)]
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def plt_var_axis(ax_j, axls, axss,score_name, burst_fraction, cell_type_here, counter, cv_name, frame_file, grid0, max_val, score,
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scores_here, var_item_names, var_types, x_axis, x_axis_names, log = False):
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@ -425,9 +436,9 @@ 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,
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x_axis[v], frame_file, max_val, scores_here[v],
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burst_fraction=burst_fraction[v], var_item=var_type)
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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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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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motivation.pdf
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motivation.pdf
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motivation.png
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motivation.png
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@ -171,7 +171,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
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# load plotting arrays
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arrays, arrays_original, spikes_pure = save_arrays_susept(
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data_dir, cell, c, b, chirps, devs, extract, group_mean, mean_type, plot_group=0,
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rocextra=False, sorted_on=sorted_on, dev_desired = dev_desired)
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rocextra=False, sorted_on=sorted_on, base_several = True, dev_desired = dev_desired)
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####################################################
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####################################################
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@ -306,17 +306,47 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
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fr = len(spikes_pure['base_0'][i])/duration
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frs.append(fr)
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fr = np.mean(frs)
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#embed()
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# embed()
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base_several = False
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if base_several:
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spikes_new = []
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for i in range(len(spikes_pure['base_0'])):
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duration = 100
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duration_full = 101#501
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dur = np.arange(0, duration_full, duration)
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for d_nr in range(len(dur) - 1):
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#embed()
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spikes_new.append(np.array(spikes_pure['base_0'][i][
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(spikes_pure['base_0'][i] > dur[d_nr]) & (
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spikes_pure['base_0'][i] < dur[
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d_nr + 1])])/1000-dur[d_nr]/1000)
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# spikes_pure['base_0'] = spikes_new
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sampling_rate = 1/np.diff(time_array)
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sampling_rate = int(sampling_rate[0]*1000)
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spikes_mats = []
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smoothed05 = []
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for i in range(len(spikes_new)):
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spikes_mat = cr_spikes_mat(spikes_new[i], sampling_rate, int(sampling_rate*duration/1000))
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spikes_mats.append(spikes_mat)
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smoothed05.append(gaussian_filter(spikes_mat, sigma=(float(dev_desired)/1000) * sampling_rate))
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smoothed_base = np.mean(smoothed05, axis=0)
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mat_base = np.mean(spikes_mats, axis=0)
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else:
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smoothed_base = arrays[0][0]
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mat_base = arrays_original[0][0]
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#embed()#arrays[0]v
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fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
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[arrays[0], arrays[2], arrays[1], arrays[3]],
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[arrays_original[0], arrays_original[2], arrays_original[1],
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[[smoothed_base], arrays[2], arrays[1], arrays[3]],
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[[mat_base], arrays_original[2], arrays_original[1],
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arrays_original[3]], spikes_pure, fr, cell, grid0, chirps, extract,
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mean_type,
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group_mean, b, devs,
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xlim=xlim, row=1 + d * 3,
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array_chosen=array_chosen,
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color0_burst=color0_burst, mean_types=[mean_type],
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color01=color01, color02=color02,
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color01=color01, color02=color02,ylim_log=(-15, 3),
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color012=color012,color012_minus = 'pink',
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color01_2=color01_2)
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@ -345,7 +375,7 @@ def motivation_all_small(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1',
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# plt.show()
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if __name__ == '__main__':
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motivation_all_small(dev_desired = '1.5', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
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if __name__ == '__main__':#2.5
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motivation_all_small(dev_desired = '1', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
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cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
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detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')#
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motivation_05_012.csv
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motivation_05_012.csv
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33906
motivation_05_base_0.csv
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@ -408,7 +408,7 @@ In this work, the influence of nonlinearities on stimulus encoding in the primar
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Nonlinear processes are fundamental in neuronal information processing. On the systemic level: deciding to take one or another action is a nonlinear process. On a finer scale, neurons are inherently nonlinear: whether an action potential is elicited depends on the membrane potential to exceed a certain threshold\citealp{Adelson1985, Brincat2004, Chacron2000, Chacron2001, Nelson1997, Gussin2007, Middleton2007, Longtin2008}. In conjunction with neuronal noise, nonlinear mechanism facilitate the encoding of weak stimuli via stochastic resonance\citealp{Wiesenfeld1995, Stocks2000,Neiman2011fish}. We can find nonlinearities in many sensory systems such as rectification in the transduction machinery of inner hair cells \citealp{Peterson2019}, signal rectification in electroreceptor cells \citealp{Chacron2000, Chacron2001}, or in complex cells of the visual system \citealp{Adelson1985}. In the auditory or the active electric sense, for example, nonlinear processes are needed to extract envelopes, i.e. amplitude modulations of a carrier signal\citealp{Joris2004, Barayeu2023} called beats. Beats are common stimuli in different sensory modalities enabling rhythm and pitch perception in human hearing \citealp{Roeber1834, Plomp1967, Joris2004, Grahn2012} and providing context for electrocommunication in weakly electric fish \citealp{Engler2001, Hupe2008, Henninger2018, Benda2020}.
