updating figures
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ampullary.pdf
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ampullary.pdf
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@ -51,5 +51,5 @@ if __name__ == '__main__':
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ampullary_punit(cells_plot2=cells_plot2, RAM=False)
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else:
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cells_plot2 = p_units_to_show(type_here='amp')#permuted = True,
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print(cells_plot2)
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#print(cells_plot2)
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ampullary_punit(eod_metrice = False, base_extra = True,color_same = False, fr_name = '$f_{base}$', tags_individual = True, isi_delta = 5, titles=[''],cells_plot2=cells_plot2, RAM=False, scale_val = False, add_texts = [0.25,1.3])#Low-CV ampullary cell,
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@ -7,5 +7,5 @@ if __name__ == '__main__':
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#stack_file = pd.read_csv('..\calc_RAM\calc_nix_RAM-eod_2022-01-28-ag-invivo-1_all__amp_20.0_filename_InputArr_400hz_30_P-unitApteronotusleptorhynchus.csv')
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cells_plot2 = p_units_to_show(type_here = 'contrasts')#permuted = True,
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print(cells_plot2)
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#print(cells_plot2)
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ampullary_punit(eod_metrice = False, color_same = False, base_extra = True, fr_name = label_fr_name_rm(), cells_plot2=[cells_plot2[0]], isi_delta = 5, titles=[''], tags_individual = True, xlim_p = [0, 1.15])#Low-CV P-unit,
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@ -6,5 +6,5 @@ from threefish.RAM.plots import ampullary_punit
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if __name__ == '__main__':
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cells_plot2 = p_units_to_show(type_here='contrasts')#permuted = True,
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print(cells_plot2)
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#print(cells_plot2)
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ampullary_punit(base_extra = True, eod_metrice = False, color_same = False, fr_name = label_fr_name_rm(), tags_individual = True, isi_delta = 5, cells_plot2=[cells_plot2[1]], titles=['', 'Ampullary cell,'], )#High-CV P-unit,
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@ -1,243 +1,242 @@
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File diff suppressed because it is too large
Load Diff
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29,0.25270000000000004
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31,0.26990000000000003
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149,1.25655
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150,1.2673
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151,1.2758
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152,1.2856
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153,1.29305
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154,1.3017
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155,1.3071000000000002
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156,1.31475
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157,1.3233000000000001
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158,1.33295
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159,1.34275
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160,1.3523500000000002
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162,1.36415
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165,1.3911
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170,1.4331500000000001
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172,1.45025
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173,1.45795
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174,1.4676
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175,1.47615
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176,1.4826000000000001
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177,1.49125
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178,1.4999500000000001
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179,1.5064
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180,1.5172
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181,1.5257500000000002
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182,1.5322
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||||
183,1.5419
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184,1.54945
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||||
185,1.5602500000000001
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||||
186,1.5656
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187,1.5742500000000001
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||||
188,1.5839
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189,1.59365
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190,1.60005
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191,1.60765
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192,1.6173000000000002
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193,1.6226500000000001
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195,1.6389500000000001
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200,1.683
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201,1.69055
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||||
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215,1.8101
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||||
217,1.8284
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||||
218,1.83485
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||||
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||||
221,1.86175
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||||
222,1.8672000000000002
|
||||
223,1.8758000000000001
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||||
224,1.8865500000000002
|
||||
225,1.8941000000000001
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||||
226,1.9006500000000002
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||||
227,1.9081000000000001
|
||||
228,1.91995
|
||||
229,1.92755
|
||||
230,1.9350500000000002
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||||
231,1.9437
|
||||
232,1.95015
|
||||
233,1.9619000000000002
|
||||
234,1.9695500000000001
|
||||
235,1.9760000000000002
|
||||
236,1.9846000000000001
|
||||
237,1.99425
|
||||
|
||||
|
Binary file not shown.
