14.11
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xaver
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ebf365dbe0
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20
apteronotus_code/avg_test.py
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20
apteronotus_code/avg_test.py
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@ -0,0 +1,20 @@
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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 jar_functions import gain_curve_fit
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from jar_functions import avgNestedLists
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import matplotlib as mpl
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from matplotlib import cm
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ab = []
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a = [1, 1, None, 1]
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b = [2, 2, 2, 2]
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ab.append(a)
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ab.append(b)
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print(ab)
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print(np.mean(ab, axis = 0))
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#av = avgNestedLists(np.array(ab))
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#print(av)
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@ -30,6 +30,7 @@ gains = []
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taus = []
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f_cs = []
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predicts = []
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maxgains = []
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for ID in identifier:
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predict = []
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@ -47,10 +48,18 @@ for ID in identifier:
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f_cs.append(f_cutoff)
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# predict of gain
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print('max gain:', np.max(gain))
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maxgains.append(np.max(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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print('absolute max gain:', np.max(maxgains))
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print('absolute min gain:', np.min(maxgains))
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print('absolute max f_c:', np.max(f_cs))
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print('absolute min f_c:', np.min(f_cs))
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sort = sorted(zip(f_cs, identifier))
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print(sort)
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114
apteronotus_code/figure_apteronotus_gain_plot_5Hz.py
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apteronotus_code/figure_apteronotus_gain_plot_5Hz.py
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@ -0,0 +1,114 @@
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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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plt.rcParams.update({'font.size': 12})
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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('5Hz_amf_%s.npy' %ID)
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amfs.append(amf)
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gain = np.load('5Hz_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 plotting: 2018lepto98, 2020lepto16, 2020lepto19, 2020lepto19, 2020lepto20
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# order of f_c: 2020lepto20, 2020lepto16, 2018lepto98, 2020lepto19
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fig = plt.figure(figsize=(8.27,11.69))
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ax0 = fig.add_subplot(221)
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fig.text(0.05, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
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fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
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ax0.set_xlim(0.0007, 1.5)
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ax0.set_ylim(0.001, 10)
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ax0.plot(amfs[0], gains[0],'o' , label = 'gain')
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ax0.plot(amfs[0], predicts[0], label = 'fit')
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ax0.axvline(x=f_cs[0], 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.axes.xaxis.set_ticklabels([])
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print('max[0]:', np.max(gain[0]))
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ax1 = fig.add_subplot(222)
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ax1.set_xlim(0.0007, 1.5)
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ax1.set_ylim(0.001, 10)
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ax1.plot(amfs[1], gains[1],'o' , label = 'gain')
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ax1.plot(amfs[1], predicts[1], label = 'fit')
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ax1.axvline(x=f_cs[1], 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.axes.yaxis.set_ticklabels([])
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ax1.axes.xaxis.set_ticklabels([])
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print('max[1]:', np.max(gain[1]))
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ax2 = fig.add_subplot(223)
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ax2.set_xlim(0.0007, 1.5)
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ax2.set_ylim(0.001, 10)
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ax2.plot(amfs[0], gains[0],'o' , label = 'gain')
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ax2.plot(amfs[0], predicts[0], label = 'fit')
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ax2.axvline(x=f_cs[0], 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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print('max[2]:', np.max(gain[2]))
