11.08
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xaver
parent
7174b03963
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2841457261
@ -163,7 +163,7 @@ def average(freq_all, time_all, start, stop, timespan, dm):
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values_all = sort_values(sv_all)
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plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color = 'g',
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plt.plot(tnew_all[tnew_all < dm], step_response(tnew_all, *sv_all)[tnew_all < dm], color = 'g', lw = 2,
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label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
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print('average: a1, a2, tau1, tau2', values_all)
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@ -9,6 +9,7 @@ from jar_functions import sin_response
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def take_second(elem):
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return elem[1]
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predict = []
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gain = []
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mgain = []
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@ -25,12 +26,13 @@ data = sorted(np.load('files.npy'), key = take_second)
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for i, d in enumerate(data):
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dd = list(d)
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jar = np.load('%s.npy' %dd)
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jm = jar - np.mean(jar)
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print(dd)
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time = np.load('time: %s.npy' %dd)
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b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True)
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y = signal.filtfilt(b, a, jar - np.mean(jar))
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y = signal.filtfilt(b, a, jm)
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#plt.plot(time, y)
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#plt.plot(time, jar)
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@ -41,12 +43,16 @@ for i, d in enumerate(data):
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gain.append(np.sqrt(sinv[2]**2))
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amfreq.append(d[1])
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Rs = []
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for ix, t in enumerate(time):
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R = (jm[ix] - sin_response(t, float(d[1]), np.sqrt(sinv[1]**2), np.sqrt(sinv[2]**2)))**2
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Rs.append(R)
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sigma = sum(Rs)
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rms = np.sqrt((1/len(time)) * sigma)
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#plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))
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# plt.legend()
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#plt.show()
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#mean over same amfreqs for phase and gain
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if currf is None or currf == d[1]:
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currf = d[1]
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idxlist.append(i)
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@ -74,10 +80,25 @@ meanedp = np.mean(meanp)
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mgain.append(meanedf)
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mphaseshift.append(meanedp)
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for f in amf:
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G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
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predict.append(G)
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(amf, mgain, 'o')
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ax.plot(amf, predict)
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ax.set_yscale('log')
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ax.set_xscale('log')
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ax.set_title('2018lepto98')
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ax.set_ylabel('gain [Hz/(mV/cm)]')
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ax.set_xlabel('AM-frequency [Hz]')
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#plt.savefig('2018lepto98_gain')
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pylab.show()
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embed()
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#betrag von A
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#phase in degree
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# Q10 / conductivity
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# AM-frequency / envelope-frequency scale title?
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@ -27,24 +27,16 @@ base_path = 'D:\\jar_project\\JAR\\sin\\2018lepto98'
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#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
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#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
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datasets = [#'2020-06-19-aa', #-5Hz delta f, horrible fit
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#'2020-06-19-ab', #-5Hz delta f, bad fit
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#'2020-06-22-aa', #-5Hz delta f, bad fit
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#'2020-06-22-ab', #-5Hz delta f, bad fit
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#'2020-06-22-ac', #-15Hz delta f, good fit
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#'2020-06-22-ad', #-15Hz delta f, horrible fit
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#'2020-06-22-ae', #-15Hz delta f, horrible fit
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#'2020-06-22-af', #-15Hz delta f, good fit
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'2020-07-21-al', #sin
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'2020-07-21-am',
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'2020-07-21-ak',
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'2020-07-21-an',
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'2020-07-21-ao',
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'2020-07-22-ai',
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'2020-07-22-aj',
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'2020-07-22-ak',
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'2020-07-22-al',
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'2020-07-22-am',
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datasets = ['2020-07-21-ak',
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'2020-07-21-al',
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'2020-07-21-am',
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'2020-07-21-an',
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'2020-07-21-ao',
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'2020-07-22-ai',
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'2020-07-22-aj',
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'2020-07-22-ak',
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'2020-07-22-al',
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'2020-07-22-am',
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]
