918 lines
44 KiB
Python
918 lines
44 KiB
Python
#sys.path.insert(0, '..')
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#from plt_RAM import plt_RAM_overview_nice
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#from utils_susept import
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from IPython import embed
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from matplotlib import gridspec as gridspec, pyplot as plt
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import numpy as np
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#embed()
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from utils_all_down import default_settings, resave_small_files,update_cell_names
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from utils_suseptibility import default_figsize, NLI_scorename, kernel_scatter, plt_burst_modulation_hists, \
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plt_model_overview2, version_final
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from utils_all import colors_overview, cv_base_name, load_overview_susept, make_log_ticks, p_units_to_show, \
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save_visualization, \
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setting_overview_score, update_ssh_file, load_folder_name
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#import lstat
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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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from heterogeneity import plt_fr_cv_base, get_grids_for_cv_fr, get_frame_for_base_plot
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def data_overview2():
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# calcdf_RAM_overview()
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save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr'
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save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s'
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save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__CutatBeginning_0.05_s_NeurDelay_0.005_s'
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save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s'
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col = 4
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row = 2 # sharex=True,
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plot_style()
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default_figsize(column=2, length=7.35)
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#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
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three = True
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if three:
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top = True
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grid00 = gridspec.GridSpec(2, 1, wspace=0.37, bottom=0.15,
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hspace=0.25, left=0.085, right=0.95, top=0.94, height_ratios = [1,2.7])
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else:
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grid00 = gridspec.GridSpec(4, 2, wspace=0.75, bottom=0.04,
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hspace=0.6, left=0.15, right=0.99, top=0.95)
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top = False
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#grid0 = gridspec.GridSpec(3, 3, wspace=0.35, bottom=0.2,
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# hspace=1, left=0.085, right=0.95, top=0.94)
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grid0 = gridspec.GridSpecFromSubplotSpec(1, 5, grid00[0],width_ratios = [1,0.4, 1,0.55,1], hspace=0.35,wspace=0)
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grid1 = gridspec.GridSpecFromSubplotSpec(2, 5, grid00[1],width_ratios = [1,0.4, 1,0.55,1], hspace=1.1, wspace=0)
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###################################
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###############################
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# Das ist der Finale Score
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scoreall = 'perc99/med'
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###################################
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#scores = [scoreall+'_diagonal_proj']
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#score = scores[0]
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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 = []
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save_names = ['calc_RAM_overview-_simplified_'+version_final()]#'calc_RAM_overview-_simplified_noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_','calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_',
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save_names = [version_final_overview()]#_abs_
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#save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__abs_']
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counter = 0
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colors = colors_overview()
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ax_j = []
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axls = []
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axss = []
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cell_types = [' Ampullary', ' P-unit', ]
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counter = 0
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tags = []
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log = False
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counterplus = 2
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max_val = 1.5
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score_burst_corr = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'#scores[0]
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score_n_burst_corr= 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
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score_n_burst_corr = 'Fr$_{BurstCorr}$/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
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score_n_burst_corr = NLI_burstcorr_name()
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score = 'max(diag5Hz)/med_diagonal_proj_fr'
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score_n = 'max(diag5Hz)\n/med_diagonal_proj_fr'
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score_n = 'Fr/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
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score_n = NLI_scorename()
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#score = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr' # scores[0]
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redo = False#True#True
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frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
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#embed()
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test_amps = False
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if test_amps:
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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', '2010-06-21-ac']
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cells_amp = update_cell_names(cells_amp)
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frame_amps = frame_load_sp[frame_load_sp.cell.isin(cells_amp)]
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fish_plot = False
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if fish_plot:
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axls, axss, counter, frame_load_sp, max_val, score_burst_corr = fish_plt_false(ax_j, axls, axss, cell_types,
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colors, counter, grid0, log,
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max_val, save_names,
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score_burst_corr)
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alpha = 0.3
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####################################################################
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# base plots
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# cells = p_units_to_show()
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# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
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burst_choice = ['', '_burst_corr_individual']
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xmax_burst = [[1.5,'no'],[0.8,'no']]
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for gg in range(len(burst_choice)):
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cells = p_units_to_show()
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species = ' Apteronotus leptorhynchus'
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cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells, save_names = save_names, species = species)
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ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
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if gg == 0:
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#ax_cv.text(1.5, 2, r'\bf{A} \textit{Apteronotus leptorhynchus}', ha = 'center', va = 'center', transform=ax_cv.transAxes)
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text_punits(ax_cv, colors)
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ax_cv.text(0.35, 0.45, r'Ampullary cells', ha='left', va='center',
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transform=ax_cv.transAxes, color = colors[' Ampullary'])#' Ampullary',
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species_letter(ax_cv)
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print(species)
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x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
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frame_load, gg, alpha = alpha, xmax = xmax_burst[gg], species = species)
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ax0.set_xticks_delta(0.5)
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tags.append(ax_cv)
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counter += 2
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#frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
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####################################################################
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# base plots
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# cells = p_units_to_show()
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# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
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burst_choice = ['']#, '_burst_corr_individual']
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#counter += 1
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nrs = [1,3]
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for gg in range(len(burst_choice)):
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cells = p_units_to_show()
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species = ' Eigenmannia virescens'
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cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells,save_names = save_names, species = species)
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ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
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#ax_cv.set_title('Eigenmannia virescens')
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#ax_cv.text(0.5, 2, B, ha='center', va='center', transform=ax_cv.transAxes)
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species_letter(ax_cv, letter='B', species=r'\textit{Eigenmannia virescens}')
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print(species)
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x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
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frame_load, gg, alpha = alpha, xmax = ['no','no'], species = species)
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ax0.set_xticks_delta(0.5)
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tags.append(ax_cv)
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counter += 2
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#embed()
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###########################
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###########################
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###########################
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# counterreset!
