453 lines
24 KiB
Python
453 lines
24 KiB
Python
import os
|
|
import sys
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from IPython import embed
|
|
from matplotlib import gridspec, pyplot as plt
|
|
|
|
from plotstyle import plot_style
|
|
from threefish.calc_time import extract_am
|
|
from threefish.core import find_code_vs_not, find_folder_name, find_project_data, info
|
|
from threefish.defaults import default_figsize, default_ticks_talks
|
|
from threefish.load import load_savedir, resave_small_files, save_visualization
|
|
from threefish.plot.limits import join_x, join_y, set_same_ylim
|
|
from threefish.RAM.calc_fft import log_calc_psd
|
|
from threefish.RAM.calc_model import chose_old_vs_new_model
|
|
from threefish.RAM.core import find_load_function
|
|
from threefish.RAM.plot_labels import label_deltaf1, label_deltaf2, label_diff, label_f_eod_name_core_rm, \
|
|
label_fbasename_small, label_sum, \
|
|
onebeat_cond, \
|
|
remove_yticks
|
|
from threefish.RAM.plot_subplots import colors_suscept_paper_dots, plt_spikes_ROC, recalc_fr_to_DF1
|
|
from threefish.RAM.values import val_cm_to_inch, vals_model_full
|
|
from threefish.twobeat.calc_model import calc_roc_amp_core_cocktail
|
|
from threefish.twobeat.colors import colors_susept, twobeat_cond
|
|
from threefish.twobeat.labels import f_stable_name, f_vary_name
|
|
from threefish.twobeat.reformat import c_dist_recalc_func, c_dist_recalc_here, dist_recalc_phaselockingchapter, \
|
|
find_dfs, \
|
|
get_frame_cell_params
|
|
from threefish.twobeat.subplots import plt_psd_saturation, plt_single_trace, plt_stim_saturation, plt_vmem_saturation, \
|
|
power_spectrum_name
|
|
from threefish.values import values_nfft_full_model, values_stimuluslength_model_full
|
|
|
|
|
|
def nonlin_regime0(yposs=[450, 450, 450], show=True):
|
|
|
|
|
|
results_diff = pd.DataFrame()
|
|
position_diff = 0
|
|
|
|
plot_style()
|
|
default_figsize(column=2, length=5.5)
|
|
|
|
|
|
########################################
|
|
# für das model_full, die Freuqnezen
|
|
DF1_frmult, DF2_frmult = vals_model_full(val=0.30833333333333335)
|
|
frame_cvs = resave_small_files('csv_model_data.csv', load_folder = 'calc_base')
|
|
frame_cell = frame_cvs[frame_cvs.cell == '2012-07-03-ak-invivo-1']
|
|
for d in range(len(DF1_frmult)):
|
|
DF2_frmult[d] = recalc_fr_to_DF1(DF2_frmult, d, frame_cell.fr_data.iloc[0])
|
|
DF1_frmult[d] = recalc_fr_to_DF1(DF1_frmult, d, frame_cell.fr_data.iloc[0])
|
|
freq1 = DF1_frmult[3]
|
|
freq2 = DF2_frmult[3]
|
|
|
|
# sachen die ich variieren will
|
|
###########################################
|
|
|
|
auci_wo = []
|
|
auci_w = []
|
|
|
|
cells_here = ["2013-01-08-aa-invivo-1"]
|
|
for cell_here in cells_here:
|
|
|
|
|
|
c_nrs_orig = [0.01,0.04, 0.1, 0.2] # 0.0002, 0.05, 0.5
|
|
