86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import os
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import nix_helpers as nh
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from IPython import embed
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from matplotlib.mlab import specgram
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#from tqdm import tqdm
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from jar_functions import parse_stimuli_dat
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from jar_functions import norm_function_eigen
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from jar_functions import mean_noise_cut_eigen
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from jar_functions import get_time_zeros
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from jar_functions import import_data_eigen
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from scipy.signal import savgol_filter
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
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identifier = ['2013eigen13','2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
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response = []
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deltaf = []
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for ID in identifier:
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for dataset in os.listdir(os.path.join(base_path, ID)):
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datapath = os.path.join(base_path, ID, dataset, '%s.nix' % dataset)
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print(datapath)
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stimuli_dat = os.path.join(base_path, ID, dataset, 'manualjar-eod.dat')
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#print(stimuli_dat)
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delta_f, duration = parse_stimuli_dat(stimuli_dat)
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dur = int(duration[0][0:2])
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print(delta_f)
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if delta_f ==[-2.0]:
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print('HANDLE WITH CARE -2Hz:', datapath)
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data, pre_data, dt = import_data_eigen(datapath)
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#hstack concatenate: 'glue' pre_data and data
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dat = np.hstack((pre_data, data))
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# data
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nfft = 2**17
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spec, freqs, times = specgram(dat[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
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dbspec = 10.0 * np.log10(spec) # in dB
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power = dbspec[:, 25]
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fish_p = power[(freqs > 200) & (freqs < 1000)]
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fish_f = freqs[(freqs > 200) & (freqs < 1000)]
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index = np.argmax(fish_p)
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eodf = fish_f[index]
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eodf4 = eodf * 4
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lim0 = eodf4 - 50
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lim1 = eodf4 + 50
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df = freqs[1] - freqs[0]
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ix0 = int(np.floor(lim0/df)) # back to index
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ix1 = int(np.ceil(lim1/df)) # back to index
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spec4= dbspec[ix0:ix1, :]
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freq4 = freqs[ix0:ix1]
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jar4 = freq4[np.argmax(spec4, axis=0)] # all freqs at max specs over axis 0
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cut_time_jar = times[:len(jar4)]
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#plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
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#plt.plot(cut_time_jar, jar4)
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#plt.show()
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b = []
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for idx, i in enumerate(times):
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if i > 0 and i < 10:
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b.append(jar4[idx])
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j = []
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for idx, i in enumerate(times):
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if i > 15 and i < 55:
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j.append(jar4[idx])
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r = np.median(j) - np.median(b)
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print(r)
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deltaf.append(delta_f[0])
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response.append(r)
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res_df = sorted(zip(deltaf,response))
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np.save('res_df_%s_new' %ID, res_df)
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# problem: rohdaten(data, pre_data) lassen sich auf grund ihrer 1D-array struktur nicht savgol filtern
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# diese bekomm ich nur über specgram in form von freq / time auftragen, was nicht mehr savgol gefiltert werden kann
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# jedoch könnte ich trotzdem einfach aus jar4 response herauslesen wobei dies dann weniger gefiltert wäre |