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xaver 2020-08-18 15:39:27 +02:00
parent 5c17008909
commit 352545004f
2 changed files with 12 additions and 20 deletions
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12
sin_response_fit.py
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@ -5,6 +5,7 @@ import pylab
from IPython import embed from IPython import embed
from scipy.optimize import curve_fit from scipy.optimize import curve_fit
from jar_functions import sin_response from jar_functions import sin_response
from jar_functions import mean_noise_cut
def take_second(elem): # function for taking the names out of files def take_second(elem): # function for taking the names out of files
return elem[1] return elem[1]
@ -33,6 +34,15 @@ for i, d in enumerate(data):
print(dd) print(dd)
time = np.load('%s time.npy' %dd) # time file time = np.load('%s time.npy' %dd) # time file
for float(d[1]) = 0.001: #ähnlich wie das, einfach passenstens n verwenden?
n = int((len(jar) / time[-1]) * (1 / 15))
print(n)
cutf, cutt = mean_noise_cut(jar, time, n = 2)
plt.plot(cutt, cutf, label='cut amfreq')
plt.plot(time, jar, label='spec')
plt.legend()
plt.show()
#embed()
b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True) # high pass filtering so our fit gets a bit better b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True) # high pass filtering so our fit gets a bit better
y = signal.filtfilt(b, a, jm) y = signal.filtfilt(b, a, jm)
@ -115,5 +125,5 @@ embed()
# AM-frequency / envelope-frequency scale title? # AM-frequency / envelope-frequency scale title?
# bevor fit noch filtern mit 15Hz damit AM-Modulation rausgefiltert wird und nur noch envelope übrig bleibt. # bevor fit noch filtern mit 15Hz damit AM-Modulation rausgefiltert wird und nur noch envelope übrig bleibt.
# Dazu running average mit n wobei n über samplingrate und delta f bestimmt wird # Dazu running average mit n wobei n über samplingrate von spectogram und delta f bestimmt wird
# samplingrate über overlap muss dabei aber größer sein als samplingrate die noch übrig bleibt wenn ich mit delta f frequency gefiltert hab # samplingrate über overlap muss dabei aber größer sein als samplingrate die noch übrig bleibt wenn ich mit delta f frequency gefiltert hab

20
sin_response_specto.py
View File

@ -23,10 +23,6 @@ from jar_functions import import_amfreq
base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto16' base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto16'
#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
datasets = ['2020-08-04-ab', datasets = ['2020-08-04-ab',
'2020-08-04-ac', '2020-08-04-ac',
'2020-08-04-ad', '2020-08-04-ad',
@ -82,7 +78,7 @@ for idx, dataset in enumerate(datasets):
if float(amfreq) < 0.01: if float(amfreq) < 0.01:
spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.8) spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.8)
else: else:
spec, freqs, times = specgram(dat, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95) spec, freqs, times = specgram(dat, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.96)
dbspec = 10.0*np.log10(spec) # in dB dbspec = 10.0*np.log10(spec) # in dB
power = dbspec[:, 50] power = dbspec[:, 50]
@ -111,22 +107,8 @@ for idx, dataset in enumerate(datasets):
np.save('%s time' % file_name, cut_times) np.save('%s time' % file_name, cut_times)
np.save('%s' % file_name, jar4) np.save('%s' % file_name, jar4)
cutf, cutt = mean_noise_cut(jar4, cut_times, 40000 * (1/15))
plt.plot(cutt, cutf)
plt.show()
embed()
#plt.plot(cut_times, jm, '-k')
#cf, ct = mean_noise_cut(jar4, cut_times, n = int(round(len(jar4)/((times[-1] - times [0]) * amfreq))))
#plt.plot(ct, cf, '-k')
#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10) #plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
#plt.legend()
#plt.ylim(lim0, lim1)
#plt.legend()
#plt.show()
np.save('files.npy', files) np.save('files.npy', files)
#embed() #embed()