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xaver 2020-08-26 18:13:49 +02:00
parent 4a58f1382f
commit 21d613222c
4 changed files with 167 additions and 136 deletions
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2
jar_functions.py
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@ -107,8 +107,6 @@ def mean_noise_cut(frequencies, time, n):
if k == 0: if k == 0:
cutf[:kkk] = f cutf[:kkk] = f
cutf[kkk] = f cutf[kkk] = f
#t = time[kk]
#cutt[kkk] = t
cutf[kkk:] = f cutf[kkk:] = f
return cutf return cutf

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jar_spectogram.png
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260
sin_response_fit.py
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@ -10,118 +10,158 @@ 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]
predict = [] identifier = ['2018lepto1',
'2018lepto4',
rootmeansquare = [] '2018lepto5',
threshold = [] '2018lepto76',
'2018lepto98',
gain = [] '2019lepto03',
mgain = [] '2019lepto24',
phaseshift = [] '2019lepto27',
mphaseshift = [] '2019lepto30',
amfreq = [] '2020lepto04',
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1] '2020lepto06',
'2020lepto16',
currf = None '2020lepto19',
idxlist = [] '2020lepto20'
]
identifier = '2020lepto06' for ident in identifier:
data = sorted(np.load('%s files.npy' %identifier), key = take_second) # list with filenames in it predict = []
for i, d in enumerate(data): rootmeansquare = []
dd = list(d) threshold = []
jar = np.load('%s.npy' %dd) # load data for every file name
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean gain = []
print(dd) mgain = []
phaseshift = []
time = np.load('%s time.npy' %dd) # time file mphaseshift = []
dt = time[1] - time[0] amfreq = []
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
n = int(1/float(d[1])/dt)
cutf = mean_noise_cut(jm, time, n = n) currf = None
cutt = time idxlist = []
plt.plot(time, jm-cutf, label='cut amfreq')
plt.plot(time, jm, label='spec') data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
#plt.legend()
#plt.show() for i, d in enumerate(data):
dd = list(d)
b, a = signal.butter(4, (float(d[1])) / (0.5/dt), 'high', analog=True) # high pass filtering so our fit gets a bit better jar = np.load('%s.npy' %dd) # load data for every file name
y = signal.filtfilt(b, a, jm) jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
#plt.plot(time, y) print(dd)
#plt.plot(time, jm)
time = np.load('%s time.npy' %dd) # time file
sinv, sinc = curve_fit(sin_response, time, y, [float(d[1]), 2, 0.5]) # fitting dt = time[1] - time[0]
print('frequency, phaseshift, amplitude:', sinv)
p = np.sqrt(sinv[1]**2) n = int(1/float(d[1])/dt)
A = np.sqrt(sinv[2] ** 2) cutf = mean_noise_cut(jm, time, n = n)
f = float(d[1]) cutt = time
phaseshift.append(p) # plt.plot(time, jm-cutf, label='cut amfreq')
gain.append(A) # plt.plot(time, jm, label='spec')
amfreq.append(f) # plt.legend()
''' # plt.show()
# root mean square
RMS = np.sqrt(np.mean((cutf_arr - sin_response(cutt_arr, sinv[0], sinv[1], sinv[2]))**2)) sinv, sinc = curve_fit(sin_response, time, jm - cutf, [float(d[1]), 2, 0.5]) # fitting
rootmeansquare.append(RMS) print('frequency, phaseshift, amplitude:', sinv)
thresh = A / np.sqrt(2) p = np.sqrt(sinv[1]**2)
threshold.append(thresh) A = np.sqrt(sinv[2] ** 2)
''' f = float(d[1])
plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv)) phaseshift.append(p)
gain.append(A)
if f not in amfreq:
amfreq.append(f)
# root mean square
RMS = np.sqrt(np.mean(((jm - cutf) - sin_response(cutt, sinv[0], sinv[1], sinv[2]))**2))
thresh = A / np.sqrt(2)
# plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))
# plt.legend()
# plt.show()
# mean over same amfreqs for phase and gain
if currf is None or currf == d[1]:
currf = d[1]
idxlist.append(i)
else: # currf != f
meanf = [] # lists to make mean of
meanp = []
meanrms = []
meanthresh = []
for x in idxlist:
meanf.append(gain[x])
meanp.append(phaseshift[x])
meanrms.append(RMS)
meanthresh.append(thresh)
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
meanedrms = np.mean(meanrms)
meanedthresh = np.mean(meanthresh)
mgain.append(meanedf)
mphaseshift.append(meanedp)
rootmeansquare.append(meanedrms)
threshold.append(meanedthresh)
currf = d[1] # set back for next loop
idxlist = [i]
meanf = []
meanp = []
meanrms = []
meanthresh = []
for y in idxlist:
meanf.append(gain[y])
meanp.append(phaseshift[y])
meanrms.append(RMS)
meanthresh.append(thresh)
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
meanedrms = np.mean(meanrms)
meanedthresh = np.mean(meanthresh)
mgain.append(meanedf)
mphaseshift.append(meanedp)
rootmeansquare.append(meanedrms)
threshold.append(meanedthresh)
# predict of gain
for f in amf:
G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
predict.append(G)
fig = plt.figure()
