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removed files for second respository

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saschuta 2024-02-26 10:20:44 +01:00
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burst_add.py
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#from plt_RAM import plt_squares_bursts_single_psd
from utils_suseptibility import *
def plt_squares_bursts_single_psd2(col = 6, cells = ['2012-07-12-ap-invivo-1', '2018-06-26-ah-invivo-1',
'2012-12-20-ae-invivo-1',
'2012-06-27-ah-invivo-1', '2011-10-25-ad-invivo-1',
'2012-12-21-ai-invivo-1',
'2012-05-10-ad-invivo-1', '2012-12-20-ad-invivo-1',
'2012-04-20-ak-invivo-1',
'2012-12-13-ah-invivo-1', '2012-12-20-ab-invivo-1'],nr_clim=10, many=False, width=0.02, row='no', HZ50=True, fs=8, hs=0.39, redo=False, nffts=['whole'],
powers=[1], col_desired=2, var_items=['contrasts'], show=False,
contrasts=[0], noises_added=[''], fft_i='forward', fft_o='forward', spikes_unit='Hz', mV_unit='mV',
D_extraction_method=['additiv_visual_d_4_scaled'], internal_noise=['eRAM'],
external_noise=['eRAM'], level_extraction=['_RAMdadjusted'], cut_off2=300, repeats=[1000000],
receiver_contrast=[1], dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9],
cut_offs1=[300],burst_corrs = [''], clims='all', restrict='restrict', label=r'$\frac{1}{mV^2S}$'):
plot_style()
duration_noise = '_short',
formula = 'code' ##'formula'
# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
stimulus_type = '_StimulusOrig_' # ,#
# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
variant = 'sinz'
mimick = 'no'
cell_recording_save_name = ''
trans = 1 # 5
aa = 0
for burst_corr, cell, var_type, stim_type_afe, trials_stim, 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, in it.product(
burst_corrs, cells, D_extraction_method, external_noise
, repeats, internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ):
#print(cell, burst_corr, cell, var_type, stim_type_afe, trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, cut_off1,trial_nrs)
# print(trial_nrs, stim_type_noise, trials_stim, power, nfft, a_fe, a_fr, var_type, cut_off1,trial_nrs)
aa += 1
default_figsize(column=2, length=4.2) #5.25.5. 6.8 2+2.25+2.25
a = 0
maxs = []
mins = []
ims = []
perc05 = []
perc95 = []
iternames = [D_extraction_method, external_noise,
internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
nr = '2'
n = 2
b = 0
ax_lines = []
ax_psds = []
ax_diagonals = []
axss = []
resize_val = None
for c, cell in enumerate(cells):
axps = []
axpsl = []
#pss = []
lines = []
diags = []
pss = []
mats = []
a = 0
#fig = plt.figure(figsize=(12, 5.5))
fig = plt.figure()
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)
#072
grid0 = gridspec.GridSpec(4, 1, wspace=0.24, bottom=0.1,
hspace=0.04, left=0.08, right=0.93, top=0.98, height_ratios = [1.1,1.3,1,5.8])#7.8
carrier, corrs_all, spikes_tr, spikes_tr_bef = save_spikes_burst_add(
a_fe, a_fr, adapt_type, burst_corr, c_noise, c_sig, cell, cell_recording_save_name, cut_off1,
cut_off2, dendrid, duration_noise, extract, fft_i, fft_o, formula, mimick, nfft, noise_added,
power, ref_type, stim_type_afe, stim_type_noise, stimulus_length, stimulus_type, trans,
trial_nrs, var_type, variant, trial_nrs_base=1, burst_corrs=burst_corrs)
# embed()
grid_stim = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[0], wspace=0.34,
hspace=0.3)
ax_stim = plt.subplot(grid_stim[0])
model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = model_cells[model_cells.cell == cell].iloc[0]
cell = model_params.pop('cell')
eod_fr = model_params['EODf']
deltat = model_params.pop("deltat")
carrier = np.array(carrier['0'])
time_s = np.arange(0, len(carrier)*deltat, deltat)
carrier_RAM = np.random.normal(0, 1, size=len(time_s )) # *0.2
#sine = np.sin(2*np.pi*750)*(1+carrier_RAM*0.2)
extracted_am, time_am = extract_am(carrier, time_s, norm=False)
time_am = time_s * 1000
ax_stim.plot(time_am, extracted_am + 0.05, color='red')
ax_stim.plot(time_am, carrier, color = 'grey', linewidth = '0.5')
ax_stim.set_xlim(0, 200)
ax_stim.show_spines('')
grid_upper = gridspec.GridSpecFromSubplotSpec(1, 1, grid0[1], wspace=0.34,
hspace=0.3)
ax_spikes = plt.subplot(grid_upper[0])
#tags.append(ax_spikes)
#axss = plt.subplot(grid_data[0])
#todo: das modell hier einmal machen für eine variante
alpha = 1/(len(burst_corrs)+1)
#ax_spikes = plt.subplot(1,1,1)
colors = colors_overview()
for i in range(len(corrs_all)):
ax_spikes.eventplot(np.array(corrs_all[i]) * 1000, lineoffsets = len(corrs_all)-i, linelengths = 0.85, alpha =alphafunc_burstadd(
alpha, i), color=colors[' P-unit'])
#ax_upper.eventplot(spikes_tr_bef, color='red')
ax_spikes.set_xlim(0, 200)
ax_spikes.xscalebar(0.9, -0.02, 20, 'ms', va='right', ha='bottom')
#ax_diagonal.xscalebar(1, 0.5, 20, 'dB', va='center', ha='right')
ax_spikes.show_spines('')
#ax_spikes.set_yticks([])
##################################
grid_lower = gridspec.GridSpecFromSubplotSpec(row,col,grid0[3], wspace=0.25,
hspace=0.3)
labelpad = 0
for r, trials_stim in enumerate(repeats):
for b, burst_corr in enumerate(burst_corrs):
save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula,
stim_type_noise, mimick, variant, trials_stim, power,
stimulus_type,
cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o, Hz=spikes_unit,
mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe, extract=extract,
noise_added=noise_added,
c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type,
var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr,
trials_nr=trial_nrs, trans=trans, zeros='ones')
path = save_name + '.pkl' # '../'+
print(save_name)
print(a)
grid_data = grid_matrices(a, grid_lower)
#grid1 = gridspec.GridSpecFromSubplotSpec(1, 2, grid[c], hspace=0)
##########596#####################
#frame = frame.sort_values(by='cv')
#fr = frame[frame.cell == cell].fr # np.array(model_cells['cell'])
#cv = frame[frame.cell == cell].cv
model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = model_cells[model_cells.cell == cell].iloc[0]
eod_fr = model_params['EODf']
#if cont_cell: # cont_cell
#grid_p_i = gridspec.GridSpecFromSubplotSpec(1, 2, grid_data[1])
#axp = plt.subplot(grid_p_i[0])
#axp.set_xlim(0,600)
sampling_calc = 40000
spikes_mat = [[]]*len(spikes_tr)
p_arrays = []
nfft_here = 2**16
diffs = []
f_array = []
#embed()
#spikes_tr = np.array(spikes_tr)
for s, sp in enumerate(corrs_all[b]):
if len(sp)>0:
#try:
np.isnan(sp[-1])
if np.isnan(sp[-1]):
sp = sp[~np.isnan(sp)]
spikes_mat[s] = cr_spikes_mat(np.array(sp), sampling_rate=sampling_calc,
length=int(sampling_calc * np.array(sp[-1])))
#embed()
p_ar, f_array = ml.psd(spikes_mat[s] - np.mean(spikes_mat[s]), Fs=sampling_calc,
NFFT=nfft_here,
noverlap=nfft_here / 2)
p_arrays.append(p_ar)
diffs.extend(np.diff(sp))
ps = np.mean(p_arrays, axis = 0)[f_array<600]
fss = f_array[f_array<600]
#axi, axs = hist_part(grid1, cell_type, burst_corrs[b], colors, cell, spikes, eod_fr, )
###############################
# square
axs = plt.subplot(grid_data[0])
axss.append(axs)
model = load_model_susept(path, cells, save_name)
#if cont:,
if len(model) > 0:
titles = ''
model_show = model[
(model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)]
#embed()
new_keys = model_show.index.unique() # [0:490]
# np.abs(
try:
stack_plot = model_show[list(map(str, new_keys))]
except:
stack_plot = model_show[new_keys]
stack_plot = stack_plot.iloc[np.arange(0, len(new_keys), 1)]
stack_plot.columns = list(map(float, stack_plot.columns))
axs.set_xlim(0, 300)
axs.set_ylim(0, 300)
axs.set_xticks_delta(100)
axs.set_yticks_delta(100)
axs.set_aspect('equal')
model_params = model_cells[model_cells['cell'] == cell]
if len(model_show) > 0:
noise_strength = model_params.noise_strength.iloc[0] # **2/2
stack_plot = RAM_norm(stack_plot, trials_stim,model_show=model_show)
if many == True:
titles = titles + ' Ef=' + str(int(model_params.EODf.iloc[0]))
color = title_color(cell)
#embed()
stack_plot, add_nonlin_title, resize_val = rescale_colorbar_and_values(stack_plot, resize_val = resize_val)
#cbar[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=100
fr = int(np.round(model_show.fr.iloc[0]))
#burst_corr +
axs.text(1, 1.15, titles + ' $f_{Base}=%s$' %(int(np.round(model_show.fr_stim.iloc[0]))) + '\,Hz \n'+ r'$\rm{CV}=%s$' %(
np.round(model_show.cv_stim.iloc[0], 2)), ha = 'right', va = 'center',
transform=axs.transAxes, alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit']) # color=color,fontsize=fs,+\n $cv_{B}$='$_{stim} + str(np.round(model_show.cv.iloc[0], 2)) + ' $fr_{B}$=' + str(fr) + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2)
perc = '' # 'perc'
im = plt_RAM_perc(axs, perc, stack_plot)
pos = np.argmin(np.abs(stack_plot.index -fr/2))