@ -21,45 +21,20 @@ except:
|
||||
|
||||
|
||||
def data_overview3():
|
||||
# calcdf_RAM_overview()
|
||||
|
||||
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr'
|
||||
save_name = 'calc_RAM_overview-noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s'
|
||||
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__CutatBeginning_0.05_s_NeurDelay_0.005_s'
|
||||
save_name = 'calc_RAM_overview-noise_data9_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s'
|
||||
|
||||
col = 4
|
||||
row = 2 # sharex=True,
|
||||
|
||||
|
||||
plot_style()
|
||||
default_figsize(column=2, length=6.2) #65.5
|
||||
#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
|
||||
var_it = 'Response Modulation [Hz]'
|
||||
var_it2 = ''
|
||||
if var_it == '':
|
||||
ws = 0.35
|
||||
|
||||
else:
|
||||
ws = 0.65
|
||||
if var_it2 != '':
|
||||
right = 0.9
|
||||
else:
|
||||
right = 0.98
|
||||
right = 0.85
|
||||
ws = 0.75
|
||||
print(right)
|
||||
#print(right)
|
||||
grid0 = gridspec.GridSpec(3, 2, wspace=ws, bottom=0.07,
|
||||
hspace=0.45, left=0.1, right=right, top=0.95)
|
||||
|
||||
###################################
|
||||
###############################
|
||||
# Das ist der Finale Score
|
||||
scoreall = 'perc99/med_diagonal_proj'
|
||||
scoreall = 'max(diag5Hz)/med_diagonal_proj_fr'#'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'#'perc99/med_diagonal_proj'
|
||||
#'max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr',
|
||||
###################################
|
||||
#scores = [scoreall+'_diagonal_proj']
|
||||
|
||||
##########################
|
||||
# Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
|
||||
|
||||
@ -88,7 +63,7 @@ def data_overview3():
|
||||
'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'] # + '_diagonal_proj'
|
||||
scores = ['max(diag5Hz)/med_diagonal_proj_fr','max(diag5Hz)/med_diagonal_proj_fr',
|
||||
] # + '_diagonal_proj'
|
||||
|
||||
printing = False # print values for paper
|
||||
max_xs = [[[],[],[]],[[],[],[]]]
|
||||
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)
|
||||
@ -105,19 +80,7 @@ def data_overview3():
|
||||
# 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])
|
||||
|
||||
#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])
|
||||
colorbar = False
|
||||
#if colorbar:
|
||||
|
||||
x_axis = ['cv_base','cv_stim','response_modulation']#,'fr_base']#
|
||||
var_item_names = [var_it,var_it,var_it2]#,var_it2]#['Response Modulation [Hz]',]
|
||||
@ -154,10 +117,6 @@ def data_overview3():
|
||||
ymin = 0
|
||||
xmin = 0
|
||||
|
||||
if (' P-unit' in cell_type_here) & ('cv' in x_axis[v]):
|
||||
xlimk = [0, 2]
|
||||
else:
|
||||
xlimk = None
|
||||
xlimk = None
|
||||
|
||||
labelpad = 0.5#-1
|
||||
@ -168,8 +127,9 @@ def data_overview3():
|
||||
xlim=xlimk, x_pos=1, burst_fraction=burst_fraction[c],
|
||||
ha='right')
|
||||
|
||||
print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]])))
|
||||
print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]])))
|
||||
if printing:
|
||||
print(cell_type_here + ' median '+scores_here[v]+''+str(np.nanmedian(frame_file[scores_here[v]])))
|
||||
print(cell_type_here + ' max ' + x_axis[v] + '' + str(np.nanmax(frame_file[x_axis[v]])))
|
||||
|
||||
if v == 0:
|
||||
colors = colors_overview()
|
||||
@ -208,44 +168,61 @@ def data_overview3():
|
||||
tags.append(axx)
|
||||
counter += 1
|
||||
#plt.show()
|
||||
|
||||
|
||||
if printing:
|
||||
printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis)
|
||||
|
||||
show = False#True
|
||||
fig = plt.gcf()
|
||||
#embed()
|
||||
tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
|
||||
fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
|
||||
save_visualization(pdf = True, show = show)
|
||||
|
||||
|
||||
