70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from read_chirp_data import *
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from utility import *
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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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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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chirp_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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# make phases together, 12 phases
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phase_vec = np.arange(0, 1+1/12, 1/12)
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phase_mat_df = {}
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for deltaf in df_map.keys():
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phase_df = {}
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for rep in df_map[deltaf]:
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for phase in spikes[rep]:
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#print(phase)
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for idx in range(len(phase_vec)-1):
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if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
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if phase_vec[idx] in phase_df.keys():
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phase_df[phase_vec[idx]].append(phase)
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else:
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phase_df[phase_vec[idx]] = [phase]
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#spikes_one_chirp = spikes[rep][phase]
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phase_mat_df[deltaf] = phase_df
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#df_spikes = spikes[df_map['-50Hz'][0]]
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#phase_spikes = df_spikes[phase_mat_df['-50Hz'][0.0][0]]
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trial_spikes = np.asarray(spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)])
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plot_spikes = trial_spikes[(trial_spikes < 50.0) & (trial_spikes > -50.0)]
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hist_data = plt.hist(plot_spikes, color='w')
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fig, ax = plt.subplots()
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ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k')
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ax.plot(hist_data[1][:-1], hist_data[0])
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plt.show()
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#mu = 1
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#sigma = 1
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#time_gauss = np.arange(-4, 4, 1)
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#gauss = gaussian(time_gauss, mu, sigma)
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# spikes during time vec (00010000001)?
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#smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
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#window = np.mean(np.diff(plot_spikes))
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#time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)
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#ax.plot(time_vec, smoothed_spikes)
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#embed()
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#exit()
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#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
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#ax.plot(hist_data[1][:-1], hist_data[0]) |