112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib as cm
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from matplotlib.colors import ListedColormap, LinearSegmentedColormap
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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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from jar_functions import average
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base_path = 'D:\\jar_project\\JAR'
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#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
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datasets = [#'2020-06-19-aa', #-5Hz delta f, horrible fit
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#'2020-06-19-ab', #-5Hz delta f, bad fit
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#'2020-06-22-aa', #-5Hz delta f, bad fit
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#'2020-06-22-ab', #-5Hz delta f, bad fit
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'2020-06-22-ac', #-15Hz delta f, good fit
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'2020-06-22-ad', #-15Hz delta f, horrible fit
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'2020-06-22-ae', #-15Hz delta f, horrible fit
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'2020-06-22-af' #-15Hz delta f, good fit
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]
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#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
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#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
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time_all = []
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freq_all = []
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ID = []
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col = ['dimgrey', 'grey', 'darkgrey', 'silver', 'lightgrey', 'gainsboro', 'whitesmoke']
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labels = zip(ID, datasets)
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for infodataset in datasets:
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infodataset = os.path.join(base_path, infodataset, 'info.dat')
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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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dataset = os.path.join(base_path, dataset, 'beats-eod.dat')
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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 = np.mean([t[0] for t in time])
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stop = np.mean([t[-1] for t in time])
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norm = norm_function(frequency, time, onset_point=dm - dm, offset_point=dm) # dm-dm funktioniert nur wenn onset = 0 sec
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mf , tnew = mean_traces(start, stop, timespan, norm, time) # maybe fixed timespan/sampling rate
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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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freq_all.append(cf_arr)
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time_all.append(ct_arr)
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plt.plot(ct_arr, cf_arr, color = col[idx], label='fish=%s' % datasets[idx])
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sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], cf_arr[ct_arr < dm], [1.0, 1.0, 5.0, 50.0], bounds=(0.0, np.inf)) # 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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# fit for each trace
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plt.plot(ct_arr[ct_arr < dm], step_response(ct_arr[ct_arr < dm], *sv), label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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#plt.plot(ft, step_response(ft, *sv), color='orange', label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
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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, values_all = average(freq_all, time_all, start, stop, timespan, dm)
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'''
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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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# norm vor mean_traces damit cutoff von -5
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# average über alle fische eigentlich mal nicht nötig, auslagern
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# nur bei -15 Hz messen
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# bei verschiedenen amplituden messen (siehe Tim)
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# natalie fragen ob sie bei verschiedenen Amplituden messen kann (siehe tim)
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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? |