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- import numpy as np
- STATES = ("AtEnd", "Backward", "Turn", "Forward", "Expect", "Lick")
- PATTERNS = dict(AtEnd="<AtEnd> Backward {turn}",
- Backward="AtEnd <Backward> {turn}",
- Turn="Backward <{turn}> Forward", # note a difference from the previous section
- Forward="{turn} <Forward>", # note a difference from the previous section
- Expect="{turn} Forward <Expect>",
- Lick="{turn} Forward Expect <Lick>")
- def collect_epochs_with_pattern(trials, pattern, property="right_whisker_angle_deg"):
- ret = []
- for trial in trials:
- # we collect traces within the matched periods, trial-by-trial
- traces = []
- for start, stop in trial.get_timeranges(pattern):
- if (stop - start) < 2:
- continue
- # frames start from 1, so we have to adjust when reading from tracking data
- epoch = np.array(trial.tracking[property][start-1:stop])
- traces.append(epoch)
- # ignore cases when the pattern did not match in this trial
- if len(traces) == 0:
- continue
- # otherwise: add with subject/session information
- ret.append(dict(session=(trial.subject, trial.session), traces=traces))
- return ret
- def normalize_time(trace, t_out):
- """sample temporal normalization using a 1-D trace.
- `t_out` is a sequence of normalized timepoints, ranging [0,1].
- """
- t_in = np.arange(0, trace.size) / (trace.size - 1)
- return np.interp(t_out, t_in, trace)
- def get_normalized_traces(epochs, bins=100):
- """returns a numpy.ndarray of time-normalized traces.
- the shape of the returned array is (bins, ntraces).
- `epochs` must be in the form as they are collected
- by `collect_epochs_with_pattern()`."""
- t_out = np.linspace(0, 1, bins, endpoint=True)
- traces = []
- for epoch in epochs:
- for trace in epoch["traces"]:
- traces.append(normalize_time(trace, t_out))
- return np.stack(traces, axis=-1)
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