redo = False # True
|
|
log = 'log' # 'log'
|
|
|
|
grid0 = gridspec.GridSpec(1, 1, bottom=0.13, top=0.94, left=0.11,
|
|
right=0.95, wspace=0.04) #
|
|
grid00 = gridspec.GridSpecFromSubplotSpec(2, 1,
|
|
wspace=0.04, hspace=0.75,
|
|
subplot_spec=grid0[0], height_ratios=[1.5,1],) # height_ratios=[2,1],
|
|
|
|
############################
|
|
# overview
|
|
grid_down = gridspec.GridSpecFromSubplotSpec(1, 1,
|
|
hspace=0.75,
|
|
wspace=0.1,
|
|
subplot_spec=grid00[1]) # 1.2hspace=0.4,wspace=0.2,len(chirps)
|
|
ax_u1, c_dist_recalc, c_nrs, color0, color01, color012, color01_2, color02, eodf, f_counter, frame_cell, frame_cell_orig, freq1, freq2, i, sampling = plt_amplitudes_for_contrasts(
|
|
c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs)
|
|
|
|
|
|
##############################
|
|
# single examples
|
|
grid_up = gridspec.GridSpecFromSubplotSpec(1, len(c_nrs_orig),
|
|
hspace=0.75,
|
|
wspace=0.25,
|
|
subplot_spec=grid00[
|
|
0]) # height_ratios=[1, 1, 0.7], 1.2hspace=0.4,wspace=0.2,len(chirps)
|
|
|
|
start = 200 # 1000
|
|
mults_period = 3
|
|
xlim = [start, start + (mults_period * 1000 / np.min([np.abs(freq1), np.abs(freq2)]))]
|
|
|
|
axts = []
|
|
axps = []
|
|
axes = []
|
|
axss = []
|
|
|
|
p_arrays_all = []
|
|
model_fit = ''#'_old_fit_' # ''#'_old_fit_'#''#'_old_fit_'#''#'_old_fit_'#''###'_old_fit_'
|
|
model_cells, reshuffled = chose_old_vs_new_model(model_fit=model_fit)
|
|
axe_all = []
|
|
|
|
for c_nn, c_nr in enumerate(c_nrs):
|
|
|
|
#################################
|
|
# hier wurden die psds vorgespeichert weil die lange brauchen
|
|
|
|
a_f2s = [c_nrs_orig[c_nn]]
|
|
xlimp = (0, 300)
|
|
runs = 1
|
|
n = 1
|
|
dev = 0.001
|
|
dev_name = ['05']
|
|
a_fr = 1
|
|
a = 0
|
|
|
|
|
|
|
|
auci_w, auci_wo, ff_p, p_arrays_here, position_diff, results_diff = load_psds_for_nonlin_regime(a_f2s, a_fr,
|
|
auci_w,
|
|
auci_wo,
|
|
c_nn,
|
|
c_nrs_orig,
|
|
cell_here,
|
|
dev,
|
|
dev_name,
|
|
eodf,
|
|
freq1,
|
|
freq2,
|
|
model_cells,
|
|
n,
|
|
position_diff,
|
|
redo,
|
|
reshuffled,
|
|
results_diff,
|
|
xlimp)
|
|
|
|
|
|
# #
|
|
#
|
|
############################
|
|
# spikes werden jedes mal neu generiert weil das schnell geht
|
|
trials_nr = 10
|
|
stimulus_length_here = 1 # values_stimuluslength_model_full()
|
|
nfft_here = stimulus_length_here * 20000
|
|
a_f2s = [c_nrs_orig[c_nn]]
|
|
|
|
_, arrays_spikes, arrays_stim, _, _, _, _, arrays, names, _, _ = calc_roc_amp_core_cocktail([freq1 + eodf],
|
|
[freq2 + eodf],
|
|
auci_wo, auci_w,
|
|
results_diff,
|
|
a_f2s,
|
|
trials_nr,
|
|
nfft_here, a_fr,
|
|
stimulus_length_here,
|
|
model_cells,
|
|
position_diff,
|
|
dev, cell_here,
|
|
a_f1s=[
|
|
c_nrs_orig[
|
|
c_nn]],
|
|
dev_name=dev_name,
|
|
min_amps='_minamps_',
|
|
n=n,
|
|
reshuffled=reshuffled,
|
|
