22.10
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xaver
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apteronotus_code/FFR_model_specgram.py
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205
apteronotus_code/FFR_model_specgram.py
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@ -0,0 +1,205 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import pylab
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from IPython import embed
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from scipy.optimize import curve_fit
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from matplotlib.mlab import specgram
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import os
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from jar_functions import import_data
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from jar_functions import import_amfreq
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from jar_functions import sin_response
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plt.rcParams.update({'font.size': 20})
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base_path = 'D:\\jar_project\\JAR\\sin'
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identifier = ['2018lepto98']
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'''
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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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time = []
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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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if dataset == 'prerecordings':
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continue
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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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amfreq = import_amfreq(datapath)
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if amfreq == '0.005' or amfreq == '0.02' or amfreq == '0.05':
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print(amfreq)
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data, pre_data, dt = import_data(datapath)
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#hstack concatenate: 'glue' pre_data and data
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if len(data) == 2:
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trace0 = np.hstack((pre_data[0], data[0]))
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trace1 = np.hstack((pre_data[1], data[1]))
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else:
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trace0 = 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(trace0, 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 - 10
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lim1 = eodf4 + 25
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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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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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time.append(times)
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np.save('spec0.npy', specs[0])
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np.save('spec1.npy', specs[1])
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np.save('spec2.npy', specs[2])
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np.save('jar0.npy', jars[0])
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np.save('jar1.npy', jars[1])
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np.save('jar2.npy', jars[2])
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np.save('sub_times0.npy', sub_times[0])
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np.save('sub_times1.npy', sub_times[1])
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np.save('sub_times2.npy', sub_times[2])
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np.save('sub_lim0_0.npy', sub_lim0[0])
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np.save('sub_lim0_1.npy', sub_lim0[1])
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np.save('sub_lim0_2.npy', sub_lim0[2])
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np.save('sub_lim1_0.npy', sub_lim1[0])
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np.save('sub_lim1_1.npy', sub_lim1[1])
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np.save('sub_lim1_2.npy', sub_lim1[2])
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np.save('time0.npy', time[0])
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np.save('time1.npy', time[1])
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np.save('time2.npy', time[2])
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'''
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spec0 = np.load('spec0.npy')
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spec1 = np.load('spec1.npy')
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spec2 = np.load('spec2.npy')
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jar0 = np.load('jar0.npy')
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jar1 = np.load('jar1.npy')
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jar2 = np.load('jar2.npy')
