Merge branch 'master' into plot_event_timeline

code/chirpdetection.py

@@ -589,16 +589,16 @@ def main(datapath: str, plot: str) -> None:
     raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
 
     # good chirp times for data: 2022-06-02-10_00
-    window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate
-    window_duration_index = 60 * data.raw_rate
+    # window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate
+    # window_duration_index = 60 * data.raw_rate
 
     #     t0 = 0
     #     dt = data.raw.shape[0]
     # window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate
     # window_duration_seconds = (28336 - 23495) * data.raw_rate
 
-    # window_start_index = 0
-    # window_duration_index = data.raw.shape[0]
+    window_start_index = 0
+    window_duration_index = data.raw.shape[0]
 
     # generate starting points of rolling window
     window_start_indices = np.arange(

code/modules/datahandling.py

@@ -1,9 +1,10 @@
 import numpy as np
 from typing import List, Any
 from scipy.ndimage import gaussian_filter1d
+from scipy.stats import gamma, norm
 
 
-def norm(data):
+def scale01(data):
     """
     Normalize data to [0, 1]
 
@@ -209,6 +210,117 @@ def flatten(list: List[List[Any]]) -> List:
     return [item for sublist in list for item in sublist]
 
 
+def causal_kde1d(spikes, time, width, shape=2):
+    """
+    causalkde computes a kernel density estimate using a causal kernel (i.e. exponential or gamma distribution).
+    A shape of 1 turns the gamma distribution into an exponential.
+
+    Parameters
+    ----------
+    spikes : array-like
+        spike times
+    time : array-like
+        sampling time
+    width : float
+        kernel width
+    shape : int, optional
+        shape of gamma distribution, by default 1
+
+    Returns
+    -------
+    rate : array-like
+        instantaneous firing rate
+    """
+
+    # compute dt
+    dt = time[1] - time[0]
+
+    # time on which to compute kernel:
+    tmax = 10 * width
+
+    # kernel not wider than time
+    if 2 * tmax > time[-1] - time[0]:
+        tmax = 0.5 * (time[-1] - time[0])
+
+    # kernel time
+    ktime = np.arange(-tmax, tmax, dt)
+
+    # gamma kernel centered in ktime:
+    kernel = gamma.pdf(
+        x=ktime,
+        a=shape,
+        loc=0,
+        scale=width,
+    )
+
+    # indices of spikes in time array:
+    indices = np.asarray((spikes - time[0]) / dt, dtype=int)
+
+    # binary spike train:
+    brate = np.zeros(len(time))
+    brate[indices[(indices >= 0) & (indices < len(time))]] = 1.0
+
+    # convolution with kernel:
+    rate = np.convolve(brate, kernel, mode="same")
+
+    return rate
+
+
+def acausal_kde1d(spikes, time, width):
+    """
+    causalkde computes a kernel density estimate using a causal kernel (i.e. exponential or gamma distribution).
+    A shape of 1 turns the gamma distribution into an exponential.
+
+    Parameters
+    ----------
+    spikes : array-like
+        spike times
+    time : array-like
+        sampling time
+    width : float
+        kernel width
+    shape : int, optional
+        shape of gamma distribution, by default 1
+
+    Returns
+    -------
+    rate : array-like
+        instantaneous firing rate
+    """
+
+    # compute dt
+    dt = time[1] - time[0]
+
+    # time on which to compute kernel:
+    tmax = 10 * width
+
+    # kernel not wider than time
+    if 2 * tmax > time[-1] - time[0]:
+        tmax = 0.5 * (time[-1] - time[0])
+
+    # kernel time
+    ktime = np.arange(-tmax, tmax, dt)
+
+    # gamma kernel centered in ktime:
+    kernel = norm.pdf(
+        x=ktime,
+        loc=0,
+        scale=width,
+    )
+
+    # indices of spikes in time array:
+    indices = np.asarray((spikes - time[0]) / dt, dtype=int)
+
+    # binary spike train:
+    brate = np.zeros(len(time))
+    brate[indices[(indices >= 0) & (indices < len(time))]] = 1.0
+
+    # convolution with kernel:
+    rate = np.convolve(brate, kernel, mode="same")
+
+    return rate
+
+
 if __name__ == "__main__":
 
