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@@ -30,6 +30,10 @@ def binspikes(path):
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frozenbin : (k, b, c) numpy.ndarray
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Binned spikes for the repeating "frozen" stimulus parts
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+
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+ moviebin : (k, b, c) numpy.ndarray
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+ Binned spikes for the repeating "movie" stimulus parts. Only
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+ returned if the experiment contained the movie stimulation
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"""
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# Load stimulus parameters from disk
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path = Path(path).expanduser()
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@@ -39,7 +43,7 @@ def binspikes(path):
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# Determine length (number of bins) of one trial
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len_run = p['RunningFrames'] * p['Nblinks']
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len_frz = p['FrozenFrames'] * p['Nblinks']
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- len_mov = p.get('MovieFrames', 0) * p.get('Nblinksmovie', 0)
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+ len_mov = p.setdefault('MovieFrames', 0) * p.setdefault('Nblinksmovie', 0)
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len_trial = len_run + len_frz + len_mov
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# Load stimulus frame times and spike times from disk
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@@ -70,14 +74,20 @@ def binspikes(path):
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# Separate running and frozen parts
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runningbin = spkbin[:, :len_run, :]
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frozenbin = spkbin[:, len_run:len_run+len_frz, :]
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+ moviebin = spkbin[:, len_run+len_frz:, :]
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# Adjust bins to blinks
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shape_r = (num_trials, p['RunningFrames'], p['Nblinks'], num_cells)
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shape_f = (num_trials, p['FrozenFrames'], p['Nblinks'], num_cells)
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+ shape_m = (num_trials, p['MovieFrames'], p['Nblinksmovie'], num_cells)
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runningbin = runningbin.reshape(shape_r).sum(axis=2, dtype='uint8')
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frozenbin = frozenbin.reshape(shape_f).sum(axis=2, dtype='uint8')
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+ moviebin = moviebin.reshape(shape_m).sum(axis=2, dtype='uint8')
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- return runningbin, frozenbin
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+ if p['MovieFrames']:
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+ return runningbin, frozenbin, moviebin
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+ else:
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+ return runningbin, frozenbin
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if __name__ == '__main__':
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