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While the sensory periphery can often be well described by linear models, this is not valid for many upstream neurons. Rather, nonlinear processes are implemented to extract special stimulus features\citealp{Gabbiani1996}. In active electrosensation, the self-generated electric field (electric organ discharge, EOD)\citealp{Salazar2013} that is quasi sinusoidal in wavetype electric fish and acts as the carrier signal that is amplitude modulated in the context of communication\citealp{Benda2013, Fotowat2013, Walz2014, Henninger2018} as well as object detection and navigation\citealp{Fotowat2013, Nelson1999}. In social contexts, the interference of the EODs of two interacting animals result in a characteristic periodic amplitude modulation, the so-called beat. The beat amplitude is defined by the smaller EOD amplitude, its frequency is defined as the difference between the two EOD frequencies ($\Delta f = f-\feod{}$, valid for $f < feod{}/2$\citealp{Barayeu2023}). Cutaneous electroreceptor organs that are distributed over the bodies of these fish \citealp{Carr1982} are tuned to the own field\citealp{Hopkins1976,Viancour1979}. P-type electroreceptor afferents (P-units) innervate these organs via ribbon synapses\citealp{Szabo1965, Wachtel1966} and project to the hindbrain where they trifurcate and synapse onto pyramidal cells in the electrosensory lateral line lobe (ELL)\citealp{Krahe2014}. The P-units ot the gymnotiform electric fish \lepto{} encode such amplitude modulations (AMs) by modulation of their firing rate\citealp{Gabbiani1996}. They fire probabilistically but phase-locked to the own EOD and the skipping of cycles leads to their characteristic multimodal interspike interval distribution. Even though the extraction of the AM itself requires a nonlinearity\citealp{Middleton2006,Stamper2012Envelope,Savard2011} encoding the time-course of the AM is linear over a wide range\citealp{Xu1996,Benda2005,Gussin2007,Grewe2017,Savard2011}. In the context of social signalling among three fish we observe an AM of the AM, also referred to as second-order envelope or just social envelope\citealp{Middleton2006, Savard2011, Stamper2012Envelope}. Encoding this again requires nonlinearities\citealp{Middleton2006} and it was shown that a subpopulation of P-units are sensitive to envelopes\citealp{Savard2011} and exhibit nonlinearities e.g. when driven by strong stimuli\citealp{Nelson1997,Chacron2004}.
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While the sensory periphery can often be well described by linear models, this is not true for many upstream neurons. Rather, nonlinear processes are implemented to extract special stimulus features\citealp{Gabbiani1996}. In active electrosensation, the self-generated electric field (electric organ discharge, EOD)\citealp{Salazar2013} that is quasi sinusoidal in wavetype electric fish and acts as the carrier signal that is amplitude modulated in the context of communication\citealp{Benda2013, Fotowat2013, Walz2014, Henninger2018} as well as object detection and navigation\citealp{Fotowat2013, Nelson1999}. In social contexts, the interference of the EODs of two interacting animals result in a characteristic periodic amplitude modulation, the so-called beat. The beat amplitude is defined by the smaller EOD amplitude, its frequency is defined as the difference between the two EOD frequencies ($\Delta f = f-\feod{}$, valid for $f < feod{}/2$)\citealp{Barayeu2023}. Cutaneous electroreceptor organs that are distributed over the bodies of these fish \citealp{Carr1982} are tuned to the own field\citealp{Hopkins1976,Viancour1979}. P-type electroreceptor afferents (P-units) innervate these organs via ribbon synapses\citealp{Szabo1965, Wachtel1966} and project to the hindbrain where they trifurcate and synapse onto pyramidal cells in the electrosensory lateral line lobe (ELL)\citealp{Krahe2014}. The P-units ot the gymnotiform electric fish \lepto{} encode such amplitude modulations (AMs) by modulation of their firing rate\citealp{Gabbiani1996}. They fire probabilistically but phase-locked to the own EOD and the skipping of cycles leads to their characteristic multimodal interspike interval distribution. Even though the extraction of the AM itself requires a nonlinearity\citealp{Middleton2006,Stamper2012Envelope,Savard2011} encoding the time-course of the AM is linear over a wide range\citealp{Xu1996,Benda2005,Gussin2007,Grewe2017,Savard2011}. In the context of social signalling among three fish we observe an AM of the AM, also referred to as second-order envelope or just social envelope\citealp{Middleton2006, Savard2011, Stamper2012Envelope}. Encoding this again requires nonlinearities\citealp{Middleton2006} and it was shown that a subpopulation of P-units are sensitive to envelopes\citealp{Savard2011} and exhibit nonlinearities e.g. when driven by strong stimuli\citealp{Nelson1997,Chacron2004}.
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\begin{figure*}[h!]