def printing_values_data_overview(cell_types, frame_load_sp, scores, species, x_axis, y_axis):
|
||||
############################
|
||||
# 1 Print scores
|
||||
print('\n')
|
||||
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
|
||||
#for species in speciess:
|
||||
# for species in speciess:
|
||||
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)
|
||||
print(cell_type_here + str(len(frame_file.cell.unique())))
|
||||
#embed()
|
||||
# embed()
|
||||
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)]
|
||||
frame_amps.cell.unique()
|
||||
#['2012-05-07-ac-invivo-1', '2012-04-26-ae','2012-05-15-ac-invivo-1', '2010-06-21-ac-invivo-1','2010-06-21-am-invivo-1', '2011-09-21-ab-invivo-1']
|
||||
#embed()
|
||||
#if len(frame_amps)
|
||||
# ['2012-05-07-ac-invivo-1', '2012-04-26-ae','2012-05-15-ac-invivo-1', '2010-06-21-ac-invivo-1','2010-06-21-am-invivo-1', '2011-09-21-ab-invivo-1']
|
||||
# embed()
|
||||
# if len(frame_amps)
|
||||
#################################################################
|
||||
# print the corrs
|
||||
score = scores[c]#'ser_base',
|
||||
score = scores[c] # 'ser_base',
|
||||
score_print = [score]
|
||||
score_print = [scores[c],scores[c],scores[c],scores[c],scores[c],'burst_fraction_burst_corr_individual_base']
|
||||
corr_vars = ['cv_base','response_modulation','fr_base','ser_first_base','burst_fraction_burst_corr_individual_stim','cv_base']
|
||||
#for score in score_print:
|
||||
for c_nr, corr_var in enumerate(corr_vars):#,'ser_sum_corr'
|
||||
score_print = [scores[c], scores[c], scores[c], scores[c], scores[c],
|
||||
'burst_fraction_burst_corr_individual_base']
|
||||
corr_vars = ['cv_base', 'response_modulation', 'fr_base', 'ser_first_base',
|
||||
'burst_fraction_burst_corr_individual_stim', 'cv_base']
|
||||
# for score in score_print:
|
||||
for c_nr, corr_var in enumerate(corr_vars): # ,'ser_sum_corr'
|
||||
score = score_print[c_nr]
|
||||
x = frame_file['fr_base']
|
||||
y = frame_file['ser_first']
|
||||
c = frame_file['cv_base']
|
||||
try:
|
||||
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var , score=score)
|
||||
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, corr_var, cv_name=corr_var,
|
||||
score=score)
|
||||
except:
|
||||
print('frame file lost')
|
||||
embed()
|
||||
corr, p_value = stats.pearsonr(x_axis, y_axis)
|
||||
pears_l = label_pearson(corr, p_value, y_axis, n=True)
|
||||
print(start_name(cell_type_here, species) + ' ' + corr_var + ' to '+str(score) + pears_l)
|
||||
#if corr_var == 'ser_first_base':#
|
||||
print(start_name(cell_type_here, species) + ' ' + corr_var + ' to ' + str(score) + pears_l)
|
||||
# if corr_var == 'ser_first_base':#
|
||||
# embed()
|
||||
print('\n')
|
||||
################################
|
||||
@ -254,17 +231,20 @@ def data_overview3():
|
||||
score='cv_base')
|
||||
corr, p_value = stats.pearsonr(x_axis, y_axis)
|
||||
pears_l = label_pearson(corr, p_value, y_axis, n=True)
|
||||
print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
|
||||
|
||||
print(start_name(cell_type_here, species) + ' ' + 'fr_base' + ' to ' + 'cv_base' + pears_l)
|
||||
|
||||
################################
|
||||
# cv to fr correlation
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='range', species=species)
|
||||
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file, 'burst_fraction_burst_corr_individual_base', cv_name='burst_fraction_burst_corr_individual_base',
|
||||
c_axis, x_axis, y_axis, exclude_here = exclude_nans_for_corr(frame_file,
|
||||
'burst_fraction_burst_corr_individual_base',
|
||||
cv_name='burst_fraction_burst_corr_individual_base',
|
||||
score='cv_base')
|
||||
corr, p_value = stats.pearsonr(x_axis, y_axis)
|
||||
pears_l = label_pearson(corr, p_value, y_axis, n=True)