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ax3 = fig.add_subplot(224)
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ax3.set_xlim(0.0007, 1.5)
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ax3.set_ylim(0.001, 10)
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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.axes.yaxis.set_ticklabels([])
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print('max[3]:', np.max(gain[3]))
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#plt.legend(loc = 'lower left')
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plt.show()
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#np.save('f_c', f_c)
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#np.save('tau', tau)
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@ -7,7 +7,9 @@ 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 = ['2018lepto98']
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plt.rcParams.update({'font.size': 12})
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identifier = ['2018lepto4']
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tau = []
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f_c = []
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@ -32,7 +34,7 @@ for ID in identifier:
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predict.append(G)
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print(np.max(gain))
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fig = plt.figure()
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fig = plt.figure(figsize=(8.27, 11.69/2))
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ax = fig.add_subplot()
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ax.plot(amf, gain,'o' , label = 'gain')
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ax.plot(amf, predict, label = 'fit')
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@ -40,9 +42,9 @@ for ID in identifier:
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_ylabel('gain [Hz/(mV/cm)]')
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ax.set_xlabel('envelope_frequency [Hz]')
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ax.set_xlabel('envelope frequency [Hz]')
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ax.set_title('gaincurve %s' %ID)
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plt.legend(loc = 'lower left')
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#plt.legend(loc = 'lower left')
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plt.show()
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@ -82,7 +82,7 @@ for ident in identifier:
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# plt.show()
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# filter by running average
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fig = plt.figure(figsize = (8,14))
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fig = plt.figure(figsize = (8.27,11.69))
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fig.suptitle('JAR trace spectogram 2018lepto98:\n subtraction of mean and running average')
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ax = fig.add_subplot(211)
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ax.plot(time, jm, color = 'C0', label = '1)')
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@ -17,22 +17,37 @@ from jar_functions import gain_curve_fit
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def take_second(elem): # function for taking the names out of files
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return elem[1]
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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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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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identifier_5Hz = [#'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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#'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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for ident in identifier:
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times = []
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@ -47,15 +62,15 @@ for ident in identifier:
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amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
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data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
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for i, d in enumerate(data):
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dd = list(d)
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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':
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jar = np.load('%s.npy' %dd) # load data for every file name
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jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
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jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
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time = np.load('%s time.npy' %dd) # time file
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time = np.load('5Hz_%s time.npy' %dd) # time file
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dt = time[1] - time[0]
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n = int(1/float(d[1])/dt)
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@ -73,7 +88,7 @@ for ident in identifier:
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jms.append(jm)
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times.append(time)
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else:
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print('1:', dd)
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#print('1:', dd)
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amfreq1.append(dd[1])
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jars1.append(jm - cutf)
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jms1.append(jm)