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time_all = []
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@ -79,7 +71,7 @@ for idx, dataset in enumerate(datasets):
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i = parse_infodataset(infodataset)
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identifier = i[0]
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if not identifier[1:-2] in ID:
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ID.append(identifier[1:-2])
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ID.append(identifier[1:-1])
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file_name.append(ID[0])
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amfreq = import_amfreq(datapath)
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@ -120,7 +112,7 @@ for idx, dataset in enumerate(datasets):
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np.save('time: %s.npy' % file_name, cut_times)
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np.save('%s.npy' % file_name, jar4)
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plt.plot(cut_times, jm, '-k')
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#plt.plot(cut_times, jm, '-k')
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#cf, ct = mean_noise_cut(jar4, cut_times, n = int(round(len(jar4)/((times[-1] - times [0]) * amfreq))))
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#plt.plot(ct, cf, '-k')
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@ -16,7 +16,7 @@ from jar_functions import step_response
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from jar_functions import sort_values
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from jar_functions import average
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base_path = 'D:\\jar_project\\JAR'
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base_path = 'D:\\jar_project\\JAR\\step\\step_2018lepto98'
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#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
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datasets = [#'2020-06-19-aa', #-5Hz delta f, horrible fit
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@ -26,8 +26,20 @@ datasets = [#'2020-06-19-aa', #-5Hz delta f, horrible fit
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#'2020-06-22-ac', #-15Hz delta f, good fit
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#'2020-06-22-ad', #-15Hz delta f, horrible fit
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#'2020-06-22-ae', #-15Hz delta f, horrible fit
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'2020-06-22-af' #-15Hz delta f, good fit
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]
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#'2020-06-22-af', #-15Hz delta f, good fit
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'2020-07-13-ad',
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'2020-07-13-ae',
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'2020-07-13-af',
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'2020-07-13-ag',
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'2020-07-13-ah',
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'2020-07-13-ai',
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'2020-07-13-aj',
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#'2020-07-13-ak',
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#'2020-07-13-al',
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'2020-07-13-am',
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#'2020-07-13-an',
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#'2020-07-13-ao'
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]
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#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
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#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
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@ -68,7 +80,7 @@ for idx, dataset in enumerate(datasets):
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freq_all.append(cf_arr)
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time_all.append(ct_arr)
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plt.plot(ct_arr, cf_arr, color = col[idx], label='fish=%s' % datasets[idx])
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plt.plot(ct_arr, cf_arr, label='fish=%s' % datasets[idx]) #, color = col[idx]
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sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], cf_arr[ct_arr < dm], [1.0, 1.0, 5.0, 50.0], bounds=(0.0, np.inf)) # step_values and step_cov
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@ -76,17 +88,17 @@ for idx, dataset in enumerate(datasets):
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values = sort_values(sv)
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# fit for each trace
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plt.plot(ct_arr[ct_arr < dm], step_response(ct_arr[ct_arr < dm], *sv), label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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#plt.plot(ct_arr[ct_arr < dm], step_response(ct_arr[ct_arr < dm], *sv), label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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#plt.plot(ft, step_response(ft, *sv), color='orange', label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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print('fish: a1, a2, tau1, tau2', values)
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'''# average over all fish
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# average over all fish
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mf_all, tnew_all, values_all = average(freq_all, time_all, start, stop, timespan, dm)
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'''
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const_line = plt.axhline(y = 0.632)
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#const_line = plt.axhline(y = 0.632)
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stimulus_duration = plt.hlines(y = -0.25, xmin = 0, xmax = 100, color = 'r', label = 'stimulus_duration')
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base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
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@ -99,14 +111,5 @@ plt.legend(loc = 'lower right')
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plt.show()
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embed()
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# norm vor mean_traces damit cutoff von -5
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# average über alle fische eigentlich mal nicht nötig, auslagern
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# nur bei -15 Hz messen
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# bei verschiedenen amplituden messen (siehe Tim)
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# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)
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# Fragen:
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# wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? oder einfach immer bei 0 onset..?
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# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
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# welche Stimulusintesität?
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# start/stop/timespan ok?
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# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)
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