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\includegraphics[width=\columnwidth]{motivation}
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216
trialnr.py
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216
trialnr.py
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@ -0,0 +1,216 @@
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#from utils_suseptibility import default_settings
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#from plt_RAM import model_and_data_isi, model_cells
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#from utils_suseptibility import model_and_data, remove_yticks
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#from utils_suseptibility import *
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#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
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# set_ylabel_arrow, \
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# xpos_y_modelanddata
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#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
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# overlap_cells, \
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# plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
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from utils_suseptibility import *#model_and_data
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#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
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def table_printen(table):
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print(table.keys())
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for l in range(len(table)):
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list_here = np.array(table.iloc[l])
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l1 = "& ".join(list_here)
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print(l1)
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def trialnr(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
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contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
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internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
|
||||
dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],
|
||||
label=r'$\frac{1}{mV^2S}$'): # ['eRAM']
|
||||
# plot_style()#['_RAMscaled']'_RAMscaled'
|
||||
|
||||
duration_noise = '_short',
|
||||
formula = 'code' ##'formula'
|
||||
# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
|
||||
stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
|
||||
trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
|
||||
stimulus_type = '_StimulusOrig_' # ,#
|
||||
# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
|
||||
variant = 'sinz'
|
||||
mimick = 'no'
|
||||
cell_recording_save_name = ''
|
||||
trans = 1 # 5
|
||||
rep = 1000000 # 500000#0
|
||||
repeats = [20, rep] # 250000
|
||||
aa = 0
|
||||
good_data, remaining = overlap_cells()
|
||||
cells_all = [good_data[0]]
|
||||
|
||||
plot_style()
|
||||
default_figsize(column=2, length=3.1) #.254.75 0.75
|
||||
#grid = gridspec.GridSpec(2, 5, wspace=0.95, bottom=0.09,
|
||||
# hspace=0.25, width_ratios = [1,0,1,1,1], left=0.09, right=0.93, top=0.9)
|
||||
|
||||
a = 0
|
||||
maxs = []
|
||||
mins = []
|
||||
mats = []
|
||||
ims = []
|
||||
perc05 = []
|
||||
perc95 = []
|
||||
iternames = [D_extraction_method, external_noise,
|
||||
internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
|
||||
c_noises,
|
||||
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
|
||||
|
||||
nr = '2'
|
||||
# embed()
|
||||
# cell_contrasts = ["2013-01-08-aa-invivo-1"]
|
||||
# cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
|
||||
# cells_triangl_contrast = 1
|
||||
# cell_contrasts = 1
|
||||
|
||||
rows = len(cells_all) # len(good_data)+len(cell_contrasts)
|
||||
perc = 'perc'
|
||||
lp = 2
|
||||
label_model = r'Nonlinearity $\frac{1}{S}$'
|
||||
for all in it.product(*iternames):
