post = 15 * pq.ms
epoch = add_epoch(
data_segment,
event1=start_event, event2=None,
pre=pre, post=post,
attach_result=False,
name='analysis_epochs')
# Create new segments of data cut according to the analysis epochs of the
# 'analysis_epochs' Neo Epoch object. The time axes of all segments are aligned
# such that each segment starts at time 0 (parameter reset_times); annotations
# describing the analysis epoch are carried over to the segments. A new Neo
# Block named "data_cut_to_analysis_epochs" is created to capture all cut
# analysis epochs.
cut_trial_block = Block(name="data_cut_to_analysis_epochs")
cut_trial_block.segments = cut_segment_by_epoch(
data_segment, epoch, reset_time=True)
# =============================================================================
# Plot data
# =============================================================================
# Determine the first existing trial ID i from the Event object containing all
# start events. Then, by calling the filter() function of the Neo Block
# "data_cut_to_analysis_epochs" containing the data cut into the analysis
# epochs, we ask to return all Segments annotated by the behavioral trial ID i.
# In this case this call should return one matching analysis epoch around TS-ON
# belonging to behavioral trial ID i. For monkey N, this is trial ID 1, for
# monkey L this is trial ID 2 since trial ID 1 is not a correct trial.
trial_id = int(np.min(start_event.annotations['trial_id']))
trial_segments = cut_trial_block.filter(
targdict={"trial_id": trial_id}, objects=Segment)
"""
Example for usecases.rst
"""
from itertools import cycle
import numpy as np
from quantities import ms, mV, kHz
import matplotlib.pyplot as plt
from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal
store_signals = False
block = Block(name="probe data", tetrode_ids=["Tetrode #1", "Tetrode #2"])
block.segments = [
Segment(name="trial #1", index=0),
Segment(name="trial #2", index=1),
Segment(name="trial #3", index=2)
]
n_units = {"Tetrode #1": 2, "Tetrode #2": 5}
# Create a group for each neuron, annotate each group with the tetrode from which it was recorded
groups = []
counter = 0
for tetrode_id, n in n_units.items():
groups.extend([
Group(name=f"neuron #{counter + i + 1}", tetrode_id=tetrode_id)
for i in range(n)
])
counter += n
block.groups.extend(groups)