final peak version with bool
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@ -12,7 +12,7 @@ from sklearn.preprocessing import normalize
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from modules.filters import bandpass_filter, envelope, highpass_filter
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from modules.filehandling import ConfLoader, LoadData
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from modules.plotstyle import PlotStyle
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from modules.timestamps import group_timestamps, group_timestamp_v2
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from modules.timestamps import group_timestamps, group_timestamps_v2
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ps = PlotStyle()
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@ -527,6 +527,7 @@ def main(datapath: str) -> None:
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if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
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continue
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# get index for each feature
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baseline_idx = np.zeros_like(baseline_ts)
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search_idx = np.ones_like(search_ts)
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@ -562,6 +563,7 @@ def main(datapath: str) -> None:
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bool_timestamps[cm] = False
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# for checking if there are chirps on multiple electrodes
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chirps_electrodes.append(current_chirps)
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for ct in current_chirps:
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@ -598,19 +600,21 @@ def main(datapath: str) -> None:
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# make index vector
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index_vector = np.arange(len(sort_chirps_electrodes))
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# make it more than only two electrodes for the search after chirps
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combinations_best_elctrodes = list(itertools.combinations(range(3), 2))
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combinations_best_elctrodes = list(
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itertools.combinations(range(3), 2))
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the_real_chirps = []
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for chirp_index, seoc in enumerate(sort_chirps_electrodes):
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if bool_vector[chirp_index] == False:
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continue
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cm = index_vector[(sort_chirps_electrodes >= seoc) & (
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sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
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sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
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for combination in combinations_best_elctrodes:
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if set(combination).issubset(sort_electrodes[cm]):
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the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
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"""
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the_real_chirps.append(
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np.mean(sort_chirps_electrodes[cm]))
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if set([0,1]).issubset(sort_electrodes[cm]):
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the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
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elif set([1,0]).issubset(sort_electrodes[cm]):
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@ -619,7 +623,6 @@ def main(datapath: str) -> None:
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the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
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elif set([1,2]).issubset(sort_electrodes[cm]):
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the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
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"""
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bool_vector[cm] = False
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for ct in the_real_chirps:
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@ -82,6 +82,7 @@ def group_timestamps_v2(sublists: List[List[Union[int, float]]], n: int, time_th
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current_group = []
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# Create a set to store the timestamps that occur in at least n of the sublists
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common_timestamps = set.intersection(*[set(lst) for lst in sublists])
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embed()
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# Iterate through the timestamps
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for i in range(len(common_timestamps)):
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# If the current timestamp is less than 50 milliseconds away from the previous timestamp
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