|
||||
|
||||
var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
|
||||
# print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs)
|
||||
fig = plt.figure()
|
||||
|
||||
hs = 0.45
|
||||
|
||||
|
||||
##################################
|
||||
# model part
|
||||
|
||||
trial_nr = 500000
|
||||
cell = '2013-01-08-aa-invivo-1'
|
||||
cell = '2012-07-03-ak-invivo-1'
|
||||
print('cell'+str(cell))
|
||||
cells_given = [cell]
|
||||
save_name_rev = load_folder_name(
|
||||
'calc_model') + '/' + 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
|
||||
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_revQuadrant_'
|
||||
# for trial in trials:#.009
|
||||
trial_nr = 1000000#1000000
|
||||
trial_nrs_here = trial_nrs_ram_model()
|
||||
stacks = []
|
||||
perc95 = []
|
||||
perc05 = []
|
||||
median = []
|
||||
|
||||
for tr in trial_nrs_here:
|
||||
save_names = [
|
||||
'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(tr)+'_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',]
|
||||
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_5000000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV'
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
#'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
|
||||
|
||||
nrs_s = [3, 4, 8, 9]#, 10, 11
|
||||
#embed()
|
||||
tr_name = trial_nr/1000000
|
||||
if tr_name == 1:
|
||||
tr_name = 1
|
||||
ax_model = []
|
||||
|
||||
for s, sav_name in enumerate(save_names):
|
||||
|
||||
save_name = load_folder_name('calc_model') + '/' + sav_name
|
||||
|
||||
cell_add, cells_save = find_cell_add(cells_given)
|
||||
perc = 'perc'
|
||||
|
||||
path = save_name + '.pkl' # '../'+
|
||||
# stack = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name)
|
||||
|
||||
# full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev))
|
||||
# stack_final = get_axis_on_full_matrix(full_matrix, stack)
|
||||
# im = plt_RAM_perc(ax, perc, np.abs(stack))
|
||||
#
|
||||
stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
|
||||
if len(stack)> 0:
|
||||
model_show, stack_plot = get_stack(cell, stack)
|
||||
stacks.append(stack_plot)
|
||||
perc95.append(np.percentile(stack_plot,95))
|
||||
perc05.append(np.percentile(stack_plot, 5))
|
||||
median.append(np.percentile(stack_plot, 50))
|
||||
else:
|
||||
stacks.append([])
|
||||
perc95.append(float('nan'))
|
||||
perc05.append(float('nan'))
|
||||
median.append(float('nan'))
|
||||
|
||||
plt.plot(trial_nrs_here, perc05)
|
||||
plt.plot(trial_nrs_here, perc95)
|
||||
plt.plot(trial_nrs_here, median)
|
||||
#embed()
|
||||
#fig.tag([axes[0:3]], xoffs=-3, yoffs=1.6) # ax_ams[3],
|
||||
#fig.tag([[axes[4]]], xoffs=-3, yoffs=1.6, minor_index=0) # ax_ams[3],
|
||||
#fig.tag([axes[3:6]], xoffs=-3, yoffs=1.6) #, major_index = 1, minor_index = 2 ax_ams[3],
|
||||
#fig.tag([[axes[7]]], xoffs=-3, yoffs=1.6, major_index=2,minor_index=0) # ax_ams[3],
|
||||
#fig.tag([axes[8::]], xoffs=-3, yoffs=1.6, major_index=2, minor_index=2) # ax_ams[3],
|
||||
#fig.tag([axes[7::]], xoffs=-3, yoffs=1.6) # ax_ams[3],
|
||||
|
||||
#fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],
|
||||
|
||||
save_visualization(pdf=True)
|
||||
|
||||
|
||||
def start_pos_modeldata():
|
||||
return 1.03
|
||||
|
||||
|
||||
def signal_component_name():
|
||||
return r'$\xi_{signal}$'#signal noise'
|
||||
|
||||
|
||||
def noise_component_name():#$\xi_{noise}$noise_name =
|
||||
return r'$\xi_{noise}$'#'Noise component'#'intrinsic noise'
|
||||
|
||||
|
||||
def ypos_x_modelanddata():
|
||||
return -0.45
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
||||
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
|
||||
cells = model.cell.unique()
|
||||
# embed()
|
||||
params = {'cells': cells}
|
||||
|
||||
show = True
|
||||
# if show == False:
|
||||
|
||||
# low CV: cells = ['2012-07-03-ak-invivo-1']
|
||||
plot_style()
|
||||
default_settings(lw=0.5, column=2, length=3.35) #8.5
|
||||
redo = False
|
||||
D_extraction_method = ['additiv_cv_adapt_factor_scaled']
|
||||