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@ -81,51 +96,184 @@ for ident in identifier:
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if len(jars) != 6:
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continue
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sample = 100000
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fig = plt.figure(figsize=(8.27,11.69))
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fig.suptitle('%s' %ident)
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fig.text(0.06, 0.5, 'frequency [Hz]', ha='center', va='center', rotation='vertical')
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fig.text(0.06, 0.5, 'fish frequency [Hz]', ha='center', va='center', rotation='vertical', color = 'C0')
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fig.text(0.97, 0.5, 'stimulus amplitude [mV/cm]', ha='center', va='center', rotation='vertical', color = 'red')
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fig.text(0.5, 0.04, 'time [s]', ha='center', va='center')
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ax0 = fig.add_subplot(611)
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ax0.plot(times[0], jms[0])
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#ax0.plot(times[0], jars[0])
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print('absolute frequency shift 0.001Hz:', np.max(jars[0]) - np.min(jars[0]))
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ax0.plot(times[0], jars[0], zorder = 20)
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#ax0.set_zorder(1)
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ax0.set_ylim(-12, 12)
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#plt.text(-0.1, 1.05, "A)", fontweight=550, transform=ax0.transAxes)
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lower0 = 0
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upper0 = 2000
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x0 = np.linspace(lower0, upper0, sample)
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y0 = (sin_response(np.linspace(lower0, upper0, sample), 0.001, -np.pi/2, .35) + 0.5)
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ax0_0 = ax0.twinx()
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ax0_0.set_ylim(-0.2, 1.2)
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ax0_0.plot(x0, y0, color = 'red', zorder = 1, alpha = 0.5)
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#ax0_0.set_zorder(2)
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ax1 = fig.add_subplot(612)
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ax1.plot(times[1], jms[1])
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#ax1.plot(times[1], jars[1])
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print('absolute frequency shift 0.005 Hz:', np.max(jars[1]) - np.min(jars[1]))
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ax1.plot(times[1], jars[1])
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ax1.set_ylim(-12, 12)
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#plt.text(-0.1, 1.05, "B)", fontweight=550, transform=ax1.transAxes)
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lower1 = 0
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upper1 = 400
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x1 = np.linspace(lower1, upper1, sample)
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y1 = (sin_response(np.linspace(lower1, upper1, sample), 0.005, -np.pi / 2, .35) + 0.5)
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ax1_0 = ax1.twinx()
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ax1_0.set_ylim(-0.2, 1.2)
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ax1_0.plot(x1, y1, color='red', alpha = 0.5)
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ax2 = fig.add_subplot(613)
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ax2.plot(times[2], jms[2])
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#ax2.plot(times[2], jars[2])
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print('absolute frequency shift 0.01 Hz:', np.max(jars[2]) - np.min(jars[2]))
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ax2.plot(times[2], jars[2])
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ax2.set_ylim(-12, 12)
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#plt.text(-0.1, 1.05, "C)", fontweight=550, transform=ax2.transAxes)
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lower2 = 0
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upper2 = 400
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x2 = np.linspace(lower2, upper2, sample)
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y2 = (sin_response(np.linspace(lower2, upper2, sample), 0.01, np.pi / 2, -0.35) + 0.5)
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ax2_0 = ax2.twinx()
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ax2_0.set_ylim(-0.2, 1.2)
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ax2_0.plot(x2, y2, color='red', alpha = 0.5)
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ax3 = fig.add_subplot(614)
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ax3.plot(times[3], jms[3])
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#ax3.plot(times[3], jars[3])
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print('absolute frequency shift 0.02 Hz:', np.max(jars[3]) - np.min(jars[3]))
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ax3.plot(times[3], jars[3])
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ax3.set_ylim(-12, 12)
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#plt.text(-0.1, 1.05, "D)", fontweight=550, transform=ax3.transAxes)
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lower3 = 0
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upper3 = 200
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x3 = np.linspace(lower3, upper3, sample)
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y3 = (sin_response(np.linspace(lower3, upper3, sample), 0.05, np.pi / 2, -0.35) + 0.5)
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ax3_0 = ax3.twinx()
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ax3_0.set_ylim(-0.2, 1.2)
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ax3_0.plot(x3, y3, color='red', alpha = 0.5)
