45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
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_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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data_rate_dict = {}
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for dataset in data_chirps:
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data_rate_dict[dataset] = []
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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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for i in df_map.keys():
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freq = list(df_map[i])
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k = freq[0]
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phase = list(chirp_spikes[k].keys())[0]
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spikes = chirp_spikes[k][phase]
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rate = len(spikes)/ 1.2
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data_rate_dict[dataset].append(rate)
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for dataset in data_rate_dict:
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plt.plot(data_rate_dict[dataset])
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plt.title('Test for sequence effects', fontsize = 20)
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plt.xlabel('Number of stimulus presentations', fontsize = 18)
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plt.ylabel('Firing rates of cells', fontsize = 18)
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plt.tick_params(axis='both', which='major', labelsize = 16)
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plt.show()
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