updating figures
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saschuta
parent
a373a3ca32
commit
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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|
Before Width: | Height: | Size: 103 KiB After Width: | Height: | Size: 103 KiB |
@ -138,7 +138,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
#
|
||||
#bias_factors = [1, 1, 1, 1] # 0.3
|
||||
bias_factors = [0.36, 0.36, 1, 1] # 0.36
|
||||
|
||||
bias_factors = [1, 1, 1, 1] # 0.36
|
||||
|
||||
#new
|
||||
save_names = [
|
||||
@ -195,6 +195,7 @@ def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1],
|
||||
|
||||
] #calc_RAM_model-2__nfft_whole_power_1_afe_2.6_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV
|
||||
bias_factors = [0.36, 0.36, 1, 1]#0.36
|
||||
bias_factors = [1, 1, 1, 1]
|
||||
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_' + str(
|
||||
|
||||
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|
Before Width: | Height: | Size: 45 KiB After Width: | Height: | Size: 46 KiB |
@ -7,13 +7,17 @@ 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_folder_name, info
|
||||
from threefish.defaults import default_figsize, default_ticks_talks
|
||||
from threefish.load import load_savedir, 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.plot_labels import label_f_eod_name_core_rm, onebeat_cond, remove_yticks
|
||||
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
|
||||
@ -206,6 +210,8 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
print('grid search problem4')
|
||||
embed()
|
||||
|
||||
labels = [label_deltaf1(), label_deltaf2(),
|
||||
label_sum(), label_diff(), label_fbasename_small()]
|
||||
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],
|
||||
@ -262,42 +268,26 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
|
||||
#################################
|
||||
# arrays plot
|
||||
save_dir = load_savedir(level=0).split('/')[0]
|
||||
name_psd = save_dir + '_psd.npy'
|
||||
name_psd_f = save_dir + '_psdf.npy'
|
||||
|
||||
do = False
|
||||
if ((not os.path.exists(name_psd)) | (redo == True)) & do:
|
||||
if log != 'log':
|
||||
stimulus_length_here = 0.5
|
||||
stimulus_length_here = values_stimuluslength_model_full()
|
||||
nfft_here = 32768
|
||||
nfft_here = stimulus_length_here*20000#values_nfft_full_model()
|
||||
else:
|
||||
stimulus_length_here = 50
|
||||
stimulus_length_here = values_stimuluslength_model_full()
|
||||
|
||||
trials_nr = 1
|
||||
nfft_here = values_nfft_full_model()
|
||||
stimulus_length_here = 100 # values_stimuluslength_model_full()
|
||||
nfft_here = 20 * 20000
|
||||
a_f2s = [c_nrs_orig[c_nn]]
|
||||
a_f2s = [c_nrs_orig[c_nn]]
|
||||
|
||||
else:
|
||||
nfft_here = 2 ** 14
|
||||
stimulus_length_here = 0.5
|
||||
save_dir = load_savedir(level=0).split('/')[0] +'_afe_'+str(a_f2s[0])+'_nfft_'+str(nfft_here)+'_len_' + str(stimulus_length_here)
|
||||
|
||||
stimulus_length_here = 10#values_stimuluslength_model_full()
|
||||
trials_nr = 10
|
||||
nfft_here = values_nfft_full_model()
|
||||
nfft_here = stimulus_length_here * 20000
|
||||
name_psd = save_dir + '_psd.npy'
|
||||
name_psd_f = save_dir + '_psdf.npy'
|
||||
|
||||
# #
|
||||
#
|
||||
############################
|
||||
# spikes generierung
|
||||
trials_nr = 10
|
||||
nfft_here = values_nfft_full_model()
|
||||
stimulus_length_here = 1 # values_stimuluslength_model_full()
|
||||
nfft_here = stimulus_length_here * 20000
|
||||
a_f2s = [c_nrs_orig[c_nn]]
|
||||
_, arrays_spikes, _, _, _, _, _, _, _, _, _ = calc_roc_amp_core_cocktail(
|
||||
do = True
|
||||
if ((not os.path.exists(name_psd)) | (redo == True)) | (do == True):
|
||||
|
||||
# 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], datapoints, auci_wo, auci_w, results_diff,
|
||||
a_f2s,
|
||||
fish_jammer, trials_nr, nfft_here, us_name, gain, runs, a_fr, nfft_for_morph,
|
||||
@ -307,17 +297,23 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
model_cells, position_diff, dev, cell_here, dev_name=dev_name,
|
||||
a_f1s=[c_nrs_orig[c_nn]],
|
||||
n=n,
|
||||
reshuffled=reshuffled, min_amps=min_amps, mean_choice = '')
|