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ax4 = fig.add_subplot(615)
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ax4.plot(times[4], jms[4])
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#ax4.plot(times[4], jars[4])
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print('absolute frequency shift 0.5 Hz:', np.max(jars[4]) - np.min(jars[4]))
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ax4.plot(times[4], jars[4])
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ax4.set_ylim(-12, 12)
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# plt.text(-0.1, 1.05, "E)", fontweight=550, transform=ax4.transAxes)
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lower4 = 0
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upper4 = 200
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x4 = np.linspace(lower4, upper4, sample)
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y4 = (sin_response(np.linspace(lower4, upper4, sample), 0.2, np.pi / 2, -0.35) + 0.5)
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ax4_0 = ax4.twinx()
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ax4_0.set_ylim(-0.2, 1.2)
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ax4_0.plot(x4, y4, color='red', alpha = 0.5)
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ax5 = fig.add_subplot(616)
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ax5.plot(times[5], jms[5])
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#ax5.plot(times[5], jars[5])
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print('absolute frequency shift 1 Hz:', np.max(jars[5]) - np.min(jars[5]))
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ax5.plot(times[5], jars[5])
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ax5.set_ylim(-12, 12)
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#plt.text(-0.1, 1.05, "F)", fontweight=550, transform=ax5.transAxes)
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lower5 = 0
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upper5 = 200
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x5 = np.linspace(lower5, upper5, sample)
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y5 = (sin_response(np.linspace(lower5, upper5, sample), 1, np.pi / 2, -0.35) + 0.5)
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ax5_0 = ax5.twinx()
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ax5_0.plot(x5, y5, color='red', lw = 0.5, alpha = 0.5)
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ax5_0.set_ylim(-0.2, 1.2)
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'''
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for ident in identifier_5Hz:
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times = []
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jars = []
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jms = []
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amfreq = []
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||||
times1 = []
|
||||
jars1 = []
|
||||
jms1 = []
|
||||
amfreq1 = []
|
||||
|
||||
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
|
||||
print('5Hz')
|
||||
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)
|
||||
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('5Hz_%s.npy' % dd) # load data for every file name
|
||||
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
|
||||
|
||||
time = np.load('5Hz_%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
|
||||
|
||||
ax6 = fig.add_subplot(622)
|
||||
ax6.plot(times1[0], jars1[0])
|
||||
print('5Hz_absolute frequency shift 0.001Hz:', np.max(jms1[0]) - np.min(jms1[0]))
|
||||
ax6.set_ylim(-12, 12)
|
||||
ax6.axes.set_yticklabels([])
|
||||
plt.text(-0.05, 1.15, "B)", fontweight=550, transform=ax6.transAxes)
|
||||
|
||||
ax7 = fig.add_subplot(624)
|
||||
ax7.plot(times1[1], jars1[1])
|
||||
print('5Hz_absolute frequency shift 0.005Hz:', np.max(jms1[1]) - np.min(jms1[1]))
|
||||
ax7.set_ylim(-12, 12)
|
||||
ax7.axes.set_yticklabels([])
|
||||
|
||||
ax8 = fig.add_subplot(626)
|
||||
ax8.plot(times1[2], jars1[2])
|
||||
print('5Hz_absolute frequency shift 0.05Hz:', np.max(jms1[2]) - np.min(jms1[2]))
|
||||
ax8.set_ylim(-12, 12)
|
||||
ax8.axes.set_yticklabels([])
|
||||
|
||||
ax9 = fig.add_subplot(6,2,8)
|
||||
ax9.plot(times1[3], jars1[3])
|
||||
print('5Hz_absolute frequency shift 0.02Hz:', np.max(jms1[3]) - np.min(jms1[3]))
|
||||
ax9.set_ylim(-12, 12)
|
||||
ax9.axes.set_yticklabels([])
|
||||
|
||||
ax10 = fig.add_subplot(6,2,10)
|
||||
ax10.plot(times1[4], jars1[4])
|
||||
print('5Hz_absolute frequency shift 0.5Hz:', np.max(jms1[4]) - np.min(jms1[4]))
|
||||
ax10.set_ylim(-12, 12)
|
||||
ax10.axes.set_yticklabels([])
|
||||
|
||||
ax11 = fig.add_subplot(6,2,12)
|
||||
ax11.plot(times1[5], jars1[5])
|
||||
print('5Hz_absolute frequency shift 1Hz:', np.max(jms1[5]) - np.min(jms1[5]))
|
||||
ax11.set_ylim(-12, 12)
|
||||
ax11.axes.set_yticklabels([])
|
||||
'''
|
||||
plt.subplots_adjust(left=0.125,
|
||||
bottom=0.1,
|
||||
right=0.9,
|
||||
top=0.9,
|
||||
wspace=0.2,
|
||||
wspace=0.1,
|
||||
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 = ['2020lepto19']
|
||||
identifier = ['2018lepto5']
|
||||
for ident in identifier:
|
||||
|
||||
predict = []
|
||||
@ -124,10 +124,10 @@ for ident in identifier:
|
||||
# condition needed to be fulfilled: RMS < threshold or RMS < mean(RMS)
|
||||
idx_arr = (rootmeansquare_arr < threshold_arr) | (rootmeansquare_arr < np.mean(rootmeansquare_arr))
|
||||
|
||||
fig = plt.figure(figsize = (8,14))
|
||||
fig = plt.figure(figsize = (8.27, 11.69))
|
||||
fig.suptitle('gaincurve and RMS %s' %ident)
|
||||
ax0 = fig.add_subplot(2, 1, 1)
|
||||
ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
|
||||
ax0.plot(amfreq_arr, mgain_arr, 'o')
|
||||
ax0.set_yscale('log')
|
||||
ax0.set_xscale('log')
|
||||
ax0.set_ylabel('gain [Hz/(mV/cm)]')
|
||||
|
||||
@ -7,125 +7,143 @@ from jar_functions import gain_curve_fit
|
||||
from jar_functions import avgNestedLists
|
||||
import matplotlib as mpl
|
||||
from matplotlib import cm
|
||||
import math
|
||||
|
||||
#plt.rcParams.update({'font.size': 18})
|
||||
#plt.rcParams.update({'font.size': 16})
