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29.09

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xaver
2020-09-29 20:27:26 +02:00
parent f444071283
commit 90d9b19d9a
9 changed files with 619 additions and 111 deletions
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86
eigenmannia_code/eigenmannia_jar_stacked.py Normal file
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@@ -0,0 +1,86 @@
import matplotlib.pyplot as plt
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\\deltaf'
identifier = ['2013eigen13','2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
response = []
deltaf = []
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')
#print(stimuli_dat)
delta_f, duration = parse_stimuli_dat(stimuli_dat)
dur = int(duration[0][0:2])
print(delta_f)
if delta_f ==[-2.0]:
print('HANDLE WITH CARE -2Hz:', datapath)
data, pre_data, dt = import_data_eigen(datapath)
#hstack concatenate: 'glue' pre_data and data
dat = np.hstack((pre_data, data))
# data
nfft = 2**17
spec, freqs, times = specgram(dat[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
dbspec = 10.0 * np.log10(spec) # in dB
power = dbspec[:, 25]
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 - 50
lim1 = eodf4 + 50
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
cut_time_jar = 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_time_jar, jar4)
#plt.show()
b = []
for idx, i in enumerate(times):
if i > 0 and i < 10:
b.append(jar4[idx])
j = []
for idx, i in enumerate(times):
if i > 15 and i < 55:
j.append(jar4[idx])
r = np.median(j) - np.median(b)
print(r)
deltaf.append(delta_f[0])
response.append(r)
res_df = sorted(zip(deltaf,response))
np.save('res_df_%s_new' %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

55
eigenmannia_code/step_response_eigen.py
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@@ -19,7 +19,7 @@ from jar_functions import average
base_path = 'D:\\jar_project\\JAR\\eigen\\step'
identifier = ['step_2015eigen8',
'step_2015eigen15',
'step_2015eigen15\\+15Hz',
'step_2015eigen16',
'step_2015eigen17',
'step_2015eigen19']
@@ -46,17 +46,19 @@ for ID in identifier:
response = []
stim_ampl = []
for idx, dataset in enumerate(os.listdir(base_path)):
dataset = os.path.join(base_path, dataset, 'beats-eod.dat')
print(dataset)
data = os.path.join(base_path, dataset, 'beats-eod.dat')
if dataset == 'prerecordings':
continue
#input of the function
frequency, time, amplitude, eodf, deltaf, stimulusf, stimulusamplitude, duration, pause = parse_dataset(dataset)
frequency, time, amplitude, eodf, deltaf, stimulusf, stimulusamplitude, duration, pause = parse_dataset(data)
dm = np.mean(duration)
pm = np.mean(pause)
timespan = dm + pm
start = np.mean([t[0] for t in time])
stop = np.mean([t[-1] for t in time])
if len(frequency) == 5:
continue
print(dataset)
mf, tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
@@ -72,49 +74,26 @@ for ID in identifier:
for index, i in enumerate(ct):
if i > -45 and i < -5:
b.append(cf[index])
j = []
for indexx, h in enumerate(ct):
if h > 195 and h < 145:
if h < 195 and h > 145:
j.append(cf[indexx])
print(h)
print(indexx)
print(cf[indexx])
''' sounds good, doesnt work somehow: in norm devision by 0 (jar) or index doesnt fit
norm, base, jar = norm_function(frequency, time, onset_point=dm - dm,
offset_point=dm) # dm-dm funktioniert nur wenn onset = 0 sec
b = []
for index, i in enumerate(ct):
if i > -45 and i < -5:
b.append(cf[index])
j = []
for indexx, h in enumerate(ct):
if h > 195 and h < 145:
j.append(cf[indexx])
print(h)
print(indexx)
print(cf[indexx])
b = np.median(cf[(ct >= onset_end) & (ct < onset_point)])
j = np.median(cf[(ct >= offset_start) & (ct < offset_point)])
'''
r = np.median(j) - np.median(b)
response.append(r)
stim_ampl.append(stimulusamplitude)
res_ampl = sorted(zip(stim_ampl, response))
base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
stim_ampl.append(float(stimulusamplitude[0]))
res_ampl = sorted(zip(stim_ampl, response))
plt.plot(stim_ampl, response, 'o')
plt.xlabel('Stimulusamplitude')
plt.ylabel('absolute JAR magnitude')
plt.title('absolute JAR')
plt.savefig('relative JAR')
plt.legend(loc = 'lower right')
plt.xticks(np.arange(0.0, 0.3, step=0.05))
#plt.savefig('relative JAR')
#plt.legend(loc = 'lower right')
plt.show()
embed()
embed()
# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)