||||
reshuffled=reshuffled, min_amps=min_amps, mean_choice='first')
|
||||
np.save(name_psd, p_arrays_p)
|
||||
np.save(name_psd_f, ff_p)
|
||||
else:
|
||||
ff_p = np.load(name_psd_f) # p_arrays_p
|
||||
p_arrays_p = np.load(name_psd) # p_arrays_p
|
||||
|
||||
###########################
|
||||
# psd generierung
|
||||
trials_nr = 1
|
||||
# #
|
||||
#
|
||||
############################
|
||||
# spikes generierung
|
||||
trials_nr = 10
|
||||
nfft_here = values_nfft_full_model()
|
||||
stimulus_length_here = 100 # values_stimuluslength_model_full()
|
||||
stimulus_length_here = 1 # values_stimuluslength_model_full()
|
||||
nfft_here = stimulus_length_here * 20000
|
||||
a_f2s = [c_nrs_orig[c_nn]]
|
||||
a_f2s = [c_nrs_orig[c_nn]]
|
||||
_, _, arrays_stim, results_diff, position_diff, auci_wo, auci_w, arrays, names, p_arrays_p, ff_p = calc_roc_amp_core_cocktail(
|
||||
_, arrays_spikes, _, _, _, _, _, _, _, _, _ = calc_roc_amp_core_cocktail(
|
||||
[freq1 + eodf], [freq2 + eodf], datapoints, auci_wo, auci_w, results_diff,
|
||||
a_f2s,
|
||||
fish_jammer, trials_nr, nfft_here, us_name, gain, runs, a_fr, nfft_for_morph,
|
||||
@ -327,7 +323,9 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
model_cells, position_diff, dev, cell_here, dev_name=dev_name,
|
||||
a_f1s=[c_nrs_orig[c_nn]],
|
||||
n=n,
|
||||
reshuffled=reshuffled, min_amps=min_amps, mean_choice = 'first')
|
||||
reshuffled=reshuffled, min_amps=min_amps, mean_choice = '')
|
||||
|
||||
|
||||
|
||||
##################################################################
|
||||
# ff_p, arrays, names, p_arrays_p, arrays_spikes, arrays_stim,
|
||||
@ -360,17 +358,18 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
freqs = [np.abs(freq2)]
|
||||
else:
|
||||
freqs = [np.abs(freq1), np.abs(freq2)]
|
||||
grid_pt = gridspec.GridSpecFromSubplotSpec(2, 1,
|
||||
grid_pt = gridspec.GridSpecFromSubplotSpec(3, 1,
|
||||
hspace=0.2,
|
||||
wspace=0.2,
|
||||
subplot_spec=grid_up[a, c_nn], height_ratios = [1,2]
|
||||
subplot_spec=grid_up[a, c_nn], height_ratios = [1, 1,2]
|
||||
) # hspace=0.4,wspace=0.2,len(chirps)
|
||||
|
||||
stim = False
|
||||
if stim:
|
||||
#if stim:
|
||||
axe = plt.subplot(grid_pt[0])
|
||||
axes.append(axe)
|
||||
plt_stim_saturation(a, [], arrays_st, axe, colors_array_here, f,
|
||||
am, time_am = extract_am(arrays_st, time, extract='',norm = False)
|
||||
plt_stim_saturation(a, [], am, axe, colors_array_here, f,
|
||||
f_counter, names, time,
|
||||
xlim=xlim) # np.array(arrays_sp)*1000
|
||||
|
||||
@ -393,7 +392,7 @@ def nonlin_regime(yposs=[450, 450, 450], freqs=[(39.5, -210.5)], printing=False,
|
||||
#############################
|
||||
|
||||
|
||||
axs = plt.subplot(grid_pt[0])
|
||||
axs = plt.subplot(grid_pt[1])
|
||||
|
||||
plt_spikes_ROC(axs, 'grey', np.array(arrays_sp[a]) * 1000, xlim, lw=1)
|
||||
|
||||
|
||||
BIN
nonlin_regime_afe_0.01_nfft_400000_len_100_psd.npy
Normal file
BIN
nonlin_regime_afe_0.01_nfft_400000_len_100_psd.npy
Normal file
Binary file not shown.
BIN
nonlin_regime_afe_0.01_nfft_400000_len_100_psdf.npy
Normal file
BIN
nonlin_regime_afe_0.01_nfft_400000_len_100_psdf.npy
Normal file
Binary file not shown.
BIN
plot_chi2.pdf
Normal file
BIN
plot_chi2.pdf
Normal file
Binary file not shown.
@ -885,10 +885,6 @@ CVs in P-units can range up to 1.5 \citep{Grewe2017, Hladnik2023}. We show the s
|
||||
|
||||
\begin{figure*}[t]
|
||||
\includegraphics[width=\columnwidth]{trialnr}
|
||||
\notejb{ylabel: absolute value of $chi_2$!}
|
||||
\notejb{sort the legend labels!}
|
||||
\notejb{one of the 99.99\% percentiles is enough}
|
||||
\notejb{add $10^7$ tick on x-axis}
|
||||
\caption{\label{fig:trialnr} Dependence of the estimate of the second-order susceptibility on the number of trials $\n$. While the estimate of the noise floor (10th and 90th percentile) of the $|\chi_2(f_1, f_2)|$ matrix does not saturate yet, the estimates of the high values in the matrix that make up the characteristic ridges saturate for $N>10^6$. The model used has the identifier 2013-01-08-aa.
|
||||
}
|
||||
\end{figure*}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user