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counter = 0
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#grid0 = grid1
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# base ready
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####################################################################
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####################################################################
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####################################################################
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####################################################################
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#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
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#
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cv_name = "cv_base"
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species = ' Apteronotus leptorhynchus'
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cell_type_here = ' P-unit'
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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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#embed()
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#setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
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x_axis = ['cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
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x_axis_names = [cv_base_name(), score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
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y_axis = ['burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
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y_axis_name = ['Burst Fraction$_{Base}$', score_n_burst_corr]
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#
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var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
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#'Response Modulation [Hz]',
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var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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logs = [False,False]#,False]
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xmins = [0,0]#,'no']
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#'response_modulation',
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#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
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#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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#['burst_fraction_burst_corr_individual_stim']
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#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
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frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
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burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
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#score_n = ['Perc99/Med','Perc99/Med']
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#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
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n = False
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xlim_here0 = [0,1.5]
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ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
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frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
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var_types, x_axis, x_axis_names, counterplus = counterplus, n = n, xlim = xlim_here0, log = logs, top = top, xmins = xmins, extra_cells = False)
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text_punits(ax_j[0], colors, xpos = 0.65, ypos = 0.9)
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#plt.show()
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#hier beide anhängen
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tags.append(ax_j[-1])
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#tags.extend(ax_j)
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xlim_here = axss[-1].get_xlim()
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ylim_here = axss[-1].get_ylim()
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axss[-1].set_xlim(xlim_here0)
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ax_j[-1].set_xlim(xlim_here0)
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start = np.max([xlim_here[0],ylim_here[0]])
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end = np.min([xlim_here[1], ylim_here[1]])
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#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
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#plt.show()
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####################################################################
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#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
