103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
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 jar_functions import parse_dataset
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from jar_functions import mean_noise_cut
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datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
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# (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
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eodf = []
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deltaf = []
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stimulusf = []
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time = []
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frequency_mean= []
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amplitude = []
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start = -10
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stop = 200
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timespan = 210
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for dataset in datasets:
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#input of the function
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t, f, a, e, d, s= parse_dataset(dataset)
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'times'
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# same for time in both loops
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minimumt = min(len(t[0]), len(t[1]))
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t0 = t[0][:minimumt]
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t1 = t[1][:minimumt]
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# new time with wished timespan because it varies for different loops
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tnew = np.arange(start, stop, timespan / minimumt) # 3rd input is stepspacing:
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# in case complete measuring time devided by total number of datapoints
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'frequencies'
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# minimum datapoint lenght of both loops of frequencies
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minimumf = min(len(f[0]), len(f[1]))
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# new frequencies to minimum for both loops
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f0 = f[0][:minimumf]
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# interpolation
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f0new = np.interp(tnew, t0, f0)
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f1 = f[1] #[:minimumf]
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# interpolation
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f1new = np.interp(tnew, t1, f1)
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#new array with frequencies of both loops as two lists put together
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frequency = np.array([f0new, f1new])
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#making a mean over both loops with the axis 0 (=averaged in y direction, axis=1 would be over x axis)
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mf = np.mean(frequency, axis=0) #.T as transition (1,0) -> (0,1)
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#appending data
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eodf.append(e)
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deltaf.append(d)
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stimulusf.append(s)
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amplitude.append(a)
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frequency_mean.append(mf)
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time.append(tnew)
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for i in range(len(frequency_mean)):
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for n in [10, 50, 100, 1000, 10000, 20000, 30000]:
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cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
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plt.plot(ct, cf, label='n=%d' % n)
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'plotting'
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plt.xlim([-10,200])
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#plt.ylim([400, 1000])
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plt.xlabel('time [s]')
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plt.ylabel('frequency [Hz]')
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#plt.title('noise_cut_n=100')
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#plt.savefig('noise_cut_n=100')
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plt.legend()
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plt.show()
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def double_exp(t, a1, a2, tau1, tau2):
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return a1*np.exp(-t/tau1)
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# plotten mit manual values for a1, ...
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# auch mal a1 oder a2 auf Null setzen.
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#evtl. normiert darstellen (frequency / baseline frequency?)?
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#Zeitkonstante: von sec. 0 bis 63%? relative JAR
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'''
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'controll of interpolation'
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fig=plt.figure()
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ax=fig.add_subplot(1,1,1)
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ax.plot(tnew, mf, c = 'r', marker = 'o', ls = 'solid', label = 'new')
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ax.plot(t0, f0, c = 'b', marker = '+', ls = '-', label = 'loop_0')
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ax.plot(t1, f1, c= 'g', marker = '+', ls = '-', label = 'loop_1')
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plt.legend(loc = 'best')
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#plt.plot(tnew, mf, marker = 'r-o', label = new, t0, f0, marker = 'b-+', label = loop_0, t1, f1, marker = 'g-+', label = loop_1)
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plt.show()
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'''
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