84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
from read_baseline_data import *
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from read_chirp_data import *
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from utility import *
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#import nix_helpers as nh
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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 imposrtieren
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data_dir = "../data"
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dataset = "2018-11-13-ad-invivo-1"
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#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-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-aa-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-ah-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-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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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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#inst_frequency = 1. / np.diff(spike_times)
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spike_rate = np.diff(spike_times)
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_rate,x)
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mu = np.mean(spike_rate)
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sigma = np.std(spike_rate)
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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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#Nyquist-Theorem Plot:
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chirp_spikes = read_chirp_spikes(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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plt.figure()
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dct_rate = {}
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for i in sort_df:
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freq = list(df_map[i])
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dct_rate[i] = []
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for k in freq:
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for phase in chirp_spikes[k]:
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spikes = chirp_spikes[k][phase]
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rate = len(spikes)/ 1.2
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dct_rate[i].append(rate)
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for h in sort_df:
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plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
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#plt.vlines(10, ymin = 190, ymax = 310)
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plt.legend()
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plt.title('Firing rate of the cell for all trials, sorted by df')
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plt.xlabel('# of trials')
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plt.ylabel('Instant firing rate of the cell')
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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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ls_mean = []
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for d in sort_df:
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mean = np.mean(dct_rate[d])
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ls_mean.append(mean)
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
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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(len(sort_df)), (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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