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#
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cv_name = "cv_base"
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species = ' Apteronotus leptorhynchus'
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cell_type_here = ' P-unit'
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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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x_axis = ['burst_fraction_burst_corr_individual_base','cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
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x_axis_names = ['Burst Fraction$_{Base}$', 'CV$_{Base}$',score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
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y_axis = [score_burst_corr, 'burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
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y_axis_name = [score_n_burst_corr, 'Burst Fraction$_{Base}$', score_n_burst_corr]
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#
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var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
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#'Response Modulation [Hz]',
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var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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logs = [False,False]#,False]
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xmins = [0,0]#,'no']
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#'response_modulation',
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#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
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#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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#['burst_fraction_burst_corr_individual_stim']
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#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
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frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
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burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
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#score_n = ['Perc99/Med','Perc99/Med']
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#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
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xmax = 6
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ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
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frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
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var_types, x_axis, x_axis_names, counterplus = counterplus, n = n, log = logs, top = top, xmins = xmins, ymaxs = [xmax], extra_cells = True)
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#text_punits(axs, colors)
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#hier beide anhängen
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reset_xlims(axls, axss, xmax)
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tags.append(ax_j[-1])
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#tags.extend(ax_j)
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xlim_here = axss[-1].get_xlim()
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ylim_here = axss[-1].get_ylim()
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start = np.max([xlim_here[0],ylim_here[0]])
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end = np.min([xlim_here[1], ylim_here[1]])
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#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
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#plt.show()
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########################################################################
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#####################################################################
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# Eigemania Zellen
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ylim_eigen = [0, 6]
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cell_types = [' P-unit',' Ampullary'] # ,' P-unit',' Ampullary', ]
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x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
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cv_name_title = [cv_base_name()] # ,'CV$_{BurstCorr}$','CV']
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species_all = [' Eigenmannia virescens'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
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#counter += counterplus
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ax_j, axls, axss, cell_types, _, max_val, _ = species_with_both_cells(grid1[counter], cell_types, ax_j,
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axls, axss, colors,
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cv_name_title,
