diff --git a/jar_functions.py b/jar_functions.py index 4bcb2c6..a809ae0 100644 --- a/jar_functions.py +++ b/jar_functions.py @@ -13,6 +13,7 @@ def parse_dataset(dataset_name): deltafs = [] stimulusfs = [] duration = [] + pause = [] # data itself times = [] @@ -34,6 +35,8 @@ def parse_dataset(dataset_name): stimulusfs.append(float(l.split(':')[-1].strip()[:-2])) if "#" in l and "Duration" in l: duration.append(float(l.split(':')[-1].strip()[:-3])) + if "#" in l and "Pause" in l: + pause.append(float(l.split(':')[-1].strip()[:-3])) if '#Key' in l: if len(time) != 0: #therefore empty in the first round @@ -55,7 +58,7 @@ def parse_dataset(dataset_name): amplitudes.append(ampl) #these append the data from the first loop to the final lists, because we overwrite them (?) frequencies.append(freq) - return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration #output of the function + return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration, pause #output of the function def parse_infodataset(dataset_name): assert(os.path.exists(dataset_name)) #see if data exists @@ -90,13 +93,11 @@ def mean_loops(start, stop, timespan, frequencies, time): def mean_noise_cut(frequencies, time, n): cutf = [] cutt = [] - for k in np.arange(0, len(frequencies), n): t = time[k] f = np.mean(frequencies[k:k+n]) cutf.append(f) cutt.append(t) - return cutf, cutt @@ -109,11 +110,11 @@ def norm_function(cf_arr, ct_arr, onset_point, offset_point): onset_end = onset_point - 10 offset_start = offset_point - 10 - base = np.mean(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)]) + base = np.median(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)]) ground = cf_arr - base - jar = np.mean(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)]) + jar = np.median(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)]) norm = ground / jar return norm @@ -123,7 +124,7 @@ def base_eod(frequencies, time, onset_point): onset_end = onset_point - 10 - base = np.mean(frequencies[(time >= onset_end) & (time < onset_point)]) + base = np.median(frequencies[(time >= onset_end) & (time < onset_point)]) base_eod.append(base) return base_eod @@ -133,7 +134,7 @@ def JAR_eod(frequencies, time, offset_point): offset_start = offset_point - 10 - jar = np.mean(frequencies[(time >= offset_start) & (time < offset_point)]) + jar = np.median(frequencies[(time >= offset_start) & (time < offset_point)]) jar_eod.append(jar) return jar_eod diff --git a/second_try.py b/second_try.py index 174bd65..cc5b94f 100644 --- a/second_try.py +++ b/second_try.py @@ -12,53 +12,84 @@ from jar_functions import mean_noise_cut from jar_functions import norm_function from jar_functions import step_response -datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), - (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))] -infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat'))] +#nicht: 19-aa, 22-ae, 22-ad (?) +datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit + #(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')), #-5Hz delta f, bad fit + #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')), #-5Hz delta f, bad fit + #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), #-5Hz delta f, bad fit + (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')), #-15Hz delta f, good fit + #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')), #-15Hz delta f, horrible fit + #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')), #-15Hz delta f, maxfev way to high so horrible + (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))] #-15Hz delta f, good fit +#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat'))) -time = [] -frequency_mean = [] +infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')), + (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\info.dat'))] + + +time_all = [] +freq_all = [] constant_factors = [] time_constants = [] -start = -10 -stop = 200 -timespan = 210 +ID = [] + for infodataset in infodatasets: - i= parse_infodataset(infodataset) + i = parse_infodataset(infodataset) identifier = i[0] + ID.append(identifier) + for dataset in datasets: #input of the function - frequency, time, amplitude, eodf, deltaf, stimulusf, duration = parse_dataset(dataset) - mf , tnew = mean_loops(start, stop, timespan, frequency, time) + frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset) dm = np.mean(duration) - frequency_mean.append(mf) - time.append(tnew) + pm = np.mean(pause) + timespan = dm + pm + start = -10 + stop = 200 + mf , tnew = mean_loops(start, stop, timespan, frequency, time) + + #for i in range(len(mf)): -for i in range(len(frequency_mean)): - cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=1000) + cf, ct = mean_noise_cut(mf, tnew, n=1250) cf_arr = np.array(cf) ct_arr = np.array(ct) norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec - plt.plot(ct_arr, norm) #, label='n=%d' % n) + freq_all.append(norm.tolist()) + time_all.append(ct_arr) + + plt.plot(ct_arr, norm) #, label='fish=%s' % ID) + + sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #step_values and step_cov - sv, sc = curve_fit(step_response, ct_arr[ct_arr < 100], norm[ct_arr < 100]) #step_values and step_cov a = sv[:2] tau = np.array(sorted(sv[2:], reverse=False)) values = np.array([a, tau]) values_flat = values.flatten() - 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)) + 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)) print('a1, a2, tau1, tau2', values_flat) constant_factors.append(a) time_constants.append(tau) +fr = [] +for j in freq_all: + fr.append(freq_all[j]) + embed() + minimumf_all = min(len(freq_all[j])) + f_all = freq_all[j][:minimumf_all] + print(freq_all[0]) +print(len((freq_all[j]))) + +#f_all_arr = np.array([f0_all], [f1_all]) +#f_mean_all = np.mean(freq_all, axis = 0) +#t_mean_all = np.mean(time_all, axis = 0) const_line = plt.axhline(y=0.632) plt.xlim([-10,220]) @@ -74,6 +105,5 @@ embed() # alle daten einlesen durch große for schleife (auch average über alle fische?) # für einzelne fische fit kontrollieren -#Fragen: wie offset point wenn nicht start bei 0 sec? -#wie a1, tau1,.. ohne array? (funkt wegen dimensionen wenn ichs nochmal in liste appende) - +# Fragen: wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? +# wie zip ich ID liste mit plot (für eine for schleife) zusammen? \ No newline at end of file