09.07
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xaver
parent
275b4bc473
commit
cf22967f48
@ -2,6 +2,15 @@ import os #compability with windows
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from IPython import embed
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from IPython import embed
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import numpy as np
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import numpy as np
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def step_response(t, a1, a2, tau1, tau2):
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r_step = (a1*(1 - np.exp(-t/tau1))) + (a2*(1 - np.exp(-t/tau2)))
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r_step[t<0] = 0
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return r_step
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def sin_response(t, f, p, A):
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r_sin = A*sin(2*np.pi*t*f + p)
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return r_sin
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def parse_dataset(dataset_name):
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def parse_dataset(dataset_name):
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assert(os.path.exists(dataset_name)) #see if data exists
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assert(os.path.exists(dataset_name)) #see if data exists
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f = open(dataset_name, 'r') #open data we gave in
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f = open(dataset_name, 'r') #open data we gave in
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@ -100,12 +109,6 @@ def mean_noise_cut(frequencies, time, n):
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cutt.append(t)
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cutt.append(t)
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return cutf, cutt
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return cutf, cutt
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def step_response(t, a1, a2, tau1, tau2):
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r_step = (a1*(1 - np.exp(-t/tau1))) + (a2*(1 - np.exp(-t/tau2)))
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r_step[t<0] = 0
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return r_step
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def norm_function(cf_arr, ct_arr, onset_point, offset_point):
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def norm_function(cf_arr, ct_arr, onset_point, offset_point):
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onset_end = onset_point - 10
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onset_end = onset_point - 10
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offset_start = offset_point - 10
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offset_start = offset_point - 10
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112
second_try.py
112
second_try.py
@ -1,112 +0,0 @@
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import matplotlib.pyplot as plt
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import os
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import glob
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import IPython
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import numpy as np
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from IPython import embed
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from scipy.optimize import curve_fit
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from jar_functions import parse_dataset
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from jar_functions import parse_infodataset
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from jar_functions import mean_traces
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from jar_functions import mean_noise_cut
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from jar_functions import norm_function
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from jar_functions import step_response
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from jar_functions import sort_values
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#nicht: 19-aa, 22-ae, 22-ad (?)
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datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')), #-5Hz delta f, bad fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')), #-15Hz delta f, good fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')), #-15Hz delta f, horrible fit
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#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')), #-15Hz delta f, maxfev way to high so horrible
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))] #-15Hz delta f, good fit
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#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')))
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infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
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(os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\info.dat'))]
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time_all = []
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freq_all = []
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ID = []
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for infodataset in infodatasets:
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i = parse_infodataset(infodataset)
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identifier = i[0]
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ID.append(identifier)
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for idx, dataset in enumerate(datasets):
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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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dm = np.mean(duration)
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pm = np.mean(pause)
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timespan = dm + pm
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start = (time[0][0] + time[1][0]) / 2
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stop = (time[0][-1] + time[1][-1]) / 2
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mf , tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
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#for i in range(len(mf)):
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cf, ct = mean_noise_cut(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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norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec
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freq_all.append(norm.tolist())
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time_all.append(ct_arr)
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plt.plot(ct_arr, norm, color = 'grey', label='fish=%s' % ID[idx])
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# fit function
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sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm]) #step_values and step_cov
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# sorted a and tau
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values = sort_values(sv)
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'''
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plt.plot(ct_arr[ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], color='orange',
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label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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'''
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print('fish: a1, a2, tau1, tau2', values)
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# average over all fish
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mf_all , tnew_all = mean_traces(start, stop, timespan, freq_all, time_all)
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plt.plot(tnew_all, mf_all, color = 'b', label = 'average', ls = 'dashed')
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# fit for average
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sv_all, sc_all = curve_fit(step_response, tnew_all[tnew_all < dm], mf_all[tnew_all < dm]) #step_values and step_cov
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values_all = sort_values(sv_all)
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plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color='orange',
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label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
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print('average: a1, a2, tau1, tau2', values_all)
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const_line = plt.axhline(y = 0.632)
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stimulus_duration = plt.hlines(y = -0.25, xmin = 0, xmax = 100, color = 'r', label = 'stimulus_duration')
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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.savefig('relative JAR')
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plt.legend(loc = 'lower right')
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plt.show()
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embed()
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# Fragen:
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# wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? oder einfach immer bei 0 onset..?
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# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
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# welche Stimulusintesität?
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# start/stop/timespan ok?
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