mean_choice='',
|
|
printing_cell=False)
|
|
|
|
##################################################################
|
|
# preprocessing der arrays
|
|
time = np.arange(0, len(arrays[a][0]) / sampling, 1 / sampling)
|
|
time = time * 1000
|
|
arrays_time = [arrays[3]] # [v_mems[1],v_mems[3]]#[1,2]#[1::]
|
|
arrays_here = [arrays[3]] # [arrays[1],arrays[3]]#arrays[1::]#
|
|
arrays_st = [arrays_stim[3]] #1:: [arrays_stim[1],arrays_stim[3]]#
|
|
arrays_sp = [arrays_spikes[3]] # [arrays_spikes[1],arrays_spikes[3]]#arrays_spikes[1::]
|
|
colors_array_here = ['grey', 'grey', 'grey'] # colors_array[1::]
|
|
p_arrays_all.append(p_arrays_here)
|
|
for a in range(len(arrays_here)):
|
|
|
|
grid_pt = gridspec.GridSpecFromSubplotSpec(3, 1,
|
|
hspace=0.3,
|
|
wspace=0.2,
|
|
subplot_spec=grid_up[a, c_nn], height_ratios = [1.2, 1,2]
|
|
)
|
|
|
|
|
|
|
|
########################################
|
|
# plot stimulus
|
|
plt_stim_nonlin(arrays_st, axe_all, axes, c_nn, c_nrs, colors_array_here, grid_pt, i, time, xlim)
|
|
|
|
#############################
|
|
# plot spikes
|
|
plt_spikes_nonlin(a, arrays_sp, axps, axss, c_nn, grid_pt, xlim)
|
|
|
|
f_counter += 1
|
|
|
|
####################
|
|
# plot psds
|
|
a = plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color01_2, color02,
|
|
colors_array_here, ff_p, frame_cell_orig, freq1, freq2, log, p_arrays_all, xlimp)
|
|
|
|
axps[0].get_shared_y_axes().join(*axps)
|
|
axps[0].get_shared_x_axes().join(*axps)
|
|
set_same_ylim(axe_all)
|
|
set_same_ylim(axps)
|
|
fig = plt.gcf()
|
|
fig.tag([axes[0], axes[1], axes[2], axes[3], ax_u1], xoffs=-2.3, yoffs=1.4)
|
|
save_visualization(cell_here, show)
|
|
|
|
|
|
def plt_spikes_nonlin(a, arrays_sp, axps, axss, c_nn, grid_pt, xlim):
|
|
axs = plt.subplot(grid_pt[1])
|
|
plt_spikes_ROC(axs, 'grey', np.array(arrays_sp[a], dtype=object) * 1000, xlim, lw=1)
|
|
if c_nn == 0:
|
|
axs.xscalebar(0.15, -0.1, 30, 'ms', va='right', ha='bottom')
|
|
axss.append(axs)
|
|
axp = plt.subplot(grid_pt[-1])
|
|
axps.append(axp)
|
|
|
|
|
|
def plt_stim_nonlin(arrays_st, axe_all, axes, c_nn, c_nrs, colors_array_here, grid_pt, i, time, xlim):
|
|
axe = plt.subplot(grid_pt[0])
|
|
axes.append(axe)
|
|
am, time_am = extract_am(arrays_st[0], time, extract='', norm=False)
|
|
am = am - np.mean(am)
|
|
if c_nn == 0:
|
|
spines = 'l'
|
|
else:
|
|
spines = ''
|
|
plt_stim_saturation(0, [], [am * 100], axe, colors_array_here, i,
|
|
c_nn, ['Contrast [$\%$]'], time,
|
|
xlim=xlim, lw=1, spines=spines) # np.array(arrays_sp)*1000
|
|
beat_here = r'$\rm{Contrast}=%s$' % (int(np.round(c_nrs[c_nn]))) + '$\%$' # +'$'
|
|
title_name = beat_here # fish + '\n' + +c1+c2#twobeat_cond(big=True, double=True,cond=False)
|
|
axe.text(1, 1.1, title_name, va='bottom', ha='right',
|
|
transform=axe.transAxes)