|
||||
print(start_name(cell_type_here, species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l)
|
||||
print(start_name(cell_type_here,
|
||||
species) + ' amprange: ' + 'burst_fraction_individual_base' + ' to ' + 'cv_base' + pears_l)
|
||||
###############################
|
||||
# fr to nonline but for both modulations
|
||||
|
||||
@ -275,29 +255,25 @@ def data_overview3():
|
||||
print(start_name(cell_type_here, species) + ' amprange: ' + ' ' + 'fr_base' + ' to score' + pears_l)
|
||||
##################################
|
||||
print('\n')
|
||||
#embed()
|
||||
#todo: hier noch die Werte für die Methodik printen
|
||||
# embed()
|
||||
# todo: hier noch die Werte für die Methodik printen
|
||||
test = False
|
||||
#embed()
|
||||
# embed()
|
||||
|
||||
if test:
|
||||
plt.hist(frame_file['lim_individual'])
|
||||
#print(frame_file['lim_individual'])
|
||||
|
||||
# print(frame_file['lim_individual'])
|
||||
############################################
|
||||
############################
|
||||
# 2 Print names
|
||||
# printe um welchen Zeitraum es sich handelt
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here = '', f_exclude=False, snippet=None, min_amp='min',
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
|
||||
species='')
|
||||
|
||||
print('\n')
|
||||
print('From ' +str(np.sort(frame_file.cell)[0])+' to '+str(np.sort(frame_file.cell)[-1])+' nr '+str(len(frame_file.cell)))
|
||||
#embed()
|
||||
|
||||
|
||||
#embed()
|
||||
|
||||
print('From ' + str(np.sort(frame_file.cell)[0]) + ' to ' + str(np.sort(frame_file.cell)[-1]) + ' nr ' + str(
|
||||
len(frame_file.cell)))
|
||||
# embed()
|
||||
# embed()
|
||||
#####################################################################
|
||||
############################
|
||||
# 3 Print , EODF
|
||||
@ -308,12 +284,14 @@ def data_overview3():
|
||||
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)
|
||||
|
||||
if len(frame_file)> 0:
|
||||
if len(frame_file) > 0:
|
||||
# ja der fisch hatte halt eine ziemlich niedriege EODf
|
||||
frame_here = frame_file[frame_file.cell != '2022-01-05-af-invivo-1']
|
||||
try:
|
||||
print(start_name(cell_type_here,
|
||||
species) + ' eodf_min ' + str(int(np.round(np.nanmin(frame_here.eod_fr)))) + ' eodf_max ' + str(int(np.round(np.nanmax(frame_here.eod_fr))))+' n '+str(len(frame_here)))
|
||||
species) + ' eodf_min ' + str(
|
||||
int(np.round(np.nanmin(frame_here.eod_fr)))) + ' eodf_max ' + str(
|
||||
int(np.round(np.nanmax(frame_here.eod_fr)))) + ' n ' + str(len(frame_here)))
|
||||
except:
|
||||
print('eodf thing')
|
||||
embed()
|
||||
@ -321,9 +299,7 @@ def data_overview3():
|
||||
new_list = [item[0:10] for item in my_list]
|
||||
fish.extend(new_list)
|
||||
|
||||
|
||||
print(species[0:7]+' n_fish '+str(len(np.unique(fish))))
|
||||
|
||||
print(species[0:7] + ' n_fish ' + str(len(np.unique(fish))))
|
||||
#####################################################################
|
||||
# Burst corr limits
|
||||
print('\n')
|
||||
@ -336,8 +312,7 @@ def data_overview3():
|
||||
lims_name = ''
|
||||
for lim in lims:
|
||||
lims_name = lims_name + str(lim) + ': ' + str(np.sum(frame_file['lim_individual'] == lim)) + ','
|
||||
print(start_name(cell_type_here, species) + ' ' + lims_name)
|
||||
|
||||
print(start_name(cell_type_here, species) + ' ' + lims_name)
|
||||
#####################################################################
|
||||
############################
|
||||
# 3 Print , EODF
|
||||
@ -346,18 +321,13 @@ def data_overview3():
|
||||
speciess = [' Apteronotus leptorhynchus', ' Eigenmannia virescens']
|
||||
for species in speciess:
|
||||
for c, cell_type_here in enumerate(cell_types):
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here,min_amp = '',species=species)
