10.09
This commit is contained in:
xaver
parent
03dc051388
commit
ea00a01fd9
@ -12,7 +12,7 @@ from jar_functions import get_time_zeros
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from jar_functions import import_data_eigen
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from scipy.signal import savgol_filter
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base_path = 'D:\\jar_project\\JAR\\eigenmannia'
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
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identifier = ['2013eigen13', '2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22', '2020eigen32']
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@ -30,9 +30,18 @@ for ID in identifier:
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# base with nh.read_eod
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time, eod = nh.read_eod(datapath, duration = 2000) # anstatt dem import data mit tag manual jar - dann sollte onset wirklich bei 10 sec sein
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dt = time[1] - time[0]
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nfft = 2 **17
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spec_0, freqs_0, times_0 = specgram(eod, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
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dbspec_0 = 10.0 * np.log10(spec_0) # in dB
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plt.imshow(dbspec_0, cmap='jet', origin='lower', extent=(times_0[0], times_0[-1], 0, 1500), aspect='auto',
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vmin=-80, vmax=-10)
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plt.show()
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zeropoints = get_time_zeros(time, eod, threshold=np.max(eod) * 0.1)
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frequencies = 1 / np.diff(zeropoints)
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window = np.ones(101) / 101
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@ -65,8 +74,10 @@ for ID in identifier:
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jm = jar4 - np.mean(jar4) # data we take
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cut_time_jar = times_0[:len(jar4)]
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#plt.imshow(spec4_0, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
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#plt.plot(cut_time_jar, jar4)
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#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80,
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#vmax=-10)
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#plt.imshow(spec4_0, cmap='jet', origin='lower', extent=(times_0[0], times_0[-1], lim0_0, lim1_0), aspect='auto', vmin=-80, vmax=-10)
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plt.plot(cut_time_jar, jm)
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#plt.ylim(lim0_0, lim1_0)
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# pre_data
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@ -75,8 +86,8 @@ for ID in identifier:
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dbspec_1 = 10.0 * np.log10(spec_1) # in dB
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power_1 = dbspec_1[:, 25]
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fish_p_1 = power_1[(freqs_1 > 200) & (freqs_1 < 1000)]
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fish_f_1 = freqs_1[(freqs_1 > 200) & (freqs_1 < 1000)]
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fish_p_1 = power_1[(freqs_1 > 200) & (freqs_1 < 500)]
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fish_f_1 = freqs_1[(freqs_1 > 200) & (freqs_1 < 500)]
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index1 = np.argmax(fish_p_1)
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eodf_1 = fish_f_1[index1]
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@ -94,13 +105,14 @@ for ID in identifier:
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bm = base4 - np.mean(base4) # data we take
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cut_time_base = times_1[:len(base4)] - times_1[-1]
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#plt.plot(cut_time_base, base4)
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plt.plot(cut_time_base, bm)
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j = []
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for idx, i in enumerate(times_0):
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if i > 45 and i < 55:
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j.append(jm[idx])
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plt.plot(j)
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plt.show()
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r = np.median(j) - np.median(bm)
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deltaf.append(df[0])
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@ -13,7 +13,7 @@ from jar_functions import import_data_eigen
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from jar_functions import get_new_zero_crossings
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from scipy.signal import savgol_filter
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base_path = 'D:\\jar_project\\JAR\\eigenmannia'
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
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identifier = ['2013eigen13', '2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22', '2020eigen32']
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20
gain_fit.py
20
gain_fit.py
@ -28,17 +28,21 @@ for ID in identifier:
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amf = np.load('amf_%s.npy' %ID)
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gain = np.load('gain_%s.npy' %ID)
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'''fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, gain, 'o')
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ax.set_yscale('log')
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ax.set_xscale('log')
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plt.show()
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'''
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
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print('tau:', sinv[0])
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tau.append(sinv[0])
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f_cutoff = 1 / (2*np.pi*sinv[0])
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print('f_cutoff:', f_cutoff)
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f_c.append(f_cutoff)
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#welche zeitkonstante ist das? was ist mit der zweiten?
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, gain, 'o')
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amff = np.logspace(-3, 0, 200)
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ax.plot(amff, gain_curve_fit(amff, *sinv))
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ax.set_yscale('log')
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ax.set_xscale('log')
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plt.show()
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#welche zeitkonstante ist das? was ist mit der zweiten? --> eher zweite zeitkonstante obwohl werte so klein?