# wenn _ am Anfang des Labels ist verändert das irgendwas, deswgen muss man das auswechseln!
if burst_corr == '':
burst_corr_name = 'no burst corr'
else:
burst_corr_name = burst_corr.replace('_', '-')
######################################
# psds
lines.append(stack_plot.iloc[pos])
diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot))
diags.append(diagonals_prj_l)
pss.append(ps)
# embed()
##########################################
ims.append(im)
mats.append(stack_plot)
maxs.append(np.max(np.array(stack_plot)))
mins.append(np.min(np.array(stack_plot)))
perc05.append(np.percentile(stack_plot, 5))
perc95.append(np.percentile(stack_plot, 95))
plt_triangle(axs, model_show.fr.iloc[0], np.round(model_show.fr_stim.iloc[0]),
model_show.eod_fr.iloc[0], 300)
#plt_peaks_several(['',''], [model_show.fr.iloc[0],np.round(model_show.fr_stim.iloc[0])],ps,0,axp,ps,['brown','red'], fss)
axs.set_aspect('equal')
if a in np.arange(col - 1, 100, col):
cbar = colorbar_outside(axs, im, fig, add=0, width=width)
cbar[0].set_label(nonlin_title(' ['+add_nonlin_title))#, labelpad=100
#if b > col-1:
# axs.set_xlabel(F1_xlabel(), labelpad=20)
#axs.text(1.05, -0.35, F1_xlabel(), ha='center', va='center',
# transform=axs.transAxes)
set_xlabel_arrow(axs, ypos=-0.28)
#else:
# remove_xticks(axs)
#axs.set_ylabel(F2_xlabel())
#if a in np.arange(0, 10, 1) * col:
if b in np.arange(0, 100,col):
#axs.set_ylabel(F2_xlabel())
axs.text(-0.35, 0.97, F2_xlabel(), ha='center', va='center',
transform=axs.transAxes, rotation=90)
else:
remove_yticks(axs)
axs.arrow_spines('lb')
#else:
# remove_tick_ymarks(axs)
else:
print('no model there')
embed()
axs.set_title(var_type[7::] + ' \n' + burst_corr, fontsize=fs,
color=color) # + '\n $D_{sig}$=' + str( np.round(D_derived, 5)) + ' s=' + str(np.round(model_show.ser_sum_stim.iloc[0], 2)
#####################################################
####################################################
# plt psds
save_name = save_ram_model(stimulus_length, cut_off1, duration_noise, nfft, a_fe, formula,
stim_type_noise, mimick, variant, trials_stim, power,
stimulus_type,
cell_recording_save_name, nr=nr, fft_i=fft_i, fft_o=fft_o,
Hz=spikes_unit,
mV=mV_unit, burst_corr=burst_corr, stim_type_afe=stim_type_afe,
extract=extract,
noise_added=noise_added,
c_noise=c_noise, c_sig=c_sig, ref_type=ref_type, adapt_type=adapt_type,
var_type=var_type, cut_off2=cut_off2, dendrid=dendrid, a_fr=a_fr,
trials_nr=trial_nrs, trans=trans, zeros='ones')
path = save_name + '.pkl' # '../'+
grid_data = grid_matrices(a, grid_lower)
#grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[b],
# hspace=1, height_ratios = [1.4,1]) #1.2 height_ratios=[1,1,1, 6],
grid_pds = gridspec.GridSpecFromSubplotSpec(1, 1, grid_data[1], wspace=0.34,
hspace = 0.8)#1.5
#ax_psd = plt.subplot(grid_pds[0])
#ax_psds.append(ax_psd)
ax_diagonal = plt.subplot(grid_pds[0])
ax_diagonals.append(ax_diagonal)
line = False
if line:
ax_line = plt.subplot(grid_pds[2])
ax_lines.append(ax_line)
colors = colors_overview()
psd_all = False
if psd_all:
psd_plot = False
if psd_plot:
##############################
# psd
max_val = pss[a] # np.array(diagonals_prj_l)
loag_val = 10 * np.log10(max_val / np.max(pss)) # / np.max(max_val)
ax_psd.plot(fss[fss < np.max(stack_plot.columns)], loag_val[fss < np.max(stack_plot.columns)],
zorder=100 - a, alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit'])
else:
model = load_model_susept(path, cells, save_name)
#frame = pd.read_pickle('../calc_model/noise2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_10000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV.pkl')
# if cont:,
if len(model) > 0:
titles = ''
model_show = model[
(model.cell == cell)] # & (model_cell.file_name == file)& (model_cell.power == power)]
try:
model_show['io_cross'] = model_show['io_cross'].astype('complex')
except:
print('io cross something')
embed()
vals = np.abs(np.array(model_show['io_cross']))/model_show['isf_psd']
#embed()
ax_psd.plot(stack_plot.columns, vals, alpha=alphafunc_burstadd(
alpha, a),
color=colors[' P-unit'])#[fss < np.max(stack_plot.columns)]
arrow = False
if arrow:
if b == 3:
set_xlabel_arrow(tranfer_xlabel(),xpos=1.15, ypos=-0.35)
#ax_psd.set_xlabel(tranfer_xlabel(), labelpad = labelpad)
else:
set_xlabel_arrow('', xpos=1.15, ypos=-0.35)
ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha = 'center', transform=ax_psd.transAxes)
if b == 0:
ax_psd.set_ylabel(trasnfer_ylabel())
else:
remove_yticks(ax_psd)
#vals = np.abs(vals) / (powers / counter)
#if 'osf' in model_show.keys():
# plt_transferfunction(alpha, ax_psd, color, model_show, eod_fr=eod_fr)
#######################################
# line throuhg
if line:
max_val = np.array(lines[a]) # stack_plot.iloc[pos]
loag_val = 10 * np.log10(max_val / np.max(lines)) # / np.max(max_val)
ax_line.plot(np.array(stack_plot.columns), loag_val, label=burst_corr_name, zorder=100 - b,
alpha=alphafunc_burstadd(
alpha, a), color=colors[' P-unit'])
colors = colors_overview()
# diag, diagonals_prj_l = get_mat_diagonals(np.array(stack_plot))
axis_d = axis_projection(stack_plot, axis='')
if psd_all:
ax_psd.show_spines('lb')
ax_psd.set_xlim(0, axis_d[-1])
a += 1
alpha = 1 / (len(burst_corrs) + 1)
a = 0
for b, burst_corr in enumerate(burst_corrs):
#######################################
# diagonal
axis_d = axis_projection(stack_plot, axis='')
max_val = np.array(diags[b])
loag_val = 10 * np.log10(max_val / np.max(diags)) # / np.max(max_val)
colors = colors_overview()
print(np.max(loag_val))
ax_diagonal = ax_diagonals[b]
ax_diagonal.plot(axis_d, loag_val,
label=burst_corr_name, zorder=100 - a, alpha=alphafunc_burstadd(
alpha, a),
color=colors[' P-unit'])
a += 1
ax_diagonal.set_xlim(0, axis_d[-1])
#remove_xticks(ax_psd)
#ax_psd.set_ylim(-12, 0)
ax_diagonal.set_ylim(-14, 0)
scalebar = False
if scalebar:
if b == 0:
ax_diagonal.text(-0.2, 0.5, chi_name(), ha='center', va='center', rotation=90,
transform=ax_diagonal.transAxes) # diagonal_xlabel())
if b == 3:#a == len(lines) - 1:
ax_diagonal.show_spines('b')
ax_diagonal.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
# ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right')
#ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
#ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center',
# transform=ax_diagonal.transAxes, rotation=90)
#ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center',
# transform=ax_psd.transAxes, rotation=90)
if line:
ax_line.text(1.25, 0.5, 'Line', ha='center', va='center',
transform=ax_line.transAxes, rotation=90)
ax_line.show_spines('b')
else:
if b == 0:
ax_diagonal.set_ylabel('dB', va='center', ha='right')
else:
remove_yticks(ax_diagonal)
# ax_line.yscalebar(1, 0.1, 10, 'dB', va='top', ha='right')
# ax_psd.yscalebar(1, 0.5, 10, 'dB', va='center', ha='right')
# ax_diagonal.text(1.25, 0.5, 'Projection', ha='center', va='center',
# transform=ax_diagonal.transAxes, rotation=90)
# ax_psd.text(1.25, 0.5, 'Output', ha='center', va='center',
# transform=ax_psd.transAxes, rotation=90)
#if line:
# ax_line.text(1.25, 0.5, 'Line', ha='center', va='center',
# transform=ax_line.transAxes, rotation=90)
# ax_line.show_spines('b')
# else:
if line:
ax_line.set_xlim(0, axis_d[-1])
ax_line.set_ylim(-25, 0)
remove_xticks(ax_diagonal)
else:
arrow = False
if arrow:
if b == 3:
set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35)
else:
set_xlabel_arrow(diagonal_xlabel(), xpos=1.15, ypos=-0.35)
#ax_diagonal.text(0.45, -0.6,diagonal_xlabel(), va='center', ha='center', transform=ax_diagonal.transAxes)
#ax_psd.text(0.45, -0.6, tranfer_xlabel(), va='center', ha='center', transform=ax_psd.transAxes)
ax_diagonal.set_xlabel(diagonal_xlabel(), labelpad = labelpad)
for a in axps:
a.set_ylim(0,np.max(axpsl))
#set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95)
#set_same_clim_perc(ims, perc05, perc95)
set_clim_same_here(ims, nr_clim='perc',mats = mats, percnr = 85,perc05=perc05, perc95=perc95, lim_type='up')
if line:
ax_lines[0].get_shared_y_axes().join(*ax_lines)
ax_diagonals[0].get_shared_y_axes().join(*ax_diagonals)
#ax_psds[0].get_shared_y_axes().join(*ax_psds)ax_psds,
tags = np.transpose([axss])#,ax_diagonals,
xoffs = -3.1#2.9
