127 lines
4.7 KiB
Python
127 lines
4.7 KiB
Python
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')
|
|
|
|
df, duration = parse_stimuli_dat(stimuli_dat)
|
|
dur = int(duration[0][0:2])
|
|
print(df)
|
|
|
|
# base with nh.read_eod
|
|
time, eod = nh.read_eod(datapath, duration = 2000) # anstatt dem import data mit tag manual jar - dann sollte onset wirklich bei 10 sec sein
|
|
dt = time[1] - time[0]
|
|
nfft = 2 **17
|
|
spec_0, freqs_0, times_0 = specgram(eod, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
|
|
dbspec_0 = 10.0 * np.log10(spec_0) # in dB
|
|
|
|
plt.imshow(dbspec_0, cmap='jet', origin='lower', extent=(times_0[0], times_0[-1], 0, 1500), aspect='auto',
|
|
vmin=-80, vmax=-10)
|
|
plt.show()
|
|
|
|
zeropoints = get_time_zeros(time, eod, threshold=np.max(eod) * 0.1)
|
|
|
|
|
|
frequencies = 1 / np.diff(zeropoints)
|
|
|
|
window = np.ones(101) / 101
|
|
freq = np.convolve(frequencies, window, mode='same')
|
|
|
|
data, pre_data, dt = import_data_eigen(datapath)
|
|
|
|
# data
|
|
nfft = 2**17
|
|
spec_0, freqs_0, times_0 = specgram(data[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
|
|
dbspec_0 = 10.0 * np.log10(spec_0) # in dB
|
|
power_0 = dbspec_0[:, 25]
|
|
|
|
fish_p_0 = power_0[(freqs_0 > 200) & (freqs_0 < 1000)]
|
|
fish_f_0 = freqs_0[(freqs_0 > 200) & (freqs_0 < 1000)]
|
|
|
|
index_0 = np.argmax(fish_p_0)
|
|
eodf_0 = fish_f_0[index_0]
|
|
eodf4_0 = eodf_0 * 4
|
|
|
|
lim0_0 = eodf4_0-20
|
|
lim1_0 = eodf4_0+20
|
|
|
|
df_0= freqs_0[1] - freqs_0[0]
|
|
ix0_0 = int(np.floor(lim0_0/df_0)) # back to index
|
|
ix1_0 = int(np.ceil(lim1_0/df_0)) # back to index
|
|
spec4_0= dbspec_0[ix0_0:ix1_0, :]
|
|
freq4_0 = freqs_0[ix0_0:ix1_0]
|
|
jar4 = freq4_0[np.argmax(spec4_0, axis=0)] # all freqs at max specs over axis 0
|
|
jm = jar4 - np.mean(jar4) # data we take
|
|
cut_time_jar = times_0[:len(jar4)]
|
|
|
|
#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80,
|
|
#vmax=-10)
|
|
#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)
|
|
plt.plot(cut_time_jar, jm)
|
|
#plt.ylim(lim0_0, lim1_0)
|
|
|
|
# pre_data
|
|
nfft = 2 ** 17
|
|
spec_1, freqs_1, times_1 = specgram(pre_data[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
|
|
dbspec_1 = 10.0 * np.log10(spec_1) # in dB
|
|
power_1 = dbspec_1[:, 25]
|
|
|
|
fish_p_1 = power_1[(freqs_1 > 200) & (freqs_1 < 500)]
|
|
fish_f_1 = freqs_1[(freqs_1 > 200) & (freqs_1 < 500)]
|
|
|
|
index1 = np.argmax(fish_p_1)
|
|
eodf_1 = fish_f_1[index1]
|
|
eodf4_1 = eodf_1 * 4
|
|
|
|
lim0_1 = eodf4_1 - 20
|
|
lim1_1 = eodf4_1 + 20
|
|
|
|
df_1 = freqs_1[1] - freqs_1[0]
|
|
ix0_1 = int(np.floor(lim0_1 / df_1)) # back to index
|
|
ix1_1 = int(np.ceil(lim1_1 / df_1)) # back to index
|
|
spec4_1 = dbspec_1[ix0_1:ix1_1, :]
|
|
freq4_1 = freqs_1[ix0_1:ix1_1]
|
|
base4 = freq4_1[np.argmax(spec4_1, axis=0)] # all freqs at max specs over axis 0
|
|
bm = base4 - np.mean(base4) # data we take
|
|
cut_time_base = times_1[:len(base4)] - times_1[-1]
|
|
|
|
plt.plot(cut_time_base, bm)
|
|
|
|
j = []
|
|
for idx, i in enumerate(times_0):
|
|
if i > 45 and i < 55:
|
|
j.append(jm[idx])
|
|
plt.plot(j)
|
|
plt.show()
|
|
r = np.median(j) - np.median(bm)
|
|
|
|
deltaf.append(df[0])
|
|
response.append(r)
|
|
embed()
|
|
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 |