raab/GP2023_chirp_detection
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge branch 'peakdetection' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection into peakdetection

Browse Source
This commit is contained in:
wendtalexander 2023-01-18 09:17:40 +01:00
commit 7034e9421b
2 changed files with 117 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
code/chirpdetection.py
View File

@ -12,6 +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
ps = PlotStyle()
@ -317,10 +318,10 @@ def main(datapath: str) -> None:
search_freq = config.default_search_freq
print(f"Search frequency: {search_freq}")
#----------- chrips on the two best electrodes-----------
# ----------- chrips on the two best electrodes-----------
chirps_electrodes = []
electrodes_of_chirps = []
electrodes_of_chirps = []
# iterate through electrodes
for el, electrode in enumerate(best_electrodes):
print(el)
@ -541,7 +542,6 @@ def main(datapath: str) -> None:
timestamps)]
timestamps = timestamps[np.argsort(timestamps)]
# # get chirps
# diff = np.empty(timestamps.shape)
# diff[0] = np.inf # always retain the 1st element
@ -549,7 +549,6 @@ def main(datapath: str) -> None:
# mask = diff < config.chirp_window_threshold
# shared_peak_indices = timestamp_idx[mask]
current_chirps = []
bool_timestamps = np.ones_like(timestamps, dtype=bool)
for bo, tt in enumerate(timestamps):
@ -561,12 +560,10 @@ def main(datapath: str) -> None:
current_chirps.append(np.mean(timestamps[cm]))
electrodes_of_chirps.append(el)
bool_timestamps[cm] = False
# for checking if there are chirps on multiple electrodes
chirps_electrodes.append(current_chirps)
for ct in current_chirps:
axs[0, el].axvline(ct, color='r', lw=1)
@ -586,17 +583,19 @@ def main(datapath: str) -> None:
np.ones_like((time_oi)[baseline_peaks]) * 600,
c=ps.red,
)
# make one array
# make one array
chirps_electrodes = np.concatenate(chirps_electrodes)
# make shure they are numpy arrays
chirps_electrodes = np.asarray(chirps_electrodes)
electrodes_of_chirps = np.asarray(electrodes_of_chirps)
# sort them
sort_chirps_electrodes = chirps_electrodes[np.argsort(chirps_electrodes)]
sort_electrodes = electrodes_of_chirps[np.argsort(chirps_electrodes)]
sort_chirps_electrodes = chirps_electrodes[np.argsort(
chirps_electrodes)]
sort_electrodes = electrodes_of_chirps[np.argsort(
chirps_electrodes)]
bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool)
# make index vector
# 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))
@ -629,10 +628,6 @@ def main(datapath: str) -> None:
plt.show()
if __name__ == "__main__":
datapath = "../data/2022-06-02-10_00/"
main(datapath)

106
code/modules/timestamps.py Normal file
View File

@ -0,0 +1,106 @@
import numpy as np
from typing import List, Union
def group_timestamps(timestamps: List[Union[int, float]], time_threshold: float = 0.05) -> List[float]:
"""
Group timestamps that are less than a certain time threshold apart.
Parameters
----------
timestamps : list of float or int
List of timestamps to group
time_threshold : float, optional
The threshold for time difference between two consecutive timestamps in milliseconds. Default is 0.05 milliseconds.
Returns
-------
list of float
List of mean of each group of timestamps
Examples
--------
>>> timestamps = [1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55, 1.6, 1.65]
>>> group_timestamps(timestamps)
[1.275, 1.425, 1.575]
"""
# Create an empty list to store the groups of timestamps
groups = []
# Create a variable to store the current group of timestamps
current_group = []
# Iterate through the timestamps
for i in range(len(timestamps)):
# If the current timestamp is less than 50 milliseconds away from the previous timestamp
if i > 0 and timestamps[i] - timestamps[i-1] < time_threshold:
# Add the current timestamp to the current group
current_group.append(timestamps[i])
else:
# If the current timestamp is not part of the current group
if current_group:
# Add the current group to the list of groups
groups.append(current_group)
# Reset the current group
current_group = []
# Add the current timestamp to a new group
current_group.append(timestamps[i])
# If there is a group left after the loop
if current_group:
# Add the current group to the list of groups
groups.append(current_group)
# Compute the mean of each group and return it
return [np.mean(group) for group in groups]
def group_timestamps_v2(sublists: List[List[Union[int, float]]], n: int, time_threshold: float = 0.05) -> List[float]:
"""
Group timestamps that are less than a certain time threshold apart and occur in at least n sublists.
Parameters
----------
sublists : list of list of float or int
List of sublists containing timestamps
n : int
Minimum number of sublists in which a timestamp should occur to be considered
time_threshold : float, optional
The threshold for time difference between two consecutive timestamps in milliseconds. Default is 0
Returns
-------
list of float
List of mean of each group of timestamps
Examples
--------
>>> sublists = [[1.2, 1.25, 1.3, 1.35, 1.4], [1.3, 1.35, 1.4, 1.45, 1.5], [1.4, 1.45, 1.5, 1.55, 1.6]]
>>> group_timestamps_v2(sublists, 2)
[1.325, 1.45]
"""
# Create an empty list to store the groups of timestamps
groups = []
# Create a variable to store the current group of timestamps
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])
# 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
if i > 0 and common_timestamps[i] - common_timestamps[i-1] < time_threshold:
# Add the current timestamp to the current group
current_group.append(common_timestamps[i])
else:
# If the current timestamp is not part of the current group
if current_group:
# Add the current group to the list of groups
groups.append(current_group)
# Reset the current group
current_group = []
# Add the current timestamp to a new group
current_group.append(common_timestamps[i])
# If there is a group left after the loop
if current_group:
# Add the current group to the list of groups
groups.append(current_group)
# Compute the mean of each group and return it
return [np.mean(group) for group in groups]