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@@ -61,8 +61,8 @@ from neo_utils import load_segment
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# Specify the path to the recording session to load, eg,
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# '/home/user/l101210-001'
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-# session_name = os.path.join('..', 'datasets', 'i140703-001')
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-session_name = os.path.join('..', 'datasets', 'l101210-001')
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+session_name = os.path.join('..', 'datasets', 'i140703-001')
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+# session_name = os.path.join('..', 'datasets', 'l101210-001')
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odml_dir = os.path.join('..', 'datasets')
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# Open the session for reading
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@@ -124,9 +124,11 @@ for anasig in data_segment.analogsignals:
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# Create LFP signal by filtering raw signal if not present already
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if filtered_anasig is None:
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- # Use the Elephant library to filter the signal
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+ # Use the Elephant library to filter the signal, filter only target channel
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+ target_channel_index = np.where(target_channel_id == raw_anasig.array_annotations['channel_ids'])[0]
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+ raw_signal = raw_anasig[:, target_channel_index]
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f_anasig = butter(
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- raw_anasig,
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+ raw_signal,
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highpass_freq=None,
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lowpass_freq=250 * pq.Hz,
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order=4)
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@@ -223,7 +225,7 @@ nsx_colors = {2: 'k', 5: 'r', 6: 'b'}
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for i, anasig in enumerate(trial_segment.analogsignals):
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# only visualize neural data
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if anasig.annotations['neural_signal']:
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- nsx = anasig.array_annotations[nsx][0]
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+ nsx = anasig.array_annotations['nsx'][0]
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target_channel_index = np.where(anasig.array_annotations['channel_ids'] == target_channel_id)[0]
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target_signal = anasig[:, target_channel_index]
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plt.plot(
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