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='', species=species)
|
||||
|
||||
if len(frame_file)> 0:
|
||||
#embed()
|
||||
if len(frame_file) > 0:
|
||||
# embed()
|
||||
print(start_name(cell_type_here, species) + ' amp_min ' + str(
|
||||
np.nanmin(frame_file.amp)) + ' amp_max ' + str(np.nanmax(frame_file[~np.isinf(frame_file.amp)].amp)))
|
||||
test = False
|
||||
#embed()
|
||||
if test:
|
||||
frame_file[['cv_stim','cv_base']]
|
||||
frame_file['cv_stim']
|
||||
embed()
|
||||
np.nanmin(frame_file.amp)) + ' amp_max ' + str(
|
||||
np.nanmax(frame_file[~np.isinf(frame_file.amp)].amp)))
|
||||
############################
|
||||
# CVs min max
|
||||
for species in speciess:
|
||||
@ -366,11 +336,14 @@ def data_overview3():
|
||||
|
||||
if len(frame_file) > 0:
|
||||
try:
|
||||
print(start_name(cell_type_here, species) + ' CV_min ' + str(np.round(np.nanmin(frame_file.cv_base), 2)) + ' CV max ' + str(np.round(np.nanmax(frame_file.cv_base), 2)) + ' FR max ' + str(np.round(np.nanmin(frame_file.fr_base))) + ' FR max ' + str(np.round(np.nanmax(frame_file.fr_base))))
|
||||
print(start_name(cell_type_here, species) + ' CV_min ' + str(
|
||||
np.round(np.nanmin(frame_file.cv_base), 2)) + ' CV max ' + str(
|
||||
np.round(np.nanmax(frame_file.cv_base), 2)) + ' FR max ' + str(
|
||||
np.round(np.nanmin(frame_file.fr_base))) + ' FR max ' + str(
|
||||
np.round(np.nanmax(frame_file.fr_base))))
|
||||
except:
|
||||
print('min here')
|
||||
embed()
|
||||
|
||||
############################################
|
||||
#######
|
||||
# N
|
||||
@ -380,51 +353,42 @@ def data_overview3():
|
||||
for c, cell_type_here in enumerate(cell_types):
|
||||
##################
|
||||
# printe wie viele Zellen es am Anfang gab
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here, f_exclude = False, snippet = None, min_amp='min', species=species)
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here, f_exclude=False, snippet=None,
|
||||
min_amp='min', species=species)
|
||||
print(start_name(cell_type_here, species) + ' nr ' + str(len(frame_file)))
|
||||
#embed()
|
||||
#if c in [0,2]:
|
||||
|
||||
# embed()
|
||||
# if c in [0,2]:
|
||||
|
||||
#cmap, _, y_axis = plt_modulation_overview(axs, c, 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])
|
||||
# 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')
|
||||
# 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::])
|
||||
|
||||
#counter += 1
|
||||
#embed()
|
||||
# set_ylim_same()
|
||||
# ax[1, 1].get_shared_y_axes().join(*ax[1, 1::])
|
||||
|
||||
# counter += 1
|
||||
# embed()
|
||||
############################################
|
||||
# printe um welchen Zeitraum es sich handelt
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here = '', f_exclude=False, snippet=None, min_amp='min',
|
||||
frame_file = setting_overview_score(frame_load_sp, cell_type_here='', f_exclude=False, snippet=None, min_amp='min',
|
||||
species='')
|
||||
print('\n sampling'+str(frame_file.sampling.unique()))
|
||||
|
||||
print('\n sampling' + str(frame_file.sampling.unique()))
|
||||
print('P-units Fr: 50-450 Hz CV: 0.15 - 1.35')
|
||||
print('Ampullary Fr: 80-200 Hz, CV: 0.08 - 0.22')
|
||||
#plt.show()
|
||||
show = False#True
|
||||
fig = plt.gcf()
|
||||
#embed()
|
||||
tags_final = np.concatenate([tags[0::3],tags[1::3],tags[2::3]])#,tags[2::3]
|
||||
fig.tag(tags_final, xoffs=-4.2, yoffs=1.12)
|
||||
save_visualization(pdf = True, show = show)
|
||||
# plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
BIN
flowchart.pdf
BIN
flowchart.pdf
Binary file not shown.
BIN
flowchart.png
BIN
flowchart.png
Binary file not shown.
|
Before Width: | Height: | Size: 53 KiB After Width: | Height: | Size: 54 KiB |
Binary file not shown.