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sub_times0 = np.load('sub_times0.npy')
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sub_times1 = np.load('sub_times1.npy')
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sub_times2 = np.load('sub_times2.npy')
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sub_lim0_0 = np.load('sub_lim0_0.npy')
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sub_lim0_1 = np.load('sub_lim0_1.npy')
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sub_lim0_2 = np.load('sub_lim0_2.npy')
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sub_lim1_0 = np.load('sub_lim1_0.npy')
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sub_lim1_1 = np.load('sub_lim1_1.npy')
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sub_lim1_2 = np.load('sub_lim1_2.npy')
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time0 = np.load('time0.npy')
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time1 = np.load('time1.npy')
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time2 = np.load('time2.npy')
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fig = plt.figure(figsize = (20,20))
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ax0 = fig.add_subplot(232)
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ax0.tick_params(width = 2, length = 5)
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ax0.imshow(spec0, cmap='jet', origin='lower', extent=(time0[0], time0[-1], sub_lim0_0, sub_lim1_0), aspect='auto', vmin=-80, vmax=-10)
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#ax0.plot(sub_times0, jar0, 'k', label = 'peak detection trace', lw = 2)
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ax0.set_xlim(time0[0],time0[-1])
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ax0.axes.xaxis.set_ticklabels([])
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ax0.axes.yaxis.set_ticklabels([])
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ax1 = fig.add_subplot(231)
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ax1.tick_params(width = 2, length = 5)
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ax1.imshow(spec1, cmap='jet', origin='lower', extent=(time1[0], time1[-1], sub_lim0_1, sub_lim1_1), aspect='auto', vmin=-80, vmax=-10)
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#ax1.plot(sub_times1, jar1, 'k', label = 'peak detection trace', lw = 2)
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ax1.set_xlim(time1[0],time1[-1])
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ax1.set_ylabel('frequency [Hz]')
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ax1.axes.xaxis.set_ticklabels([])
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plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax1.transAxes)
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ax2 = fig.add_subplot(233)
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ax2.tick_params(width = 2, length = 5)
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ax2.imshow(spec2, cmap='jet', origin='lower', extent=(time2[0], time2[-1], sub_lim0_2, sub_lim1_2), aspect='auto', vmin=-80, vmax=-10)
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#ax2.plot(sub_times2, jar2, 'k', label = 'peak detection trace', lw = 2)
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ax2.set_xlim(time2[0],time2[-1])
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ax2.axes.xaxis.set_ticklabels([])
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ax2.axes.yaxis.set_ticklabels([])
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# AM model: 0.05 Hz
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lower0 = 50
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upper0 = 250
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sample0 = 2000
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x0 = np.linspace(lower0, upper0, sample0)
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y0_0 = (sin_response(np.linspace(lower0, upper0, sample0), 0.05, np.pi/2, -0.35) - 0.5)
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y0_1 = (sin_response(np.linspace(lower0, upper0, sample0), 0.05, np.pi/2, 0.35) + 0.5)
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ax3 = fig.add_subplot(234)
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ax3.tick_params(width = 2, length = 5)
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plt.hlines(y = 0, xmin = 0, xmax = 50, color = 'red')
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plt.vlines(x = 50, ymin = -0.15, ymax = 0.15, color = 'red')
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ax3.plot(x0, y0_0, c = 'red')
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ax3.plot(x0, y0_1, c = 'red')
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ax3.fill_between(x0, y0_0, y0_1)
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ax3.set_ylabel('amplitude [mV/cm]')
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ax3.set_xlabel('time [s]')
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ax3.set_xlim(0,250)
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plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax3.transAxes)
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# AM model: 0.02 Hz
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lower1 = 50