# D_extraction_method = ['additiv_visual_d_4_scaled']
|
||||
|
||||
##########################
|
||||
# hier printen wir die table Werte zum kopieren in den Text
|
||||
path = 'print_table_suscept-model_params_suscept_table.csv'
|
||||
if os.path.exists(path):
|
||||
table = pd.read_csv(path)
|
||||
table_printen(table)
|
||||
|
||||
path = 'print_table_all-model_params_suscept_table.csv'
|
||||
if os.path.exists(path):
|
||||
table = pd.read_csv()
|
||||
print('model big')
|
||||
table_printen(table)
|
||||
#embed()
|
||||
|
||||
##########################
|
||||
#embed()
|
||||
trialnr(eod_metrice = False, width=0.005, show=show, D_extraction_method=D_extraction_method,
|
||||
label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1 @@
|
||||
,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,112.0,113.0,114.0,115.0,116.0,117.0,118.0,119.0,120.0,121.0,122.0,123.0,124.0,125.0,126.0,127.0,128.0,129.0,130.0,131.0,132.0,133.0,134.0,135.0,136.0,137.0,138.0,139.0,140.0,141.0,142.0,143.0,144.0,145.0,146.0,147.0,148.0,149.0,150.0,151.0,152.0,153.0,154.0,155.0,156.0,157.0,158.0,159.0,160.0,161.0,162.0,163.0,164.0,165.0,166.0,167.0,168.0,169.0,170.0,171.0,172.0,173.0,174.0,175.0,176.0,177.0,178.0,179.0,180.0,181.0,182.0,183.0,184.0,185.0,186.0,187.0,188.0,189.0,190.0,191.0,192.0,193.0,194.0,195.0,196.0,197.0,198.0,199.0,200.0,201.0,202.0,203.0,204.0,205.0,206.0,207.0,208.0,209.0,210.0,211.0,212.0,213.0,214.0,215.0,216.0,217.0,218.0,219.0,220.0,221.0,222.0,223.0,224.0,225.0,226.0,227.0,228.0,229.0,230.0,231.0,232.0,233.0,234.0,235.0,236.0,237.0,238.0,239.0,240.0,241.0,242.0,243.0,244.0,245.0,246.0,247.0,248.0,249.0,250.0,251.0,252.0,253.0,254.0,255.0,256.0,257.0,258.0,259.0,260.0,261.0,262.0,263.0,264.0,265.0,266.0,267.0,268.0,269.0,270.0,271.0,272.0,273.0,274.0,275.0,276.0,277.0,278.0,279.0,280.0,281.0,282.0,283.0,284.0,285.0,286.0,287.0,288.0,289.0,290.0,291.0,292.0,293.0,294.0,295.0,296.0,297.0,298.0,299.0,isf_psd,osf_psd,io_cross,d_isf_all,d_osf_all,var_RAM,trial_nr,cell,file_name,eod_fr,cv,fr,fr_stim,cv_stim,ser_stim,ser_first_stim,ser_sum_stim,fr_stim_mean,cv_stim_mean,ser_stim_mean,ser_first_stim_mean,ser_sum_stim_mean
|
||||
|
@ -0,0 +1 @@
|
||||
,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,112.0,113.0,114.0,115.0,116.0,117.0,118.0,119.0,120.0,121.0,122.0,123.0,124.0,125.0,126.0,127.0,128.0,129.0,130.0,131.0,132.0,133.0,134.0,135.0,136.0,137.0,138.0,139.0,140.0,141.0,142.0,143.0,144.0,145.0,146.0,147.0,148.0,149.0,150.0,151.0,152.0,153.0,154.0,155.0,156.0,157.0,158.0,159.0,160.0,161.0,162.0,163.0,164.0,165.0,166.0,167.0,168.0,169.0,170.0,171.0,172.0,173.0,174.0,175.0,176.0,177.0,178.0,179.0,180.0,181.0,182.0,183.0,184.0,185.0,186.0,187.0,188.0,189.0,190.0,191.0,192.0,193.0,194.0,195.0,196.0,197.0,198.0,199.0,200.0,201.0,202.0,203.0,204.0,205.0,206.0,207.0,208.0,209.0,210.0,211.0,212.0,213.0,214.0,215.0,216.0,217.0,218.0,219.0,220.0,221.0,222.0,223.0,224.0,225.0,226.0,227.0,228.0,229.0,230.0,231.0,232.0,233.0,234.0,235.0,236.0,237.0,238.0,239.0,240.0,241.0,242.0,243.0,244.0,245.0,246.0,247.0,248.0,249.0,250.0,251.0,252.0,253.0,254.0,255.0,256.0,257.0,258.0,259.0,260.0,261.0,262.0,263.0,264.0,265.0,266.0,267.0,268.0,269.0,270.0,271.0,272.0,273.0,274.0,275.0,276.0,277.0,278.0,279.0,280.0,281.0,282.0,283.0,284.0,285.0,286.0,287.0,288.0,289.0,290.0,291.0,292.0,293.0,294.0,295.0,296.0,297.0,298.0,299.0,isf_psd,osf_psd,io_cross,d_isf_all,d_osf_all,var_RAM,trial_nr,cell,file_name,eod_fr,cv,fr,fr_stim,cv_stim,ser_stim,ser_first_stim,ser_sum_stim,fr_stim_mean,cv_stim_mean,ser_stim_mean,ser_first_stim_mean,ser_sum_stim_mean
|
||||
|
@ -0,0 +1 @@
|
||||
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|
||||
