139 lines
5.0 KiB
Python
139 lines
5.0 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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plt.rcParams.update({'font.size': 18})
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
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#2015eigen8 no nix files
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identifier = [#'2013eigen13',
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'2015eigen16'] #,'2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
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response = []
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deltaf = []
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specs = []
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jars = []
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sub_times = []
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sub_lim0 = []
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sub_lim1 = []
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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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if delta_f == [-2.0] or delta_f == [2.0] or delta_f == [-10.0] or delta_f == [10.0]:
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print(delta_f)
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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 - 40
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lim1 = eodf4 + 40
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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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ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
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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('response:', r)
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deltaf.append(delta_f[0])
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response.append(r)
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specs.append(spec4)
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jars.append(jar4)
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sub_times.append(cut_time_jar)
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sub_lim0.append(lim0)
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sub_lim1.append(lim1)
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if len(specs) == 4:
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break
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# plt.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
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# plt.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
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# plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
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# plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
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# plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
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# plt.xlim(times[0],times[-1])
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fig = plt.figure(figsize = (20,20))
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ax0 = fig.add_subplot(221)
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ax0.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[0]), aspect='auto', vmin=-80, vmax=-10)
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ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
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ax0.set_xlim(times[0],times[-1])
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ax0.set_ylabel('frequency [Hz]')
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ax0.axes.xaxis.set_ticklabels([])
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ax0.set_title('∆F -2 Hz')
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ax1 = fig.add_subplot(222)
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ax1.imshow(specs[1], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[1], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
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ax1.plot(sub_times[1], jars[1], 'k', label = 'peak detection trace', lw = 2)
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ax1.set_xlim(times[0],times[-1])
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ax1.axes.xaxis.set_ticklabels([])
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ax1.axes.yaxis.set_ticklabels([])
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ax1.set_title('∆F -10 Hz')
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ax2 = fig.add_subplot(223)
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ax2.imshow(specs[2], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[2], sub_lim1[2]), aspect='auto', vmin=-80, vmax=-10)
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ax2.plot(sub_times[2], jars[2], 'k', label = 'peak detection trace', lw = 2)
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ax2.set_xlim(times[0],times[-1])
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ax2.set_ylabel('frequency [Hz]')
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ax2.set_xlabel('time [s]')
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ax2.set_title('∆F 2 Hz')
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ax3 = fig.add_subplot(224)
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ax3.imshow(specs[3], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[3], sub_lim1[3]), aspect='auto', vmin=-80, vmax=-10)
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ax3.plot(sub_times[3], jars[3], 'k', label = 'peak detection trace', lw = 2)
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ax3.set_xlim(times[0],times[-1])
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ax3.set_xlabel('time [s]')
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ax3.axes.yaxis.set_ticklabels([])
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ax3.set_title('∆F 10 Hz')
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plt.show()
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embed()
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