fig.tag(ax_stim, xoffs=xoffs)
fig.tag(ax_spikes,xoffs=xoffs)#,-4.5
tag2(fig,axss,xoffs=xoffs, yoffs = 1.9)#.35#, -0.2
save_visualization(pdf=True)
def grid_matrices(a, grid_lower):
grid_data = gridspec.GridSpecFromSubplotSpec(2, 1, grid_lower[a], hspace=0.5,
height_ratios=[1.7, 1]) # height_ratios=[1,1,1, 6],
return grid_data
def alphafunc_burstadd(alpha, i):
return 1 - alpha * i
def set_same_clim_perc(ims, perc05, perc95):
for i, im in enumerate(ims):
im.set_clim(np.min(perc05), np.max(perc95))
def set_clim_same_remainer(clims, ims, maxs, mins, nr_clim, perc05, perc95):
if 'all' in clims:
remainer = clims.replace('all', '')
if remainer == '':
remainer = 0
else:
remainer = int(remainer)
# embed()
# remainer = 0
# set_clim_same_here()
for i, im in enumerate(ims):
if i < remainer:
# im.set_clim(np.min(np.min(mins)) * nr_clim, np.max(np.max(maxs) / nr_clim))
im.set_clim(perc05[i], perc95[i])
else:
if nr_clim == 'perc':
im.set_clim(perc05[i], perc95[i])
else:
im.set_clim(np.min(perc05), np.max(perc95))
# im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim)
else:
for i, im in enumerate(ims):
if nr_clim == 'perc':
im.set_clim(perc05[i], perc95[i])
else:
im.set_clim(mins[i] * nr_clim, maxs[i] / nr_clim)
#####################################################
if __name__ == '__main__':
##################################################
# compare all scales with 0.2, 0.25, 0.3, 0.35
model = resave_small_files("models_big_fit_d_right.csv", load_folder = 'calc_model_core')
#e#mbed()
cells = model.cell.unique()
cells_m = np.array(model.cell)
cells = []
cells = ['2018-05-08-af-invivo-1', '2018-05-08-ad-invivo-1', '2012-12-20-ae-invivo-1', '2015-01-20-af-invivo-1',
'2012-07-12-ap-invivo-1', '2018-05-08-ae-invivo-1',
'2014-12-11-aa-invivo-1',
'2015-01-15-ab-invivo-1', '2018-06-25-ad-invivo-1',
'2011-10-25-ad-invivo-1', '2018-05-08-ad-invivo-1',
'2018-05-08-ab-invivo-1', '2014-06-06-ag-invivo-1',
'2014-06-06-ac-invivo-1', '2012-04-20-ak-invivo-1',
'2018-05-08-aa-invivo-1']
cells.extend(cells_m)
cells = ["2013-01-08-aa-invivo-1"]
#cells = ['2012-04-20-ak-invivo-1']
burst_corrs = ['', '_burst_added1_','_burst_added2_','_burst_added3_',]#'_burst_added2only_',]#'_burst_added2_',
params = {'burst_corrs': burst_corrs}
########
# das mit den niedriegeren CVs machen
redo = True
show = True
repeats = [1000000] #250000 250000**params,
#repeats = [10000]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.2','additiv_cv_adapt_factor_scaled0.25',
'additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.3','additiv_cv_adapt_factor_scaled0.35',
'additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled0.4','additiv_cv_adapt_factor_scaled0.45',]
D_extraction_method = ['additiv_cv_adapt_factor_scaled',
]#'additiv_visual_d_4_scaled',
#cells = ['2018-05-08-ad-invivo-1','2018-05-08-af-invivo-1','2018-06-25-ad-invivo-1']
#todo:hier das mit dem psd machen
#embed()#'perc'
plt_squares_bursts_single_psd2(row = 1, level_extraction=[''], col = 4, cells = cells, burst_corrs=burst_corrs, D_extraction_method=D_extraction_method, nr_clim=10,
many=False,internal_noise=['RAM'],
external_noise=['RAM'], width=0.005, HZ50=False, fs=7, hs=0.8, redo=redo,
var_items=[k for k in params], clims='all0', repeats=repeats, show=show, label=r'$\frac{1}{S}$')#r'$\frac{1}{mV^2S}$'
#embed()

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from utils_suseptibility import *#p_units_to_show,burst_cells
#from plt_RAM import plt_punit
# plt_cellbody_singlecell
def plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type, plus=1, ax3=[],
burst_corr='_burst_corr_individual', cells_plot2 = []):
#plt.rcParams["font.family"] = "Times New Roman"
colors = colors_overview()
stack = []
axis = []
ims = []
tags_cell = []
#titles = ['Low CV P-unit', 'High CV P-unit', 'Ampullary cell']
for c, cell in enumerate(cells_plot):
print(cell)
frame_cell = frame[(frame['cell'] == cell)]
frame_cell = unify_cell_names(frame_cell, cell_type=cell_type_type)
try:
cell_type = frame_cell[cell_type_type].iloc[0]
except:
print('cell type prob')
embed()
spikes = frame_cell.spikes.iloc[0]
wss = [0.15, 0.66]
# embed()
# eod, sampling_rate, ds, time_eod = find_eod(frame_cell)
tags = []
spikes_base = []
isi = []
frs_calc = []
fr = frame_cell.fr.iloc[0]
cv = frame_cell.cv.iloc[0]
vs = frame_cell.vs.iloc[0]
eod_fr = frame_cell.EODf.iloc[0]
print('EODF' + str(eod_fr))
spikes_base, isi, frs_calc, cont_spikes = load_spikes(spikes, eod_fr)
save_name_type = ['_allspikes_', '_burstIndividual_']
colors_b = ['grey', colors[cell_type]]
ims = []
wr_l = [1, 0.1, 1, 1]
wr_l = wr_l_cells_susept()
wr_u = [1, 0.1, 1, 1]
grid_cell, grid_upper = grids_upper_susept_pics(c, grid1, hs=0.75, row = 2, hr = [1, 0.8], wr_u = wr_u)
wss = ws_for_susept_pic()
# todo: das wenn die zwei ungleich ist ist noch ein Problem
widht_ratios = [2 + wss[0], 2 + wss[1]]
grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1, wspace=0.15,
width_ratios=widht_ratios)
'''grid_s1 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[0],
hspace=0.1, wspace=wss[0],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
# plot the same also to the next plot
grid_s2 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[1],
hspace=0.1, wspace=wss[1],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
'''
several = False
extra_input = False#>
axes = []
axos = []
axds = []
axd2, axi, axo2, grid_lower, grid_s1, grid_s2 = grids_for_psds(save_names, extra_input, grid_cell,
several, widht_ratios = widht_ratios, wss = wss, wr = wr_l)
#axo2 = None
#axd2 = None
test_clim = False
zorder = [100,1]
mats = []
ax_psds = []
add_nonlin_title = None
title_squares = ['All spikes, ', 'First spike, ']
var = ['fr','fr_burst_corr_individual']
for aa, save_name in enumerate(save_names):
add_save = '_cell' + cell + save_name_type[aa] #
# grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1,
# wspace=0.15)
title_square = title_squares[aa]
load_name = load_folder_name('calc_RAM') + '/' + save_name + '_' + cell
# embed()
# axes = []
stack = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv', cells = cells_plot2)
if len(stack) > 0:
files = stack['file_name'].unique()
amps = stack['amp'].unique()
file_name = files[0]
stack_file = stack[stack['file_name'] == file_name]
amps_defined = [np.min(stack_file['amp'].unique())]
# embed()
xpos_xlabel = -0.24
for aaa, amp in enumerate(amps_defined):
if amp in np.array(stack_file['amp']):
alpha = find_alpha_val(aa, save_names)
#add_save = '_cell' + str(cell) + '_amp_' + str(amp)
xlim = [0,1.1]#
if not several:
#embed()
#lim = find_lim_here(cell, 'individual')
fr = frame[frame.cell == cell][var[aa]].iloc[0]
fr_bc = frame[frame.cell == cell][var[-1]].iloc[0]
#embed()
diagonals_prj_l, axi, eod_fr, fr, stack_final1, axds, axos, ax_square, axo2, axd2,mat, add_nonlin_title = plt_psds_in_one_squares_next(
aa, add_save, amp, amps_defined, axds, axes, axis, axos, c, cells_plot, colors_b,
eod_fr, file_name, grid_lower, ims, load_name, save_names, stack_file, wss, xlim = [],
test_clim=test_clim, zorder=zorder[aa], alpha=alpha, extra_input=extra_input, fr=fr,
title_square=title_square,fr_diag = fr_bc, xpos_xlabel=xpos_xlabel, add_nonlin_title=add_nonlin_title,
color=colors[cell_type], axo2=axo2, peaks_extra = True, axd2=axd2, axi=axi, iterate_var=save_names, amp_give = False)
mats.append(mat)
print(np.max(np.max(mat)))
else:
axi, eod_fr, fr, stack_final1, stack_spikes, axds, axos, ax_square, axo2, axd2 = plt_psds_in_one_squares(
aa, add, amp,
amps_defined, axds, axes,
axis, axos, c, cells_plot,
colors_b, eod_fr,
file_name, files, fr,
grid_s1, grid_s2, ims,
load_name, save_names,
stack_file, wss, xlim, axo2=axo2, axd2=axd2, iterate_var=save_names)
if aa == 0:
if extra_input:
tags.append(axi)
else:
tags.append(axo2)
tags.append(ax_square)
if aa == 1:
tags.append(axd2)
ax_psds.append(axo2)
ax_psds.append(axd2)
################################
# do the scatter of these cells
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
# embed()
if len(stack) > 0:
load_name = load_folder_name('calc_RAM') + '/' + save_names[aa] + '_' + cell
if ax3 != []:
try:
frame_g = base_to_stim(load_name, frame, cell_type_type, cell_type, stack=stack)