|
||||
|
||||
identifier_uniform = ['2018lepto1',
|
||||
identifier = [#'2018lepto1',
|
||||
#'2018lepto4',
|
||||
'2018lepto5',
|
||||
'2018lepto76',
|
||||
#'2018lepto5',
|
||||
#'2018lepto76',
|
||||
'2018lepto98',
|
||||
#'2019lepto03',
|
||||
'2019lepto24',
|
||||
#'2019lepto24',
|
||||
#'2019lepto27',
|
||||
#'2019lepto30',
|
||||
#'2020lepto04',
|
||||
'2020lepto06',
|
||||
#'2020lepto16',
|
||||
#'2020lepto19',
|
||||
#'2020lepto20'
|
||||
]
|
||||
identifier = ['2018lepto1',
|
||||
'2018lepto4',
|
||||
'2018lepto5',
|
||||
'2018lepto76',
|
||||
'2018lepto98',
|
||||
'2019lepto03',
|
||||
'2019lepto24',
|
||||
'2019lepto27',
|
||||
'2019lepto30',
|
||||
'2020lepto04',
|
||||
'2020lepto06',
|
||||
#'2020lepto06',
|
||||
'2020lepto16',
|
||||
'2020lepto19',
|
||||
'2020lepto20'
|
||||
]
|
||||
|
||||
custom_amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
|
||||
#colors = ['dimgray', 'dimgrey', 'gray', 'grey', 'darkgray', 'darkgrey', 'silver', 'lightgray', 'lightgrey', 'gainsboro', 'whitesmoke']
|
||||
colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k']
|
||||
all = []
|
||||
new_all = []
|
||||
for ident in identifier:
|
||||
data = np.load('gain_%s.npy' %ident)
|
||||
all.append(data)
|
||||
for ident in identifier_uniform:
|
||||
data = np.load('gain_%s.npy' % ident)
|
||||
new_all.append(data)
|
||||
|
||||
av = avgNestedLists(all)
|
||||
new_av = avgNestedLists(new_all)
|
||||
|
||||
fig = plt.figure(figsize=(8.27, 11.69/2))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(custom_amf, av, 'o', label = 'normal')
|
||||
#ax.plot(amf, new_av, 'o', label = 'uniform')
|
||||
|
||||
sinv, sinc = curve_fit(gain_curve_fit, custom_amf, av, [2, 3])
|
||||
predict = []
|
||||
for f in custom_amf:
|
||||
G = np.max(av) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
|
||||
predict.append(G)
|
||||
|
||||
custom_amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
|
||||
tau = []
|
||||
IDs = []
|
||||
f_c = []
|
||||
fit = []
|
||||
fit_amf = []
|
||||
|
||||
all_gains = []
|
||||
for ID in identifier:
|
||||
|
||||
print(ID)
|
||||
amf = np.load('amf_%s.npy' %ID)
|
||||
gain = np.load('gain_%s.npy' %ID)
|
||||
IDs.append(ID)
|
||||
gain_10 = np.zeros(10)
|
||||
amf = np.load('5Hz_amf_%s.npy' % ID)
|
||||
gain = np.load('5Hz_gain_%s.npy' % ID)
|
||||
b = 0
|
||||
for aa, a in enumerate(custom_amf):
|
||||
if a in amf:
|
||||
gain_10[aa] = gain[b]
|
||||
b += 1
|
||||
else:
|
||||
gain_10[aa] = None
|
||||
print(gain_10)
|
||||
#print(amf)
|
||||
all_gains.append(gain_10)
|
||||
|
||||
|
||||
sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
|
||||
#print('tau:', sinv[0])
|
||||
print('tau:', sinv[0])
|
||||
tau.append(sinv[0])
|
||||
f_cutoff = abs(1 / (2*np.pi*sinv[0]))
|
||||
print('f_cutoff:', f_cutoff)
|
||||
f_c.append(f_cutoff)
|
||||
fit.append(gain_curve_fit(amf, *sinv))
|
||||
fit_amf.append(amf)
|
||||
ax.axvline(x=f_cutoff, ymin=0, ymax=5, color='C0', ls='-', alpha=0.5, label='cutoff frequency')
|
||||
|
||||
ax.axvline(x=f_cutoff, ymin=0, ymax=5, color='C0', ls='-', alpha=0.5)
|
||||
|
||||
f_c_ID = zip(ID, f_c)
|
||||
|
||||
mean = []
|
||||
|
||||
g0 = []
|
||||
g1 = []
|
||||
g2 = []
|
||||
g3 = []
|
||||
g4 = []
|
||||
g5 = []
|
||||
g6 = []
|
||||
g7 = []
|
||||
g8 = []
|
||||
g9 = []
|
||||
for g in all_gains:
|
||||
if math.isnan(g[0]) is False:
|
||||
g0.append(g[0])
|
||||
if math.isnan(g[1]) is False:
|
||||
g1.append(g[1])
|
||||
if math.isnan(g[2]) is False:
|
||||
g2.append(g[2])
|
||||
if math.isnan(g[3]) is False:
|
||||
g3.append(g[3])
|
||||
if math.isnan(g[4]) is False:
|
||||
g4.append(g[4])
|
||||
if math.isnan(g[5]) is False:
|
||||
g5.append(g[5])
|
||||
if math.isnan(g[6]) is False:
|
||||
g6.append(g[6])
|
||||
if math.isnan(g[7]) is False:
|
||||
g7.append(g[7])
|
||||
if math.isnan(g[8]) is False:
|
||||
g8.append(g[8])
|
||||
if math.isnan(g[9]) is False:
|
||||
g9.append(g[9])
|
||||
print(g0)
|
||||
print(np.mean(g0))
|
||||
print(g1)
|
||||
print(np.mean(g1))
|
||||
print(g2)
|
||||
print(np.mean(g2))
|
||||
print(g3)
|
||||
print(np.mean(g3))
|
||||
print(g4)
|
||||
print(np.mean(g4))
|
||||
print(g5)
|
||||
print(np.mean(g5))
|
||||
print(g6)
|
||||
print(np.mean(g6))
|
||||
print(g7)
|
||||
print(np.mean(g7))
|
||||
print(g8)
|
||||
print(np.mean(g8))
|
||||
print(g9)
|
||||
print(np.mean(g9))
|
||||
|
||||
mean.append(np.mean(g0))
|
||||
mean.append(np.mean(g1))
|
||||
mean.append(np.mean(g2))
|
||||
mean.append(np.mean(g3))
|
||||
mean.append(np.mean(g4))
|
||||
mean.append(np.mean(g5))
|
||||
mean.append(np.mean(g6))
|
||||
mean.append(np.mean(g7))
|
||||
mean.append(np.mean(g8))
|
||||
mean.append(np.mean(g9))
|
||||
|
||||
print('maximum of mean:', np.max(mean))
|
||||
|
||||
ax.plot(custom_amf, mean, 'o')
|
||||
|
||||
# uniformed: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto06
|
||||
|
||||
tau_uniform = []
|
||||
f_c_uniform = []
|
||||
fit_uniform = []
|
||||
fit_amf_uniform = []
|
||||
for ID in identifier_uniform:
|
||||
print(ID)
|
||||
amf = np.load('amf_%s.npy' %ID)
|
||||
gain = np.load('gain_%s.npy' %ID)
|
||||
|
||||
sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
|
||||
print('tau:', sinv[0])
|
||||
tau_uniform.append(sinv[0])
|
||||
f_cutoff = abs(1 / (2*np.pi*sinv[0]))
|
||||
print('f_cutoff:', f_cutoff)
|
||||
print('alpha:', sinv[1])
|
||||
f_c_uniform.append(f_cutoff)
|
||||
fit_uniform.append(gain_curve_fit(amf, *sinv))
|
||||
fit_amf_uniform.append(amf)
|
||||
|
||||
|
||||
colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
|
||||
|
||||
#for ff ,f in enumerate(fit_uniform):
|
||||
# ax.plot(fit_amf_uniform[ff], fit_uniform[ff],color = colorss[ff])
|
||||
# ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[ff])#colors_uniform[ff])
|
||||
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
ax.set_ylabel('gain [Hz/(mV/cm)]')
|
||||
ax.set_xlabel('envelope frequency [Hz]')
|
||||
ax.set_xlim(0.0007, 1.5)
|
||||
ax.set_ylim(0.001, 10)
|
||||
ax.plot(custom_amf, predict)
|
||||
#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', color = 'C0', alpha = 0.5, label = 'normal cutoff frequencies')
|
||||
#ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies')
|
||||