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@ -13,7 +13,7 @@ def sin_response(t, f, p, A):
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r_sin = A*np.sin(2*np.pi*t*f + p)
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return r_sin
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def gain_curve_fit(tau, alpha, amf):
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def gain_curve_fit(amf, tau, alpha):
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gain = alpha / np.sqrt(1 + (2*np.pi*amf*tau)**2)
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return gain
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12
notes
12
notes
@ -1,15 +1,19 @@
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+ fit für gain kurven: über tau = 1/cutoff_f * 2 * pi --> wie bestimm ich cutoff_f?
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+ daten von natalie zu eigenmannia mit + / - delta f anschauen ob unterschiede
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+ größe/gewicht/dominanz/temp in csv und über split aufteilen und mit ID verknüpfen oder mit pandar,
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eod basefrequenz rausziehen, scatter plot gegen cutoff frequency, ...
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+ cutoff frequencies rausziehen und zu gain_all plotten, dann punkte so aussortieren dass uniform
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verteilt ist um zu zeigen wie metzen chacron zu dem ergebnis gekommen sind (hoffentlich)
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dabei noch absolutwerte von cutoff und tau verwenden da wir wurzel in formel nehmen
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+ specgram von pre_data neben specgram von data machen um zu sehen ob analyse fehler oder fehler in import_data
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long term:
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- extra datei mit script drin um fertige daten darzustellen, den fit-code nur zur datenverarbeitung verwenden
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- darstellung: specgram --> rausgezogene jarspur darüber --> filterung --> fit und daten zusammen dargestellt, das ganze für verschiedene frequenzen
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+ größe/evtl. gewicht nachtragen, eod basefrequenz rausziehen und zuweisen --> wie macht man das schnell und nicht manuell?
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- liste mit eigenschaften der fische (dominanz/größe), messvariablen (temp/conductivity), eodf und evtl ampl machen um diese plotten zu können
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- unterschiedliche nffts auf anderem rechner laufen lassen evtl um unterschiede zu sehen
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- phase in degree: phase % (2pi) - modulo 2pi
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(
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- mit zu hohem RMS rauskicken: evtl nur ein trace rauskicken wenn nur da RMS zu hoch
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- 2019lepto27/30: 27 - 0.05Hz (7-27-af, erste dat mit len(dat)=1), 30 - 0.001Hz (7-30-ah mit 0.005 anstatt gewollten 0.001Hz --> fehlt)
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)
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@ -7,7 +7,7 @@ from IPython import embed
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identifier = ['2013eigen13', '2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22', '2020eigen32']
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for ID in identifier:
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res_df = np.load('res_df_%s_new.npy' %ID)
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res_df = np.load('res_df_%s.npy' %ID)
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mres = []
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mdf = []
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11
sin_all.py
11
sin_all.py
@ -40,16 +40,21 @@ identifier = ['2018lepto4',
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amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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all = []
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new_all = []
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for ident in identifier:
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data = np.load('gain_%s.npy' %ident)
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max = np.max(data)
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new_data = data / max
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all.append(data)
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new_all.append(new_data)
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av = avgNestedLists(all)
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new_av = avgNestedLists(new_all)
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, av, 'o')
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ax.plot(amf, av, 'o', label = 'not normed')
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ax.plot(amf, new_av, 'o', label = 'normed')
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ax.legend()
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_title('gaincurve_average_allfish')
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@ -22,7 +22,7 @@ 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\\2019lepto27'
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\sin\\2015eigen8'
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time_all = []
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freq_all = []
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@ -38,15 +38,12 @@ for idx, dataset in enumerate(os.listdir(base_path)):
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#print(datapath)
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data, pre_data, dt = import_data(datapath)
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embed()
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nfft = 2**17
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for d, dat in enumerate(data):
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if len(dat) == 1:
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print(datapath)
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embed()
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else:
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continue
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file_name = []
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ID = []
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@ -62,7 +59,7 @@ for idx, dataset in enumerate(os.listdir(base_path)):
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file_name.append(ID[0])
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amfreq = import_amfreq(datapath)
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#print(amfreq)
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print(amfreq)
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file_name.append(str(amfreq))
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file_name.append(str(d))
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@ -97,7 +94,9 @@ for idx, dataset in enumerate(os.listdir(base_path)):
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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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plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
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plt.plot(cut_times, jar4)
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plt.show()
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# save data
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#np.save('%s time' % file_name, cut_times)
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@ -89,7 +89,7 @@ for idx, dataset in enumerate(datasets):
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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(ft, step_response(ft, *sv), color='orange', 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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