except:
print('stim problem')
embed()
try:
ax3.scatter(frame_g['cv'], frame_g['cv_stim'], zorder=2, alpha=1,
label=cell_type, s=15,
color=colors[str(cell_type)], facecolor='white')
except:
print('scatter problem')
embed()
######################################################
#if aa == 0:
# color_here = 'grey'
#else:
color_here = ['grey', colors[str(cell_type)]][aa]#[colors[str(cell_type)]
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
#colors_hist = ['grey', colors[str(cell_type)]]
# if len(hists_both) > 1:
# colors_hist = ['grey', colors[str(cell_type)]]
# else:
# colors_hist = [colors[str(cell_type)]]
# for gg in range(len(hists_both)):
# if len(hists_both) > 1:
# hists_here = hists_both[gg]
# embed()
# spikes_base, hists, frs_calc, cont_spikes = load_spikes(spikes, eod_fr, spikes_base, hists, frs_calc)
xlim_e = [0, 200]
#if 'spikes' in stack_final1.keys():
if aa == 0:
axss = plt.subplot(grid_upper[1, -2::])
# axii = plt.subplot(grid_upper[:, 0])
# try:
# embed()
# ax_spikes = plt.subplot(grid_upper[1, 1::])
eod_fr, length, stack_final, stack_final1, trial_nr = stack_preprocessing(amp, stack_file)
stack_spikes = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv',
load_type='spikes',
trial_nr=trial_nr, stimulus_length=length, amp=amp,
file_name=file_name, redo = True)
# das mache ich damit der Stimulus für Mascha hier einmal mit abgespeichert ist
#stack_stim = load_data_susept(load_name + '.pkl', load_name, add=add + '_sampling_' + str(sampling),
# load_version='csv', load_type='stimulus',
# trial_nr=trial_nr, stimulus_length=length, redo=True, amp=amp,
# file_name=file_name)
plt_spikes(amps_defined, aa, c, cell, cell_type, cells_plot, color_here, eod_fr, fr, axss,
stack_final1, stack_spikes, xlim, axi=axi,xlim_e = [0,150], alpha = alpha, spikes_max = 3)
# print('spikes not there yet')
#else:
# eod_mt, sampling, spikes_loaded = nix_load(cell, stack_final1)
##############################
# isi
if len(isi) > 0:
if aa == 0:
grid_p = gridspec.GridSpecFromSubplotSpec(1, 2, grid_upper[:, 0], width_ratios=[1.4, 2],
wspace=0.3,
hspace=0.55)
#grid_p = gridspec.GridSpecFromSubplotSpec(2, 1, grid_upper[:, 0], height_ratios=[1.4, 2],
# hspace=0.55)
# hspace=0.25)
ax_isi = plt.subplot(grid_p[0])
ax_p = plt.subplot(grid_p[1])
tags.insert(0, ax_isi)
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
_, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
else:
# da machen wir die Burst corr spikes anders
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
isi, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
#embed()
#if len(isi[0])<1:
# print('len thing')
# embed()
right = False
ax_isi = base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_fr, frame,
isi, right, spikes_ex,stack,xlim,add_texts = [-3.1,0],texts_left = [250.3,0],peaks = True, pos = -0.55, titles = ['Bursty P-unit,','Bursty P-unit,'],fr_name = '$f_{BaseCorrected}$')
#plt_susept_isi_base(c, cell_type, cells_plot,'grey', ax_isi, isi, xlim=[])# color_here
#ax_p = plt_susept_psd_base(cell_type, 'grey', eod_fr, ax_p, spikes_base, xlim,
# right) #colors[str(cell_type)]
#remove_xticks(ax_p)
#else:
# embed()
##################################
# stimulus
xlim_e = [0, 100]
if aa == 0:
axe = plt.subplot(grid_upper[0, -2::])
plt_stimulus(eod_fr, axe, stack_final1, xlim_e, files[0])
tags.insert(1, axe)
#embed()
set_same_ylimscale(ax_psds)
labels_for_psds(axd2, axi, axo2, extra_input, right = right, xpos_xlabel = xpos_xlabel)
tags_cell.append(tags)
if not test_clim:
set_clim_same_here(ims, mats=mats, lim_type='up', mean_type = True, percnr = 94)
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
# join_y(axes)
# axds[0].get_shared_y_axes().join(*axds)
# embed()
# todo: das muss noch der gleiche ylim sein
try:
set_same_ylim(axos)
set_same_ylim(axds)
except:
print('axo thing')
#fig = plt.gcf()
#fig.tag(tags_cell, xoffs=-4, yoffs=1.9) # -1.5diese Offsets sind nicht intuitiv
if not test_clim:
try:
if len(cells_plot2) ==1:
tags_susept_pictures(tags_cell)
else:
tags_susept_pictures(tags_cell,yoffs=np.array([1.1, 1.1, 2.9, 2.9, 2.9, 2.9]))
except:
print('tag thing')
embed()
def burst_cells(amp_desired=[0.5, 1, 5], cell_class=' Ampullary', cells_plot2=[], show=False, annotate=False):
plot_style()
default_figsize(column=2, width=12, length=7.7) #ts=10, fs=10, ls=10,
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr']
# 'noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s',#__burstIndividual_
# ]
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual_']
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr']
save_names = ['noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr',
'noise_data8_nfft1sec_original__LocalEOD_CutatBeginning_0.05_s_NeurDelay_0.005_s_burst_corr',
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_']
save_names = [
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_',
'noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s__burstIndividual__spikes_']
save_names = [version_final(),version_final()+'__burstIndividual_']
# amps_desired, cell_type_type, cells_plot, frame, cell_types = load_isis(save_names, amps_desired = amp_desired, cell_class = cell_class)
cell_type_type = 'cell_type_reclassified'
#frame = load_cv_base_frame(cells_plot2, cell_type_type=cell_type_type)
#
frame, frame_spikes = load_cv_vals_susept(cells_plot2, EOD_type='synch',
names_keep=['spikes','gwn', 'fs', 'EODf', 'cv', 'fr', 'width_75', 'vs',
'cv_burst_corr_individual',
'fr_burst_corr_individual',
'width_75_burst_corr_individual',
'vs_burst_corr_individual', 'cell_type_reclassified',
'cell'], path_sp = '/calc_base_data-base_frame_overview.pkl',frame_general = False)
#print(cell)
#embed()
# if len(cells_plot2)>0:
cells_plot = cells_plot2
# '2012-05-15-ac-invivo-1','2010-06-21-ac-invivo-1','2010-06-18-ah-invivo-1'
# elif len(cells_plot)>6:
# cells_plot = cells_plot[0:6]
# cells_plot = cells_plot[0:6]
# embed()
################################################
# fig = plt.figure(figsize=(11.5, 6))
# das entscheided ob wir ein burst pic haben werden
#default_settings(ts=10, fs=10, ls=10, width=12, length=3.25 * len(cells_plot), column=2)
default_settings_cells_susept(cells_plot, l=3.65)
if len(cells_plot) > 1:
grid1 = big_grid_susept_pics(cells_plot, top=0.96, bottom=0.065)
else:
grid1 = big_grid_susept_pics(cells_plot, top=0.9, bottom=0.12)
plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type,
plus=1, burst_corr='_burst_corr_individual', cells_plot2 = cells_plot2)
# embed()
# plt.show()
fig = plt.gcf()
save_visualization(pdf=True)
# fig.savefig()
# save_all(0, cell+save_name+cell_sorted, show, '')
# show_func(show = show)
if __name__ == '__main__':
cells_plot1 = p_units_to_show(type_here = 'contrasts')
cells_plot1 = p_units_to_show(type_here='contrasts')
cells_plot2 = p_units_to_show(type_here = 'bursts')
burst_cells(cells_plot2 = [cells_plot2[0]], show = True, cell_class =' P-unit')#

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from utils_suseptibility import *#p_units_to_show,burst_cells
#from plt_RAM import plt_punit
from burst_cells_suscept import burst_cells
# plt_cellbody_singlecell
def plt_cellbody_singlecell_bursts2(grid1, frame, save_names, cells_plot, cell_type_type, plus=1, ax3=[],
burst_corr='_burst_corr_individual'):
#plt.rcParams["font.family"] = "Times New Roman"
colors = colors_overview()
stack = []
axis = []
ims = []
tags_cell = []
#titles = ['Low CV P-unit', 'High CV P-unit', 'Ampullary cell']
for c, cell in enumerate(cells_plot):
print(cell)
frame_cell = frame[(frame['cell'] == cell)]
frame_cell = unify_cell_names(frame_cell, cell_type=cell_type_type)
try:
cell_type = frame_cell[cell_type_type].iloc[0]
except:
print('cell type prob')
embed()
spikes = frame_cell.spikes.iloc[0]
wss = [0.15, 0.66]
# embed()
# eod, sampling_rate, ds, time_eod = find_eod(frame_cell)
tags = []
spikes_base = []
isi = []
frs_calc = []
fr = frame_cell.fr.iloc[0]
cv = frame_cell.cv.iloc[0]
vs = frame_cell.vs.iloc[0]
eod_fr = frame_cell.EODf.iloc[0]
print('EODF' + str(eod_fr))
spikes_base, isi, frs_calc, cont_spikes = load_spikes(spikes, eod_fr)
save_name_type = ['_allspikes_', '_burstIndividual_']
colors_b = ['grey', colors[cell_type]]