@ -74,7 +74,6 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
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
|
||||
@ -103,7 +102,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
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 = find_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(
|
||||
@ -287,7 +286,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
|
||||
deltat = model_params.pop("deltat") # .iloc[0]
|
||||
v_offset = model_params.pop("v_offset") # .iloc[0]
|
||||
eod_fr = stack.eod_fr.iloc[0]
|
||||
print(var_types[g] + ' a_fe ' + str(a_fes[g]))
|
||||
|
||||
noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params(
|
||||
a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
|
||||
eod_fe=eod_fe)
|
||||
@ -315,7 +314,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
|
||||
elif len(np.unique(frame.RAM_afe)) > 1:
|
||||
color_timeseries = 'red'
|
||||
nr_plot = 0
|
||||
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
#print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
try:
|
||||
ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
|
||||
(frame.RAM_afe + frame.RAM_noise) * 100,
|
||||
@ -339,7 +338,7 @@ def plt_model_flowcharts(a_fr, ax_external, c, cs, grid, stack, stimulus_length)
|
||||
elif len(np.unique(frame.RAM_noise)) > 1:
|
||||
color_timeseries = 'purple'
|
||||
nr_plot = 1
|
||||
print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
#print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
|
||||
try:
|
||||
ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
|
||||
(frame.RAM_afe + frame.RAM_noise) * 100,
|
||||
@ -454,5 +453,5 @@ if __name__ == '__main__':
|
||||
|
||||
##########################
|
||||
#embed()
|
||||
print('hi')
|
||||
#print('hi')
|
||||
model_and_data2(eod_metrice=False, width=0.005, D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$'
|
||||
|
||||
BIN
model_full.pdf
BIN
model_full.pdf
Binary file not shown.
BIN
model_full.png
BIN
model_full.png
Binary file not shown.
|
Before Width: | Height: | Size: 194 KiB After Width: | Height: | Size: 194 KiB |
BIN
motivation.pdf
BIN
motivation.pdf
Binary file not shown.
BIN
plot_chi2.pdf
BIN
plot_chi2.pdf
Binary file not shown.
BIN
trialnr.pdf
BIN
trialnr.pdf
Binary file not shown.
59
trialnr.py
59
trialnr.py
@ -86,7 +86,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
trial_nr = 500000
|
||||
cell = '2013-01-08-aa-invivo-1'
|
||||
cell = '2012-07-03-ak-invivo-1'
|
||||
print('cell'+str(cell))
|
||||
#print('cell'+str(cell))
|
||||
cells_given = [cell]
|
||||
save_name_rev = find_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(
|
||||
@ -127,12 +127,8 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
]
|
||||
nrs_s = [3, 4, 8, 9]#, 10, 11
|
||||
save_name = save_names[s]
|
||||
#embed()
|
||||
tr_name = trial_nr/1000000
|
||||
if tr_name == 1:
|
||||
tr_name = 1
|
||||
ax_model = []
|
||||
|
||||
|
||||
|
||||
save_name = find_folder_name('calc_model') + '/' + save_name
|
||||
@ -141,12 +137,7 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
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:
|
||||
@ -184,14 +175,8 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
perc05_wo_norm.append(float('nan'))
|
||||
median_wo_norm.append(float('nan'))
|
||||
counter.append(float('nan'))
|
||||
# vars, cv_stims, fr_stims
|
||||
print((np.array(counter)+1)/trial_nrs_here)
|
||||
print((np.array(counter)) / trial_nrs_here)
|
||||
#embed()
|
||||
#ax.plot(trial_nrs_here, perc05, color = 'grey')
|
||||
|
||||
ax[0].plot(trial_nrs_here, perc95, color = 'black', clip_on = False, label = '99.99th percentile', alpha = alphas[s])
|
||||
#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
|
||||
#ax.scatter(trial_nrs_here, perc05, color = 'grey')
|
||||
ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False, alpha = alphas[s])
|
||||
ax[0].set_xscale('log')#colors[s]
|
||||
ax[0].set_yscale('log')
|
||||
@ -203,52 +188,12 @@ def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_ext
|
||||
if s == 1:
|
||||
ax[0].plot(trial_nrs_here, perc05, color='lightgrey', clip_on=False, label = '10th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc05, color='lightgrey', clip_on=False, alpha = alphas[s])
|
||||
|
||||
|
||||
ax[0].plot(trial_nrs_here, perc90, color='grey', clip_on=False, label='90th percentile', alpha = alphas[s])
|
||||
ax[0].scatter(trial_nrs_here, perc90, color='grey', clip_on=False, alpha = alphas[s])
|
||||
ax[0].legend()
|
||||
#ax[0].set_xscale('log')
|
||||
#ax[0].set_yscale('log')
|
||||
#ax[0].set_xlabel('Trials [$N$]')
|
||||
#ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
|
