07.07
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xaver
parent
e91b648b5c
commit
85a4b51680
@ -13,6 +13,7 @@ def parse_dataset(dataset_name):
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deltafs = []
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stimulusfs = []
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duration = []
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pause = []
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# data itself
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times = []
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@ -34,6 +35,8 @@ def parse_dataset(dataset_name):
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stimulusfs.append(float(l.split(':')[-1].strip()[:-2]))
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if "#" in l and "Duration" in l:
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duration.append(float(l.split(':')[-1].strip()[:-3]))
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if "#" in l and "Pause" in l:
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pause.append(float(l.split(':')[-1].strip()[:-3]))
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if '#Key' in l:
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if len(time) != 0: #therefore empty in the first round
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@ -55,7 +58,7 @@ def parse_dataset(dataset_name):
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amplitudes.append(ampl) #these append the data from the first loop to the final lists, because we overwrite them (?)
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frequencies.append(freq)
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return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration #output of the function
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return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration, pause #output of the function
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def parse_infodataset(dataset_name):
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assert(os.path.exists(dataset_name)) #see if data exists
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@ -90,13 +93,11 @@ def mean_loops(start, stop, timespan, frequencies, time):
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def mean_noise_cut(frequencies, time, n):
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cutf = []
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cutt = []
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for k in np.arange(0, len(frequencies), n):
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t = time[k]
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f = np.mean(frequencies[k:k+n])
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cutf.append(f)
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cutt.append(t)
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return cutf, cutt
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@ -109,11 +110,11 @@ def norm_function(cf_arr, ct_arr, onset_point, offset_point):
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onset_end = onset_point - 10
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offset_start = offset_point - 10
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base = np.mean(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)])
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base = np.median(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)])
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ground = cf_arr - base
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jar = np.mean(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)])
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jar = np.median(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)])
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norm = ground / jar
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return norm
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@ -123,7 +124,7 @@ def base_eod(frequencies, time, onset_point):
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onset_end = onset_point - 10
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base = np.mean(frequencies[(time >= onset_end) & (time < onset_point)])
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base = np.median(frequencies[(time >= onset_end) & (time < onset_point)])
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base_eod.append(base)
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return base_eod
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@ -133,7 +134,7 @@ def JAR_eod(frequencies, time, offset_point):
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offset_start = offset_point - 10
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jar = np.mean(frequencies[(time >= offset_start) & (time < offset_point)])
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jar = np.median(frequencies[(time >= offset_start) & (time < offset_point)])
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jar_eod.append(jar)
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return jar_eod
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@ -12,53 +12,84 @@ from jar_functions import mean_noise_cut
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from jar_functions import norm_function
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from jar_functions import step_response
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datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')),
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
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infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat'))]
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#nicht: 19-aa, 22-ae, 22-ad (?)
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datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')), #-5Hz delta f, bad fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')), #-15Hz delta f, good fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')), #-15Hz delta f, horrible fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')), #-15Hz delta f, maxfev way to high so horrible
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))] #-15Hz delta f, good fit
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#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')))
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time = []
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frequency_mean = []
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infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\info.dat'))]
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time_all = []
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freq_all = []
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constant_factors = []
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time_constants = []
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start = -10
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stop = 200
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timespan = 210
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ID = []
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for infodataset in infodatasets:
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i= parse_infodataset(infodataset)
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i = parse_infodataset(infodataset)
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identifier = i[0]
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ID.append(identifier)
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for dataset in datasets:
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#input of the function
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frequency, time, amplitude, eodf, deltaf, stimulusf, duration = parse_dataset(dataset)
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mf , tnew = mean_loops(start, stop, timespan, frequency, time)
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frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
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dm = np.mean(duration)
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frequency_mean.append(mf)
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time.append(tnew)
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pm = np.mean(pause)
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timespan = dm + pm
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start = -10
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stop = 200
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mf , tnew = mean_loops(start, stop, timespan, frequency, time)
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#for i in range(len(mf)):
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for i in range(len(frequency_mean)):
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cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=1000)
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cf, ct = mean_noise_cut(mf, tnew, n=1250)
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cf_arr = np.array(cf)
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ct_arr = np.array(ct)
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norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec
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plt.plot(ct_arr, norm) #, label='n=%d' % n)
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freq_all.append(norm.tolist())
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time_all.append(ct_arr)
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plt.plot(ct_arr, norm) #, label='fish=%s' % ID)
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sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #step_values and step_cov
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sv, sc = curve_fit(step_response, ct_arr[ct_arr < 100], norm[ct_arr < 100]) #step_values and step_cov
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a = sv[:2]
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tau = np.array(sorted(sv[2:], reverse=False))
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values = np.array([a, tau])
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values_flat = values.flatten()
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plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], 'r-', label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat))
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plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat))
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print('a1, a2, tau1, tau2', values_flat)
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constant_factors.append(a)
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time_constants.append(tau)
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fr = []
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for j in freq_all:
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fr.append(freq_all[j])
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embed()
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minimumf_all = min(len(freq_all[j]))
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f_all = freq_all[j][:minimumf_all]
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print(freq_all[0])
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print(len((freq_all[j])))
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#f_all_arr = np.array([f0_all], [f1_all])
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#f_mean_all = np.mean(freq_all, axis = 0)
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#t_mean_all = np.mean(time_all, axis = 0)
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const_line = plt.axhline(y=0.632)
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plt.xlim([-10,220])
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@ -74,6 +105,5 @@ embed()
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# alle daten einlesen durch große for schleife (auch average über alle fische?)
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# für einzelne fische fit kontrollieren
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#Fragen: wie offset point wenn nicht start bei 0 sec?
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#wie a1, tau1,.. ohne array? (funkt wegen dimensionen wenn ichs nochmal in liste appende)
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# Fragen: wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt?
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# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
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