ims = []
wr_l = [1, 0.1, 1, 1]
wr_l = wr_l_cells_susept()
wr_u = [1, 0.1, 1, 1]
grid_cell, grid_upper = grids_upper_susept_pics(c, grid1, hs=0.75, row = 2, hr = [1, 0.8], wr_u = wr_u)
wss = ws_for_susept_pic()
# todo: das wenn die zwei ungleich ist ist noch ein Problem
widht_ratios = [2 + wss[0], 2 + wss[1]]
grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1, wspace=0.15,
width_ratios=widht_ratios)
'''grid_s1 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[0],
hspace=0.1, wspace=wss[0],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
# plot the same also to the next plot
grid_s2 = gridspec.GridSpecFromSubplotSpec(2, 2, grid_lower[1],
hspace=0.1, wspace=wss[1],
width_ratios=[0.8,
1]) # height_ratios=[1.5, 1.5, 5],
'''
several = False
extra_input = False#>
axes = []
axos = []
axds = []
axd2, axi, axo2, grid_lower, grid_s1, grid_s2 = grids_for_psds(save_names, extra_input, grid_cell,
several, widht_ratios = widht_ratios, wss = wss, wr = wr_l)
#axo2 = None
#axd2 = None
test_clim = False
zorder = [100,1]
mats = []
ax_psds = []
add_nonlin_title = None
title_squares = ['All spikes, ', 'First spike, ']
var = ['fr','fr_burst_corr_individual']
for aa, save_name in enumerate(save_names):
add_save = '_cell' + cell + save_name_type[aa] #
# grid_lower = gridspec.GridSpecFromSubplotSpec(1, len(save_names), grid_cell[1], hspace=0.1,
# wspace=0.15)
title_square = title_squares[aa]
load_name = load_folder_name('calc_RAM') + '/' + save_name + '_' + cell
# embed()
# axes = []
stack = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv', cells = cells_plot2)
if len(stack) > 0:
files = stack['file_name'].unique()
amps = stack['amp'].unique()
file_name = files[0]
stack_file = stack[stack['file_name'] == file_name]
amps_defined = [np.min(stack_file['amp'].unique())]
# embed()
xpos_xlabel = -0.24
for aaa, amp in enumerate(amps_defined):
if amp in np.array(stack_file['amp']):
alpha = find_alpha_val(aa, save_names)
#add_save = '_cell' + str(cell) + '_amp_' + str(amp)
xlim = [0,1.1]#
if not several:
#embed()
#lim = find_lim_here(cell, 'individual')
fr = frame[frame.cell == cell][var[aa]].iloc[0]
fr_bc = frame[frame.cell == cell][var[-1]].iloc[0]
#embed()
diagonals_prj_l, axi, eod_fr, fr, stack_final1, axds, axos, ax_square, axo2, axd2,mat, add_nonlin_title = plt_psds_in_one_squares_next(
aa, add_save, amp, amps_defined, axds, axes, axis, axos, c, cells_plot, colors_b,
eod_fr, file_name, grid_lower, ims, load_name, save_names, stack_file, wss, xlim = [],
test_clim=test_clim, zorder=zorder[aa], alpha=alpha, extra_input=extra_input, fr=fr,
title_square=title_square,fr_diag = fr_bc, xpos_xlabel=xpos_xlabel, add_nonlin_title=add_nonlin_title,
color=colors[cell_type], axo2=axo2, peaks_extra = True, axd2=axd2, axi=axi, iterate_var=save_names, amp_give = False)
mats.append(mat)
print(np.max(np.max(mat)))
else:
axi, eod_fr, fr, stack_final1, stack_spikes, axds, axos, ax_square, axo2, axd2 = plt_psds_in_one_squares(
aa, add, amp,
amps_defined, axds, axes,
axis, axos, c, cells_plot,
colors_b, eod_fr,
file_name, files, fr,
grid_s1, grid_s2, ims,
load_name, save_names,
stack_file, wss, xlim, axo2=axo2, axd2=axd2, iterate_var=save_names)
if aa == 0:
if extra_input:
tags.append(axi)
else:
tags.append(axo2)
tags.append(ax_square)
if aa == 1:
tags.append(axd2)
ax_psds.append(axo2)
ax_psds.append(axd2)
################################
# do the scatter of these cells
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
# embed()
if len(stack) > 0:
load_name = load_folder_name('calc_RAM') + '/' + save_names[aa] + '_' + cell
if ax3 != []:
try:
frame_g = base_to_stim(load_name, frame, cell_type_type, cell_type, stack=stack)
except:
print('stim problem')
embed()
try:
ax3.scatter(frame_g['cv'], frame_g['cv_stim'], zorder=2, alpha=1,
label=cell_type, s=15,
color=colors[str(cell_type)], facecolor='white')
except:
print('scatter problem')
embed()
######################################################
#if aa == 0:
# color_here = 'grey'
#else:
color_here = ['grey', colors[str(cell_type)]][aa]#[colors[str(cell_type)]
add = ['', '_burst_corr', ]
add = ['', '_burst_corr_individual']
#colors_hist = ['grey', colors[str(cell_type)]]
# if len(hists_both) > 1:
# colors_hist = ['grey', colors[str(cell_type)]]
# else:
# colors_hist = [colors[str(cell_type)]]
# for gg in range(len(hists_both)):
# if len(hists_both) > 1:
# hists_here = hists_both[gg]
# embed()
# spikes_base, hists, frs_calc, cont_spikes = load_spikes(spikes, eod_fr, spikes_base, hists, frs_calc)
xlim_e = [0, 200]
#if 'spikes' in stack_final1.keys():
if aa == 0:
axss = plt.subplot(grid_upper[1, -2::])
# axii = plt.subplot(grid_upper[:, 0])
# try:
# embed()
# ax_spikes = plt.subplot(grid_upper[1, 1::])
eod_fr, length, stack_final, stack_final1, trial_nr = stack_preprocessing(amp, stack_file)
stack_spikes = load_data_susept(load_name + '.pkl', load_name, add=add_save, load_version='csv',
load_type='spikes',
trial_nr=trial_nr, stimulus_length=length, amp=amp,
file_name=file_name, redo = True)
# das mache ich damit der Stimulus für Mascha hier einmal mit abgespeichert ist
#stack_stim = load_data_susept(load_name + '.pkl', load_name, add=add + '_sampling_' + str(sampling),
# load_version='csv', load_type='stimulus',
# trial_nr=trial_nr, stimulus_length=length, redo=True, amp=amp,
# file_name=file_name)
plt_spikes(amps_defined, aa, c, cell, cell_type, cells_plot, color_here, eod_fr, fr, axss,
stack_final1, stack_spikes, xlim, axi=axi,xlim_e = [0,150], alpha = alpha, spikes_max = 3)
# print('spikes not there yet')
#else:
# eod_mt, sampling, spikes_loaded = nix_load(cell, stack_final1)
##############################
# isi
if len(isi) > 0:
if aa == 0:
grid_p = gridspec.GridSpecFromSubplotSpec(1, 2, grid_upper[:, 0], width_ratios=[1.4, 2],
wspace=0.3,
hspace=0.55)
#grid_p = gridspec.GridSpecFromSubplotSpec(2, 1, grid_upper[:, 0], height_ratios=[1.4, 2],
# hspace=0.55)
# hspace=0.25)
ax_isi = plt.subplot(grid_p[0])
ax_p = plt.subplot(grid_p[1])
tags.insert(0, ax_isi)
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
_, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
else:
# da machen wir die Burst corr spikes anders
lim_here = find_lim_here(cell, burst_corr=burst_corr)
if np.min(np.concatenate(isi)) < lim_here:
isi, spikes_ex, frs_calc2 = correct_burstiness(isi, spikes_base,
[eod_fr] * len(spikes_base),
[eod_fr] * len(spikes_base), lim=lim_here,
burst_corr=burst_corr)
#embed()
#if len(isi[0])<1:
# print('len thing')
# embed()
right = False
ax_isi = base_cells_susept(ax_isi, ax_p, c, cell, cell_type, cells_plot, colors, eod_fr, frame,
isi, right, spikes_ex,stack,xlim,add_texts = [-3.1,0],texts_left = [250.3,0],peaks = True, pos = -0.55, titles = ['Bursty P-unit,','Bursty P-unit,'],fr_name = '$f_{BaseCorrected}$')
#plt_susept_isi_base(c, cell_type, cells_plot,'grey', ax_isi, isi, xlim=[])# color_here
#ax_p = plt_susept_psd_base(cell_type, 'grey', eod_fr, ax_p, spikes_base, xlim,
# right) #colors[str(cell_type)]
#remove_xticks(ax_p)
#else:
# embed()
##################################
# stimulus
xlim_e = [0, 100]
if aa == 0:
axe = plt.subplot(grid_upper[0, -2::])
plt_stimulus(eod_fr, axe, stack_final1, xlim_e, files[0])
tags.insert(1, axe)
set_same_ylimscale(ax_psds)
labels_for_psds(axd2, axi, axo2, extra_input, right = right, xpos_xlabel = xpos_xlabel)
tags_cell.append(tags)
if not test_clim:
set_clim_same_here(ims, mats=mats, lim_type='up', mean_type = True, percnr = 94)
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
#set_clim_same_here(ims, clims='all', same = 'same', lim_type='up')
# join_y(axes)
# axds[0].get_shared_y_axes().join(*axds)
# embed()
# todo: das muss noch der gleiche ylim sein
try:
set_same_ylim(axos)
set_same_ylim(axds)
except:
print('axo thing')
#fig = plt.gcf()
#fig.tag(tags_cell, xoffs=-4, yoffs=1.9) # -1.5diese Offsets sind nicht intuitiv
if not test_clim:
try:
if len(cells_plot2) ==1:
tags_susept_pictures(tags_cell)
else:
tags_susept_pictures(tags_cell,yoffs=np.array([1.1, 1.1, 2.9, 2.9, 2.9, 2.9]))
except:
print('tag thing')
embed()
if __name__ == '__main__':
cells_plot2 = p_units_to_show(type_here = 'bursts')
