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08.09

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xaver 2020-09-08 14:49:58 +02:00
parent fb14104325
commit da1bc387b4
7 changed files with 202 additions and 135 deletions

116
eigenmannia_jar.py
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@ -3,68 +3,74 @@ import numpy as np
import os
import nix_helpers as nh
from IPython import embed
from matplotlib.mlab import specgram
#from tqdm import tqdm
from jar_functions import parse_stimuli_dat
from jar_functions import norm_function_eigen
from jar_functions import mean_noise_cut_eigen
from jar_functions import get_time_zeros
from jar_functions import import_data_eigen
from scipy.signal import savgol_filter
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\2015eigen16'
base_path = 'D:\\jar_project\\JAR\\eigenmannia'
datasets = ['2020-07-08-aa', '2020-07-08-as']
identifier = ['2013eigen13', '2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22', '2020eigen32']
response = []
deltaf = []
for dataset in os.listdir(base_path):
datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
print(datapath)
stimuli_dat = os.path.join(base_path, dataset, 'manualjar-eod.dat')
df, duration = parse_stimuli_dat(stimuli_dat)
dur = int(duration[0][0:2])
print(df)
time, eod = nh.read_eod(datapath, duration = 2000)
zeropoints = get_time_zeros(time, eod, threshold = np.max(eod)*0.1)
frequencies = 1 / np.diff(zeropoints)
# norm, base, jar = norm_function_eigen(frequencies, zeropoints[:-1], onset_point=(dur - dur)+10, offset_point=dur+10) # dm-dm funktioniert nur wenn onset = 0 sec
# cf, ct = mean_noise_cut_eigen(frequencies, zeropoints[:-1], n=200)
window = np.ones(101) / 101
freq = np.convolve(frequencies, window, mode='same')
''' # plt.plot(ct, cf)
plt.plot(zeropoints[:-1], freq)
plt.ylabel('EOD_frequency [Hz]')
plt.xlabel('time [s]')
plt.xlim(1, 140)
plt.ylim(np.median(freq) - 10, np.median(freq) + 10)
plt.title('JAR_deltaf_%s' % deltaf)
plt.show()
'''
j = []
for idx, i in enumerate(zeropoints):
if i > 20 and i < 80:
j.append(freq[idx])
b = []
for idx, i in enumerate(zeropoints):
if i < 20:
b.append(freq[idx])
r = np.median(j) - np.median(b)
response.append(r)
deltaf.append(df[0])
res_df1 = sorted(zip(deltaf[:32],response[:32]))
res_df2 = sorted(zip(deltaf[33:],response[33:]))
res_df = sorted(zip(deltaf,response))
np.save('res_df1', res_df1)
np.save('res_df2', res_df2)
np.save('res_df', res_df)
for ID in identifier:
for dataset in os.listdir(os.path.join(base_path, ID)):
datapath = os.path.join(base_path, ID, dataset, '%s.nix' % dataset)
print(datapath)
stimuli_dat = os.path.join(base_path, ID, dataset, 'manualjar-eod.dat')
df, duration = parse_stimuli_dat(stimuli_dat)
dur = int(duration[0][0:2])
print(df)
# time, eod = nh.read_eod(datapath, duration = 2000) # anstatt dem import data mit tag manual jar - dann sollte onset wirklich bei 10 sec sein
data, pre_dat, dt = import_data_eigen(datapath)
nfft = 2**17
spec, freqs, times = specgram(data[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
dbspec = 10.0 * np.log10(spec) # in dB
power = dbspec[:, 50]
fish_p = power[(freqs > 200) & (freqs < 1000)]
fish_f = freqs[(freqs > 200) & (freqs < 1000)]
index = np.argmax(fish_p)
eodf = fish_f[index]
eodf4 = eodf * 4
lim0 = eodf4-20
lim1 = eodf4+20
df = freqs[1] - freqs[0]
ix0 = int(np.floor(lim0/df)) # back to index
ix1 = int(np.ceil(lim1/df)) # back to index
spec4 = dbspec[ix0:ix1, :]
freq4 = freqs[ix0:ix1]
jar4 = freq4[np.argmax(spec4, axis=0)] # all freqs at max specs over axis 0
jar = jar4 / 4
jm = jar4 - np.mean(jar4) # data we take
cut_times = times[:len(jar4)]
#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
plt.plot(cut_times, jm)
plt.plot(cut_times, savgol)
#plt.ylim(lim0, lim1)
plt.show()
# nicht unbedingt filtern, einfach wie unten median/mean
'''
res_df = sorted(zip(deltaf,response))
np.save('res_df_%s' %ID, res_df)
'''
# problem: rohdaten(data, pre_data) lassen sich auf grund ihrer 1D-array struktur nicht savgol filtern
# diese bekomm ich nur über specgram in form von freq / time auftragen, was nicht mehr savgol gefiltert werden kann
# jedoch könnte ich trotzdem einfach aus jar4 response herauslesen wobei dies dann weniger gefiltert wäre