||||
#ax[0].legend()
|
||||
|
||||
''' ax = plt.subplot(1,3,2)
|
||||
ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
|
||||
ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
|
||||
ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
|
||||
ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
|
||||
#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
|
||||
|
||||
ax = plt.subplot(1,3,3)
|
||||
ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
|
||||
ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
|
||||
ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
|
||||
ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
|
||||
#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
|
||||
|
||||
|
||||
|
||||
ax.legend()
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
|
||||
'''
|
||||
plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95)
|
||||
#plt.set_scale
|
||||
#embed()
|
||||
#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],
|
||||
|
||||
#plt.show()
|
||||
save_visualization(pdf=True)
|
||||
|
||||
|
||||
|
||||
@ -1,188 +0,0 @@
|
||||
'''import os
|
||||
|
||||
try:
|
||||
from numba import jit
|
||||
|
||||
except ImportError:
|
||||
def jit(nopython):
|
||||
def decorator_jit(func):
|
||||
return func
|
||||
|
||||
return decorator_jit
|
||||
|
||||
import inspect
|
||||
if 'cv_cell_types' not in inspect.stack()[-1][1]:
|
||||
try:
|
||||
from plotstyle import plot_style, spines_params
|
||||
except:
|
||||
a = 5
|
||||
|
||||
import sys
|
||||
from IPython import embed
|
||||
|
||||
|
||||
# utils susept wird in utils paper copiert und von utisl_susepitbility gestartet
|
||||
utils_suseptibility_name = 'utils1'
|
||||
utils_susept_name = 'utils1_project'
|
||||
|
||||
utils_suseptibility_name2 = 'utils_susept2'
|
||||
utils_susept_name2 = 'utils_paper2'
|
||||
|
||||
utils_suseptibility_name_all = 'utils0'
|
||||
utils_susept_name_all = 'utils0_project'#_down
|
||||
|
||||
|
||||
|
||||
try:# this will not load but I want this to be reference for the refractoring in pycharm
|
||||
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
cont_other_dir = True#
|
||||
except:
|
||||
cont_other_dir = False#then I know that I am on alexandras PC and I can update my code
|
||||
|
||||
#embed()
|
||||
if cont_other_dir == False:
|
||||
import filecmp
|
||||
# I can also update the folder in another directy to double check its dependencies
|
||||
|
||||
|
||||
if not os.path.exists('../'+utils_suseptibility_name+'.py'):
|
||||
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
|
||||
else:
|
||||
#embed()
|
||||
if filecmp.cmp('../'+utils_suseptibility_name+'.py', utils_susept_name+'.py'):
|
||||
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
|
||||
if os.path.exists('../'+utils_suseptibility_name+'.py'):# das mache ich um in dem richtigen embed zu arbeiten
|
||||
sys.path.insert(0, '..')
|
||||
from threefish.utils1 import *#resave_small_files,remove_yticks, unify_cell_names,load_cv_table, colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
|
||||
else:
|
||||
# wir schauen erstmal ohne sys dass das immer zu teilen da ist
|
||||
import shutil
|
||||
if os.path.exists('../'+utils_suseptibility_name+'.py'):
|
||||
shutil.copyfile('../'+utils_suseptibility_name+'.py', utils_susept_name+'.py')
|
||||
import shutil
|
||||
if os.path.exists('../' + utils_suseptibility_name + '.py'):
|
||||
shutil.copyfile('../' + utils_suseptibility_name_all + '.py', utils_susept_name_all + '.py')
|
||||
print('copied utils all')
|
||||
# wenn wir auf meinem Computer sind ziehen wir es aber immer vom code
|
||||
# damit das refractors und wenn wir wo anders sind von dem extra kopierten file
|
||||
if not os.path.exists('../'+utils_suseptibility_name+'.py'):
|
||||
from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
#from utils_paper2 import * # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
|
||||
sys.path.insert(0, '..')
|
||||
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
else:
|
||||
|
||||
sys.path.insert(0, '..')
|
||||
from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
|
||||
|
||||
##############################
|
||||
# find out if we are in the code or develop mode (alexandra) or the public mode!
|
||||
names_extra_modules = []
|
||||
version = 'code'
|
||||
# for name in names_extra_modules:
|
||||
if 'suseptibility' in inspect.stack()[-1][1]: # 'code' not in # da ist jetzt die Starter Directory
|
||||
version = 'develop'
|
||||
if ('code' not in inspect.stack()[-1][1]) | (not (('alex' in os.getlogin()) | ('rudnaya' in os.getlogin()))):
|
||||
version = 'public' # für alle sollte version public sein!