,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,112.0,113.0,114.0,115.0,116.0,117.0,118.0,119.0,120.0,121.0,122.0,123.0,124.0,125.0,126.0,127.0,128.0,129.0,130.0,131.0,132.0,133.0,134.0,135.0,136.0,137.0,138.0,139.0,140.0,141.0,142.0,143.0,144.0,145.0,146.0,147.0,148.0,149.0,150.0,151.0,152.0,153.0,154.0,155.0,156.0,157.0,158.0,159.0,160.0,161.0,162.0,163.0,164.0,165.0,166.0,167.0,168.0,169.0,170.0,171.0,172.0,173.0,174.0,175.0,176.0,177.0,178.0,179.0,180.0,181.0,182.0,183.0,184.0,185.0,186.0,187.0,188.0,189.0,190.0,191.0,192.0,193.0,194.0,195.0,196.0,197.0,198.0,199.0,200.0,201.0,202.0,203.0,204.0,205.0,206.0,207.0,208.0,209.0,210.0,211.0,212.0,213.0,214.0,215.0,216.0,217.0,218.0,219.0,220.0,221.0,222.0,223.0,224.0,225.0,226.0,227.0,228.0,229.0,230.0,231.0,232.0,233.0,234.0,235.0,236.0,237.0,238.0,239.0,240.0,241.0,242.0,243.0,244.0,245.0,246.0,247.0,248.0,249.0,250.0,251.0,252.0,253.0,254.0,255.0,256.0,257.0,258.0,259.0,260.0,261.0,262.0,263.0,264.0,265.0,266.0,267.0,268.0,269.0,270.0,271.0,272.0,273.0,274.0,275.0,276.0,277.0,278.0,279.0,280.0,281.0,282.0,283.0,284.0,285.0,286.0,287.0,288.0,289.0,290.0,291.0,292.0,293.0,294.0,295.0,296.0,297.0,298.0,299.0,isf_psd,osf_psd,io_cross,d_isf_all,d_osf_all,var_RAM,trial_nr,cell,file_name,eod_fr,cv,fr,fr_stim,cv_stim,ser_stim,ser_first_stim,ser_sum_stim,fr_stim_mean,cv_stim_mean,ser_stim_mean,ser_first_stim_mean,ser_sum_stim_mean
|
||||
|
@ -0,0 +1 @@
|
||||
,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,112.0,113.0,114.0,115.0,116.0,117.0,118.0,119.0,120.0,121.0,122.0,123.0,124.0,125.0,126.0,127.0,128.0,129.0,130.0,131.0,132.0,133.0,134.0,135.0,136.0,137.0,138.0,139.0,140.0,141.0,142.0,143.0,144.0,145.0,146.0,147.0,148.0,149.0,150.0,151.0,152.0,153.0,154.0,155.0,156.0,157.0,158.0,159.0,160.0,161.0,162.0,163.0,164.0,165.0,166.0,167.0,168.0,169.0,170.0,171.0,172.0,173.0,174.0,175.0,176.0,177.0,178.0,179.0,180.0,181.0,182.0,183.0,184.0,185.0,186.0,187.0,188.0,189.0,190.0,191.0,192.0,193.0,194.0,195.0,196.0,197.0,198.0,199.0,200.0,201.0,202.0,203.0,204.0,205.0,206.0,207.0,208.0,209.0,210.0,211.0,212.0,213.0,214.0,215.0,216.0,217.0,218.0,219.0,220.0,221.0,222.0,223.0,224.0,225.0,226.0,227.0,228.0,229.0,230.0,231.0,232.0,233.0,234.0,235.0,236.0,237.0,238.0,239.0,240.0,241.0,242.0,243.0,244.0,245.0,246.0,247.0,248.0,249.0,250.0,251.0,252.0,253.0,254.0,255.0,256.0,257.0,258.0,259.0,260.0,261.0,262.0,263.0,264.0,265.0,266.0,267.0,268.0,269.0,270.0,271.0,272.0,273.0,274.0,275.0,276.0,277.0,278.0,279.0,280.0,281.0,282.0,283.0,284.0,285.0,286.0,287.0,288.0,289.0,290.0,291.0,292.0,293.0,294.0,295.0,296.0,297.0,298.0,299.0,isf_psd,osf_psd,io_cross,d_isf_all,d_osf_all,var_RAM,trial_nr,cell,file_name,eod_fr,cv,fr,fr_stim,cv_stim,ser_stim,ser_first_stim,ser_sum_stim,fr_stim_mean,cv_stim_mean,ser_stim_mean,ser_first_stim_mean,ser_sum_stim_mean,var_stim
|
||||
|
@ -4442,13 +4442,14 @@ def load_model_susept(path, cells, save_name, save=True, redo=False):
|
||||
|
||||
redo = False
|
||||
print(name1)
|
||||
#embed()
|
||||
if (versions_comp == 'develop'):
|
||||
#embed()
|
||||
if (os.path.exists(name1)):
|
||||
cont = check_creation_time(load_function, name1)
|
||||
else:
|
||||
cont = True
|
||||
|
||||
#embed()
|
||||
if (redo == True) | cont: # (not os.path.exists(name1))
|
||||
print('redo model')
|
||||
model = resave_model_susept(cells, load_function, name1, path, remove_old, save, versions_comp)
|
||||
@ -4502,8 +4503,10 @@ def load_model_susept(path, cells, save_name, save=True, redo=False):
|
||||
def resave_model_susept(cells, load_function, name1, path, remove_old, save, versions_comp):
|
||||
if not os.path.exists(path):
|
||||
dated_up = update_ssh_file(path)
|
||||
|
||||
if os.path.exists(path):
|
||||
embed()
|
||||
if os.path.exists(
|
||||
|
||||
):
|
||||
################################
|
||||
# wenn es den localen Computer von Sascha findet soll es die Versionen nochmal updaten
|
||||
# if (redo == True) : # | (not os.path.exists(name1))& (not os.path.exists(name0))
|
||||
|
||||
@ -2,6 +2,7 @@ import ast
|
||||
import csv
|
||||
import warnings
|
||||
|
||||
import numpy
|
||||
from scipy.optimize import curve_fit
|
||||
from scipy.signal import vectorstrength
|
||||
from scipy.stats import alpha, gaussian_kde
|
||||
@ -2837,13 +2838,7 @@ def plt_squares_special(params, col_desired=2, var_items=['contrasts'], show=Fal
|
||||
|
||||
|
||||
