108 lines
3.1 KiB
Python
108 lines
3.1 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib as cm
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from matplotlib.colors import ListedColormap, LinearSegmentedColormap
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from matplotlib.mlab import specgram
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import os
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import glob
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import IPython
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import numpy as np
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#import DataLoader as dl
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from IPython import embed
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#from tqdm import tqdm
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from scipy.optimize import curve_fit
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from jar_functions import step_response
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from jar_functions import sin_response
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from jar_functions import parse_dataset
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from jar_functions import parse_infodataset
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from jar_functions import mean_traces
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from jar_functions import mean_noise_cut
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from jar_functions import norm_function
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from jar_functions import sort_values
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from jar_functions import average
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from jar_functions import import_data
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from jar_functions import import_amfreq
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\sin\\2015eigen8'
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time_all = []
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freq_all = []
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amfrequencies = []
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gains = []
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files = []
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for idx, dataset in enumerate(os.listdir(base_path)):
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if dataset == 'prerecordings':
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continue
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datapath = os.path.join(base_path, dataset, '%s.nix' % dataset)
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#print(datapath)
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data, pre_data, dt = import_data(datapath)
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nfft = 2**17
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for d, dat in enumerate(data):
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if len(dat) == 1:
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print(datapath)
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file_name = []
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ID = []
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# identifier for file_name
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infodatapath = os.path.join(base_path, dataset, 'info.dat')
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i = parse_infodataset(infodatapath)
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identifier = i[0]
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if not identifier[1:-2] in ID:
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ID.append(identifier[1:-1])
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# file_name
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file_name.append(ID[0])
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amfreq = import_amfreq(datapath)
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print(amfreq)
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file_name.append(str(amfreq))
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file_name.append(str(d))
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files.append(file_name)
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# spectogram
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if float(amfreq) < 0.01:
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spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.8)
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else:
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spec, freqs, times = specgram(dat, 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[:, 50]
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fish_p = power[(freqs > 400) & (freqs < 1000)]
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fish_f = freqs[(freqs > 400) & (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 = eodf4-10
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lim1 = eodf4+15
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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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jar = jar4 / 4
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jm = jar4 - np.mean(jar4) # data we take
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cut_times = 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_times, jar4)
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plt.show()
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# save data
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#np.save('%s time' % file_name, cut_times)
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#np.save('%s' % file_name, jar4)
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# save filenames for this fish
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#np.save('%s files' %ID[0], files)
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embed() |