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upper1 = 250
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sample1 = 2000
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x1 = np.linspace(lower1, upper1, sample1)
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y1_0 = (sin_response(np.linspace(lower1, upper1, sample1), 0.02, -np.pi/2 , -0.35) - 0.5)
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y1_1 = (sin_response(np.linspace(lower1, upper1, sample1), 0.02, -np.pi/2, 0.35) + 0.5)
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ax4 = fig.add_subplot(235)
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ax4.tick_params(width = 2, length = 5)
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plt.hlines(y = 0, xmin = 0, xmax = 50, color = 'red')
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plt.vlines(x = 50, ymin = -0.15, ymax = 0.15, color = 'red')
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ax4.plot(x1, y1_0, c = 'red')
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ax4.plot(x1, y1_1, c = 'red')
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ax4.fill_between(x1, y1_0, y1_1)
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ax4.set_xlabel('time [s]')
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ax4.set_xlim(0,250)
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ax4.axes.yaxis.set_ticklabels([])
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# AM model: 0.005 Hz
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lower2 = 50
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upper2 = 450
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sample2 = 2000
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x2 = np.linspace(lower2, upper2, sample2)
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y2_0 = (sin_response(np.linspace(lower2, upper2, sample2), 0.005, -np.pi , -0.35) - 0.5)
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y2_1 = (sin_response(np.linspace(lower2, upper2, sample2), 0.005, -np.pi, 0.35) + 0.5)
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ax5 = fig.add_subplot(236)
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ax5.tick_params(width = 2, length = 5)
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plt.hlines(y = 0, xmin = 0, xmax = 50, color = 'red')
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plt.vlines(x = 50, ymin = -0.15, ymax = 0.15, color = 'red')
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ax5.plot(x2, y2_0, c = 'red')
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ax5.plot(x2, y2_1, c = 'red')
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ax5.fill_between(x2, y2_0, y2_1)
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ax5.set_xlabel('time [s]')
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ax5.set_xlim(0,450)
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ax5.axes.yaxis.set_ticklabels([])
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plt.show()
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embed()
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@ -9,22 +9,24 @@ from jar_functions import mean_traces
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from jar_functions import mean_noise_cut_eigen
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from jar_functions import adjust_eodf
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base_path = 'D:\\jar_project\\JAR\\sin'
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\sin'
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identifier = ['2018lepto1',
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'2018lepto4',
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'2018lepto5',
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'2018lepto76',
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'2018lepto98',
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'2019lepto03',
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'2019lepto24',
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'2019lepto27',
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'2019lepto30',
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'2020lepto04',
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'2020lepto06',
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'2020lepto16',
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'2020lepto19',
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'2020lepto20'
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identifier = ['2015eigen8',
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'2015eigen16','2015eigen17', '2015eigen19', '2015eigen15'
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# '2018lepto1',
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# '2018lepto4',
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# '2018lepto5',
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# '2018lepto76',
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# '2018lepto98',
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# '2019lepto03',
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# '2019lepto24',
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# '2019lepto27',
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# '2019lepto30',