def plt_single_square_modl(ax, cell, model, perc, titles, width,eod_metrice = True, nr = 3, titles_plot=False, resize=False):
|
||||
try:
|
||||
model_show = model[(model.cell == cell)]
|
||||
except:
|
||||
print('cell something')
|
||||
embed()
|
||||
stack_plot = change_model_from_csv_to_plots(model_show)
|
||||
stack_plot = RAM_norm(stack_plot, model_show=model_show)
|
||||
model_show, stack_plot = get_stack(cell, model)
|
||||
if resize:
|
||||
stack_plot, add_nonlin_title, resize_val = rescale_colorbar_and_values(stack_plot)
|
||||
else:
|
||||
@ -2889,6 +2884,17 @@ def plt_single_square_modl(ax, cell, model, perc, titles, width,eod_metrice = Tr
|
||||
return add_nonlin_title,cbar, fig, stack_plot, im
|
||||
|
||||
|
||||
def get_stack(cell, model):
|
||||
try:
|
||||
model_show = model[(model.cell == cell)]
|
||||
except:
|
||||
print('cell something')
|
||||
embed()
|
||||
stack_plot = change_model_from_csv_to_plots(model_show)
|
||||
stack_plot = RAM_norm(stack_plot, model_show=model_show)
|
||||
return model_show, stack_plot
|
||||
|
||||
|
||||
#[1, 0, 0][1, 0, 0.4]
|
||||
|
||||
|
||||
@ -8824,7 +8830,7 @@ def motivation_small_roc(ylim=[-1.25, 1.25], c1=10, dfs=['m1', 'm2'], mult_type=
|
||||
data_dir, cell, c, b, chirps, devs, extract, group_mean, mean_type,
|
||||
plot_group=0,
|
||||
rocextra=False, sorted_on=sorted_on)
|
||||
# embed()
|
||||
#embed()
|
||||
fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single3(
|
||||
[arrays[0], arrays[2], arrays[1], arrays[3]],
|
||||
[arrays_original[0], arrays_original[2], arrays_original[1],
|
||||
@ -10031,6 +10037,7 @@ def plot_arrays_ROC_psd_single3(arrays, arrays_original, spikes_pure, fr, cell,
|
||||
color_psd = 'black'
|
||||
# embed()
|
||||
ax_as = []
|
||||
#embed()
|
||||
for j in range(len(arrays)):
|
||||
###################################
|
||||
# plt spikes
|
||||
@ -10054,7 +10061,7 @@ def plot_arrays_ROC_psd_single3(arrays, arrays_original, spikes_pure, fr, cell,
|
||||
# hier kann man aussuchen welches power spektrum machen haben will
|
||||
f, nfft = get_psds_ROC(array_chosen, arrays, arrays_original, j, mean_type, names, p_means_all)
|
||||
ax_as.append(ax_a)
|
||||
|
||||
#embed()
|
||||
|
||||
#########################################
|
||||
# plot the psds
|
||||
@ -10189,7 +10196,7 @@ def plt_psds_ROC(add_burst_corr, arrays, ax00, ax_ps, cell, color_psd, colors_p,
|
||||
embed()
|
||||
pp, pp_mean = decide_log_ROCs(j, log, names, p_means_all, ref)
|
||||
|
||||
# embed()
|
||||
#embed()
|
||||
# add_log = 10.5#2.5
|
||||
try: # todo: if log müsste hier was anderes rein, das log veränderte nämlich!#2.5
|
||||
plt_peaks_several(np.array(labels)[choice[j]], np.array(freqs)[choice[j]], pp, j,
|
||||
@ -10205,6 +10212,7 @@ def plt_psds_ROC(add_burst_corr, arrays, ax00, ax_ps, cell, color_psd, colors_p,
|
||||
ax00.show_spines('b')
|
||||
if j == 0:
|
||||
ax00.yscalebar(-0.02, 0.5, 10, 'dB', va='center', ha='left')
|
||||
#embed()
|
||||
return ax00, fr_isi
|
||||
|
||||
|
||||
@ -10221,7 +10229,7 @@ def f2_core(DF1):
|
||||
|
||||
|
||||
def f1_core(DF2):
|
||||
return '$2 |\Delta f_{1}|=%s$' %(DF2 * 2) + '\,Hz'
|
||||
return '$2 |\Delta f_{1}|=%s$' %(np.abs(DF2) * 2) + '\,Hz'
|
||||
|
||||
|
||||
def fdiff_core(DF1, DF2):
|
||||
@ -10229,7 +10237,7 @@ def fdiff_core(DF1, DF2):
|
||||
|
||||
|
||||
def fsum_core(DF1, DF2):
|
||||
return '$||\Delta f_{1}| +|\Delta f_{2}||=%s$' %(np.abs(DF1) + np.abs(DF2)) + '\,Hz'#)
|
||||
return '$||\Delta f_{1}| + |\Delta f_{2}||=%s$' %(np.abs(DF1) + np.abs(DF2)) + '\,Hz'#)
|
||||
|
||||
|
||||
def decide_log_ROCs(j, log, names, p_means_all, ref):
|
||||
@ -10349,7 +10357,7 @@ def plt_traces_ROC(array_chosen, arrays, ax00, colors, datapoints, group_mean, j
|
||||
|
||||
|
||||
def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean, mean_type, plot_group, rocextra,
|
||||
sorted_on='LocalReconst0.2Norm', dev_desired = '1'):
|
||||
sorted_on='LocalReconst0.2Norm', base_several = False, dev_desired = '1',mean_type0 = ''):#'_MeanTrialsIndex'
|
||||
# sorted_on = 'LocalReconst' # 'EodLocSynch'
|
||||
|
||||