37
gain_fit.py Normal file
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@ -0,0 +1,37 @@
from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
import pylab
from IPython import embed
from scipy.optimize import curve_fit
from jar_functions import gain_curve_fit
identifier = ['2018lepto1',
'2018lepto4',
'2018lepto5',
'2018lepto76',
'2018lepto98',
'2019lepto03',
'2019lepto24',
'2019lepto27',
'2019lepto30',
'2020lepto04',
'2020lepto06',
'2020lepto16',
'2020lepto19',
'2020lepto20'
]
for ID in identifier:
amf = np.load('amf_%s.npy' %ID)
gain = np.load('gain_%s.npy' %ID)
rms = np.load('rms_%s.npy' %ID)
thresh = np.load('thresh_%s.npy' % ID)
idx_arr = (rms < thresh) | (rms < np.mean(rms))
embed()
sinv, sinc = curve_fit(gain_curve_fit, amf[idx_arr], gain[idx_arr])
print(sinv[0])
f_cutoff = 1 / (2*np.pi*sinv[0])
print(f_cutoff)

23
jar_functions.py
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@ -13,6 +13,10 @@ def sin_response(t, f, p, A):
r_sin = A*np.sin(2*np.pi*t*f + p)
return r_sin
def gain_curve_fit(tau, alpha, amf):
gain = alpha / np.sqrt(1 + (2*np.pi*amf*tau)**2)
return gain
def parse_dataset(dataset_name):
assert(os.path.exists(dataset_name)) #see if data exists
f = open(dataset_name, 'r') #open data we gave in
@ -265,6 +269,25 @@ def import_data(dataset):
return dat, pre_dat, dt
#nf.close()
def import_data_eigen(dataset):
import nixio as nix
nf = nix.File.open(dataset, nix.FileMode.ReadOnly)
b = nf.blocks[0]
eod = b.data_arrays['EOD-1']
dt = eod.dimensions[0].sampling_interval
di = int(10.0/dt)
t = b.tags['ManualJAR_1']
#amfreq = t.metadata['RePro-Info']['settings']['amfreq']
dat = []
pre_dat = []
for mt in b.multi_tags:
data = mt.retrieve_data(0, 'EOD-1')[:] # data[0]
dat.append(data)
i0 = int(mt.positions[0][0]/dt)
pre_data = eod[i0-di:i0]
pre_dat.append(pre_data)
return dat, pre_dat, dt
def import_amfreq(dataset):
import nixio as nix
nf = nix.File.open(dataset, nix.FileMode.ReadOnly)