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# '2020lepto04',
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# '2020lepto06',
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# '2020lepto16',
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# '2020lepto19',
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# '2020lepto20'
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]
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eod = []
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for ID in identifier:
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@ -73,4 +75,8 @@ Q10_eod = []
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for et in eod_temp:
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Q10 = adjust_eodf(et[0], et[1])
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Q10_eod.append(Q10)
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print('MAXI KING', np.max(Q10_eod))
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print('MINI KING', np.min(Q10_eod))
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embed()
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136
apteronotus_code/figure_apteronotus_gain_plot.py
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136
apteronotus_code/figure_apteronotus_gain_plot.py
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import matplotlib.pyplot as plt
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import numpy as np
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import pylab
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from IPython import embed
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from scipy.optimize import curve_fit
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from matplotlib.mlab import specgram
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import os
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from jar_functions import gain_curve_fit
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identifier = ['2018lepto1',
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#'2018lepto4',
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'2018lepto5',
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'2018lepto76',
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'2018lepto98',
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#'2019lepto03',
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'2019lepto24',
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#'2019lepto27',
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#'2019lepto30',
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#'2020lepto04',
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'2020lepto06',
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#'2020lepto16',
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#'2020lepto19',
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#'2020lepto20'
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]
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amfs = []
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gains = []
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taus = []
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f_cs = []
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predicts = []
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for ID in identifier:
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predict = []
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print(ID)
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amf = np.load('amf_%s.npy' %ID)
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amfs.append(amf)
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gain = np.load('gain_%s.npy' %ID)
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gains.append(gain)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
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#print('tau:', sinv[0])
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taus.append(sinv[0])
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f_cutoff = abs(1 / (2*np.pi*sinv[0]))
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print('f_cutoff:', f_cutoff)
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f_cs.append(f_cutoff)
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# predict of gain
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for f in amf:
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G = np.max(gain) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
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predict.append(G)
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predicts.append(predict)
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sort = sorted(zip(f_cs, identifier))
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print(sort)
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# order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5
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fig = plt.figure(figsize=(8.27,11.69))
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ax0 = fig.add_subplot(321)
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ax0.set_xlim(0.0007, 1.5)
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ax0.plot(amfs[4], gains[4],'o' , label = 'gain')
