20.08
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xaver
parent
352545004f
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4a58f1382f
@ -99,14 +99,18 @@ def mean_traces(start, stop, timespan, frequencies, time):
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return mf, tnew
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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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f = np.mean(frequencies[k:k+n])
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t = time[k]
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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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cutf = np.zeros(len(frequencies))
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for k in range(0, len(frequencies) - n):
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kk = int(k)
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f = np.mean(frequencies[kk:kk+n])
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kkk = int(kk+n/2)
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if k == 0:
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cutf[:kkk] = f
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cutf[kkk] = f
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#t = time[kk]
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#cutt[kkk] = t
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cutf[kkk:] = f
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return cutf
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def norm_function(f, t, onset_point, offset_point):
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onset_end = onset_point - 10
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@ -25,7 +25,9 @@ amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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currf = None
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idxlist = []
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data = sorted(np.load('files.npy'), key = take_second) # list with filenames in it
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identifier = '2020lepto06'
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data = sorted(np.load('%s files.npy' %identifier), key = take_second) # list with filenames in it
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for i, d in enumerate(data):
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dd = list(d)
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@ -34,41 +36,40 @@ for i, d in enumerate(data):
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print(dd)
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time = np.load('%s time.npy' %dd) # time file
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for float(d[1]) = 0.001: #ähnlich wie das, einfach passenstens n verwenden?
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n = int((len(jar) / time[-1]) * (1 / 15))
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print(n)
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cutf, cutt = mean_noise_cut(jar, time, n = 2)
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plt.plot(cutt, cutf, label='cut amfreq')
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plt.plot(time, jar, label='spec')
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plt.legend()
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plt.show()
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#embed()
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dt = time[1] - time[0]
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n = int(1/float(d[1])/dt)
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cutf = mean_noise_cut(jm, time, n = n)
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cutt = time
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plt.plot(time, jm-cutf, label='cut amfreq')
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plt.plot(time, jm, label='spec')
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#plt.legend()
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#plt.show()
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b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True) # high pass filtering so our fit gets a bit better
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b, a = signal.butter(4, (float(d[1])) / (0.5/dt), 'high', analog=True) # high pass filtering so our fit gets a bit better
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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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#plt.plot(time, jm)
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sinv, sinc = curve_fit(sin_response, time, y, [float(d[1]), 2, 0.5]) # fitting
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print('frequency, phaseshift, amplitude:', sinv)
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plt.show()
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p = np.sqrt(sinv[1]**2)
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A = np.sqrt(sinv[2] ** 2)
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f = float(d[1])
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phaseshift.append(p)
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gain.append(A)
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amfreq.append(f)
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'''
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# root mean square
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RMS = np.sqrt(np.mean((jm - sin_response(time, sinv[0], sinv[1], sinv[2]))**2))
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RMS = np.sqrt(np.mean((cutf_arr - sin_response(cutt_arr, sinv[0], sinv[1], sinv[2]))**2))
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rootmeansquare.append(RMS)
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thresh = A / np.sqrt(2)
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threshold.append(thresh)
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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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#mean over same amfreqs for phase and gain
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'''
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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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@ -85,7 +86,6 @@ for i, d in enumerate(data):
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mphaseshift.append(meanedp)
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currf = d[1] # set back for next loop
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idxlist = [i]
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meanf = []
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meanp = []
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for y in idxlist:
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@ -101,8 +101,6 @@ 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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@ -111,19 +109,19 @@ ax.set_yscale('log')
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ax.set_xscale('log')
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ax.set_title('%s' % data[0][0])
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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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ax.set_xlabel('envelope_frequency [Hz]')
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#plt.savefig('%s gain' % data[0][0])
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pylab.show()
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plt.plot(threshold, label = 'threshold')
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plt.plot(rootmeansquare, label = 'RMS')
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plt.legend()
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plt.show()
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embed()
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#phase in degree
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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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# bevor fit noch filtern mit 15Hz damit AM-Modulation rausgefiltert wird und nur noch envelope übrig bleibt.
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# Dazu running average mit n wobei n über samplingrate von spectogram und delta f bestimmt wird
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# samplingrate über overlap muss dabei aber größer sein als samplingrate die noch übrig bleibt wenn ich mit delta f frequency gefiltert hab
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# einfach passendes n verwenden um AM-beats rauszufiltern? ...Menge Datenpunkte zu gering
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@ -21,18 +21,18 @@ from jar_functions import average
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from jar_functions import import_data
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from jar_functions import import_amfreq
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base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto16'
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datasets = ['2020-08-04-ab',
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'2020-08-04-ac',
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'2020-08-04-ad',
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'2020-08-04-ae',
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'2020-08-04-af',
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'2020-08-05-ab',
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'2020-08-05-ac',
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'2020-08-05-ad',
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'2020-08-05-ae',
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'2020-08-05-af']
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base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto04'
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datasets = ['2020-07-22-ab',
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'2020-07-22-ac',
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'2020-07-22-ad',
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'2020-07-22-ae',
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'2020-07-22-af',
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'2020-07-22-ag',
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'2020-07-23-ab',
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'2020-07-23-ac',
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'2020-07-23-ad',
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'2020-07-23-ae']
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time_all = []
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freq_all = []
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@ -41,11 +41,7 @@ amfrequencies = []
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gains = []
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files = []
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ID = []
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col = ['dimgrey', 'grey', 'darkgrey', 'silver', 'lightgrey', 'gainsboro', 'whitesmoke']
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labels = zip(ID, datasets)
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for idx, dataset in enumerate(datasets):
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datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
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@ -53,13 +49,11 @@ for idx, dataset in enumerate(datasets):
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data, pre_data, dt = import_data(datapath)
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nfft = 2**17
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for d, dat in enumerate(data):
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file_name = []
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for infodataset in datasets:
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infodataset = os.path.join(base_path, infodataset, 'info.dat')
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@ -67,6 +61,8 @@ for idx, dataset in enumerate(datasets):
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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:-1])
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# file_name
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file_name.append(ID[0])
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amfreq = import_amfreq(datapath)
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@ -75,10 +71,12 @@ for idx, dataset in enumerate(datasets):
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file_name.append(str(d))
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files.append(file_name)
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# spectogram
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if float(amfreq) < 0.01:
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spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.8)
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else:
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spec, freqs, times = specgram(dat, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.96)
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spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
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dbspec = 10.0*np.log10(spec) # in dB
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power = dbspec[:, 50]
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@ -93,7 +91,6 @@ for idx, dataset in enumerate(datasets):
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lim0 = eodf4-10
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lim1 = eodf4+15
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# control of plt.imshow
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df = freqs[1] - freqs[0]
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ix0 = int(np.floor(lim0/df)) # back to index
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ix1 = int(np.ceil(lim1/df)) # back to index
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@ -102,14 +99,16 @@ for idx, dataset in enumerate(datasets):
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jar4 = freq4[np.argmax(spec4, axis=0)] # all freqs at max specs over axis 0
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jar = jar4 / 4
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jm = jar4 - np.mean(jar4) # data we take
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cut_times = times[:len(jar4)]
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# plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
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# save data
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np.save('%s time' % file_name, cut_times)
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np.save('%s' % file_name, jar4)
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#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
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np.save('files.npy', files)
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#embed()
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# save filenames for this fish
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np.save('%s files' %ID[0], files)
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embed()
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# running average over on AM-period?
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