burst_cells( cells_plot2 = [cells_plot2[1]], show = True, cell_class =' P-unit')

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from utils_suseptibility import cells_eigen
from utils_all import p_units_to_show
#from utils_all import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':
#'2010-06-21-ac-invivo-1'
# Bei dem ampullary cells machen wir auch die Bursts weg
['2010-11-26-al-invivo-1', '2010-11-26-am-invivo-1',
'2010-11-08-ab-invivo-1', '2010-11-08-ag-invivo-1', '2010-11-08-ah-invivo-1', '2010-11-08-ai-invivo-1',
'2010-11-11-ak-invivo-1', '2010-11-11-ac-invovo-1',
'2013-04-11-ab-invivo-1',
]
cells = ['2010-11-26-al-invivo-1', '2010-11-26-am-invivo-1'
]
# 10-08-11-ar
# 5 + 10+20
# 0.5, 1, 5, 10, 20
# 2.5 5 10
# 5 10 20 (2012-06-21-av, 2010-08-27-ag)
# 0.5, 1, 5 (2021-08-03-ab)
# 0.1, 0.5, 1, 5 (2021-08-03-ac)
# 1 5 10 20, (2020-10-21-ac)
# 0.1,1,10 (2021-12-17-ad)
# 0.1, 0.5, 1, 5, 10 ,20 (2020-10-27-af)
# 1, 2, 5, 10, 20 (2021-06-18-ae)
unklar = ['2010-07-29-ae-invivo-1']
# für mehrere
# ampullary
## 2.5, 5, 10 (2011-10-25-ab, 2012-07-11-ag, 2012-12-13-ao)
# amp_desired = [2.5, 10, 20]# '2010-08-31-ad-invivo-1',
# cells_plot2 = ['2011-10-25-ac-invivo-1', '2012-05-15-ac-invivo-1','2012-06-08-ae-invivo-1']#['2010-06-21-av-invivo-1', '2010-08-27-ag-invivo-1','2020-10-27-ae-invivo-1']
amp_desired = [1, 5, 10, 20]
amp_desired = [0.1, 1, 10]
amp_desired = [0.5, 1, 5]
amp_desired = [0.5, 1, 5],
# 0.1, 0.5, 1, 5 (2021-08-03-ac,2021-08-03-ae)
amp_desired = [0.1, 0.5, 1, 5]
cells_plot2 = []
# 0.5, 1, 5 (2021-08-03-ab)
amp_desired = [0.5, 1, 5]
cells_plot2 = ['2021-08-03-ab-invivo-1','2021-08-03-ac-invivo-1','2021-08-03-ae-invivo-1']
# 1 5 10 20, (2020-10-21-ac)
amp_desired = [1,5,10,20]
cells_plot2 = []
# 0.1,1,10 (2021-12-17-ad)
# 0.1, 0.5, 1, 5, 10 ,20 (2020-10-20-ab, 2020-10-27-af)
#amp_desired = [1,5,10,20]
#cells_plot2 = []
# das hat uns glaube ich ganz gut gefallen
# 5 10 20 (2010-06-21-av, 2010-08-27-ag, 2010-08-31-ad)
amp_desired = [5,20]# '2010-08-31-ad-invivo-1', 10,
cells_plot2 = p_units_to_show(type_here = 'eigen_small')
# I like: '2020-10-21-ac' '2010-06-21-av-invivo-1', '2010-08-27-ag-invivo-1' '2021-08-03-ag-invivo-1, '20-10-21-aa-invivo-1
#2021-12-17-ad
#embed()
#'2017-10-25-am-invivo-1', '2010-11-26-an-invivo-1'
#save_names = ['noise_data10_nfft1sec_original__StimPreSaved4__mean5__CutatBeginning_0.05_s_NeurDelay_0.005_s_spikes_']
#load_name = load_folder_name('calc_RAM') + '/' + save_names[0] + '_' +'2017-10-25-am-invivo-1'# '2010-11-26-an-invivo-1'#'2017-10-25-am-invivo-1'
#stack = pd.read_pickle(load_name + '.pkl')
#embed()#[0, 250]#'High CV P-unit', at Fr \n at 1/2 EODf'
cells_eigen(titles = ['Low-CV P-unit,','Low-CV P-unit,'], cells_plot2 = cells_plot2, amp_desired = amp_desired, show = True,cell_class = ' P-unit')

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#sys.path.insert(0, '..')
#from plt_RAM import plt_RAM_overview_nice
#from utils_susept import
from IPython import embed
from matplotlib import gridspec as gridspec, pyplot as plt
import numpy as np
#embed()
from utils_all_down import default_settings, resave_small_files,update_cell_names
from utils_suseptibility import default_figsize, NLI_scorename, kernel_scatter, plt_burst_modulation_hists, \
plt_model_overview2, version_final
from utils_all import colors_overview, cv_base_name, load_overview_susept, make_log_ticks, p_units_to_show, \
save_visualization, \
setting_overview_score, update_ssh_file, load_folder_name
#import lstat
try:
from plotstyle import plot_style, spines_params
except:
a = 5
from heterogeneity import plt_fr_cv_base, get_grids_for_cv_fr, get_frame_for_base_plot
def data_overview2():
# 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=7.35)
#fig, ax = plt.subplots(4, 2) # , figsize=(14, 7.5) constrained_layout=True,
three = True
if three:
top = True
grid00 = gridspec.GridSpec(2, 1, wspace=0.37, bottom=0.15,
hspace=0.25, left=0.085, right=0.95, top=0.94, height_ratios = [1,2.7])
else:
grid00 = gridspec.GridSpec(4, 2, wspace=0.75, bottom=0.04,
hspace=0.6, left=0.15, right=0.99, top=0.95)
top = False
#grid0 = gridspec.GridSpec(3, 3, wspace=0.35, bottom=0.2,
# hspace=1, left=0.085, right=0.95, top=0.94)
grid0 = gridspec.GridSpecFromSubplotSpec(1, 5, grid00[0],width_ratios = [1,0.4, 1,0.55,1], hspace=0.35,wspace=0)
grid1 = gridspec.GridSpecFromSubplotSpec(2, 5, grid00[1],width_ratios = [1,0.4, 1,0.55,1], hspace=1.1, wspace=0)
###################################
###############################
# Das ist der Finale Score
scoreall = 'perc99/med'
###################################
#scores = [scoreall+'_diagonal_proj']
#score = scores[0]
##########################
# Auswahl: wir nehmen den mean um nicht Stimulus abhängigen Noise rauszumitteln
#save_names = []
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_',
save_names = [version_final_overview()]#_abs_
#save_names = ['calc_RAM_overview-_simplified_noise_data12_nfft0.5sec_original__StimPreSaved4__abs_']
counter = 0
colors = colors_overview()
ax_j = []
axls = []
axss = []
cell_types = [' Ampullary', ' P-unit', ]
counter = 0
tags = []
log = False
counterplus = 2
max_val = 1.5
score_burst_corr = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr'#scores[0]
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']
score_n_burst_corr = 'Fr$_{BurstCorr}$/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
score_n_burst_corr = NLI_burstcorr_name()
score = 'max(diag5Hz)/med_diagonal_proj_fr'
score_n = 'max(diag5Hz)\n/med_diagonal_proj_fr'
score_n = 'Fr/Median' # , 'max(diag5Hz)/med \n diagonal_proj \n fr_base_w_burstcorr']
score_n = NLI_scorename()
#score = 'max(diag5Hz)/med_diagonal_proj_fr_base_w_burstcorr' # scores[0]
redo = False#True#True
frame_load_sp = load_overview_susept(save_names[0], redo=redo, redo_class=redo)
#embed()
test_amps = False
if test_amps:
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_load_sp[frame_load_sp.cell.isin(cells_amp)]
fish_plot = False
if fish_plot:
axls, axss, counter, frame_load_sp, max_val, score_burst_corr = fish_plt_false(ax_j, axls, axss, cell_types,
colors, counter, grid0, log,
max_val, save_names,
score_burst_corr)
alpha = 0.3
####################################################################
# base plots
# cells = p_units_to_show()
# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
burst_choice = ['', '_burst_corr_individual']
xmax_burst = [[1.5,'no'],[0.8,'no']]
for gg in range(len(burst_choice)):
cells = p_units_to_show()
species = ' Apteronotus leptorhynchus'
cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells, save_names = save_names, species = species)
ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
if gg == 0:
#ax_cv.text(1.5, 2, r'\bf{A} \textit{Apteronotus leptorhynchus}', ha = 'center', va = 'center', transform=ax_cv.transAxes)
text_punits(ax_cv, colors)
ax_cv.text(0.35, 0.45, r'Ampullary cells', ha='left', va='center',
transform=ax_cv.transAxes, color = colors[' Ampullary'])#' Ampullary',
species_letter(ax_cv)
print(species)
x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
frame_load, gg, alpha = alpha, xmax = xmax_burst[gg], species = species)
ax0.set_xticks_delta(0.5)
tags.append(ax_cv)
counter += 2
#frame_file = setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
####################################################################
# base plots
# cells = p_units_to_show()
# cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells)
burst_choice = ['']#, '_burst_corr_individual']
#counter += 1
nrs = [1,3]
for gg in range(len(burst_choice)):
cells = p_units_to_show()
species = ' Eigenmannia virescens'
cell_type_type, frame_load, frame_spikes = get_frame_for_base_plot(cells,save_names = save_names, species = species)
ax0, ax_cv, ax_fr = get_grids_for_cv_fr(gg, grid0[counter])#counter
#ax_cv.set_title('Eigenmannia virescens')
#ax_cv.text(0.5, 2, B, ha='center', va='center', transform=ax_cv.transAxes)
species_letter(ax_cv, letter='B', species=r'\textit{Eigenmannia virescens}')
print(species)
x_axis, y_axis = plt_fr_cv_base(ax0, ax_cv, ax_fr, burst_choice,
frame_load, gg, alpha = alpha, xmax = ['no','no'], species = species)
ax0.set_xticks_delta(0.5)
tags.append(ax_cv)
counter += 2
#embed()
