From 6fd3323c6cd2304a968e3d310ef0898e029a3693 Mon Sep 17 00:00:00 2001
From: sprause <sprause95@gmail.com>
Date: Wed, 25 Jan 2023 09:11:51 +0100
Subject: [PATCH] inserted two functions from CTCPTC stuff
---
code/modules/behaviour_handling.py | 46 ++++++++++++++++++++++++++----
1 file changed, 41 insertions(+), 5 deletions(-)
diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py
index 90a18ab..6641309 100644
--- a/code/modules/behaviour_handling.py
+++ b/code/modules/behaviour_handling.py
@@ -8,6 +8,7 @@ from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
+from modules.datahandling import causal_kde1d, acausal_kde1d
logger = makeLogger(__name__)
@@ -76,14 +77,14 @@ def correct_chasing_events(
offset_ids = np.arange(
len(category))[category == 1]
- woring_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
+ wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0]
- woring_bh = np.append(woring_bh, help_index)
+ wrong_bh = np.append(wrong_bh[help_index])
- category = np.delete(category, woring_bh)
- timestamps = np.delete(timestamps, woring_bh)
+ category = np.delete(category, wrong_bh)
+ timestamps = np.delete(timestamps, wrong_bh)
# Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids):
@@ -94,6 +95,41 @@ def correct_chasing_events(
logger.info(f'Offsets are greater than onsets by {len_diff}')
elif len(onset_ids) == len(offset_ids):
logger.info('Chasing events are equal')
+
+ return category, timestamps
- return category, timestamps
\ No newline at end of file
+def event_triggered_chirps(
+ event: np.ndarray,
+ chirps:np.ndarray,
+ time_before_event: int,
+ time_after_event: int,
+ dt: float,
+ width: float,
+ )-> tuple[np.ndarray, np.ndarray, np.ndarray]:
+
+ event_chirps = [] # chirps that are in specified window around event
+ centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
+
+ for event_timestamp in event:
+ start = event_timestamp - time_before_event
+ stop = event_timestamp + time_after_event
+ chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
+ event_chirps.append(chirps_around_event)
+ if len(chirps_around_event) == 0:
+ continue
+ else:
+ centered_chirps.append(chirps_around_event - event_timestamp)
+
+ time = np.arange(-time_before_event, time_after_event, dt)
+
+ # Kernel density estimation with some if's
+ if len(centered_chirps) == 0:
+ centered_chirps = np.array([])
+ centered_chirps_convolved = np.zeros(len(time))
+ else:
+ centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
+ centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
+
+ return event_chirps, centered_chirps, centered_chirps_convolved
+