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save_names, score,
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species_all, x_axis,ylim = ylim_eigen,
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max_val=max_val,n = n, alpha = alpha, log = False, score_n = score_n)
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tags.append(ax_j[-1])
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axss[-1].set_title('')
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ax_j[-1].set_title('')
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counter += 1
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####################################################################
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# grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
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#
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cv_name = "cv_base"
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species = ' Apteronotus leptorhynchus'
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cell_type_here = ' P-unit'
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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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x_axis = [
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score] # 'burst_fraction_burst_corr_individual_base','cv_base']#,score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
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x_axis_names = [
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score_n] # ,'Burst Fraction$_{Base}$', 'CV$_{Base}$']#,score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
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y_axis = [
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score_burst_corr] # , 'burst_fraction_burst_corr_individual_base']#, score_burst_corr] # , 'coherence_', ]
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y_axis_name = [score_n_burst_corr] # , 'Burst Fraction$_{Base}$']#, score_n_burst_corr]
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#
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var_item_names = [
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'Burst Fraction$_{Base}$'] # 'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
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# 'Response Modulation [Hz]',
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var_types = [
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'burst_fraction_burst_corr_individual_base'] # 'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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logs = [False]
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xmins = ['no']
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ymins = ['no']
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# 'response_modulation',
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# var_item_names = ['burst_diff']#['Modulation [Hz]']#,
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# var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
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# ['burst_fraction_burst_corr_individual_stim']
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|
# score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
|
|
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim'] - frame_file[
|
|
'burst_fraction_burst_corr_individual_base'])
|
|
|
|
burst_fraction = [1, 1, 1] # , 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
|
|
|
|
# score_n = ['Perc99/Med','Perc99/Med']
|
|
# score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
|
|
|
|
|
|
xmax = 7
|
|
ax_j, axls, axs, axss, counter = plt_var_axis(ax_j, axls, axss, y_axis_name, burst_fraction, cell_type_here,
|
|
counter, cv_name,
|
|
frame_file, grid1, max_val, score_burst_corr, y_axis, var_item_names,
|
|
var_types, x_axis, x_axis_names, counterplus = counterplus,n = n, log=logs, top=top, xmins=xmins, ymaxs = [xmax], extra_cells= False)
|
|
|
|
reset_xlims(axls, axss, xmax)
|
|
axss[-1].set_xticks_delta(2)
|
|
axss[-1].set_yticks_delta(2)
|
|
|
|
axss[-1].set_xlim(0.5, xmax)
|
|
ax_j[-1].get_shared_x_axes().join(*[axss[-1], ax_j[-1]])
|
|
#text_punits(axs, colors)
|
|
# embed()
|
|
tags.append(ax_j[-1])
|
|
|
|
xlim_here = axss[-1].get_xlim()
|
|
ylim_here = axss[-1].get_ylim()
|
|
start = np.max([xlim_here[0], ylim_here[0]])
|
|
end = np.min([xlim_here[1], ylim_here[1]])
|
|
axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
|
|
|
|
# embed()
|
|
|
|
####################################################################
|
|
|
|
cv_name = "cv_base"
|
|
species = ' Apteronotus leptorhynchus'
|
|
cell_type_here = ' P-unit'
|
|
# for c, cell_type_here in enumerate(cell_types):
|
|
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
|
|
|
|
var_types = ['response_modulation']#,'response_modulation']
|
|
x_axis = ['burst_fraction_burst_corr_individual_base']#,score]
|
|
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#'Modulatoin'
|
|
x_axis_names = ['Burst Fraction$_{Base}$']#,score_n]
|
|