#ax.legend(loc = 'center left')
|
||||
|
||||
|
||||
plt.show()
|
||||
embed()
|
||||
|
||||
@ -5,28 +5,24 @@ from IPython import embed
|
||||
from scipy.optimize import curve_fit
|
||||
from jar_functions import gain_curve_fit
|
||||
from jar_functions import avgNestedLists
|
||||
from matplotlib import gridspec
|
||||
|
||||
|
||||
identifier = ['2018lepto1',
|
||||
'2018lepto4',
|
||||
'2018lepto5',
|
||||
'2018lepto76',
|
||||
'2018lepto98',
|
||||
'2019lepto03',
|
||||
'2019lepto24',
|
||||
'2019lepto27',
|
||||
'2019lepto30',
|
||||
'2020lepto04',
|
||||
'2020lepto06',
|
||||
'2020lepto16',
|
||||
'2020lepto19',
|
||||
'2020lepto20'
|
||||
]
|
||||
#plt.rcParams.update({'font.size': 16})
|
||||
|
||||
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
|
||||
|
||||
custom_f = np.logspace(-2, -1, 10)
|
||||
custom_alpha = np.logspace(1.5, 1, 10)
|
||||
low_lim = 0.005
|
||||
high_lim = 6
|
||||
|
||||
# subplot 1
|
||||
fig = plt.figure(figsize=(8.27,11.69))
|
||||
gs = gridspec.GridSpec(2, 2)
|
||||
ax0 = fig.add_subplot(gs[0,:])
|
||||
fig.text(0.06, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
|
||||
fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
|
||||
|
||||
custom_f = np.logspace(-2, -1, 4)
|
||||
custom_alpha = np.logspace(0.6, 0.1, 4)
|
||||
c_gain = []
|
||||
custom_tau = abs(1 / (2 * np.pi * custom_f))
|
||||
for t, a in zip(custom_tau, custom_alpha):
|
||||
@ -35,70 +31,68 @@ for t, a in zip(custom_tau, custom_alpha):
|
||||
custom_g = gain_curve_fit(am, t, a)
|
||||
custom_gain.append(custom_g)
|
||||
c_gain.append(custom_gain)
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
for cc, c in enumerate(c_gain):
|
||||
ax.plot(amf, c)
|
||||
ax.axvline(x=custom_f[cc], ymin=0, ymax=5, alpha=0.8) # colors_uniform[ff])
|
||||
col = ['blue', 'orange', 'green', 'purple']
|
||||
for cc, c in enumerate(c_gain):
|
||||
ax0.plot(amf, c, c = col[cc])
|
||||
ax0.axvline(x=custom_f[cc], c = col[cc], ymin=0, ymax=5, alpha=0.5)
|
||||
|
||||
plt.show()
|
||||
mean = avgNestedLists(c_gain)
|
||||
|
||||
ax0.set_xscale('log')
|
||||
ax0.set_yscale('log')
|
||||
ax0.set_ylim(low_lim, high_lim)
|
||||
ax0.plot(amf, mean, lw = 3, c = 'r')
|
||||
|
||||
# subplot 2
|
||||
ax1 = fig.add_subplot(gs[1,0])
|
||||
|
||||
custom_f = np.logspace(-2, -1, 10)
|
||||
custom_alpha = np.logspace(0.6, 0.1, 10)
|
||||
c_gain = []
|
||||
custom_tau = abs(1 / (2 * np.pi * custom_f))
|
||||
for t, a in zip(custom_tau, custom_alpha):
|
||||
custom_gain = []
|
||||
for am in amf:
|
||||
custom_g = gain_curve_fit(am, t, a)
|
||||
custom_gain.append(custom_g)
|
||||
c_gain.append(custom_gain)
|
||||
col = ['blue', 'orange', 'green']
|
||||
for cc, c in enumerate(c_gain):
|
||||
ax1.plot(amf, c, c = 'C0')
|
||||
ax1.axvline(x=custom_f[cc], c = 'C0', ymin=0, ymax=5, alpha=0.5) # colors_uniform[ff])
|
||||
|
||||
mean = avgNestedLists(c_gain)
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
ax.plot(amf, mean)
|
||||
plt.show()
|
||||
|
||||
all = []
|
||||
ax1.set_xscale('log')
|
||||
ax1.set_yscale('log')
|
||||
ax1.set_ylim(low_lim, high_lim)
|
||||
ax1.plot(amf, mean, lw = 3, c = 'r')
|
||||
|
||||
for ident in identifier:
|
||||
data = np.load('gain_%s.npy' %ident)
|
||||
all.append(data)
|
||||
# subplot 3
|
||||
ax2 = fig.add_subplot(gs[1,1])
|
||||
|
||||
av = avgNestedLists(all)
|
||||
embed()
|
||||
custom_f = np.logspace(-2.75, -0.25, 10)
|
||||
custom_alpha = np.logspace(0.6, 0.1, 10)
|
||||
c_gain = []
|
||||
custom_tau = abs(1 / (2 * np.pi * custom_f))
|
||||
for t, a in zip(custom_tau, custom_alpha):
|
||||
custom_gain = []
|
||||
for am in amf:
|
||||
custom_g = gain_curve_fit(am, t, a)
|
||||
custom_gain.append(custom_g)
|
||||
c_gain.append(custom_gain)
|
||||
col = ['blue', 'orange', 'green']
|
||||
for cc, c in enumerate(c_gain):
|
||||
ax2.plot(amf, c, c = 'C0')
|
||||
ax2.axvline(x=custom_f[cc], c = 'C0', ymin=0, ymax=5, alpha=0.5) # colors_uniform[ff])
|
||||
|
||||
fig = plt.figure(figsize=(8.27,11.69/2))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(amf, av, 'o', c = 'C0', label = 'gain')
|
||||
|
||||
#plt.show()
|
||||
|
||||
tau = []
|
||||
f_c = []
|
||||
fit = []
|
||||
fit_amf = []
|
||||
for ID in identifier:
|
||||
print(ID)
|
||||
amf = np.load('amf_%s.npy' %ID)
|
||||
gain = np.load('gain_%s.npy' %ID)
|
||||
|
||||
sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
|
||||
#print('tau:', sinv[0])
|
||||
tau.append(sinv[0])
|
||||
f_cutoff = abs(1 / (2*np.pi*sinv[0]))
|
||||
print('f_cutoff:', f_cutoff)
|
||||
f_c.append(f_cutoff)
|
||||
fit.append(gain_curve_fit(amf, *sinv))
|
||||
fit_amf.append(amf)
|
||||
col = plt.cm.magma(np.linspace(0,0.8,len(fit)))
|
||||
for ff ,f in enumerate(fit):
|
||||
ax.plot(fit_amf[ff], fit[ff], c = col[ff])
|
||||
ax.axvline(x=f_c[ff], ymin=0, ymax=5, alpha=0.8, c = col[ff]) # colors_uniform[ff])
|
||||
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
ax.set_title('gain average all fish')
|
||||
ax.set_ylabel('gain [Hz/(mV/cm)]')
|
||||
ax.set_xlabel('envelope_frequency [Hz]')
|
||||
ax.set_ylim(0.0008, )
|
||||
#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'cutoff frequencies')
|
||||
ax.legend(loc = 'center left')
|
||||
mean = avgNestedLists(c_gain)
|
||||
|
||||
ax2.set_xscale('log')
|
||||
ax2.set_yscale('log')
|
||||
ax2.set_ylim(low_lim, high_lim)
|
||||
ax2.set_yticklabels([])
|
||||
ax2.plot(amf, mean, lw = 3, c = 'r')
|
||||
plt.show()
|
||||
|
||||
embed()
|
||||
|
||||
@ -12,12 +12,12 @@ from jar_functions import get_time_zeros
|
||||
from jar_functions import import_data_eigen
|
||||
from scipy.signal import savgol_filter
|
||||
|
||||
plt.rcParams.update({'font.size': 18})
|
||||
plt.rcParams.update({'font.size': 12})
|
||||
|
||||
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
|
||||
|
||||
#2015eigen8 no nix files
|
||||
identifier = [#'2013eigen13',
|
||||
identifier = ['2013eigen13',
|
||||
'2015eigen16','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
|
||||
|
||||
|
||||
@ -32,8 +32,8 @@ for ID in identifier:
|
||||
delta_f, duration = parse_stimuli_dat(stimuli_dat)
|
||||
dur = int(duration[0][0:2])
|
||||
print(delta_f)
|
||||
if delta_f != [4.0]:
|
||||
continue
|
||||
