325 lines
15 KiB
Python
325 lines
15 KiB
Python
from matplotlib import gridspec as gridspec, pyplot as plt
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import numpy as np
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from scipy import stats
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from threefish.defaults import default_figsize
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from threefish.load import save_visualization
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from threefish.RAM.calc_bursts import setting_overview_score
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from threefish.RAM.plot_colors import colors_overview
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from threefish.RAM.plot_grid import get_grid_4
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from threefish.RAM.plot_labels import basename_small, label_NLI_scorename2_small, label_pearson, label_stimname_small, \
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make_log_ticks
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from threefish.RAM.plot_subplots import plt_specific_cells, scatter_with_marginals_colorcoded
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from threefish.RAM.reformat_population import load_overview_susept, update_cell_names_to_unify_invivo_nomenclature
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from threefish.RAM.values import start_name
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from threefish.reformat import exclude_nans_for_corr
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try:
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from plotstyle import plot_style, spines_params
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except:
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a = 5
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def data_overview3():
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plot_style()
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default_figsize(column=2, length=6.2) #65.5
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var_it = 'Response Modulation [Hz]'
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var_it2 = ''
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right = 0.85
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ws = 0.75
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grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.06,
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hspace=0.45, left=0.1, right=right, top=0.95)
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##########################
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# Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
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save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__direct_']
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cell_types = [' P-unit',' Ampullary', ]#, ' P-unit',]#' P-unit',
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cell_types_name = ['P-units','Ampullary cells',]
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species = ' Apteronotus leptorhynchus'
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burst_fraction = [1, 1] # ,1,1]
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burst_corr_reset = 'burst_fraction_burst_corr_individual_stim'
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redo = False
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counter = 0
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tags = []
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frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
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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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printing = False # print values for paper
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max_xs = [[[],[],[]],[[],[],[]]]
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for c, cell_type_here in enumerate(cell_types):# iteriert über die columns
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frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
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frame_file = frame_file[frame_file.cv_stim <5]
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colorbar_value = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
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colorbar_name = ['response_modulation','response_modulation','']#,'']#'response_modulation'
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x_axis = ['cv_base', 'cv_stim', 'response_modulation'] # ,'fr_base']#
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x_axis_names = ['CV$' + basename_small() +'$','CV$' + label_stimname_small() + '$', 'Response modulation [Hz]']#$'+basename()+'$,'Fr$'+basename()+'$',]
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score = scores[c]
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y_axis_values = [score,score,score]#,score]
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y_axis_name = [label_NLI_scorename2_small(), label_NLI_scorename2_small(), label_NLI_scorename2_small()]#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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max_x = max_xs[c]
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log = ''#'logall'#''#'logy','logall'True#False
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for v, colorbar_name in enumerate(colorbar_name):# iteriert über die rows
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axx, axy, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[v,counter])
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if log == 'logy':
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ymin = 'no'
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else:
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ymin = 0
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#frame.filter(like = 'fr')#wo_burstcorr
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xmin = 0
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xlimk = None
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labelpad = 0.5#-1
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cmap, _, y_axis = scatter_with_marginals_colorcoded(colorbar_value[v], axs, cell_type_here, x_axis[v],
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frame_file, y_axis_values[v], axy, axx, ymin=ymin,
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xmin=xmin, burst_fraction_reset=burst_corr_reset,
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var_item=colorbar_name, labelpad=labelpad, max_x=max_x[v],
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xlim=None, x_pos=1, burst_fraction=burst_fraction[c],
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ha='right')
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if printing:
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print(cell_type_here + ' median '+y_axis_values[v]+''+str(np.nanmedian(frame_file[y_axis_values[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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if v == 0:
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colors = colors_overview()
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axx.set_title(cell_types_name[c], color = colors[cell_type_here])
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axx.show_spines('')
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axy.show_spines('')
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axs.set_ylabel(y_axis_name[v])
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axs.set_xlabel(x_axis_names[v], labelpad = labelpad)
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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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axx.set_xlim(xlimk)
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#embed()
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#remove_yticks(axl)
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if log == 'logy':
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axy.set_yscale('log')
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axs.set_yscale('log')
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make_log_ticks([axs])
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axy.minorticks_off()
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elif log == 'logall':
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axy.set_yscale('log')
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axs.set_yscale('log')
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make_log_ticks([axs])
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axy.minorticks_off()
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axx.set_xscale('log')
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axs.set_xscale('log')
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make_log_ticks([axs])
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axx.minorticks_off()
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axy.set_yticks_blank()
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plt_specific_cells(axs, cell_type_here, x_axis[v], frame_file, y_axis_values[v], marker = ['o',"s"])
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tags.append(axx)
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counter += 1
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if printing:
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printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis)
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show = False#True
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fig = plt.gcf()
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tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
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fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
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save_visualization(pdf = True, show = show)
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def printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis):
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############################
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# 1 Print scores
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print('\n')
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speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
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# for species in speciess:
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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='min', species=species)
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print(cell_type_here + str(len(frame_file.cell.unique())))
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# embed()
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cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac',
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'2010-06-21-ac']
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cells_amp = update_cell_names_to_unify_invivo_nomenclature(cells_amp)
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frame_amps = frame_file[frame_file.cell.isin(cells_amp)]
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frame_amps.cell.unique()
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#################################################################
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# print the corrs
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score = scores[c] # 'ser_base',
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score_print = [score]
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score_print = [scores[c], scores[c], scores[c], scores[c], scores[c],
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'burst_fraction_burst_corr_individual_base']
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corr_vars = ['cv_base', 'response_modulation', 'fr_base', 'ser_first_base',
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'burst_fraction_burst_corr_individual_stim', 'cv_base']