burst_fraction = [1, 1, 1, 1] # burst_fraction_burst_corr_individual_base
|
|
y_axis = ['burst_fraction_burst_corr_individual_stim']#,score_burst_corr]
|
|
y_axis_name = ['Burst Fraction$_{Stim}$']#,score_n_burst_corr]
|
|
|
|
#embed()
|
|
test = False
|
|
if test:
|
|
burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name = test_eodfr(
|
|
burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name)
|
|
#embed()
|
|
for v, var_type in enumerate(var_types):
|
|
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
|
|
|
|
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid1[counter])
|
|
|
|
cmap, _, _ = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
|
|
x_axis[v], frame_file, max_val, y_axis[v],
|
|
burst_fraction=burst_fraction[v], n = n,top = top, var_item=var_type)
|
|
axs.set_ylabel(y_axis_name[v])
|
|
axs.set_xlabel(x_axis_names[v])
|
|
#text_punits(axs, colors)
|
|
plot_extra_cells(axs, frame_file, y_axis, v, x_axis, type_here = 'contrasts')
|
|
|
|
#if v == 1:
|
|
if test == False:
|
|
axs.plot([0, 1.01], [0, 1.01], color='grey', linewidth=0.5)
|
|
|
|
tags.append(axk)
|
|
counter += counterplus
|
|
|
|
|
|
####################################################################
|
|
|
|
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
|
|
#
|
|
mutual_information = False
|
|
if mutual_information:
|
|
cv_name = "cv_base"
|
|
species = ' Apteronotus leptorhynchus'
|
|
cell_type_here = ' P-unit'
|
|
#for c, cell_type_here in enumerate(cell_types):
|
|
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
|
|
|
|
x_axis = ['burst_fraction_burst_corr_individual_base','cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
|
|
x_axis_names = ['Burst Fraction$_{Base}$', 'CV$_{Base}$',score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
|
|
|
|
y_axis = ['contrast_integral', 'burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
|
|
y_axis_name = ['(MI$_{R}$-MI$_{C}$)/MI$_{C}$', 'Burst Fraction$_{Base}$', score_n_burst_corr]
|
|
|
|
|
|
|
|
#
|
|
var_item_names = ['Response Modulation [Hz]']#,'Response Modulation [Hz]']#,'Burst Fraction$_{Base}$']#'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
|
|
#'Response Modulation [Hz]',
|
|
var_types = ['response_modulation']#,'response_modulation']#,'burst_fraction_burst_corr_individual_base']#'response_modulation']#,score_burst_corr,'response_modulation']#,'burst_fraction_burst_corr_individual_stim','cv_base''response_modulation'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
|
|
logs = [False,False]#,False]
|
|
xmins = [0,0]#,'no']
|
|
#'response_modulation',
|
|
|
|
|
|
#var_item_names = ['burst_diff']#['Modulation [Hz]']#,
|
|
#var_types = ['burst_diff'] # [ 'response_modulation']#,'cv_base''response_modulation']#,'burst_fraction_burst_corr_individual_base', ]
|
|
|
|
#['burst_fraction_burst_corr_individual_stim']
|
|
#score_name = ['Burst Fraction$_{Stim}$']# 'CV$_{Base}$']#,'Burst Fraction', ]
|
|
frame_file['burst_diff'] = np.abs(frame_file['burst_fraction_burst_corr_individual_stim']-frame_file['burst_fraction_burst_corr_individual_base'])
|
|
|
|
|
|
burst_fraction = [1,1,1]#, 0.5, 1, 1]#05burst_fraction_burst_corr_individual_base
|
|
|
|
#score_n = ['Perc99/Med','Perc99/Med']
|
|
#score = ['max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr']
|
|
|
|
|
|
ax_j, axls, axs, axss, counter = plt_var_axis([], axls, axss,y_axis_name, burst_fraction, cell_type_here, counter, cv_name,
|
|
frame_file, grid0, max_val, score_burst_corr, y_axis, var_item_names,
|
|
var_types, x_axis, x_axis_names,counterplus = coutnerplus, n = n, extra_cells = True, log = logs, top = top, xmins = xmins)
|
|
#hier beide anhängen
|
|
tags.append(ax_j[-1])
|
|
#tags.extend(ax_j)
|
|
|
|
xlim_here = axss[-1].get_xlim()
|
|
ylim_here = axss[-1].get_ylim()
|
|
start = np.max([xlim_here[0],ylim_here[0]])
|
|
end = np.min([xlim_here[1], ylim_here[1]])
|
|
#axss[-1].plot([start, end], [start, end], color='grey', linewidth=0.5)
|
|
|
|
#####################################################################
|
|
# Eigemania Zellen
|
|
cell_types = [' P-unit',' Ampullary'] # ,' P-unit',' Ampullary', ]
|
|
x_axis = ["response_modulation"] # , "cv_base_w_burstcorr","cv_base", ]
|
|
cv_name_title = ['Response Modulation [Hz]'] # ,'CV$_{BurstCorr}$','CV']
|
|
species_all = [' Eigenmannia virescens'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
|
|
#counter += 1
|
|
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid1[counter], cell_types, ax_j,
|
|
axls, axss, colors,
|
|
cv_name_title,
|
|
save_names, score,
|
|
species_all, x_axis, alpha = alpha,
|
|
max_val=max_val, n = n, ylim = ylim_eigen, log = False, score_n = score_n)
|
|
tags.append(ax_j[-1])
|
|
axss[-1].set_title('')
|
|
ax_j[-1].set_title('')
|
|
counter+= 1#counterplus
|
|
|
|
#####################################################
|
|
#grid_lower_lower = gridspec.GridSpecFromSubplotSpec(1, 2, grid0[1], wspace = 0.5, hspace=0.55)#, height_ratios = [1,3]
|
|
cell_types = [' P-unit']#,' P-unit',' Ampullary', ]
|
|
|
|
restrict = False
|
|
if restrict:
|
|
ax_j, axls, axss, counter = modulation_restrict(ax_j, axls, axs, axss, cell_types, counter, cv_name,