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ax0.plot(amfs[4], predicts[4], label = 'fit')
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ax0.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax0.set_xscale('log')
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ax0.set_yscale('log')
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ax0.set_ylabel('gain [Hz/(mV/cm)]')
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#ax0.set_xlabel('envelope_frequency [Hz]')
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#ax0.set_title('gaincurve %s' %ID)
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ax1 = fig.add_subplot(322)
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ax1.set_xlim(0.0007, 1.5)
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ax1.get_shared_y_axes().join(ax0, ax1)
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ax1.axes.yaxis.set_ticklabels([])
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ax1.plot(amfs[5], gains[5],'o' , label = 'gain')
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ax1.plot(amfs[5], predicts[5], label = 'fit')
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ax1.axvline(x=f_cs[5], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax1.set_xscale('log')
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ax1.set_yscale('log')
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#ax1.set_ylabel('gain [Hz/(mV/cm)]')
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#ax1.set_xlabel('envelope_frequency [Hz]')
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#ax1.set_title('gaincurve %s' %ID)
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ax2 = fig.add_subplot(323)
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ax2.set_xlim(0.0007, 1.5)
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ax2.plot(amfs[4], gains[4],'o' , label = 'gain')
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ax2.plot(amfs[4], predicts[4], label = 'fit')
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ax2.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax2.set_xscale('log')
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ax2.set_yscale('log')
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ax2.set_ylabel('gain [Hz/(mV/cm)]')
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#ax2.set_xlabel('envelope_frequency [Hz]')
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#ax2.set_title('gaincurve %s' %ID)
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ax3 = fig.add_subplot(324)
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ax3.set_xlim(0.0007, 1.5)
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ax3.get_shared_y_axes().join(ax2, ax3)
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ax3.axes.yaxis.set_ticklabels([])
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ax3.plot(amfs[2], gains[2],'o' , label = 'gain')
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ax3.plot(amfs[2], predicts[2], label = 'fit')
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ax3.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax3.set_xscale('log')
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ax3.set_yscale('log')
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#ax3.set_ylabel('gain [Hz/(mV/cm)]')
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#ax3.set_xlabel('envelope_frequency [Hz]')
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#ax3.set_title('gaincurve %s' %ID)
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ax4 = fig.add_subplot(325)
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ax4.set_xlim(0.0007, 1.5)
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#ax4.get_shared_y_axes().join(ax0, ax4)
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ax4.plot(amfs[0], gains[0],'o' , label = 'gain')
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ax4.plot(amfs[0], predicts[0], label = 'fit')
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ax4.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax4.set_xscale('log')
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ax4.set_yscale('log')
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ax4.set_ylabel('gain [Hz/(mV/cm)]')
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#ax4.set_xlabel('envelope_frequency [Hz]')
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#ax4.set_title('gaincurve %s' %ID)
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ax5 = fig.add_subplot(326)
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ax5.set_xlim(0.0007, 1.5)
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ax5.axes.yaxis.set_ticklabels([])
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ax5.get_shared_y_axes().join(ax4, ax5)
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ax5.plot(amfs[1], gains[1],'o' , label = 'gain')
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ax5.plot(amfs[1], predicts[1], label = 'fit')
|
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ax5.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
|
||||
ax5.set_xscale('log')
|
||||
ax5.set_yscale('log')
|
||||
#ax5.set_ylabel('gain [Hz/(mV/cm)]')
|
||||
#ax5.set_xlabel('envelope_frequency [Hz]')
|
||||
#ax5.set_title('gaincurve %s' %ID)
|
||||
|
||||
#plt.legend(loc = 'lower left')
|
||||
plt.show()
|
||||
|
||||
|
||||
#np.save('f_c', f_c)
