65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
from read_baseline_data import *
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from read_chirp_data 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 importieren
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data_dir = "../data"
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dataset = "2018-11-09-ad-invivo-1"
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#data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-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", "2018-11-09-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-aa-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")
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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 = {}
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#Keys werden nach df sortiert ausgegeben
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for k in chirp_spikes.keys():
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df = k[1]
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ch = k[3]
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if df in df_map.keys():
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df_map[df].append(k)
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else:
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df_map[df] = [k]
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for i in df_map.keys():
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freq = list(df_map[i])
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for k in freq:
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spikes = chirp_spikes[k]
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trial = spikes[1]
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print(trial)
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#
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# plt.plot(spikes, rate)
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# plt.show()
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