|
|
frame_load_sp, grid0, log, max_val, score_burst_corr, score_n,
|
|
species, tags, top)
|
|
|
|
################################
|
|
# coherence
|
|
coherence_plot = False
|
|
if coherence_plot:
|
|
coherence_plot_modulation(ax_j, axls, axss, counter, frame_load_sp, grid0, max_val, tags, top)
|
|
|
|
##############################################
|
|
# jetzt kommen die extra P-unit Eigen statistiken
|
|
#species = ' Eigenmannia virescens'
|
|
|
|
#cv_name = 'cv_base'
|
|
#ax = plt.subplot(grid[4, 0])
|
|
#for c, cell_type_here in enumerate(cell_types):
|
|
# frame_file = setting_overview_score(cell_type_here, frame_load_sp, species=species)
|
|
# plt_eigen(cv_name, ax, c, cell_type_here, cells_extra, colors, frame_file, max_val, score,
|
|
# species, x_axis, y_axis)
|
|
|
|
|
|
########################
|
|
# modell
|
|
model_plot = False
|
|
if model_plot:
|
|
model_overview_plot(grid0, scoreall)
|
|
|
|
#ax_j[0].get_shared_y_axes().join(*[ax_j[0],ax_j[2],ax_j[3]])
|
|
#ax_j[0].get_shared_y_axes().join(*[ax_j[1], ax_j[3], ax_j[5], axls[0], axls[1], axls[2]])
|
|
|
|
#ax_j[0].get_shared_x_axes().join(*ax_j)
|
|
#ax_j[0].get_shared_x_axes().join(*[ax_j[0],ax_j[1]])
|
|
#ax_j[2].get_shared_x_axes().join(*[ax_j[2], ax_j[3]])
|
|
#ax_j[4].get_shared_x_axes().join(*[ax_j[4], ax_j[5]])
|
|
|
|
#plt.show()
|
|
fig = plt.gcf()
|
|
fig.tag([[tags[0], tags[1], tags[3], tags[4], tags[6], tags[7]],[tags[2], tags[5], tags[8]]], xoffs=-4.2, yoffs=1.12)
|
|
show = False#True
|
|
save_visualization(pdf = True, show = show)
|
|
|
|
|
|
def reset_xlims(axls, axss, xmax):
|
|
axss[-1].set_ylim(0.5, xmax)
|
|
axls[-1].get_shared_y_axes().join(*[axss[-1], axls[-1]])
|
|
|
|
|
|
def species_letter(ax_cv, letter = 'A', species = r'\textit{Apteronotus leptorhynchus}'):
|
|
ax_cv.text(-0.26, 1.8, letter, ha='left',
|
|
transform=ax_cv.transAxes, fontsize=13.5)
|
|
ax_cv.text(-0.1, 1.8, species, ha='left',
|
|
transform=ax_cv.transAxes) # va='center',
|
|
|
|
|
|
def text_punits(ax_cv, colors, xpos = 0.35, ypos = 0.85):
|
|
ax_cv.text(xpos, ypos, r'P-units', ha='left', va='center',
|
|
transform=ax_cv.transAxes, color=colors[' P-unit'])
|
|
|
|
|
|
def fish_plt_false(ax_j, axls, axss, cell_types, colors, counter, grid0, log, max_val, save_names, score_burst_corr):
|
|
#####################################################################
|
|
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
|
|
cv_name_title = ['CV'] # ,'CV$_{BurstCorr}$','CV']
|
|
species_all = [' Apteronotus leptorhynchus'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
|
|
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid0[counter],
|
|
cell_types, ax_j,
|
|
axls, axss, colors,
|
|
cv_name_title,
|
|
save_names,
|
|
score_burst_corr,
|
|
species_all,
|
|
x_axis, log=log,
|
|
max_val=max_val)
|
|
counter += 1
|
|
#####################################################################
|
|
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
|
|
cv_name_title = ['CV'] # ,'CV$_{BurstCorr}$','CV']
|
|
# embed()
|
|
species_all = [' Apteronotus albifrons'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
|
|
ax_j, axls, axss, cell_types, frame_load_sp, max_val, score_burst_corr = species_with_both_cells(grid0[-2],
|
|
cell_types, ax_j,
|
|
axls, axss, colors,
|
|
cv_name_title,
|
|
save_names,
|
|
score_burst_corr,
|
|
species_all,
|
|
x_axis,
|
|
max_val=max_val)
|
|
return axls, axss, counter, frame_load_sp, max_val, score_burst_corr
|
|
|
|
|
|
def version_final_overview():
|
|
return 'calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__direct_'
|
|
|
|
|
|
def NLI_burstcorr_name():
|
|
return 'NLI$(f_{BaseCorrected})$'#f_{BaseCorr}
|
|
|
|
|
|
def test_eodfr(burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name):
|
|
########################################
|
|
# test
|
|
var_types = ['response_modulation']
|
|
x_axis = ['burst_fraction_burst_corr_individual_base']
|
|
var_item_names = ['Response Modulation [Hz]'] # 'Modulatoin'
|
|
x_axis_names = ['Burst Fraction$_{Base}$']
|
|
burst_fraction = [1, 1, 1, 1] # burst_fraction_burst_corr_individual_base
|
|
y_axis = ['eod_fr']
|
|
y_axis_name = ['EOD Fr']
|
|
return burst_fraction, var_item_names, var_types, x_axis, x_axis_names, y_axis, y_axis_name
|
|
|
|
|
|
def model_overview_plot(grid0, scoreall):
|
|
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
|
|
cells = model.cell.unique()
|
|
axm = plt.subplot(grid0[-3])
|
|
plt_model_overview2(axm, cells, scores=[scoreall + '_'])
|
|
plt.subplots_adjust(left=0.07, right=0.95, top=0.98, bottom=0.05, wspace=0.45, hspace=0.55)
|
|
|
|
|
|
def coherence_plot_modulation(ax_j, axls, axss, counter, frame_load_sp, grid0, max_val, tags, top):
|
|
cv_name = "cv_base"
|
|
species = ' Apteronotus leptorhynchus'
|
|
cell_type_here = ' P-unit'
|
|
# for c, cell_type_here in enumerate(cell_types):
|
|
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
|
|
var_types = ['burst_fraction_burst_corr_individual_base'] # ,'response_modulation']
|
|
x_axis = ['cv_base'] # ,'burst_fraction_burst_corr_individual_base']
|
|
var_item_names = ['Burst Fraction'] # , 'Modulatoin' ]
