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Update multirecording_spikeanalysis.py #1

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Update multirecording_spikeanalysis.py #1

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28 changes: 12 additions & 16 deletions multirecording_spikeanalysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import pandas as pd
import matplotlib.pyplot as plt

from collections import defaultdict
from scipy.stats import sem, ranksums, fisher_exact, wilcoxon
from statistics import mean, StatisticsError
from sklearn.decomposition import PCA
Expand Down Expand Up @@ -439,9 +440,9 @@ def get_spike_specs(self):

def get_unit_timestamps(self):
"""
creates a dictionary of units to spike timestamps
keys are unit ids (int) and values are spike timestamps for
that unit (numpy arrays)and assigns dictionary to self.unit_timestamps
Creates a dictionary of units to spike timestamps.
Keys are unit ids (int) and values are spike timestamps for that unit (numpy arrays),
and assigns dictionary to self.unit_timestamps.

Args:
None
Expand All @@ -450,21 +451,16 @@ def get_unit_timestamps(self):
None
"""

unit_timestamps = {}
for spike in range(len(self.timestamps_var)):
if self.unit_array[spike] in unit_timestamps.keys():
timestamp_list = unit_timestamps[self.unit_array[spike]]
timestamp_list = np.append(
timestamp_list, self.timestamps_var[spike]
)
unit_timestamps[self.unit_array[spike]] = timestamp_list
else:
unit_timestamps[self.unit_array[spike]] = self.timestamps_var[
spike
]
# Initialize a defaultdict for holding lists
unit_timestamps = defaultdict(list)
default_dict = defaultdict(lambda: np.array([]))

self.unit_timestamps = unit_timestamps
# Loop through each spike only once
for spike, unit in enumerate(self.unit_array):
# Append the timestamp to the list for the corresponding unit
unit_timestamps[unit].append(self.timestamps_var[spike])

self.unit_timestamps = unit_timestamps

class EphysRecordingCollection:
"""
Expand Down