95 lines
3.8 KiB
Python
95 lines
3.8 KiB
Python
from read_baseline_data import *
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from read_chirp_data import *
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from func_spike import *
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed #Funktionen importieren
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data_dir = "../data"
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data_base = ("2018-11-09-ab-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-af-invivo-1", "2018-11-13-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
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data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
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dataset = "2018-11-14-al-invivo-1"
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#for dataset in data_base:
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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spike_iv = np.diff(spike_times)
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_iv,x)
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mu = np.mean(spike_iv)
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sigma = np.std(spike_iv)
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cv = sigma/mu
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plt.title('A.lepto ISI Histogramm', fontsize = 14)
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plt.xlabel('duration ISI[ms]', fontsize = 12)
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plt.ylabel('number of ISI', fontsize = 12)
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plt.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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plt.show()
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#for dataset in data_chirps:
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#Nyquist-Theorem Plot:
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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plt.figure()
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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plt.show()
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#mittlere Feuerrate einer Frequenz auf Frequenz:
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plt.figure()
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
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plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r)
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plt.title('Mean firing rate of a cell for a range of frequency differences')
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plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
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plt.xlabel('Range of frequency differences [Hz]')
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plt.ylabel('Mean firing rate of the cell')
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plt.show()
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#Adaption der Zellen:
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#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
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adapt = adaptation_df(sort_df, dct_rate)
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plt.figure()
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plt.boxplot(adapt)
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plt.title('Adaptation of cell firing rate during a trial')
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plt.xlabel('Cell')
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plt.ylabel('Adaptation size [Hz]')
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plt.show()
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'''
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#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
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name = str(dataset.replace('-invivo-1', ''))
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f = open('../results/Nyquist/Ny_' + name + '.txt' , 'w')
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f.write(str(sort_df))
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f.write(str(df_map))
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f.write(str(chirp_spikes))
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f.write(str(times))
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f.write(str(ls_mean))
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f.write(str(over_r))
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f.write(str(adapt))
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f.close()
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'''
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