|
|
x_axis_names = ['Burst Fraction$_{Base}$'] # ,'Burst Fraction$_{Base}$']
|
|
burst_fraction = [1] # , 1, 1, 1]#burst_fraction_burst_corr_individual_base
|
|
y_axis = ['mutual_informaiton_'] # , 'coherence_''burst_fraction_burst_corr_individual_stim']
|
|
y_axis_name = ['Mutual Information'] # , 'coherence''Burst Fraction$_{Stim}$']
|
|
# embed()
|
|
for v, var_type in enumerate(var_types):
|
|
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
|
|
|
|
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
|
|
|
|
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
|
|
x_axis[v], frame_file, max_val, y_axis[v],
|
|
burst_fraction=burst_fraction[v], xmin=0, ymin=0, top=top,
|
|
var_item=var_type)
|
|
axs.set_ylabel(y_axis_name[v])
|
|
axs.set_xlabel(x_axis_names[v])
|
|
|
|
# if v == 1:
|
|
# axs.plot([0, 1], [0, 1], color='grey', linewidth=0.5)
|
|
|
|
tags.append(axk)
|
|
counter += 1
|
|
# ax_j
|
|
|
|
|
|
def modulation_restrict(ax_j, axls, axs, axss, cell_types, counter, cv_name, frame_load_sp, grid0, log, max_val,
|
|
score_burst_corr, score_n, species, tags, top):
|
|
burst_corr_reset = 'response_modulation'
|
|
burst_fraction = [50] # , 1000, 1000] # ,1,1]
|
|
burst_fraction = [0.01] # , 1, 1] # ,1,1]
|
|
burst_corr_reset = 'burst_fraction_burst_corr_individual_stim'
|
|
for c, cell_type_here in enumerate(cell_types):
|
|
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
|
|
|
|
##############################################
|
|
# modulatoin comparison for both cell_types
|
|
################################
|
|
# Modulation, cell type comparison
|
|
# todo: hier die diff werte über die zellen
|
|
|
|
# ax_here = []
|
|
# axd = plt.subplot(grid_lower_lower[0, c])
|
|
# embed()
|
|
# kernel_histogram(axk, colors[str(cell_type_here)], np.array(x_axis), norm=True, step=0.03, alpha=0.5)
|
|
# embed()
|
|
|
|
# axk.show_spines('lb')
|
|
|
|
# axs = plt.subplot(grid0[6+c])
|
|
var_types = ['response_modulation']
|
|
x_axis = ['cv_base']
|
|
var_item_names = ['Response Modulation [Hz]', ] # 'Modulatoin'
|
|
x_axis_names = ['CV$_{Base}$']
|
|
|
|
y_axis = [score_burst_corr]
|
|
y_axis_name = [score_n]
|
|
for v, var_type in enumerate(var_types):
|
|
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
|
|
|
|
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
|
|
|
|
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
|
|
x_axis[v], frame_file, max_val, y_axis[v],
|
|
burst_fraction=burst_fraction[c], top=top,
|
|
burst_fraction_reset=burst_corr_reset, var_item=var_type)
|
|
axs.set_ylabel(y_axis_name[v])
|
|
axs.set_xlabel(x_axis_names[v])
|
|
# remove_yticks(axl)
|
|
|
|
if log:
|
|
axl.set_yscale('log')
|
|
axs.set_yscale('log')
|
|
axl.minorticks_off()
|
|
|
|
axl.set_yticks_blank()
|
|
tags.append(axk)
|
|
counter += 1
|
|
|
|
if c in [0, 2]:
|
|
######################################################
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|
# hier kommen die kontrast Punkte dazu
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|
# für die Zellen spielt Burst correctin ja keine Rolle
|
|
# if cv_n == 0:
|
|
if cell_type_here == ' P-unit':
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|
cells_plot2 = p_units_to_show(type_here='contrasts')[0:2]
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|
else:
|
|
cells_plot2 = [p_units_to_show(type_here='contrasts')[-1]]
|
|
|
|
# for cell_plt in cells_plot2:
|
|
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
|
|
# ax = plt.subplot(grid[1, cv_n])
|
|
# todo: hier nur noch die kleinste und größte Amplitude nehmen
|
|
# embed()
|
|
axs.scatter(frame_file[cv_name].loc[cells_extra], frame_file[score_burst_corr].loc[cells_extra],
|
|
s=5, color='white', edgecolor='black', alpha=0.5, clip_on=False) # colors[str(cell_type_here)]
|
|
|
|
# cmap, _, y_axis = plt_modulation_overview(axs, c, cell_type_here,
|
|
# cv_name, frame_file, max_val, score,
|
|
# species)
|
|
# axs.set_ylabel(score)
|
|
# embed()#frame_file[(frame_file.cv_base < 0.65) & (frame_file.response_modulation > 200)].response_modulation
|
|
# axs.set_xlabel(cv_name)
|
|
|
|
# axs.get_shared_x_axes().join(*[axs, axd])
|
|
|
|
# elif species == ' Apteronotus albifrons':
|
|
# plt_albi(ax[4, 1], cell_type_here, colors, max_val, species, x_axis, y_axis)
|
|
|
|
# ax[1,cv_n].set_xlim(0, max_val)
|
|
# set_same_ylim(np.concatenate(ax[1::, :]))
|
|
# set_same_ylim(np.concatenate(ax[1::, :]),ylim_type ='xlim')
|
|
# set_same_ylim(ax[0, :], ylim_type='xlim')
|
|
|
|
# set_ylim_same()
|
|
# ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
|
|
# embed()
|
|
# counter += 1
|
|
# embed()
|
|
return ax_j, axls, axss, counter
|
|
|
|
|
|
def plt_var_axis(ax_j, axls, axss,score_name, burst_fraction, cell_type_here, counter, cv_name, frame_file, grid0, max_val, score,
|
|
scores_here, var_item_names, var_types, x_axis, x_axis_names, ymaxs = ['no'], n = True,counterplus = 1, extra_cells_false = False, extra_cells = True, ymins = ['no'], xmins = [], xlim = None, log = [False], top = False):
|
|
for v, var_type in enumerate(var_types):
|
|
# ax = plt.subplot(grid0[1+v])#grid_lower[0, v]
|
