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@ -90,10 +90,10 @@ def kde(event_dt, max_dt = 60):
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for e in event_dt:
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conv_array += gauss(conv_t, e, kernal_w, kernal_h, norm=True)
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plt.plot(conv_t, conv_array)
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# plt.plot(conv_t, conv_array)
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def permulation_kde(event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB = 4):
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def permulation_kde(event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB = 4, norm_count = 1):
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def chunk_permutation(select_event_dt, conv_tt, n_chuck, max_jitter, kernal_w, kernal_h):
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# array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
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# event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
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@ -105,6 +105,8 @@ def permulation_kde(event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB =
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# conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
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gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
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gauss_3d /= np.sum(gauss_3d, axis=0)
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kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
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try:
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@ -122,7 +124,7 @@ def permulation_kde(event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB =
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kernal_w = 1
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kernal_h = 0.2
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max_jitter = 5
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max_jitter = 120
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select_event_dt = event_dt[np.abs(event_dt) <= max_dt + max_jitter*2]
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conv_t = cp.arange(-max_dt, max_dt, 1)
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@ -165,6 +167,7 @@ def main(base_path):
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trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
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lose_chrips_centered_on_ag_off_t = []
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norm_count = []
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for index, trial in tqdm(trial_summary.iterrows()):
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trial_path = os.path.join(base_path, trial['recording'])
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@ -196,14 +199,12 @@ def main(base_path):
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rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
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lose_chrips_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], chirp_times[1]))
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norm_count.append(len(chirp_times[1]))
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kde(np.hstack(lose_chrips_centered_on_ag_off_t))
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permulation_kde(np.hstack(lose_chrips_centered_on_ag_off_t))
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permulation_kde(np.hstack(lose_chrips_centered_on_ag_off_t), norm_count=norm_count)
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embed()
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quit()
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pass
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if __name__ == '__main__':
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