###########################
###########################
###########################
# counterreset!
counter = 0
#grid0 = grid1
# base ready
####################################################################
####################################################################
####################################################################
####################################################################
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
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)
#embed()
#setting_overview_score(frame_load_sp, cell_type_here, min_amp='min', species=species)
x_axis = ['cv_base',score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = [cv_base_name(), score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = ['burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
y_axis_name = ['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']
n = False
xlim_here0 = [0,1.5]
ax_j, axls, axs, axss, counter = plt_var_axis([], 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, xlim = xlim_here0, log = logs, top = top, xmins = xmins, extra_cells = False)
text_punits(ax_j[0], colors, xpos = 0.65, ypos = 0.9)
#plt.show()
#hier beide anhängen
tags.append(ax_j[-1])
#tags.extend(ax_j)
xlim_here = axss[-1].get_xlim()
ylim_here = axss[-1].get_ylim()
axss[-1].set_xlim(xlim_here0)
ax_j[-1].set_xlim(xlim_here0)
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)
#plt.show()
####################################################################
#grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
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 = [score_burst_corr, 'burst_fraction_burst_corr_individual_base', score_burst_corr] # , 'coherence_', ]
y_axis_name = [score_n_burst_corr, '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']
xmax = 6
ax_j, axls, axs, axss, counter = plt_var_axis([], 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 = True)
#text_punits(axs, colors)
#hier beide anhängen
reset_xlims(axls, axss, xmax)
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)
#plt.show()
########################################################################
#####################################################################
# Eigemania Zellen
ylim_eigen = [0, 6]
cell_types = [' P-unit',' Ampullary'] # ,' P-unit',' Ampullary', ]
x_axis = ["cv_base"] # , "cv_base_w_burstcorr","cv_base", ]
cv_name_title = [cv_base_name()] # ,'CV$_{BurstCorr}$','CV']
species_all = [' Eigenmannia virescens'] # ,' Apteronotus leptorhynchus',' Eigenmannia virescens']
#counter += counterplus
ax_j, axls, axss, cell_types, _, max_val, _ = species_with_both_cells(grid1[counter], cell_types, ax_j,
axls, axss, colors,
cv_name_title,
save_names, score,
species_all, x_axis,ylim = ylim_eigen,
max_val=max_val,n = n, alpha = alpha, log = False, score_n = score_n)
tags.append(ax_j[-1])
axss[-1].set_title('')
ax_j[-1].set_title('')
counter += 1
####################################################################
# grid_lower = gridspec.GridSpecFromSubplotSpec(2, 2, grid0[2],hspace = 0.55, wspace = 0.5)
#
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 = [
score] # 'burst_fraction_burst_corr_individual_base','cv_base']#,score]#,'burst_fraction_burst_corr_individual_stim']#, 'cv_base']#,'cv_base',]
x_axis_names = [
score_n] # ,'Burst Fraction$_{Base}$', 'CV$_{Base}$']#,score_n] # ,'Burst Fraction$_{Stim}$' 'CV$_{Base}$']#,'Burst Fraction$_{Base}$',]
y_axis = [
score_burst_corr] # , 'burst_fraction_burst_corr_individual_base']#, score_burst_corr] # , 'coherence_', ]
y_axis_name = [score_n_burst_corr] # , 'Burst Fraction$_{Base}$']#, score_n_burst_corr]
#
var_item_names = [
'Burst Fraction$_{Base}$'] # 'Response Modulation',]#,score_n_burst_corr,'Response Modulation [Hz]']#'Burst Fraction$_{Stim}$',['Modulation']#,
# 'Response Modulation [Hz]',
var_types = [
'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]
xmins = ['no']
ymins = ['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']
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]:
######################################################
# hier kommen die kontrast Punkte dazu
# für die Zellen spielt Burst correctin ja keine Rolle
# if cv_n == 0:
if cell_type_here == ' P-unit':
cells_plot2 = p_units_to_show(type_here='contrasts')[0:2]
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()

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#from utils_suseptibility import default_settings
#from plt_RAM import model_and_data_isi, model_cells
#from utils_suseptibility import model_and_data, remove_yticks
#from utils_suseptibility import *
#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
# set_ylabel_arrow, \
# xpos_y_modelanddata
#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
# overlap_cells, \
# plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
from utils_suseptibility import *#model_and_data
#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
def table_printen(table):
print(table.keys())
for l in range(len(table)):
list_here = np.array(table.iloc[l])
l1 = "& ".join(list_here)
print(l1)
def model_and_data2(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],
label=r'$\frac{1}{mV^2S}$'): # ['eRAM']
# plot_style()#['_RAMscaled']'_RAMscaled'
duration_noise = '_short',
formula = 'code' ##'formula'
# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
stimulus_type = '_StimulusOrig_' # ,#
# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
variant = 'sinz'
mimick = 'no'
cell_recording_save_name = ''
trans = 1 # 5
rep = 1000000 # 500000#0
repeats = [20, rep] # 250000
aa = 0
good_data, remaining = overlap_cells()
cells_all = [good_data[0]]
plot_style()
default_figsize(column=2, length=4.75) # 0.75
grid = gridspec.GridSpec(3, 4, wspace=0.85, bottom=0.07,
hspace=0.18, left=0.09, right=0.93, top=0.94)
a = 0
maxs = []
mins = []
mats = []
ims = []
perc05 = []
perc95 = []
iternames = [D_extraction_method, external_noise,
internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
c_noises,
ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
nr = '2'
# embed()
# cell_contrasts = ["2013-01-08-aa-invivo-1"]
# cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
# cells_triangl_contrast = 1
# cell_contrasts = 1
rows = len(cells_all) # len(good_data)+len(cell_contrasts)
perc = 'perc'
lp = 2
label_model = r'Nonlinearity $\frac{1}{S}$'
for all in it.product(*iternames):
var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
# print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs)
fig = plt.figure()
hs = 0.45
#################################
# data cells
# embed()
grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 1],
hspace=hs)
#ypos_x_modelanddata()
nr = 1
ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width,
cbar_label=True, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
for ax_external in ax_data:
# ax.set_ylabel(F2_xlabel())
# remove_xticks(ax)
ax_external.set_xticks_delta(100)
set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87)
#embed()
set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
# ax.text(-0.42, 0.87, F2_xlabel(), ha='center', va='center',
# transform=ax.transAxes, rotation = 90)
# ax.text(1.66, 0.5, nonlin_title(), rotation=90, ha='center', va='center',
# transform=ax.transAxes)
ax_external.arrow_spines('lb')
#embed()
#plt.show()
##################################
# model part
trial_nr = 500000
cell = '2013-01-08-aa-invivo-1'
cell = '2012-07-03-ak-invivo-1'
print('cell'+str(cell))
cells_given = [cell]
save_name_rev = load_folder_name(
'calc_model') + '/' + 'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV_revQuadrant_'
# for trial in trials:#.009
trial_nr = 1000000#1000000
save_names = [
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
]
nrs_s = [2, 3, 6, 7, 10, 11]
#embed()
tr_name = trial_nr/1000000
if tr_name == 1:
tr_name = 1
titles = ['Model\n$N=11$ \n $c=1\,\%$', 'Model\n$N=%s $' % (tr_name) +'\,million \n $c=1\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=0\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million \n $c=0\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=1\,\%$',
'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ ']#%
ax_model = []
for s, sav_name in enumerate(save_names):
try:
ax_external = plt.subplot(grid[nrs_s[s]])
except:
print('vers something')
embed()
ax_model.append(ax_external)
save_name = load_folder_name('calc_model') + '/' + sav_name
cell_add, cells_save = find_cell_add(cells_given)
perc = 'perc'
path = save_name + '.pkl' # '../'+
# stack = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name)
# full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev))
# stack_final = get_axis_on_full_matrix(full_matrix, stack)
# im = plt_RAM_perc(ax, perc, np.abs(stack))
# embed()
stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
if len(stack)> 0:
add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s],
width, titles_plot=True,
resize=True, nr = nr)
# if s in [1,3,5]:
ims.append(im)
mats.append(stack_plot)
maxs.append(np.max(np.array(stack_plot)))
mins.append(np.min(np.array(stack_plot)))
col = 2
row = 3
ax_external.set_xticks_delta(100)
ax_external.set_yticks_delta(100)
# cbar[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=100
cbar.set_label(nonlin_title(' ['+add_nonlin_title), labelpad=lp) # rotation=270,
if (s in np.arange(col - 1, 100, col)) | (s == 0):
remove_yticks(ax_external)
else:
set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87)
if s >= row * col - col:
set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