|
||||
#np.save('tau', tau)
|
||||
@ -7,7 +7,7 @@ from matplotlib.mlab import specgram
|
||||
import os
|
||||
from jar_functions import gain_curve_fit
|
||||
|
||||
identifier = ['2020lepto19']
|
||||
identifier = ['2018lepto98']
|
||||
|
||||
tau = []
|
||||
f_c = []
|
||||
|
||||
131
apteronotus_code/figure_apteronotus_jar_plot.py
Normal file
131
apteronotus_code/figure_apteronotus_jar_plot.py
Normal file
@ -0,0 +1,131 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pylab
|
||||
from IPython import embed
|
||||
from scipy.optimize import curve_fit
|
||||
from scipy.optimize import curve_fit
|
||||
from matplotlib.mlab import specgram
|
||||
import os
|
||||
|
||||
from jar_functions import import_data
|
||||
from jar_functions import import_amfreq
|
||||
from jar_functions import sin_response
|
||||
from jar_functions import mean_noise_cut
|
||||
from jar_functions import gain_curve_fit
|
||||
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
|
||||
def take_second(elem): # function for taking the names out of files
|
||||
return elem[1]
|
||||
|
||||
identifier = [#'2018lepto1',
|
||||
#'2018lepto4',
|
||||
#'2018lepto5',
|
||||
#'2018lepto76',
|
||||
'2018lepto98',
|
||||
'2019lepto03',
|
||||
#'2019lepto24',
|
||||
#'2019lepto27',
|
||||
#'2019lepto30',
|
||||
#'2020lepto04',
|
||||
#'2020lepto06',
|
||||
'2020lepto16',
|
||||
'2020lepto19',
|
||||
'2020lepto20'
|
||||
]
|
||||
for ident in identifier:
|
||||
|
||||
times = []
|
||||
jars = []
|
||||
jms = []
|
||||
amfreq = []
|
||||
|
||||
times1 = []
|
||||
jars1 = []
|
||||
jms1 = []
|
||||
amfreq1 = []
|
||||
|
||||
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
|
||||
|
||||
data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
|
||||
|
||||
for i, d in enumerate(data):
|
||||
dd = list(d)
|
||||
if dd[1] == '1' or dd[1] == '0.2' or dd[1] == '0.05' or dd[1] == '0.01' or dd[1] == '0.005' or dd[1] == '0.001':
|
||||
jar = np.load('%s.npy' %dd) # load data for every file name
|
||||
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
|
||||
|
||||
time = np.load('%s time.npy' %dd) # time file
|
||||
dt = time[1] - time[0]
|
||||
|
||||
n = int(1/float(d[1])/dt)
|
||||
cutf = mean_noise_cut(jm, n = n)
|
||||
cutt = time
|
||||
if dd[1] == '0.001':
|
||||
amfreq1.append(dd[1])
|
||||
jars1.append(jm - cutf)
|
||||
jms1.append(jm)
|
||||
times1.append(time)
|
||||
if dd[1] not in amfreq:
|
||||
print(dd)
|
||||
amfreq.append(dd[1])
|
||||
jars.append(jm - cutf)
|
||||
jms.append(jm)
|
||||
times.append(time)
|
||||
else:
|
||||
print('1:', dd)
|
||||
amfreq1.append(dd[1])
|
||||
jars1.append(jm - cutf)
|
||||
jms1.append(jm)
|
||||
times1.append(time)
|
||||
if len(jars) != 6:
|
||||
continue
|
||||
|
||||
fig = plt.figure(figsize=(8.27,11.69))
|
||||
fig.suptitle('%s' %ident)
|
||||
fig.text(0.06, 0.5, 'frequency [Hz]', ha='center', va='center', rotation='vertical')
|
||||
fig.text(0.5, 0.04, 'time [s]', ha='center', va='center')
|
||||
|
||||
ax0 = fig.add_subplot(611)
|
||||
ax0.plot(times[0], jms[0])
|
||||
#ax0.plot(times[0], jars[0])
|
||||
ax0.set_ylim(-12, 12)
|
||||
#plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax0.transAxes)
|
||||
|
||||
ax1 = fig.add_subplot(612)
|
||||
ax1.plot(times[1], jms[1])
|
||||
#ax1.plot(times[1], jars[1])
|
||||
ax1.set_ylim(-12, 12)
|
||||
#plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
|
||||
|
||||
ax2 = fig.add_subplot(613)
|
||||
ax2.plot(times[2], jms[2])
|
||||
#ax2.plot(times[2], jars[2])
|
||||
ax2.set_ylim(-12, 12)
|
||||
#plt.text(-0.1, 1.05, "C)", fontweight=550, transform=ax2.transAxes)
|
||||
|
||||
ax3 = fig.add_subplot(614)
|
||||
ax3.plot(times[3], jms[3])
|
||||
#ax3.plot(times[3], jars[3])
|
||||
ax3.set_ylim(-12, 12)
|
||||
#plt.text(-0.1, 1.05, "D)", fontweight=550, transform=ax3.transAxes)
|
||||
|
||||
ax4 = fig.add_subplot(615)
|
||||
ax4.plot(times[4], jms[4])
|
||||
#ax4.plot(times[4], jars[4])
|
||||
ax4.set_ylim(-12, 12)
|
||||
# plt.text(-0.1, 1.05, "E)", fontweight=550, transform=ax4.transAxes)
|
||||
|
||||
ax5 = fig.add_subplot(616)
|
||||
ax5.plot(times[5], jms[5])
|
||||
#ax5.plot(times[5], jars[5])
|
||||
ax5.set_ylim(-12, 12)
|
||||
#plt.text(-0.1, 1.05, "F)", fontweight=550, transform=ax5.transAxes)
|
||||
|
||||
plt.subplots_adjust(left=0.125,
|
||||
bottom=0.1,
|
||||
right=0.9,
|
||||
top=0.9,
|
||||
wspace=0.2,
|
||||
hspace=0.35)
|
||||
plt.show()
|
||||
@ -19,7 +19,7 @@ plt.rcParams.update({'font.size': 12})
|
||||
def take_second(elem): # function for taking the names out of files
|
||||
return elem[1]
|
||||
|
||||
identifier = ['2018lepto4']
|
||||
identifier = ['2020lepto16']
|
||||
for ident in identifier:
|
||||
|
||||
predict = []
|
||||
@ -37,16 +37,16 @@ for ident in identifier:
|
||||
currf = None
|
||||
idxlist = []
|
||||
|
||||
data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
|
||||
data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
|
||||
|
||||
for i, d in enumerate(data):
|
||||
dd = list(d)
|
||||
|
||||
jar = np.load('%s.npy' %dd) # load data for every file name
|
||||
jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
|
||||
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
|
||||
print(dd)
|
||||
|
||||
time = np.load('%s time.npy' %dd) # time file
|
||||
time = np.load('5Hz_%s time.npy' %dd) # time file