#if delta_f != [-4.0]:
|
||||
# continue
|
||||
data, pre_data, dt = import_data_eigen(datapath)
|
||||
|
||||
#hstack concatenate: 'glue' pre_data and data
|
||||
@ -74,24 +74,24 @@ for ID in identifier:
|
||||
if i > 15 and i < 55:
|
||||
j.append(jar4[idx])
|
||||
|
||||
r = np.median(j) - np.median(b)
|
||||
r = (np.median(j) - np.median(b)) / 4 # divided by 4 cause of data at 4th harmonic, therefore response 4 times higher
|
||||
print('response:', r)
|
||||
deltaf.append(delta_f[0])
|
||||
response.append(r)
|
||||
|
||||
plt.figure(figsize = (14,8))
|
||||
plt.figure(figsize = (8.27,11.69/2))
|
||||
plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
|
||||
plt.plot(cut_time_jar, jar4, 'k', label = 'peak detection trace', lw = 2)
|
||||
plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
|
||||
plt.plot(cut_time_jar, jar4, color = 'k', label = 'peak detection trace', lw = 2)
|
||||
plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
|
||||
plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='gold', label='stimulus duration')
|
||||
plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
|
||||
plt.xlim(times[0],times[-1])
|
||||
#embed()
|
||||
#plt.xticks((times[0], 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
|
||||
plt.xticks((times[0], 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
|
||||
plt.xlabel('time [s]')
|
||||
plt.ylabel('frequency [Hz]')
|
||||
plt.legend(loc = 'best')
|
||||
plt.show()
|
||||
#plt.show()
|
||||
delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
|
||||
|
||||
plt.close()
|
||||
@ -99,4 +99,4 @@ for ID in identifier:
|
||||
|
||||
res_df = sorted(zip(deltaf,response))
|
||||
|
||||
#np.save('res_df_%s_new' %ID, res_df)
|
||||
np.save('res_df_%s_new' %ID, res_df)
|
||||
|
||||
@ -12,7 +12,7 @@ from jar_functions import get_time_zeros
|
||||
from jar_functions import import_data_eigen
|
||||
from scipy.signal import savgol_filter
|
||||
|
||||
#plt.rcParams.update({'font.size': 18})
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
|
||||
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
|
||||
|
||||
@ -107,6 +107,7 @@ ax0.set_xlim(times[0],times[-1])
|
||||
ax0.set_ylabel('frequency [Hz]')
|
||||
ax0.axes.xaxis.set_ticklabels([])
|
||||
ax0.set_title('∆F -2 Hz')
|
||||
plt.xticks((1.7, 10, 20, 30, 40, 50, 60, times[-1]))
|
||||
|
||||
ax1 = fig.add_subplot(222)
|
||||
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)
|
||||
@ -116,6 +117,7 @@ ax1.axes.xaxis.set_ticklabels([])
|
||||
#ax1.axes.yaxis.set_ticklabels([])
|
||||
ax1.set_title('∆F 2 Hz')
|
||||
ax1.get_shared_y_axes().join(ax0, ax1)
|
||||
plt.xticks((1.7, 10, 20, 30, 40, 50, 60, times[-1]))
|
||||
|
||||
ax2 = fig.add_subplot(223)
|
||||
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)
|
||||
@ -124,6 +126,7 @@ ax2.set_xlim(times[0],times[-1])
|
||||
ax2.set_ylabel('frequency [Hz]')
|
||||
ax2.set_xlabel('time [s]')
|
||||
ax2.set_title('∆F -10 Hz')
|
||||
plt.xticks((1.7, 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
|
||||
|
||||
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)
|
||||
@ -132,7 +135,7 @@ ax3.set_xlim(times[0],times[-1])
|
||||
ax3.set_xlabel('time [s]')
|
||||
#ax3.axes.yaxis.set_ticklabels([])
|
||||
ax3.set_title('∆F 10 Hz')
|
||||
|
||||
plt.xticks((1.7, 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
|
||||
plt.subplots(sharex = True, sharey = True)
|
||||
plt.show()
|
||||
|
||||
|
||||
128
eigenmannia_code/figure_eigen_gain_plot.py
Normal file
128
eigenmannia_code/figure_eigen_gain_plot.py
Normal file
@ -0,0 +1,128 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pylab
|
||||
from IPython import embed
|
||||
from scipy.optimize import curve_fit
|
||||
from matplotlib.mlab import specgram
|
||||
import os
|
||||
from jar_functions import gain_curve_fit
|
||||
|
||||
plt.rcParams.update({'font.size': 10})
|
||||
|
||||
identifier = ['2015eigen8',
|
||||
'2015eigen15',
|
||||
'2015eigen16',
|
||||
'2015eigen17',
|
||||
'2015eigen19'
|
||||
]
|
||||
|
||||
amfs = []
|
||||
gains = []
|
||||
maxgains = []
|
||||
mingains = []
|
||||
taus = []
|
||||
f_cs = []
|
||||
predicts = []
|
||||
for ID in identifier:
|
||||
predict = []
|
||||
|
||||
print(ID)
|
||||
amf = np.load('eigen_amf_%s.npy' % ID)
|
||||
amfs.append(amf)
|
||||
gain = np.load('eigen_gain_%s.npy' % ID)
|
||||
gains.append(gain)
|
||||
print(np.max(gain))
|
||||
sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
|
||||
# print('tau:', sinv[0])
|
||||
taus.append(sinv[0])
|
||||
f_cutoff = abs(1 / (2 * np.pi * sinv[0]))
|
||||
print('f_cutoff:', f_cutoff)
|
||||
f_cs.append(f_cutoff)
|
||||
|
||||
print('min gain:', np.min(gain))
|
||||
print('max gain:', np.max(gain))
|
||||
maxgains.append(np.max(gain))
|
||||
mingains.append(np.min(gain))
|
||||
# predict of gain
|
||||
for f in amf:
|
||||
G = np.max(gain) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
|
||||
predict.append(G)
|
||||
predicts.append(predict)
|
||||
|
||||
print('max of absolute max gain:', np.max(maxgains))
|
||||
print('min of absolute max gain:', np.min(maxgains))
|
||||
print('max of absolute min gain:', np.max(mingains))
|
||||
print('min of absolute min gain:', np.min(mingains))
|
||||
print('absolute max f_c:', np.max(f_cs))
|
||||
print('absolute min f_c:', np.min(f_cs))
|
||||
|
||||
sort = sorted(zip(f_cs, identifier))
|
||||
print(sort)
|
||||
# order of plotting: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2019lepto24, 2020lepto06
|
||||
# order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5
|
||||
|
||||
fig = plt.figure(figsize=(8.27, 11.69))
|
||||
# ax0 = plt.subplot2grid(shape=(3,4), loc=(0,0), colspan = 2)
|
||||
# ax1 = plt.subplot2grid((3,4), (0,2), colspan = 2)
|
||||
# ax2 = plt.subplot2grid((3,4), (1,0), colspan = 2)
|
||||
# ax3 = plt.subplot2grid((3,4), (1,2), colspan = 2)
|
||||
# ax4 = plt.subplot2grid((3,4), (2,0), colspan = 2)
|
||||
|
||||
ax0 = fig.add_subplot(321)
|
||||
fig.text(0.05, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
|
||||
fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
|
||||
|
||||
ax0.set_xlim(0.0007, 1.5)
|
||||
ax0.set_ylim(0.001, 10)
|
||||
ax0.plot(amfs[1], gains[1], 'o', label='gain')
