21.09
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xaver
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042bdd7aa3
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64
apteronotus_code/sin_all_normal.py
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64
apteronotus_code/sin_all_normal.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 jar_functions import gain_curve_fit
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from jar_functions import avgNestedLists
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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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tau = []
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f_c = []
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for ID in identifier:
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print(ID)
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amf = np.load('5Hz_amf_%s.npy' %ID)
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gain = np.load('5Hz_gain_%s.npy' %ID)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
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#print('tau:', sinv[0])
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tau.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_c.append(f_cutoff)
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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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all = []
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for ident in identifier:
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data = np.load('5Hz_gain_%s.npy' %ident)
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all.append(data)
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av = avgNestedLists(all)
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, av, 'o')
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_title('gaincurve_average_allfish_5Hz')
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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_ylim(0.0008, )
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ax.plot(f_c, np.full((len(identifier)), 0.0015), 'o', label = 'cutoff frequencies')
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ax.legend()
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plt.show()
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embed()
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97
apteronotus_code/sin_all_uniform.py
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97
apteronotus_code/sin_all_uniform.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 jar_functions import gain_curve_fit
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from jar_functions import avgNestedLists
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identifier_uniform = ['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 = ['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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tau = []
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f_c = []
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for ID in identifier:
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print(ID)
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amf = np.load('amf_%s.npy' %ID)
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gain = np.load('gain_%s.npy' %ID)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
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#print('tau:', sinv[0])
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tau.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_c.append(f_cutoff)
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tau_uniform = []
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f_c_uniform = []
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for ID in identifier_uniform:
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#print(ID)
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amf = np.load('amf_%s.npy' %ID)
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gain = np.load('gain_%s.npy' %ID)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
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#print('tau:', sinv[0])
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tau_uniform.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_c_uniform.append(f_cutoff)
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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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all = []
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new_all = []
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for ident in identifier:
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data = np.load('gain_%s.npy' %ident)
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all.append(data)
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for ident in identifier_uniform:
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data = np.load('gain_%s.npy' % ident)
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new_all.append(data)
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av = avgNestedLists(all)
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new_av = avgNestedLists(new_all)
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lim = 0.001
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, av, 'o', color = 'orange', label = 'normal')
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ax.plot(amf, new_av, 'o', color = 'blue', label = 'uniformed')
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_title('gaincurve_average_allfish')
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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_ylim(0.0008, )
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ax.plot(f_c, np.full((len(identifier)), 0.0015), 'o', color = 'orange', label = 'all cutoff frequencies')
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ax.plot(f_c_uniform, np.full((len(identifier_uniform)), 0.001), 'o', color = 'blue', label = 'uniformed cutoff frequencies')
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ax.legend()
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plt.show()
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embed()
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@ -43,12 +43,13 @@ for infodataset in datasets:
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for ID in identifier:
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base_path = 'D:\\jar_project\\JAR\\eigenmannia\\step\\%s' %ID
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res_df = []
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response = []
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stim_ampl = []
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for idx, dataset in enumerate(os.listdir(base_path)):
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dataset = os.path.join(base_path, dataset, 'beats-eod.dat')
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print(dataset)
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#input of the function
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frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
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frequency, time, amplitude, eodf, deltaf, stimulusf, stimulusamplitude, duration, pause = parse_dataset(dataset)
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dm = np.mean(duration)
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pm = np.mean(pause)
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timespan = dm + pm
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@ -57,20 +58,15 @@ for ID in identifier:
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if len(frequency) == 5:
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continue
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norm, base, jar = norm_function(frequency, time, onset_point=dm - dm, offset_point=dm) # dm-dm funktioniert nur wenn onset = 0 sec
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print(jar)
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if jar[0] == 0.0:
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continue
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mf, tnew = mean_traces(start, stop, timespan, norm, time) # maybe fixed timespan/sampling rate
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mf, tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
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cf, ct = mean_noise_cut_eigen(mf, tnew, n=1250)
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cf_arr = np.array(cf)
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ct_arr = np.array(ct)
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onset_point = dm - dm
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offset_point = dm
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onset_end = onset_point - 10
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offset_start = offset_point - 10
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freq_all.append(cf_arr)
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time_all.append(ct_arr)
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b = []
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for index, i in enumerate(ct):
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@ -84,15 +80,37 @@ for ID in identifier:
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print(h)
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print(indexx)
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print(cf[indexx])
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''' sounds good, doesnt work somehow: in norm devision by 0 (jar) or index doesnt fit
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norm, base, jar = norm_function(frequency, time, onset_point=dm - dm,
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offset_point=dm) # dm-dm funktioniert nur wenn onset = 0 sec
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b = []
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for index, i in enumerate(ct):
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if i > -45 and i < -5:
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b.append(cf[index])
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j = []
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for indexx, h in enumerate(ct):
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if h > 195 and h < 145:
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j.append(cf[indexx])
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print(h)
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print(indexx)
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print(cf[indexx])
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b = np.median(cf[(ct >= onset_end) & (ct < onset_point)])
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j = np.median(cf[(ct >= offset_start) & (ct < offset_point)])
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'''
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r = np.median(j) - np.median(b)
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#response.append(r)
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embed()
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response.append(r)
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stim_ampl.append(stimulusamplitude)
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res_ampl = sorted(zip(stim_ampl, response))
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base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
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plt.xlim([-10,220])
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plt.xlabel('time [s]')
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plt.ylabel('rel. JAR magnitude')
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plt.title('relative JAR')
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plt.xlabel('Stimulusamplitude')
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plt.ylabel('absolute JAR magnitude')
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plt.title('absolute JAR')
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plt.savefig('relative JAR')
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plt.legend(loc = 'lower right')
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plt.show()
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12
notes
12
notes
@ -1,10 +1,11 @@
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+ größe/gewicht/dominanz/temp in csv und über split aufteilen und mit ID verknüpfen oder mit pandar,
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eod basefrequenz rausziehen, scatter plot gegen cutoff frequency, ...
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- zeigen: sin_all_uniform - sin_all_normal (also 5Hz, let away 0.001Hz?, gain_fit), eigenmannia_jar, plot_eigenmannia_jar(compare res_df_%s / res_df_%s_new),
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fish_properties(step_response_eigen does not work (norming/mean_trace)), phaseshift_all, gewicht größe von vanessa
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+ größe/gewicht/dominanz/temp/eod basefrequenz/... , scatter plot gegen cutoff frequency, ...
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- cutoff - dominance score
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- cutoff - basefrequency
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- gain - dominance_score: für gain predict machen pro fish,
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hab ich dazu die richtige zeitckonstante aus gain_fit?
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... da ich ja prediction auch über sin und nicht step mache dann
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- gain - dominance_score: für gain predict machen pro fish, passt diese zeitkonstante?
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- mit daten befassen (fish_properties!
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+ eigenmannia: specgram von pre_data neben specgram von data machen um zu sehen ob analyse fehler oder fehler in import_data
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- erkenntnis: hab bei bm/jm nicht den gleichen mean abgezogen..
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- an sich res_df besser, jedoch immer noch relativ variabel
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@ -13,6 +14,7 @@ eod basefrequenz rausziehen, scatter plot gegen cutoff frequency, ...
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+ look at step eigen data
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- norming of data: what if in norm = ground / jar with jar == 0.0?
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+ look at 5Hz data - compare
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+ to step response eigenmannia and eigenmannia response to deltaf, to now absolute JAR --> should i go to relative? (relative didnt work for me somehow)
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long term:
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- extra datei mit script drin um fertige daten darzustellen, den fit-code nur zur datenverarbeitung verwenden
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