44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
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
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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-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")
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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df_map = {}
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for k in spikes.keys():
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df = k[1]
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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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print(df_map.keys())
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e1 = eod[0, '-50Hz', '20%', '100Hz']
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#plt.plot(e1[1])
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#plt.show()
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plt.title('EOD chirps')
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plt.xlabel('Frequency')
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plt.ylabel('Amplitude')
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plt.plot(e1[0],e1[1])
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plt.show()
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ct = times[0, '-50Hz', '20%', '100Hz']
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#plt.scatter(ct*1000, np.ones(len(ct)))
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plt.scatter(np.asarray(ct)*1000, np.ones(len(ct)))
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plt.show()
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#plt.scatter(spikes[0, '-50Hz', '20%', '100Hz'][0, 0.614])
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#print(len(spikes))
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#print(len(eod))
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#print(len(times))
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