version_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not()
|
||||
@ -10374,7 +10382,7 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
|
||||
|
||||
|
||||
#embed()
|
||||
mean_type0 = '_MeanTrialsIndex'
|
||||
|
||||
spikes_pure, fish_number_base, chirp, fish_cuts, time_array, fish_number, smoothened2, smoothed05, eod_mt, eod_interp, effective_duration, cut, devname, frame = cut_spikes_and_eod_three(
|
||||
group_mean, b, extract, chirps=chirps, emb=False,
|
||||
mean_type=mean_type, sorted_on=sorted_on,
|
||||
@ -10384,6 +10392,7 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
|
||||
# group_mean,
|
||||
# mean_type)
|
||||
|
||||
|
||||
if printing: # todo: also das dauert lange das könnte man optimizieren
|
||||
print('arrays1 ' + str(time.time() - t3))
|
||||
# embed()
|
||||
@ -10409,6 +10418,7 @@ def save_arrays_susept(data_dir, cell, c, b, chirps, devs, extract, group_mean,
|
||||
|
||||
# embed()
|
||||
if ('Phase' not in mean_type0) & (mean_type0 != ''):
|
||||
#embed()
|
||||
for i in range(len(delays_length['base_0'])):
|
||||
delays_length['base_0'][i] = np.arange(0, delays_length['base_0'][i][-1], 1)
|
||||
try:
|
||||
@ -10671,7 +10681,7 @@ def plt_single_pds(nfft, f, p_means, p_mean_all_here, ylim_psd, xlim_psd, color_
|
||||
# remove_xticks(ax00)
|
||||
if j != 0:
|
||||
remove_yticks(ax00)
|
||||
|
||||
#embed()
|
||||
return ref, ax00
|
||||
|
||||
|
||||
@ -15368,7 +15378,7 @@ def fft_matrix(deltat, osf, f_range, isf, norm='', quadrant=''): # stimulus,
|
||||
return np.array(f_mat1), np.array(f_mat2), np.array(f_idx_sum), np.array(cross)
|
||||
|
||||
|
||||
def exclude_nans_for_corr(file_here, var_item, x=[], y=[], cv_name='cv_base', score='perc99/med'):
|
||||
def exclude_nans_for_corr(file_here, var_item, x=[], y=[], max_x = None, cv_name='cv_base', score='perc99/med'):
|
||||
# embed()
|
||||
if len(x) == 0:
|
||||
x = file_here[cv_name]
|
||||
@ -15384,6 +15394,19 @@ def exclude_nans_for_corr(file_here, var_item, x=[], y=[], cv_name='cv_base', sc
|
||||
x = x[~exclude_here]
|
||||
y = y[~exclude_here]
|
||||
c_axis = c_axis[~exclude_here]
|
||||
|
||||
if max_x:
|
||||
#embed()
|
||||
if np.sum(x > max_x) > 0:
|
||||
y = y[x < max_x]
|
||||
try:
|
||||
c_axis = c_axis.loc[x < max_x]
|
||||
except:
|
||||
print('c something')
|
||||
embed()
|
||||
x = x[x < max_x]
|
||||
|
||||
|
||||
return c_axis, x, y, exclude_here
|
||||
|
||||
|
||||
@ -15674,10 +15697,11 @@ def get_axis(cv_name, frame_file, score):
|
||||
return x_axis, y_axis
|
||||
|
||||
|
||||
def plt_burst_modulation_hists(axk, axl, var_item_name, ax, cell_type_here, cv_name, frame_file, max_val,
|
||||
score, ymin='no', xmin='no', ymax = 'no', top=False,
|
||||
def plt_burst_modulation_hists(axk, axl, var_item_name, ax, cell_type_here, cv_name, frame_file, score, ymin='no',
|
||||
xmin='no', ymax='no', top=False,
|
||||
burst_fraction_reset='burst_fraction_burst_corr_individual_base',
|
||||
var_item='response_modulation', n = True, xlim = None, x_pos = 0, burst_fraction=1, ha = 'left'):
|
||||
var_item='response_modulation', max_x=None, n=True, xlim=None, x_pos=0, burst_fraction=1,
|
||||
ha='left'):
|
||||
cmap = []
|
||||
x_axis = []
|
||||
y_axis = []
|
||||
@ -15727,7 +15751,7 @@ def plt_burst_modulation_hists(axk, axl, var_item_name, ax, cell_type_here, cv_n
|
||||
y_axis = frame_file[score] # np.array(frame_file[score])[frame_file[score] > 0]
|
||||
# c_axis = np.array(frame_file['response_modulation'])[frame_file[score] > 0]
|
||||
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, var_item, cv_name=cv_name,
|
||||
score=score)
|
||||
score=score, max_x = max_x)
|
||||
# corr, p_value = stats.pearsonr(x, y)
|
||||
# y =
|
||||
# c=c_axis[x_axis < max_val], cmap=cm,
|
||||
@ -39979,3 +40003,6 @@ def end_fi_s():
|
||||
return 90
|
||||
|
||||
|
||||
def trial_nrs_ram_model():
|
||||
trial_nrs_here = np.array([9, 11, 20, 30, 100, 500, 1000, 10000, 100000, 250000, 500000, 750000, 1000000])
|
||||
return trial_nrs_here
|
||||
Loading…
Reference in New Issue
Block a user