|
||||
dt = time[1] - time[0]
|
||||
|
||||
n = int(1/float(d[1])/dt)
|
||||
@ -125,7 +125,7 @@ for ident in identifier:
|
||||
idx_arr = (rootmeansquare_arr < threshold_arr) | (rootmeansquare_arr < np.mean(rootmeansquare_arr))
|
||||
|
||||
fig = plt.figure(figsize = (8,14))
|
||||
fig.suptitle('gaincurve and RMS 2018lepto4')
|
||||
fig.suptitle('gaincurve and RMS %s' %ident)
|
||||
ax0 = fig.add_subplot(2, 1, 1)
|
||||
ax0.plot(amfreq_arr, mgain_arr, 'o')
|
||||
ax0.set_yscale('log')
|
||||
|
||||
@ -3,7 +3,7 @@ import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from jar_functions import sin_response
|
||||
|
||||
plt.rcParams.update({'font.size': 12})
|
||||
plt.rcParams.update({'font.size': 27})
|
||||
|
||||
# AM model
|
||||
lower = 0
|
||||
@ -13,33 +13,32 @@ x = np.linspace(lower, upper, sample)
|
||||
y1 = (sin_response(np.linspace(lower, upper, sample), 0.02, -np.pi/2, -0.75) - 1)
|
||||
y2 = (sin_response(np.linspace(lower, upper, sample), 0.02, -np.pi/2, 0.75) + 1)
|
||||
|
||||
fig = plt.figure(figsize = (12,6))
|
||||
ax = fig.add_subplot(121)
|
||||
ax.plot(x, y1, c = 'red')
|
||||
ax.plot(x, y2, c = 'red')
|
||||
ax.fill_between(x, y1, y2)
|
||||
|
||||
ax.set_xlabel('time[s]')
|
||||
ax.set_ylabel('amplitude')
|
||||
ax.set_xlim(0,200)
|
||||
ax.axes.yaxis.set_ticks([])
|
||||
plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax.transAxes)
|
||||
fig = plt.figure(figsize = (6,6))
|
||||
# ax = fig.add_subplot(121)
|
||||
# ax.plot(x, y1, c = 'red')
|
||||
# ax.plot(x, y2, c = 'red')
|
||||
# ax.fill_between(x, y1, y2)
|
||||
#
|
||||
# ax.set_xlabel('time[s]')
|
||||
# ax.set_ylabel('amplitude')
|
||||
# ax.set_xlim(0,200)
|
||||
# ax.axes.yaxis.set_ticks([])
|
||||
# plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax.transAxes)
|
||||
|
||||
# carrier
|
||||
lower = 0
|
||||
upper = 10
|
||||
sample = 1000
|
||||
upper = 100
|
||||
sample = 10000
|
||||
x = np.linspace(lower, upper, sample)
|
||||
y1 = (sin_response(np.linspace(lower, upper, sample), 800, np.pi, -0.75) - 1)
|
||||
|
||||
ax1 = fig.add_subplot(122)
|
||||
ax1.plot(x, y1)
|
||||
ax1.axhline(y = -0.25, c = 'red', lw = 2)
|
||||
ax1.axhline(y = -1.75, c = 'red', lw = 2)
|
||||
ax1 = fig.add_subplot(111)
|
||||
ax1.plot(x, y1, lw = 4)
|
||||
ax1.axhline(y = -0.25, c = 'red', lw = 4)
|
||||
ax1.axhline(y = -1.75, c = 'red', lw = 4)
|
||||
|
||||
ax1.set_xlabel('time[ms]')
|
||||
ax1.set_xlim(0,10)
|
||||
ax1.set_xlim(0,100)
|
||||
ax1.axes.get_yaxis().set_visible(False)
|
||||
plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
|
||||
|
||||
plt.xticks((0,50,100), [0,5,10])
|
||||
plt.show()
|
||||
@ -58,8 +58,8 @@ for ID in identifier:
|
||||
eodf = fish_f[index]
|
||||
eodf4 = eodf * 4
|
||||
|
||||
lim0 = eodf4 - 40
|
||||
lim1 = eodf4 + 40
|
||||
lim0 = eodf4 - 42
|
||||
lim1 = eodf4 + 42
|
||||
|
||||
df = freqs[1] - freqs[0]
|
||||
ix0 = int(np.floor(lim0 / df)) # back to index
|
||||
@ -102,36 +102,38 @@ for ID in identifier:
|
||||
fig = plt.figure(figsize = (20,20))
|
||||
ax0 = fig.add_subplot(221)
|
||||
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)
|
||||
ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
|
||||
#ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax0.set_xlim(times[0],times[-1])
|
||||
ax0.set_ylabel('frequency [Hz]')
|
||||
ax0.axes.xaxis.set_ticklabels([])
|
||||
ax0.set_title('∆F -2 Hz')
|
||||
|
||||
ax1 = fig.add_subplot(222)
|
||||
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)
|
||||
ax1.plot(sub_times[1], jars[1], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax1.imshow(specs[2], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[2], sub_lim1[2]), aspect='auto', vmin=-80, vmax=-10)
|
||||
#ax1.plot(sub_times[2], jars[2], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax1.set_xlim(times[0],times[-1])
|
||||
ax1.axes.xaxis.set_ticklabels([])
|
||||
ax1.axes.yaxis.set_ticklabels([])
|
||||
ax1.set_title('∆F -10 Hz')
|
||||
#ax1.axes.yaxis.set_ticklabels([])
|
||||
ax1.set_title('∆F 2 Hz')
|
||||
ax1.get_shared_y_axes().join(ax0, ax1)
|
||||
|
||||
ax2 = fig.add_subplot(223)
|
||||
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)
|
||||
ax2.plot(sub_times[2], jars[2], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax2.imshow(specs[1], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[1], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
|
||||
#ax2.plot(sub_times[1], jars[1], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax2.set_xlim(times[0],times[-1])
|
||||
ax2.set_ylabel('frequency [Hz]')
|
||||
ax2.set_xlabel('time [s]')
|
||||
ax2.set_title('∆F 2 Hz')
|
||||
ax2.set_title('∆F -10 Hz')
|
||||
|
||||
ax3 = fig.add_subplot(224)
|
||||
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)
|
||||
ax3.plot(sub_times[3], jars[3], 'k', label = 'peak detection trace', lw = 2)
|
||||
#ax3.plot(sub_times[3], jars[3], 'k', label = 'peak detection trace', lw = 2)
|
||||
ax3.set_xlim(times[0],times[-1])
|
||||
ax3.set_xlabel('time [s]')
|
||||
ax3.axes.yaxis.set_ticklabels([])
|
||||
#ax3.axes.yaxis.set_ticklabels([])
|
||||
ax3.set_title('∆F 10 Hz')
|
||||
|
||||
plt.subplots(sharex = True, sharey = True)
|
||||
plt.show()
|
||||
|
||||
embed()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user