From 844f65e24e886c3428d2cc0ecdb33c93fa003335 Mon Sep 17 00:00:00 2001
From: wendtalexander <wendtalexander@protonmail.com>
Date: Wed, 18 Jan 2023 09:38:23 +0100
Subject: [PATCH] final peak version with bool
---
code/chirpdetection.py | 19 +++++++++++--------
code/modules/timestamps.py | 1 +
2 files changed, 12 insertions(+), 8 deletions(-)
diff --git a/code/chirpdetection.py b/code/chirpdetection.py
index 1391f7c..26a9b52 100644
--- a/code/chirpdetection.py
+++ b/code/chirpdetection.py
@@ -12,7 +12,7 @@ from sklearn.preprocessing import normalize
from modules.filters import bandpass_filter, envelope, highpass_filter
from modules.filehandling import ConfLoader, LoadData
from modules.plotstyle import PlotStyle
-from modules.timestamps import group_timestamps, group_timestamp_v2
+from modules.timestamps import group_timestamps, group_timestamps_v2
ps = PlotStyle()
@@ -527,6 +527,7 @@ def main(datapath: str) -> None:
if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
continue
+
# get index for each feature
baseline_idx = np.zeros_like(baseline_ts)
search_idx = np.ones_like(search_ts)
@@ -562,6 +563,7 @@ def main(datapath: str) -> None:
bool_timestamps[cm] = False
# for checking if there are chirps on multiple electrodes
+
chirps_electrodes.append(current_chirps)
for ct in current_chirps:
@@ -597,20 +599,22 @@ def main(datapath: str) -> None:
bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool)
# make index vector
index_vector = np.arange(len(sort_chirps_electrodes))
- # make it more than only two electrodes for the search after chirps
- combinations_best_elctrodes = list(itertools.combinations(range(3), 2))
+ # make it more than only two electrodes for the search after chirps
+ combinations_best_elctrodes = list(
+ itertools.combinations(range(3), 2))
the_real_chirps = []
for chirp_index, seoc in enumerate(sort_chirps_electrodes):
if bool_vector[chirp_index] == False:
continue
cm = index_vector[(sort_chirps_electrodes >= seoc) & (
- sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
-
+ sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
+
for combination in combinations_best_elctrodes:
if set(combination).issubset(sort_electrodes[cm]):
- the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
- """
+ the_real_chirps.append(
+ np.mean(sort_chirps_electrodes[cm]))
+
if set([0,1]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([1,0]).issubset(sort_electrodes[cm]):
@@ -619,7 +623,6 @@ def main(datapath: str) -> None:
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
elif set([1,2]).issubset(sort_electrodes[cm]):
the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
- """
bool_vector[cm] = False
for ct in the_real_chirps:
diff --git a/code/modules/timestamps.py b/code/modules/timestamps.py
index a342271..1f3aa8d 100644
--- a/code/modules/timestamps.py
+++ b/code/modules/timestamps.py
@@ -82,6 +82,7 @@ def group_timestamps_v2(sublists: List[List[Union[int, float]]], n: int, time_th
current_group = []
# Create a set to store the timestamps that occur in at least n of the sublists
common_timestamps = set.intersection(*[set(lst) for lst in sublists])
+ embed()
# Iterate through the timestamps
for i in range(len(common_timestamps)):
# If the current timestamp is less than 50 milliseconds away from the previous timestamp