ax0 = fig.add_subplot(2, 1, 1)
ax0.plot(amfreq, mgain(RMS<threshold), 'o')
#ax0.plot(amf, predict)
ax0.set_yscale('log')
ax0.set_xscale('log')
ax0.set_title('%s' % data[0][0])
ax0.set_ylabel('gain [Hz/(mV/cm)]')
ax0.set_xlabel('envelope_frequency [Hz]')
#plt.savefig('%s gain' % data[0][0])
ax1 = fig.add_subplot(2, 1, 2, sharex = ax0)
ax1.plot(amfreq, threshold, 'o-', label = 'threshold', color = 'b')
ax1.set_xscale('log')
ax1.plot(amfreq, rootmeansquare, 'o-', label = 'RMS', color = 'orange')
ax1.set_xscale('log')
ax1.set_xlabel('envelope_frequency [Hz]')
ax1.set_ylabel('RMS')
plt.legend() plt.legend()
plt.show() pylab.show()
# mean over same amfreqs for phase and gain
if currf is None or currf == d[1]:
currf = d[1]
idxlist.append(i)
else: # currf != f
meanf = [] # lists to make mean of
meanp = []
for x in idxlist:
meanf.append(gain[x])
meanp.append(phaseshift[x])
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
mgain.append(meanedf)
mphaseshift.append(meanedp)
currf = d[1] # set back for next loop
idxlist = [i]
meanf = []
meanp = []
for y in idxlist:
meanf.append(gain[y])
meanp.append(phaseshift[y])
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
mgain.append(meanedf)
mphaseshift.append(meanedp)
# predict of gain
for f in amf:
G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
predict.append(G)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(amf, mgain, 'o')
#ax.plot(amf, predict)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_title('%s' % data[0][0])
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]')
#plt.savefig('%s gain' % data[0][0])
pylab.show()
plt.plot(threshold, label = 'threshold')
plt.plot(rootmeansquare, label = 'RMS')
plt.legend()
plt.show()
embed() embed()
# phase in degree # zu eigenmannia: jeden fisch mit amplituden von max und min von modulationstiefe und evtl 1 oder 2 dazwischen
# Q10 / conductivity # und dann für die am frequenzen von apteronotus für 15Hz delta f messen
# AM-frequency / envelope-frequency scale title?
# einfach passendes n verwenden um AM-beats rauszufiltern? ...Menge Datenpunkte zu gering # mit zu hohem RMS rauskicken: gain/rms < ... (?)
# gain kurven als array abspeichern
# daten von natalie zu eigenmannia mit + / - delta f anschauen ob unterschiede
# unterschiedliche nffts auf anderem rechner laufen lassen evtl um unterschiede zu sehen?
# long term: extra datei mit script drin um fertige daten darzustellen, den code hier als datenverarbeitung allein verwenden
# darstellung: specgram --> rausgezogene jarspur darüber --> filterung --> fit und daten zusammen dargestellt, das ganze für verschiedene frequenzen
# liste mit eigenschaften der fische (dominanz/größe) und messvariablen (temp/conductivity) machen um diese plotten zu können
# phase in degree

41
sin_response_specto.py
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@ -21,18 +21,7 @@ from jar_functions import average
from jar_functions import import_data from jar_functions import import_data
from jar_functions import import_amfreq from jar_functions import import_amfreq
base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto04' base_path = 'D:\\jar_project\\JAR\\sin\\2019lepto03'
datasets = ['2020-07-22-ab',
'2020-07-22-ac',
'2020-07-22-ad',
'2020-07-22-ae',
'2020-07-22-af',
'2020-07-22-ag',
'2020-07-23-ab',
'2020-07-23-ac',
'2020-07-23-ad',
'2020-07-23-ae']
time_all = [] time_all = []
freq_all = [] freq_all = []
@ -41,26 +30,28 @@ amfrequencies = []
gains = [] gains = []
files = [] files = []
ID = [] for idx, dataset in enumerate(os.listdir(base_path)):
if dataset == 'prerecordings':
for idx, dataset in enumerate(datasets): continue
datapath = os.path.join(base_path, dataset, '%s.nix' % dataset) datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
print(datapath) print(datapath)
data, pre_data, dt = import_data(datapath) data, pre_data, dt = import_data(datapath)
nfft = 2**17 nfft = 2**17
for d, dat in enumerate(data): for d, dat in enumerate(data):
file_name = [] if len(dat) == 1:
continue
for infodataset in datasets: file_name = []
infodataset = os.path.join(base_path, infodataset, 'info.dat') ID = []
i = parse_infodataset(infodataset) # identifier for file_name
identifier = i[0] infodatapath = os.path.join(base_path, dataset, 'info.dat')
if not identifier[1:-2] in ID: i = parse_infodataset(infodatapath)
ID.append(identifier[1:-1]) identifier = i[0]
if not identifier[1:-2] in ID:
ID.append(identifier[1:-1])
# file_name # file_name
file_name.append(ID[0]) file_name.append(ID[0])
@ -109,6 +100,8 @@ for idx, dataset in enumerate(datasets):
# save filenames for this fish # save filenames for this fish
np.save('%s files' %ID[0], files) np.save('%s files' %ID[0], files)
print(ID)
embed() embed()
# running average over on AM-period? # running average over on AM-period?