|
|
axe_all.append(axe)
|
|
|
|
|
|
def plt_psd_nonlin(a, arrays_here, axps, c_nrs, color0, color01, color012, color01_2, color02, colors_array_here, ff_p,
|
|
frame_cell_orig, freq1, freq2, log, p_arrays_all, xlimp):
|
|
pps = []
|
|
for c_nn, c_nr in enumerate(c_nrs):
|
|
for a in range(len(arrays_here)):
|
|
|
|
axps_here = [[axps[0], axps[1], axps[2], axps[3]]] # [axps[3], axps[4], axps[5]
|
|
axp = axps_here[a][c_nn]
|
|
pp = log_calc_psd(log, p_arrays_all[c_nn][a][0],
|
|
np.nanmax(p_arrays_all))
|
|
pps.append(pp)
|
|
|
|
colors_peaks = [color01, color02, color012, color01_2, color0]
|
|
markeredgecolors = [color01, color02, color012, color01_2, color0]
|
|
fr = frame_cell_orig.fr.unique()[0]
|
|
labels = [label_deltaf1(), label_deltaf2(),
|
|
label_sum(), label_diff(), label_fbasename_small(), label_fbasename_small()]
|
|
freqs = [np.abs(freq1), np.abs(freq2), np.abs(freq1) + np.abs(freq2), np.abs(np.abs(freq1) - np.abs(freq2)),
|
|
fr]
|
|
plt_psd_saturation(pp, ff_p, a, axp, colors_array_here, freqs=freqs,
|
|
colors_peaks=colors_peaks, xlim=xlimp,
|
|
markeredgecolor=markeredgecolors, labels=labels, log=log, add_log=5)
|
|
if c_nn == 0:
|
|
axp.legend(ncol=5, loc=(-0, -1.1))
|
|
if log:
|
|
scalebar = False
|
|
if scalebar:
|
|
axp.show_spines('b')
|
|
if c_nn == 0:
|
|
axp.yscalebar(-0.05, 0.5, 20, 'dB', va='center', ha='left')
|
|
axp.set_ylim(-33, 5)
|
|
else:
|
|
axp.show_spines('lb')
|
|
if c_nn == 0:
|
|
axp.set_ylabel('dB') # , va='center', ha='left'
|
|
else:
|
|
remove_yticks(axp)
|
|
axp.set_ylim(-39, 5)
|
|
else:
|
|
axp.show_spines('lb')
|
|
if c_nn != 0:
|
|
remove_yticks(axp)
|
|
else:
|
|
axp.set_ylabel(power_spectrum_name())
|
|
axp.set_xlabel('Frequency [Hz]')
|
|
return a
|
|
|
|
|
|
def load_psds_for_nonlin_regime(a_f2s, a_fr, auci_w, auci_wo, c_nn, c_nrs_orig, cell_here, dev,
|
|
dev_name, eodf, freq1, freq2, model_cells, n,
|
|
position_diff, redo, reshuffled,
|
|
results_diff, xlimp):
|
|
trials_nr = 1
|
|
stimulus_length_here = 500 # 500 # values_stimuluslength_model_full()
|
|
nfft_here = 20 * 20000
|
|
|
|
save_dir = load_savedir(level=0).split('/')[0] + '_afe_' + str(a_f2s[0]) + '_nfft_' + str(
|
|
nfft_here) + '_len_' + str(stimulus_length_here)
|
|
|
|
name_psd = find_project_data() + save_dir + '_psd.npy'
|
|
name_psd_f = find_project_data() + save_dir + '_psdf.npy'
|
|
if ((not os.path.exists(name_psd)) | (redo == True)): # | (do == True)
|
|
|
|
auci_w, auci_wo, ff_p, p_arrays_here, position_diff, results_diff = develop_save_nonlin_psd_values(a_f2s, a_fr,
|
|
auci_w,
|
|
auci_wo,
|
|
c_nn,
|
|
c_nrs_orig,
|
|
cell_here,
|
|
dev,
|
|
dev_name,
|
|
eodf,
|
|
freq1, freq2,
|
|
model_cells,
|
|
n, name_psd,
|
|
name_psd_f,
|
|
nfft_here,
|
|
position_diff,
|
|
reshuffled,
|
|
results_diff,
|
|