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# for score in score_print:
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for c_nr, corr_var in enumerate(corr_vars): # ,'ser_sum_corr'
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score = score_print[c_nr]
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x = frame_file['fr_base']
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y = frame_file['ser_first']
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c = frame_file['cv_base']
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c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var,
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score=score)
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corr, p_value = stats.pearsonr(x_axis, y_axis)
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pears_l = label_pearson(corr, p_value, y_axis, n=True)
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print(start_name(cell_type_here, species) + ' ' + corr_var + ' to ' + str(score) + pears_l)
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print('\n')
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################################
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# cv to fr correlation
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c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'fr_base', cv_name='fr_base',
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score='cv_base')
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corr, p_value = stats.pearsonr(x_axis, y_axis)
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pears_l = label_pearson(corr, p_value, y_axis, n=True)
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print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
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################################
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# cv to fr correlation
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frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
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c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file,
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'burst_fraction_burst_corr_individual_base',
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cv_name='burst_fraction_burst_corr_individual_base',
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score='cv_base')
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corr, p_value = stats.pearsonr(x_axis, y_axis)
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pears_l = label_pearson(corr, p_value, y_axis, n=True)
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print(start_name(cell_type_here,
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species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l)
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###############################
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# fr to nonline but for both modulations
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c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'fr_base', cv_name='fr_base',
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score=score)
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corr, p_value = stats.pearsonr(x_axis, y_axis)
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pears_l = label_pearson(corr, p_value, y_axis, n=True)
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print(start_name(cell_type_here, species) + ' amprange: ' + ' ' + 'fr_base' + ' to score' + pears_l)
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##################################
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print('\n')
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# embed()
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# todo: hier noch die Werte für die Methodik printen
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test = False
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# embed()
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if test:
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plt.hist(frame_file['lim_individual'])
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# print(frame_file['lim_individual'])
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############################################
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############################
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# 2 Print names
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# printe um welchen Zeitraum es sich handelt
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frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
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species='')
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print('\n')
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print('From ' + str(np.sort(frame_file.cell)[0]) + ' to ' + str(np.sort(frame_file.cell)[-1]) + ' nr ' + str(
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len(frame_file.cell)))
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# embed()
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# embed()
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#####################################################################
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############################
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# 3 Print , EODF
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print('\n')
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speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
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for species in speciess:
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fish = []
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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='min', species=species)
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if len(frame_file) > 0:
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# ja der fisch hatte halt eine ziemlich niedriege EODf
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frame_here = frame_file[frame_file.cell != '2022-01-05-af-invivo-1']
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print(start_name(cell_type_here,
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species) + ' eodf_min ' + str(
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int(np.round(np.nanmin(frame_here.eod_fr)))) + ' eodf_max ' + str(
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int(np.round(np.nanmax(frame_here.eod_fr)))) + ' n ' + str(len(frame_here)))
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my_list = np.unique(frame_here.cell)
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new_list = [item[0:10] for item in my_list]
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fish.extend(new_list)
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print(species[0:7] + ' n_fish ' + str(len(np.unique(fish))))
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#####################################################################
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# Burst corr limits
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print('\n')
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for species in speciess:
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fish = []
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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='min', species=species)
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lims = np.unique(frame_file['lim_individual'])
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lims_name = ''
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for lim in lims:
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lims_name = lims_name + str(lim) + ': ' + str(np.sum(frame_file['lim_individual'] == lim)) + ','
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print(start_name(cell_type_here, species) + ' ' + lims_name)
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#####################################################################
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############################
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# 3 Print , EODF
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# Amplituden
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print('\n')
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speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
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for species in speciess:
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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='', species=species)
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if len(frame_file) > 0:
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# embed()
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print(start_name(cell_type_here, species) + ' amp_min ' + str(
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np.nanmin(frame_file.amp)) + ' amp_max ' + str(
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np.nanmax(frame_file[~np.isinf(frame_file.amp)].amp)))
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############################
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# CVs min max
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for species in speciess:
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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='', species=species)
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if len(frame_file) > 0:
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print(start_name(cell_type_here, species) + ' CV_min ' + str(
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np.round(np.nanmin(frame_file.cv_base), 2)) + ' CV max ' + str(
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np.round(np.nanmax(frame_file.cv_base), 2)) + ' FR max ' + str(
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np.round(np.nanmin(frame_file.fr_base))) + ' FR max ' + str(
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np.round(np.nanmax(frame_file.fr_base))))
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############################################
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#######
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# N
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print('\n N')
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speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
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for species in speciess:
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for c, cell_type_here in enumerate(cell_types):
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##################
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# printe wie viele Zellen es am Anfang gab
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frame_file = setting_overview_score(frame_load_sp, cell_type_here, f_exclude=False, snippet=None,
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min_amp='min', species=species)
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print(start_name(cell_type_here, species) + ' nr ' + str(len(frame_file)))
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############################################
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# printe um welchen Zeitraum es sich handelt
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frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
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species='')
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print('\n sampling' + str(frame_file.sampling.unique()))
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print('P-units Fr: 50-450 Hz CV: 0.15 - 1.35')
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print('Ampullary Fr: 80-200 Hz, CV: 0.08 - 0.22')
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if __name__ == '__main__':
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data_overview3() |