|
|
|
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0[counter])
|
|
counter += counterplus
|
|
try:
|
|
cmap, _, y_axis = plt_burst_modulation_hists(axk, axl, var_item_names[v], axs, cell_type_here,
|
|
x_axis[v], frame_file, max_val, scores_here[v],
|
|
ymax = ymaxs[v], burst_fraction=burst_fraction[v], n = n, xlim = xlim, xmin = xmins[v],ymin =ymins[v], var_item=var_type, top = top)
|
|
except:
|
|
print('burst thing')
|
|
embed()
|
|
axk.show_spines('')
|
|
axl.show_spines('')
|
|
axs.set_ylabel(score_name[v])
|
|
axs.set_xlabel(x_axis_names[v])
|
|
if v in [0, 1]:
|
|
if log[v]:
|
|
axl.set_yscale('log')
|
|
axs.set_yscale('log')
|
|
axes = [axl, axs]
|
|
make_log_ticks(axes)
|
|
axl.set_yticks_blank()
|
|
# remove_yticks(axl)
|
|
axl.minorticks_off()
|
|
|
|
if extra_cells:
|
|
plot_extra_cells(axs, frame_file, scores_here, v, x_axis)
|
|
|
|
return ax_j, axls, axs, axss, counter
|
|
|
|
|
|
def plot_extra_cells(axs, frame_file, scores_here, v, x_axis, type_here = 'bursts'):
|
|
############################
|
|
# extra Zellen Scatter
|
|
# todo: diese Zellen müssen noch runter konvertiert werden
|
|
# todo: extra funktion für Zellen über 9 Snippets schreiben und die nochmal extra machen
|
|
cells_plot2 = p_units_to_show(type_here=type_here)
|
|
# for cell_plt in cells_plot2:
|
|
cells_extra = frame_file[frame_file['cell'].isin(cells_plot2)].index
|
|
# ax = plt.subplot(grid[1, cv_n])
|
|
#embed()
|
|
test_amps = False
|
|
if test_amps:
|
|
cells_here = frame_file.cell.unique()
|
|
cells_amp = ['2011-09-21-ab', '2010-06-21-am', '2012-05-15-ac', '2012-04-26-ae', '2012-05-07-ac',
|
|
'2010-06-21-ac']
|
|
cells_amp = update_cell_names(cells_amp)
|
|
# frame_amps = frame_file[frame_file.cell.isin(cells_amp)]
|
|
for cell_amp in cells_amp:
|
|
if cell_amp in cells_here:
|
|
print(cell_amp)
|
|
# cells_plot = update_cell_names(cells_search)
|
|
try:
|
|
axs.scatter(frame_file[x_axis[v]].loc[cells_extra], frame_file[scores_here[v]].loc[cells_extra],
|
|
s=5, color='white', edgecolor='black', alpha=0.5,
|
|
clip_on=False) # colors[str(cell_type_here)]
|
|
except:
|
|
print('small things')
|
|
embed()
|
|
|
|
def species_with_both_cells(grid0, cell_types, ax_j, axls, axss, colors, cv_name_title, save_names, score, species_all, x_axis, log = True, n = True,ylim = None, alpha = 1, max_val = 1.5, score_n ='Perc(99)/Median'):
|
|
for cv_n, cv_name in enumerate(x_axis):
|
|
if cv_n == 0:
|
|
redo = False
|
|
else:
|
|
redo = False
|
|
redo = False
|
|
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
|
|
if len(frame_load_sp.cell.unique()) <470:
|
|
# double check if the file is the newest
|
|
|
|
#hostname, password, root, username = credencials0()
|
|
dated_up = update_ssh_file(load_folder_name('calc_RAM') + '/' + save_names[0] + '.csv')
|
|
if dated_up == 'yes':
|
|
frame_load_sp = load_overview_susept(save_names[0], redo=True, redo_class=redo)
|
|
|
|
#res = pysftp.put(filepath, destination)
|
|
#ssh_stdin, ssh_stdout, ssh_stderr = ssh.exec_command(cmd_to_execute)
|
|
#ssh
|
|
#embed()
|
|
# frame_file = setting_overview_score(cell_type_here, frame_load_sp, min_amp=True, species=species)
|
|
|
|
# print(np.isnan(species))
|
|
|
|
|
|
|
|
|
|
for c, cell_type_here in enumerate(cell_types):
|
|
#if c == 1:
|
|
# embed()
|
|
|
|
species = species_all[cv_n]
|
|
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
|
|
|
|
##################################
|
|
# modulation and species comparison
|
|
# x_axis, y_axis = get_axis(cv_name, frame_file, score)
|
|
# if cv_n == 0:
|
|
|
|
###############################
|
|
#######################
|
|
# Kernel Histogram
|
|
|
|
# plot the histograms of the values above the according vals
|
|
# grid = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0],hspace=0, wspace = 0.15)#grid[0, cv_n]
|
|
#
|
|
if c == 0:
|
|
axk, axl, axs, axls, axss, ax_j = get_grid_4(ax_j, axls, axss, grid0)
|
|
|
|
if c in [0, 2]:
|
|
axk.set_title(species)
|
|
|
|
# embed()
|
|
if len(frame_file) > 0:
|
|
if log:
|
|
ymin = 'no'
|
|
else:
|
|
ymin = 0
|
|
xmin = 0
|
|
if ylim:
|
|
axs.set_ylim(ylim)
|
|
axs, x_axis = kernel_scatter(axl, cell_types, axk, axs, ax_j, c, cell_type_here, colors, cv_n, cv_name,
|
|
frame_file, grid0, max_val,
|
|
score, log = log,alpha = alpha, n = n,xmin = xmin, ymin = ymin)
|
|
if log:
|
|
axl.set_yscale('log')
|
|
make_log_ticks([axs])
|
|
axl.set_yticks_blank()
|
|
axl.minorticks_off()
|
|
axs.set_xlabel(cv_name_title[cv_n])
|
|
if cv_n == 0:
|
|
axs.set_ylabel(score_n)
|
|
axl.show_spines('')
|
|
axk.show_spines('')
|
|
if cv_n == 0:
|
|
axm = [axs]
|
|
return ax_j, axls, axss, cell_types, frame_load_sp, max_val, score
|
|
|
|
|
|
def get_grid_4(ax_j, axls, axss, grid0):
|
|
grid_k = gridspec.GridSpecFromSubplotSpec(2, 2, grid0,
|
|
hspace=0.1, wspace=0.1, height_ratios=[0.35, 3], width_ratios=[3, 0.5])
|
|
axk = plt.subplot(grid_k[0, 0])
|
|
ax_j.append(axk)
|
|
axs = plt.subplot(grid_k[1, 0])
|
|
axss.append(axs)
|
|
axl = plt.subplot(grid_k[1, 1])
|
|
axls.append(axl)
|
|
return axk, axl, axs, axls, axss, ax_j
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
data_overview2() |