|
||||
|
||||
|
||||
# this we do only in the develop mode
|
||||
if version == 'develop': #
|
||||
copy = True
|
||||
if copy:
|
||||
if __name__ == '__main__':
|
||||
from distutils.dir_util import copy_tree
|
||||
import time
|
||||
import shutil
|
||||
#shutil.rmtree("../../make_folder/suseptibility")#delet
|
||||
t1 = time.time()
|
||||
dirs = os.listdir("../suseptibility")
|
||||
d_count = 0
|
||||
for d, directory in enumerate(dirs):
|
||||
if (('.csv' in directory) | ('.npy' in directory) | ('.pkl' in directory) | ('.py' in directory)| ('suseptibility' in directory)| ('make' in dir.lower()) | ('.dat' in directory)) & ('__pycache__' not in directory):
|
||||
new_folder_make = '../../../make_folder'
|
||||
if not os.path.exists(new_folder_make):
|
||||
os.mkdir(new_folder_make)
|
||||
new_dir = new_folder_make+"/suseptibility/"
|
||||
if not os.path.exists(new_dir):
|
||||
|
||||
os.mkdir(new_dir)
|
||||
# clean the previous directory
|
||||
#embed()
|
||||
if d_count == 0:
|
||||
dirs_new = os.listdir(new_dir)
|
||||
for dir_n in dirs_new:
|
||||
if '__pycache__' not in dir_n:
|
||||
os.remove(new_dir + dir_n)
|
||||
# nicht remove three sonst macht es alles weg
|
||||
#shutil.rmtree(new_dir)
|
||||
#os.mkdir(new_dir)
|
||||
# redo the directory
|
||||
|
||||
#
|
||||
if os.path.exists(new_dir + directory):
|
||||
embed()
|
||||
#print(new_dir+dir)
|
||||
#try:
|
||||
shutil.copy2("../suseptibility/" + directory,new_dir + directory)
|
||||
#print(new_dir + directory)
|
||||
#shutil.copy("../suseptibility/" + directory,new_dir + directory)
|
||||
#shutil.copyfile("../suseptibility/"+dir, new_dir+dir)
|
||||
#except:
|
||||
# print('shutil stuff')
|
||||
# embed()
|
||||
d_count += 1
|
||||
t2 = time.time() -t1
|
||||
|
||||
print('Time='+str(t2))
|
||||
print('copied')
|
||||
#copy_tree("../suseptibility", "../../make_folder/suseptibility")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def plt_scatter_four(grid, frame, cell_types, cell_type_type, annotate, colors):
|
||||
grid2 = gridspec.GridSpecFromSubplotSpec(1, 4, grid[0], wspace=0.4,
|
||||
hspace=0.2)
|
||||
add = ['', '_burst_corr', ]
|
||||
ax0 = plt.subplot(grid2[0])
|
||||
|
||||
# ok hier plotten wir nur den scatter der auch ein gwn hat, aber was ist wenn es mehr sind?
|
||||
# ok im prinzip sollte das zwar schon stimmen aber für das Bild kann man wirklich mehr machen
|
||||
for c, cell_type_it in enumerate(cell_types):
|
||||
frame_g = frame[
|
||||
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
|
||||
|
||||
plt_cv_fr(annotate, ax0, add[0], frame_g, colors, cell_type_it)
|
||||
|
||||
|
||||
|
||||
ax1 = plt.subplot(grid2[1])
|
||||
for c, cell_type_it in enumerate(cell_types):
|
||||
frame_g = frame[
|
||||
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
|
||||
plt_cv_vs(frame_g, ax1, add[0], annotate, colors, cell_type_it)
|
||||
|
||||
|
||||
ax2 = plt.subplot(grid2[2])
|
||||
ax2.set_title('burst')
|
||||
for c, cell_type_it in enumerate(cell_types):
|
||||
# frame_all = frame[(frame[cell_type_type] == cell_type)]
|
||||
frame_g = frame[
|
||||
(frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
|
||||
|
||||
plt_cv_fr(annotate, ax2, add[1], frame_g, colors, cell_type_it)
|
||||
|
||||
|
||||
|
||||
return ax0, ax1, ax2,'''
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user