# ax.set_xlabel(F1_xlabel(), labelpad=20)
else:
remove_xticks(ax_external)
if len(cells) > 1:
a += 1
set_clim_same_here(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=95)
#################################################
# Flowcharts
var_types = ['', 'additiv_cv_adapt_factor_scaled', 'additiv_cv_adapt_factor_scaled']
##additiv_cv_adapt_factor_scaled
a_fes = [0.009, 0, 0.009]
eod_fe = [750, 750, 750]
ylim = [-0.5, 0.5]
c_sigs = [0, 0.9, 0.9]
grid_left = [[], grid[1, 0], grid[2, 0]]
ax_ams = []
for g, grid_here in enumerate([grid[0, 0], grid[1, 0], grid[2, 0]]):
grid_lowpass = gridspec.GridSpecFromSubplotSpec(4, 1,
subplot_spec=grid_here, hspace=0.3,
height_ratios=[1, 1,1, 0.1])
models = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
model_params = models[models['cell'] == '2012-07-03-ak-invivo-1'].iloc[0]
cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
eod_fr = model_params['EODf'] # .iloc[0]
deltat = model_params.pop("deltat") # .iloc[0]
v_offset = model_params.pop("v_offset") # .iloc[0]
# embed()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)
# embed()
if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1,
subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.2)
# if (np.unique(frame.RAM_afe) != 0):grid_left[g]
ax_external = plt_time_arrays('red', grid_lowpass2, 1, (frame.RAM_afe)*100, time=time, nr=0)
# if (np.unique(frame.RAM_noise) != 0):
remove_xticks(ax_external)
ax_intrinsic = plt_time_arrays('purple', grid_lowpass2, 1, (frame.RAM_noise)*100, time=time, nr=1)
ax_intrinsic.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=ax_intrinsic.transAxes)
ax_intrinsic.show_spines('l')
ax_external.show_spines('l')
# ax_ams.append(axt_p2)
#color_timeseries = 'black'
#axt_p2.set_xlabel('Time [ms]')
#axt_p2.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=axt_p2.transAxes)
#ax_ams.append(axt_p2)
vers = 'all'
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)))
try:
ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
(frame.RAM_afe + frame.RAM_noise)*100,
deltat, eod_fr,
color1=color_timeseries, lw=1, extract=False)
except:
print('add up thing')
embed()
ax_external.show_spines('l')
ax_intrinsic = plt.subplot(grid_lowpass[1])
ax_intrinsic.show_spines('l')
ax_intrinsic.axhline(0, color='black', lw=0.5)
ax_intrinsic.axhline(0, color='purple', lw=0.5)
remove_xticks(ax_external)
remove_xticks(ax_intrinsic)
join_x([ax_intrinsic, ax_external])
join_y([ax_intrinsic, ax_external])
vers = 'first'
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)))
try:
ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, (frame.RAM_afe + frame.RAM_noise)*100,
deltat, eod_fr,
color1=color_timeseries, lw=1, extract=False)
except:
print('add up thing')
embed()
ax_external = plt.subplot(grid_lowpass[0])
ax_external.show_spines('l')
ax_intrinsic.show_spines('l')
ax_external.axhline(0, color='black', lw=0.5)
ax_external.axhline(0, color='red', lw=0.5)
join_x([ax_intrinsic,ax_external])
join_y([ax_intrinsic, ax_external])
vers = 'second'
ax_external.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_external.transAxes)
ax_intrinsic.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_intrinsic.transAxes)
remove_xticks(ax_intrinsic)
# if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
ax_external.set_xlabel('')
# remove_yticks(ax)
ax_ams.append(ax_external)
remove_xticks(ax_external)
# embed()
ax_n, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[2]], time, noise_final_c, deltat, eod_fr,
extract=False, color1='grey', lw=1)
remove_yticks(ax_n)
if g == 2:
ax_n.set_xlabel('Time [ms]', labelpad = -0.5)
else:
remove_xticks(ax_n)
ax_n.set_ylim(ylim)
if vers == 'first':
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_n.text(start_pos_modeldata(), 1.1, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
elif vers == 'second':
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
transform=ax_intrinsic.transAxes)
ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
else:
ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
transform=ax_n.transAxes)
ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
transform=ax_intrinsic.transAxes)
set_same_ylim(ax_ams, up='up')
# embed()
axes = np.concatenate([ax_data, ax_model])
axes = [ax_ams[0], axes[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ax_ams[2], axes[5],
axes[6], ]
axd1 = plt.subplot(grid[1, 1])
axd2 = plt.subplot(grid[2, 1])
#ax_data.extend([,])
axd1.show_spines('')
axd2.show_spines('')
#embed()
#axes = [[ax_ams[0],ax_data[0],axes[2], axes[3]],[ax_ams[1],axd1,axes[4], axes[5]],[axd2,axd2, axes[6], axes[7]]]
fig.tag([axes[0:4]], 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[5:7]], xoffs=-3, yoffs=1.6, major_index = 1, minor_index = 2) # ax_ams[3],
fig.tag([[axes[7]]], xoffs=-3, yoffs=1.6, major_index=2,minor_index=0) # ax_ams[3],
fig.tag([axes[8::]], xoffs=-3, yoffs=1.6, major_index=2, minor_index=2) # ax_ams[3],
#fig.tag([axes[7::]], xoffs=-3, yoffs=1.6) # ax_ams[3],
#fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],
save_visualization(pdf=True)
def start_pos_modeldata():
return 1.03
def signal_component_name():
return 'Signal component'#'signal noise'
def noise_component_name():
return 'Noise component'#'intrinsic noise'
def ypos_x_modelanddata():
return -0.45
if __name__ == '__main__':
model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
cells = model.cell.unique()
# embed()
params = {'cells': cells}
show = True
# if show == False:
# low CV: cells = ['2012-07-03-ak-invivo-1']
plot_style()
default_settings(lw=0.5, column=2, length=3.35) #8.5
redo = False
D_extraction_method = ['additiv_cv_adapt_factor_scaled']
# D_extraction_method = ['additiv_visual_d_4_scaled']
##########################
# hier printen wir die table Werte zum kopieren in den Text
table = pd.read_csv('print_table_suscept-model_params_suscept_table.csv')
table_printen(table)
table = pd.read_csv('print_table_all-model_params_suscept_table.csv')
print('model big')
table_printen(table)
#embed()
##########################
#embed()
model_and_data2(width=0.005, show=show, D_extraction_method=D_extraction_method,
label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'

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from utils_suseptibility import *
def model_full():
plot_style()
default_figsize(column=2, length=2.3)
grid = gridspec.GridSpec(1, 2, wspace=0.6, bottom = 0.1,hspace=0.15, top=0.95, left=0.075, right=0.87)
axes = []
##################################
# model part
ls = '--'
lw = 0.5
ax = plt.subplot(grid[1])
axes.append(ax)
perc,im,stack_final = plt_model_big(ax, ls = ls, lw = 0.5)
# if len(cbar) > 0:
###############################
# data part
ax = plt.subplot(grid[0])
axes.append(ax)
cell = '2012-07-03-ak-invivo-1'
mat_rev,stack_final_rev = load_stack_data_susept(cell, save_name = version_final(), end = '_revQuadrant_')
mat, stack = load_stack_data_susept(cell, save_name=version_final(), end = '')
#embed()
#try:
full_matrix = create_full_matrix2(np.array(mat),np.array(mat_rev))
#except:
# print('full matrix something')
# embed()
stack_final = get_axis_on_full_matrix(full_matrix, mat)
abs_matrix = np.abs(stack_final)
#embed()
#if np.
abs_matrix, add_nonlin_title, resize_val = rescale_colorbar_and_values(abs_matrix)
ax.axhline(0, color = 'white', linestyle = ls, linewidth = lw)
ax.axvline(0, color='white', linestyle = ls, linewidth = lw)
im = plt_RAM_perc(ax, perc, abs_matrix)
cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01)
set_clim_same_here([im], mats=[abs_matrix], lim_type='up', nr_clim='perc', clims='', percnr=95)
#clim = im.get_clim()
#if clim[1]> 1000:
#todo: change clim values with different Hz values
#embed()
cbar.set_label(nonlin_title(add_nonlin_title = ' ['+add_nonlin_title), rotation=90, labelpad=8)
set_ylabel_arrow(ax, xpos = -0.07, ypos = 0.97)
set_xlabel_arrow(ax, xpos=1, ypos=-0.07)
''' eod_fr, stack_spikes = plt_data_suscept_single(ax, cbar_label, cell, cells, f, fig, file_names_exclude, lp, title,
width)'''
cbar, left, bottom, width, height = colorbar_outside(ax, im, add=5, width=0.01)
#print('finished model_full')
fig = plt.gcf()
#axes = plt.gca()
fig.tag(axes[::-1], xoffs=-4.5, yoffs=0.4) # ax_ams[3],
save_visualization()
def load_stack_data_susept(cell, save_name, end = ''):
load_name = load_folder_name('calc_RAM') + '/' + save_name+end
add = '_cell' + cell +end# str(f) # + '_amp_' + str(amp)
#embed()
stack_cell = load_data_susept(load_name + '_' + cell + '.pkl', load_name + '_' + cell, add=add,
load_version='csv')
file_names_exclude = get_file_names_exclude()
stack_cell = stack_cell[~stack_cell['file_name'].isin(file_names_exclude)]
# if len(stack_cell):
file_names = stack_cell.file_name.unique()
#embed()
file_names = exclude_file_name_short(file_names)
cut_off_nr = get_cutoffs_nr(file_names)
try:
maxs = list(map(float, cut_off_nr))
except:
embed()
file_names = file_names[np.argmax(maxs)]
#embed()
stack_file = stack_cell[stack_cell['file_name'] == file_names]
amps = [np.min(stack_file.amp.unique())]
amps = restrict_punits(cell, amps)
amp = np.min(amps)#[0]
# for amp in amps:
stack_amps = stack_file[stack_file['amp'] == amp]
lengths = stack_amps.stimulus_length.unique()
trial_nr_double = stack_amps.trial_nr.unique()
trial_nr = np.max(trial_nr_double)
stack_final = stack_amps[
(stack_amps['stimulus_length'] == np.max(lengths)) & (stack_amps.trial_nr == trial_nr)]
mat, new_keys = get_mat_susept(stack_final)
return mat,stack_final
if __name__ == '__main__':
model_full()

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