stimulus_length_here,
|
|
trials_nr,
|
|
xlimp)
|
|
else:
|
|
ff_p = np.load(name_psd_f) # p_arrays_p
|
|
p_arrays_here = np.load(name_psd) # p_arrays_p
|
|
return auci_w, auci_wo, ff_p, p_arrays_here, position_diff, results_diff
|
|
|
|
|
|
def develop_save_nonlin_psd_values(a_f2s, a_fr, auci_w, auci_wo, c_nn, c_nrs_orig, cell_here, dev,
|
|
dev_name, eodf, freq1, freq2, model_cells, n, name_psd,
|
|
name_psd_f, nfft_here, position_diff, reshuffled,
|
|
results_diff, stimulus_length_here, trials_nr, xlimp):
|
|
# psd generierung
|
|
_, _, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
|
|
[freq1 + eodf], [freq2 + eodf], auci_wo, auci_w, results_diff, a_f2s, trials_nr, nfft_here, a_fr,
|
|
stimulus_length_here, model_cells, position_diff, dev, cell_here,a_f1s=[c_nrs_orig[c_nn]], dev_name=dev_name, min_amps='_minamps_', n=n, reshuffled=reshuffled, mean_choice='first')
|
|
p_arrays_here = [p_arrays_p[3]]
|
|
# embed()
|
|
for p in range(len(p_arrays_here)):
|
|
p_arrays_here[p][0] = p_arrays_here[p][0][ff_p < xlimp[1]]
|
|
ff_p = ff_p[ff_p < xlimp[1]]
|
|
np.save(name_psd, p_arrays_here)
|
|
np.save(name_psd_f, ff_p)
|
|
return auci_w, auci_wo, ff_p, p_arrays_here, position_diff, results_diff
|
|
|
|
|
|
def plt_amplitudes_for_contrasts(c_nrs_orig, cell_here, freq1, freq2, grid_down, yposs):
|
|
full_names = [
|
|
'calc_model_amp_freqs-_F1_0.22833333333333333Fr_F2_1Fr_af_coupled__C1Len_100_FirstC1_0.0001_LastC1_0.3_FRrelavtiv__start_0.0001_end_0.3_StimLen_100_nfft_20000_trialsnr_1_mult_minimum_1_power_1_minamps__dev_original_05_not_log__point_1_fft2_temporal']
|
|
for i, full_name in enumerate(full_names):
|
|
|
|
frame = load_nonlin_regime_values(full_name)
|
|
|
|
frame_cell_orig = frame[(frame.cell == cell_here)].copy()
|
|
if len(frame_cell_orig) > 0:
|
|
|
|
f_counter = 0
|
|
frame_cell_orig, df1s, df2s, f1s, f2s = find_dfs(frame_cell_orig)
|
|
eodf = frame_cell_orig.f0.unique()[0]
|
|
#######################################################################################
|
|
# übersicht
|
|
|
|
frame_cell = frame_cell_orig[
|
|
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
|
if len(frame_cell) < 1:
|
|
freq1 = frame_cell_orig.iloc[(np.argmin(np.abs(frame_cell_orig.df1 - freq1)))].df1
|
|
freq2 = frame_cell_orig.iloc[(np.argmin(np.abs(frame_cell_orig.df2 - freq2)))].df2
|
|
|
|
frame_cell = frame_cell_orig[
|
|
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
|
|
|
# embed()
|
|
labels, alpha, color01, color01_012, color02, color02_012, colors, colors_array, linestyles, scores, linewidths = colors_susept(
|
|
add='_mean_original', nr=4)
|
|
|
|
sampling = 20000
|
|
|
|
c_dist_recalc = dist_recalc_phaselockingchapter()
|
|
|
|
c_nrs = c_dist_recalc_func(frame_cell, c_nrs=c_nrs_orig, cell=cell_here,
|
|