18
notes
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@ -1,13 +1,15 @@
- mit zu hohem RMS rauskicken: evtl nur ein trace rauskicken wenn nur da RMS zu hoch
- 2019lepto27/30 nochmal anschauen, gainpunkt fehlt
- fit für gain kurven: über tau = 1/cutoff_f * 2 * pi --> wie bestimm ich cutoff_f?
- daten von natalie zu eigenmannia mit + / - delta f anschauen ob unterschiede
- unterschiedliche nffts auf anderem rechner laufen lassen evtl um unterschiede zu sehen
+ fit für gain kurven: über tau = 1/cutoff_f * 2 * pi --> wie bestimm ich cutoff_f?
+ daten von natalie zu eigenmannia mit + / - delta f anschauen ob unterschiede
long term:
- extra datei mit script drin um fertige daten darzustellen, den fit-code als datenverarbeitung allein verwenden
- extra datei mit script drin um fertige daten darzustellen, den fit-code nur zur datenverarbeitung verwenden
- darstellung: specgram --> rausgezogene jarspur darüber --> filterung --> fit und daten zusammen dargestellt, das ganze für verschiedene frequenzen
- größe/evtl. gewicht nachtragen, eod basefrequenz rausziehen und zuweisen
+ größe/evtl. gewicht nachtragen, eod basefrequenz rausziehen und zuweisen --> wie macht man das schnell und nicht manuell?
- liste mit eigenschaften der fische (dominanz/größe), messvariablen (temp/conductivity), eodf und evtl ampl machen um diese plotten zu können
- unterschiedliche nffts auf anderem rechner laufen lassen evtl um unterschiede zu sehen
- phase in degree
- phase in degree: phase % (2pi) - modulo 2pi
- mit zu hohem RMS rauskicken: evtl nur ein trace rauskicken wenn nur da RMS zu hoch
- 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)

79
plot_eigenmannia_jar.py
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@ -4,47 +4,48 @@ import os
import nix_helpers as nh
from IPython import embed
res_df1 = np.load('res_df1.npy')
res_df2 = np.load('res_df2.npy')
res_df = np.load('res_df.npy')
identifier = ['2013eigen13', '2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22', '2020eigen32']
mres = []
mdf = []
for ID in identifier:
res_df = np.load('res_df_%s.npy' %ID)
currf = None
idxlist = []
mres = []
mdf = []
for i, d in enumerate(res_df):
if currf is None or currf == d[0]:
currf = d[0]
idxlist.append(i)
currf = None
idxlist = []
else: # currf != f
meanres = [] # lists to make mean of
meandf = []
for x in idxlist:
meanres.append(res_df[x][1])
meandf.append(res_df[x][0])
meanedres = np.mean(meanres)
meaneddf = np.mean(meandf)
mres.append(meanedres)
mdf.append(meaneddf)
currf = d[0] # set back for next loop
idxlist = [i]
meanres = [] # lists to make mean of
meandf = []
for y in idxlist:
meanres.append(res_df[y][1])
meandf.append(res_df[y][0])
meanedres = np.mean(meanres)
meaneddf = np.mean(meandf)
mres.append(meanedres)
mdf.append(meaneddf)
for i, d in enumerate(res_df):
if currf is None or currf == d[0]:
currf = d[0]
idxlist.append(i)
plt.plot(mdf, mres, 'o')
plt.xlabel('deltaf [Hz]')
plt.ylabel('JAR_respones [Hz]')
plt.axhline(0, color='grey', lw =1)
plt.axvline(0, color='grey', lw = 1)
plt.title('JAR_response_to_deltaf_eigenmannia')
plt.show()
else: # currf != f
meanres = [] # lists to make mean of
meandf = []
for x in idxlist:
meanres.append(res_df[x][1])
meandf.append(res_df[x][0])
meanedres = np.mean(meanres)
meaneddf = np.mean(meandf)
mres.append(meanedres)
mdf.append(meaneddf)
currf = d[0] # set back for next loop
idxlist = [i]
meanres = [] # lists to make mean of
meandf = []
for y in idxlist:
meanres.append(res_df[y][1])
meandf.append(res_df[y][0])
meanedres = np.mean(meanres)
meaneddf = np.mean(meandf)
mres.append(meanedres)
mdf.append(meaneddf)
plt.plot(mdf, mres, 'o')
plt.xlabel('deltaf [Hz]')
plt.ylabel('JAR_respones [Hz]')
plt.axhline(0, color='grey', lw =1)
plt.axvline(0, color='grey', lw = 1)
plt.title('JAR_response_to_deltaf_%s' %ID)
plt.show()

