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

code/chirpdetection.py

@@ -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()
 
@@ -317,7 +318,7 @@ 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 = []
 
@@ -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):
@@ -562,11 +561,9 @@ def main(datapath: str) -> None:
                         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)
 
@@ -593,8 +590,10 @@ def main(datapath: str) -> None:
             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
             index_vector = np.arange(len(sort_chirps_electrodes))
@@ -629,10 +628,6 @@ def main(datapath: str) -> None:
             plt.show()
 
 
-                
-   
-
-
 if __name__ == "__main__":
     datapath = "../data/2022-06-02-10_00/"
     main(datapath)

code/modules/timestamps.py

@@ -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]
+