c_dist_recalc=c_dist_recalc)
|
|
if not c_dist_recalc:
|
|
c_nrs = np.array(c_nrs) * 100
|
|
|
|
letters = ['A', 'B', 'C', 'D']
|
|
scores = ['c_B1_012_original', 'c_B2_012_original', 'c_B1+B2_012_original', 'c_B1-B2_012_original']
|
|
color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
|
|
colors = [color01, color02, color012, color01_2]
|
|
linestyles = ['-', '-', '-', '-']
|
|
index = [0, 1, 2, 3]
|
|
ax_u1 = plt.subplot(grid_down[0, i])
|
|
|
|
labels = [label_deltaf1(), label_deltaf2(),
|
|
label_sum(), label_diff(), label_fbasename_small()]
|
|
# ax_u1.legend(ncol = 4, loc = (0,1.2))
|
|
plt_single_trace([], ax_u1, frame_cell_orig, freq1, freq2,
|
|
scores=np.array(scores)[index], labels=np.array(labels)[index],
|
|
colors=np.array(colors)[index],
|
|
linestyles=np.array(linestyles)[index],
|
|
linewidths=np.array(linewidths)[index],
|
|
alpha=np.array(alpha)[index],
|
|
thesum=False, B_replace='F', default_colors=False,
|
|
c_dist_recalc=c_dist_recalc)
|
|
# ax_us.append(ax_u1)
|
|
frame_cell = frame_cell_orig[
|
|
(frame_cell_orig.df1 == freq1) & (frame_cell_orig.df2 == freq2)]
|
|
c1 = c_dist_recalc_here(c_dist_recalc, frame_cell)
|
|
ax_u1.set_xlim(0, 25)
|
|
|
|
if i != 0:
|
|
ax_u1.set_ylabel('')
|
|
remove_yticks(ax_u1)
|
|
|
|
ax_u1.scatter(c_nrs, (np.array(yposs[i]) - 0) * np.ones(len(c_nrs)), color='black',
|
|
marker='v',
|
|
clip_on=False)
|
|
|
|
#
|
|
for c_nn, c_nr in enumerate(c_nrs):
|
|
ax_u1.text(c_nr, yposs[i][c_nn] + 30, letters[c_nn], color='black', ha='center',
|
|
va='top')
|
|
# ax_u1.plot([c_nr, c_nr], [0, 435], color='black', linewidth=0.8, clip_on=False)
|
|
ylim = ax_u1.get_ylim()
|
|
ax_u1.set_ylim(0, ylim[-1])
|
|
ax_u1.set_xlabel('Contrast [$\%$]')
|
|
ax_u1.set_ylabel('Amplitude [Hz]')
|
|
return ax_u1, c_dist_recalc, c_nrs, color0, color01, color012, color01_2, color02, eodf, f_counter, frame_cell, frame_cell_orig, freq1, freq2, i, sampling
|
|
|
|
|
|
def load_nonlin_regime_values(full_name):
|
|
version_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not(
|
|
)
|
|
if os.path.exists(find_folder_name('calc_cocktailparty') + '/' + full_name + '.csv') & (version_comp != 'public'):
|
|
frame = pd.read_csv(find_folder_name('calc_cocktailparty') + '/' + full_name + '.csv')
|
|
load_function = find_load_function()
|
|
frame.to_csv(find_project_data() + load_function + '-traces.csv')
|
|
else:
|
|
load_function = find_load_function()
|
|
frame = pd.read_csv(
|
|
find_project_data() + load_function + '-traces.csv') # resave_small_files(full_name + '.csv', load_folder='calc_cocktailparty')
|
|
return frame
|
|
|
|
|
|
if __name__ == '__main__':
|
|
sys.excepthook = info
|
|
nonlin_regime0(yposs = [[220, 220, 220, 220]],)#, [430,470], [200,200], [200,200, 200, 200], [200,200, 200, 200], [200,200, 200, 200] |