     timestamps = [

code/plot_event_timeline.py

@@ -0,0 +1,203 @@
+import numpy as np
+
+import os 
+
+import numpy as np
+import matplotlib.pyplot as plt 
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+class Behavior:
+    """Load behavior data from csv file as class attributes
+        Attributes
+    ----------
+    behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
+    behavior_type:         
+    behavioral_category:   
+    comment_start:         
+    comment_stop:          
+    dataframe: pandas dataframe with all the data            
+    duration_s:             
+    media_file:            
+    observation_date:      
+    observation_id:        
+    start_s: start time of the event in seconds               
+    stop_s:  stop time of the event in seconds               
+    total_length:          
+    """
+
+    def __init__(self, folder_path: str) -> None:
+        
+
+        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
+
+        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
+        logger.info(f'CSV file: {csv_filename}')
+        self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
+
+        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
+
+        self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
+        self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
+        self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
+        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
+        self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)    
+
+        for k, key in enumerate(self.dataframe.keys()):
+            key = key.lower() 
+            if ' ' in key:
+                key = key.replace(' ', '_')
+                if '(' in key:
+                    key = key.replace('(', '')
+                    key = key.replace(')', '')
+            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
+        
+        last_LED_t_BORIS = LED_on_time_BORIS[-1]
+        real_time_range = self.time[-1] - self.time[0]
+        factor = 1.034141
+        shift = last_LED_t_BORIS - real_time_range * factor
+        self.start_s = (self.start_s - shift) / factor
+        self.stop_s = (self.stop_s - shift) / factor
+
+def correct_chasing_events(
+    category: np.ndarray, 
+    timestamps: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray]:
+
+    onset_ids = np.arange(
+        len(category))[category == 0]
+    offset_ids = np.arange(
+        len(category))[category == 1]
+
+    # Check whether on- or offset is longer and calculate length difference
+    if len(onset_ids) > len(offset_ids):
+        len_diff = len(onset_ids) - len(offset_ids)
+        longer_array = onset_ids
+        shorter_array = offset_ids
+        logger.info(f'Onsets are greater than offsets by {len_diff}')
+    elif len(onset_ids) < len(offset_ids):
+        len_diff = len(offset_ids) - len(onset_ids)
+        longer_array = offset_ids
+        shorter_array = onset_ids
+        logger.info(f'Offsets are greater than offsets by {len_diff}')
+    elif len(onset_ids) == len(offset_ids):
+        logger.info('Chasing events are equal')
+        return category, timestamps
+
+
+    # Correct the wrong chasing events; delete double events
+    wrong_ids = []
+    for i in range(len(longer_array)-(len_diff+1)):
+        if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
+            pass
+        else:
+            wrong_ids.append(longer_array[i])
+            longer_array = np.delete(longer_array, i)
+        
+    category = np.delete(
+        category, wrong_ids)
+    timestamps = np.delete(
+        timestamps, wrong_ids)
+    return category, timestamps
+
+
+
+def main(datapath: str):
+    # behabvior is pandas dataframe with all the data
+    bh = Behavior(datapath)
+    # chirps are not sorted in time (presumably due to prior groupings)
+    # get and sort chirps and corresponding fish_ids of the chirps
+    chirps = bh.chirps[np.argsort(bh.chirps)]
+    chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
+    category = bh.behavior
+    timestamps = bh.start_s
+    # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
+    # Get rid of tracking faults (two onsets or two offsets after another)
+    category, timestamps = correct_chasing_events(category, timestamps)
+
+    # split categories
+    chasing_onset = (timestamps[category == 0]/ 60) /60
+    chasing_offset = (timestamps[category == 1]/ 60) /60
+    physical_contact = (timestamps[category == 2] / 60) /60
+
+    all_fish_ids = np.unique(chirps_fish_ids)
+    fish1_id = all_fish_ids[0]
+    fish2_id = all_fish_ids[1]
+    # Associate chirps to inidividual fish
+    fish1 = (chirps[chirps_fish_ids == fish1_id] / 60) /60
+    fish2 = (chirps[chirps_fish_ids == fish2_id] / 60) /60
+    fish1_color = ps.red
+    fish2_color = ps.orange
+
+    fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
+    # marker size 
+    s = 200
+    ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
+    ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
+    ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
+    ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
+   
+
+    freq_temp = bh.freq[bh.ident==fish1_id]
+    time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
+    ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
+
+    freq_temp = bh.freq[bh.ident==fish2_id]
+    time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
+    ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
+
+    #ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
+
+        # Hide grid lines
+    ax[0].grid(False)
+    ax[0].set_frame_on(False)
+    ax[0].set_xticks([])
+    ax[0].set_yticks([])
+    ps.hide_ax(ax[0])
+
+
+    ax[1].grid(False)
+    ax[1].set_frame_on(False)
+    ax[1].set_xticks([])
+    ax[1].set_yticks([])
+    ps.hide_ax(ax[1])
+
+    ax[2].grid(False)
+    ax[2].set_frame_on(False)
+    ax[2].set_yticks([])
+    ax[2].set_xticks([])
+    ps.hide_ax(ax[2])
+
+
+
+    ax[3].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5)
+    ax[3].set_xticks(np.arange(0, 6.1, 0.5))
+
+    labelpad = 40
+    ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
+    ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad)
+    ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad)
+    ax[3].set_ylabel('EODf')
+
+    ax[3].set_xlabel('Time [h]')
+
+    plt.show()
+    embed()
+
+    # plot chirps
+
+
+if __name__ == '__main__':
+    # Path to the data
+    datapath = '../data/mount_data/2020-05-13-10_00/'
+    main(datapath)