|
||||
ax0.plot(amfs[1], predicts[1], label='fit')
|
||||
ax0.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label='cutoff frequency')
|
||||
ax0.set_xscale('log')
|
||||
ax0.set_yscale('log')
|
||||
ax0.axes.xaxis.set_ticklabels([])
|
||||
|
||||
ax1 = fig.add_subplot(322)
|
||||
ax1.set_xlim(0.0007, 1.5)
|
||||
ax1.set_ylim(0.001, 10)
|
||||
ax1.plot(amfs[0], gains[0], 'o', label='gain')
|
||||
ax1.plot(amfs[0], predicts[0], label='fit')
|
||||
ax1.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label='cutoff frequency')
|
||||
ax1.set_xscale('log')
|
||||
ax1.set_yscale('log')
|
||||
ax1.axes.yaxis.set_ticklabels([])
|
||||
ax1.axes.xaxis.set_ticklabels([])
|
||||
|
||||
ax2 = fig.add_subplot(323)
|
||||
ax2.set_xlim(0.0007, 1.5)
|
||||
ax2.set_ylim(0.001, 10)
|
||||
ax2.plot(amfs[4], gains[4], 'o', label='gain')
|
||||
ax2.plot(amfs[4], predicts[4], label='fit')
|
||||
ax2.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label='cutoff frequency')
|
||||
ax2.set_xscale('log')
|
||||
ax2.set_yscale('log')
|
||||
ax2.axes.xaxis.set_ticklabels([])
|
||||
|
||||
ax3 = fig.add_subplot(324)
|
||||
ax3.set_xlim(0.0007, 1.5)
|
||||
ax3.set_ylim(0.001, 10)
|
||||
ax3.plot(amfs[2], gains[2], 'o', label='gain')
|
||||
ax3.plot(amfs[2], predicts[2], label='fit')
|
||||
ax3.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label='cutoff frequency')
|
||||
ax3.set_xscale('log')
|
||||
ax3.set_yscale('log')
|
||||
ax3.axes.yaxis.set_ticklabels([])
|
||||
|
||||
ax4 = fig.add_subplot(325)
|
||||
ax4.set_xlim(0.0007, 1.5)
|
||||
ax4.set_ylim(0.001, 10)
|
||||
ax4.plot(amfs[3], gains[3], 'o', label='gain')
|
||||
ax4.plot(amfs[3], predicts[3], label='fit')
|
||||
ax4.axvline(x=f_cs[3], ymin=0, ymax=5, ls='-', alpha=0.5, label='cutoff frequency')
|
||||
ax4.set_xscale('log')
|
||||
ax4.set_yscale('log')
|
||||
|
||||
# plt.legend(loc = 'lower left')
|
||||
plt.show()
|
||||
|
||||
# np.save('f_c', f_c)
|
||||
# np.save('tau', tau)
|
||||
168
eigenmannia_code/figure_eigen_jar_plot.py
Normal file
168
eigenmannia_code/figure_eigen_jar_plot.py
Normal file
@ -0,0 +1,168 @@
|
||||
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 = ['2015eigen8',
|
||||
'2015eigen15',
|
||||
'2015eigen16',
|
||||
'2015eigen17',
|
||||
'2015eigen19'
|
||||
]
|
||||
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('eigen_%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('eigen_%s.npy' %dd) # load data for every file name
|
||||
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
|
||||
|
||||
time = np.load('eigen_%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
|
||||
|
||||
ssample = 100000
|
||||
|
||||
fig = plt.figure(figsize=(8.27, 11.69))
|
||||
fig.suptitle('%s' % ident)
|
||||
fig.text(0.06, 0.5, 'fish frequency [Hz]', ha='center', va='center', rotation='vertical', color='C0')
|
||||
fig.text(0.97, 0.5, 'stimulus amplitude [mV/cm]', ha='center', va='center', rotation='vertical', color='red')
|
||||
fig.text(0.5, 0.04, 'time [s]', ha='center', va='center')
|
||||
|
||||
ax0 = fig.add_subplot(611)
|
||||
print('absolute frequency shift 0.001Hz:', np.max(jars[0]) - np.min(jars[0]))
|
||||
ax0.plot(times[0], jars[0], zorder=20)
|
||||
# ax0.set_zorder(1)
|
||||
ax0.set_ylim(-12, 12)
|
||||
|
||||
lower0 = 0
|
||||
upper0 = 2000
|
||||
x0 = np.linspace(lower0, upper0, sample)
|
||||
y0 = (sin_response(np.linspace(lower0, upper0, sample), 0.001, np.pi / 2, .35) + 0.5)
|
||||
ax0_0 = ax0.twinx()
|
||||
ax0_0.set_ylim(-0.2, 1.2)
|
||||
ax0_0.plot(x0, y0, color='red', zorder=1, alpha=0.5)
|
||||
# ax0_0.set_zorder(2)
|
||||
|
||||
ax1 = fig.add_subplot(612)
|
||||
print('absolute frequency shift 0.005 Hz:', np.max(jars[1]) - np.min(jars[1]))
|
||||
ax1.plot(times[1], jars[1])
|
||||
ax1.set_ylim(-12, 12)
|
||||
|
||||
lower1 = 0
|
||||
upper1 = 400
|
||||
x1 = np.linspace(lower1, upper1, sample)
|
||||
y1 = (sin_response(np.linspace(lower1, upper1, sample), 0.005, np.pi / 2, .35) + 0.5)
|
||||
ax1_0 = ax1.twinx()
|
||||
ax1_0.set_ylim(-0.2, 1.2)
|
||||
ax1_0.plot(x1, y1, color='red', alpha=0.5)
|
||||
|
||||
ax2 = fig.add_subplot(613)
|
||||
print('absolute frequency shift 0.01 Hz:', np.max(jars[2]) - np.min(jars[2]))
|
||||
ax2.plot(times[2], jars[2])
|
||||
ax2.set_ylim(-12, 12)
|
||||
|
||||
lower2 = 0
|
||||
upper2 = 400
|
||||
x2 = np.linspace(lower2, upper2, sample)
|
||||
y2 = (sin_response(np.linspace(lower2, upper2, sample), 0.01, np.pi / 2, 0.35) + 0.5)
|
||||
ax2_0 = ax2.twinx()
|
||||
ax2_0.set_ylim(-0.2, 1.2)
|
||||
ax2_0.plot(x2, y2, color='red', alpha=0.5)
|
||||
|
||||
ax3 = fig.add_subplot(614)
|
||||
print('absolute frequency shift 0.02 Hz:', np.max(jars[3]) - np.min(jars[3]))
|
||||
ax3.plot(times[3], jars[3])
|
||||
ax3.set_ylim(-12, 12)
|
||||
|
||||
lower3 = 0
|
||||
upper3 = 200
|
||||
x3 = np.linspace(lower3, upper3, sample)
|
||||
y3 = (sin_response(np.linspace(lower3, upper3, sample), 0.05, np.pi / 2, 0.35) + 0.5)
|
||||
ax3_0 = ax3.twinx()
|
||||
ax3_0.set_ylim(-0.2, 1.2)
|
||||
ax3_0.plot(x3, y3, color='red', alpha=0.5)
|
||||
|
||||
ax4 = fig.add_subplot(615)
|
||||
print('absolute frequency shift 0.5 Hz:', np.max(jars[4]) - np.min(jars[4]))
|
||||
ax4.plot(times[4], jars[4])
|
||||
ax4.set_ylim(-12, 12)
|
||||
|
||||
lower4 = 0
|
||||
upper4 = 200
|
||||
x4 = np.linspace(lower4, upper4, sample)
|
||||
y4 = (sin_response(np.linspace(lower4, upper4, sample), 0.2, np.pi / 2, 0.35) + 0.5)
|
||||
ax4_0 = ax4.twinx()
|
||||
ax4_0.set_ylim(-0.2, 1.2)
|
||||
ax4_0.plot(x4, y4, color='red', alpha=0.5)
|
||||
|
||||
ax5 = fig.add_subplot(616)
|
||||
print('absolute frequency shift 1 Hz:', np.max(jars[5]) - np.min(jars[5]))
|
||||
ax5.plot(times[5], jars[5])
|
||||
ax5.set_ylim(-12, 12)
|
||||
|
||||
lower5 = 0
|
||||
upper5 = 200
|
||||
x5 = np.linspace(lower5, upper5, sample)
|
||||
y5 = (sin_response(np.linspace(lower5, upper5, sample), 1, np.pi / 2, 0.35) + 0.5)
|
||||
ax5_0 = ax5.twinx()
|
||||
ax5_0.plot(x5, y5, color='red', lw=0.5, alpha=0.5)
|
||||
ax5_0.set_ylim(-0.2, 1.2)
|
||||
plt.subplots_adjust(left=0.125,
|
||||
bottom=0.1,
|
||||
right=0.9,
|
||||
top=0.9,
|
||||
wspace=0.1,
|
||||
hspace=0.35)
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue
Block a user