38
sin_response_fit.py
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@ -6,24 +6,25 @@ from IPython import embed
from scipy.optimize import curve_fit
from jar_functions import sin_response
from jar_functions import mean_noise_cut
from jar_functions import gain_curve_fit
def take_second(elem): # function for taking the names out of files
return elem[1]
identifier = [#'2018lepto1',
#'2018lepto4',
#'2018lepto5',
#'2018lepto76',
#'2018lepto98',
#'2019lepto03',
#'2019lepto24',
#'2019lepto27',
identifier = ['2018lepto1',
'2018lepto4',
'2018lepto5',
'2018lepto76',
'2018lepto98',
'2019lepto03',
'2019lepto24',
'2019lepto27',
'2019lepto30',
#'2020lepto04',
#'2020lepto06',
#'2020lepto16',
#'2020lepto19',
#'2020lepto20'
'2020lepto04',
'2020lepto06',
'2020lepto16',
'2020lepto19',
'2020lepto20'
]
for ident in identifier:
@ -54,7 +55,7 @@ for ident in identifier:
dt = time[1] - time[0]
n = int(1/float(d[1])/dt)
cutf = mean_noise_cut(jm, time, n = n)
cutf = mean_noise_cut(jm, n = n)
cutt = time
#plt.plot(time, jm-cutf, label='cut amfreq')
#plt.plot(time, jm, label='spec')
@ -157,9 +158,10 @@ for ident in identifier:
ax1.set_xlabel('envelope_frequency [Hz]')
ax1.set_ylabel('RMS [Hz]')
plt.legend()
pylab.show()
np.save('gain_%s' %ident, mgain_arr[idx_arr])
np.save('amf%s' %ident, amfreq_arr[idx_arr])
#pylab.show()
#np.save('gain_%s' %ident, mgain_arr[idx_arr])
#np.save('amf_%s' %ident, amfreq_arr[idx_arr])
np.save('rms_%s' %ident, rootmeansquare_arr)
np.save('thresh_%s' %ident, threshold_arr)
embed()

26
sin_response_specto.py
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@ -8,7 +8,7 @@ import IPython
import numpy as np
#import DataLoader as dl
from IPython import embed
from tqdm import tqdm
#from tqdm import tqdm
from scipy.optimize import curve_fit
from jar_functions import step_response
from jar_functions import sin_response
@ -22,7 +22,7 @@ from jar_functions import average
from jar_functions import import_data
from jar_functions import import_amfreq
base_path = 'D:\\jar_project\\JAR\\sin\\2019lepto30'
base_path = 'D:\\jar_project\\JAR\\sin\\2019lepto27'
time_all = []
freq_all = []
@ -31,27 +31,23 @@ amfrequencies = []
gains = []
files = []
for idx, dataset in tqdm(enumerate(os.listdir(base_path))):
for idx, dataset in enumerate(os.listdir(base_path)):
if dataset == 'prerecordings':
continue
datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
print(datapath)
#print(datapath)
data, pre_data, dt = import_data(datapath)
embed()
nfft = 2**17
for d, dat in enumerate(data):
if len(dat) == 1:
continue
'''if len(data) > 0:
print(datapath)
amfreq = import_amfreq(datapath)
print(amfreq)
continue
else:
embed()
'''
else:
continue
file_name = []
ID = []
@ -104,10 +100,10 @@ for idx, dataset in tqdm(enumerate(os.listdir(base_path))):
# plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
# save data
np.save('%s time' % file_name, cut_times)
np.save('%s' % file_name, jar4)
#np.save('%s time' % file_name, cut_times)
#np.save('%s' % file_name, jar4)
# save filenames for this fish
np.save('%s files' %ID[0], files)
#np.save('%s files' %ID[0], files)
embed()