python versions

.gitignore

@@ -14,6 +14,7 @@ output
 __pycache__/
 *.py[cod]
 *$py.class
+poster/main.pdf
 
 # C extensions
 *.so

.python-version

@@ -0,0 +1 @@
+chirp

code/chirp_sim.py

@@ -1,24 +1,37 @@
-from modules.filters import create_chirp, bandpass_filter
-import matplotlib.pyplot as plt 
-from chirpdetection import instantaneos_frequency
+import numpy as np
 from IPython import embed
-# create chirp
+import matplotlib.pyplot as plt
+from thunderfish import fakefish
 
-time, signal, ampl, freq = create_chirp(chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
+from modules.filters import bandpass_filter
+from modules.datahandling import instantaneous_frequency
+from modules.simulations import create_chirp
 
-# filter signal with bandpass_filter 
 
-signal = bandpass_filter(signal, 1/0.00001, 495, 505)
+
+# trying thunderfish fakefish chirp simulation ---------------------------------
+samplerate = 44100
+freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
+data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3, samplerate=samplerate)
+
+# filter signal with bandpass_filter
+data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
 embed()
-exit()
-fig, axs = plt.subplots(2, 1, figsize=(10, 10))
-axs[0].plot(time, signal)
+data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
 
-# plot instatneous frequency
 
-baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
-axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
+fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
 
+ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
+#ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
+ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
+ax[2].plot(np.arange(len(freq))/samplerate, freq)
+ax[3].plot(data_freq_time, data_freq)
 
 
 plt.show()
+embed()
+
+
+
+

code/eventchirpsplots.py

@@ -1,55 +1,62 @@
-import os 
+import os
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 
 from tqdm import tqdm
 from IPython import embed
 from pandas import read_csv
 from modules.logger import makeLogger
 from modules.plotstyle import PlotStyle
-from modules.datahandling import causal_kde1d, acausal_kde1d
+from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
 
 logger = makeLogger(__name__)
 ps = PlotStyle()
 
+
 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:          
+    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:
         print(f'{folder_path}')
-        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
-        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
-        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
+        LED_on_time_BORIS = np.load(os.path.join(
+            folder_path, 'LED_on_time.npy'), allow_pickle=True)
+        self.time = np.load(os.path.join(
+            folder_path, "times.npy"), allow_pickle=True)
+        csv_filename = [f for f in os.listdir(folder_path) if f.endswith(
+            '.csv')][0]  # check if there are more than one csv file
         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, 'chirp_ids.npy'), allow_pickle=True)
+        self.chirps = np.load(os.path.join(
+            folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(
+            folder_path, 'chirp_ids.npy'), allow_pickle=True)
 
         for k, key in enumerate(self.dataframe.keys()):
-            key = key.lower() 
+            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]]))
+            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]
@@ -58,6 +65,7 @@ class Behavior:
         self.start_s = (self.start_s - shift) / factor
         self.stop_s = (self.stop_s - shift) / factor
 
+
 """
 1 - chasing onset
 2 - chasing offset
@@ -87,16 +95,17 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
 
 
 def correct_chasing_events(
-    category: np.ndarray, 
+    category: np.ndarray,
     timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
 
     onset_ids = np.arange(
         len(category))[category == 0]
     offset_ids = np.arange(
         len(category))[category == 1]
 
-    wrong_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]
@@ -105,7 +114,6 @@ def correct_chasing_events(
     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):
         len_diff = len(onset_ids) - len(offset_ids)
@@ -115,21 +123,22 @@ 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
 
 
 def event_triggered_chirps(
-    event: np.ndarray, 
-    chirps:np.ndarray,
+    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]:
+) -> 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
+    # timestamps of chirps around event centered on the event timepoint
+    centered_chirps = []
 
     for event_timestamp in event:
         start = event_timestamp - time_before_event
@@ -138,25 +147,28 @@ def event_triggered_chirps(
         event_chirps.append(chirps_around_event)
         if len(chirps_around_event) == 0:
             continue
-        else: 
+        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)
+        # convert list of arrays to one array for plotting
+        centered_chirps = np.concatenate(centered_chirps, axis=0)
+        centered_chirps_convolved = (acausal_kde1d(
+            centered_chirps, time, width)) / len(event)
 
     return event_chirps, centered_chirps, centered_chirps_convolved
 
 
 def main(datapath: str):
 
-    foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
 
     nrecording_chirps = []
     nrecording_chirps_fish_ids = []
@@ -171,7 +183,7 @@ def main(datapath: str):
             continue
 
         bh = Behavior(folder)
-        
+
         # Chirps are already sorted
         category = bh.behavior
         timestamps = bh.start_s
@@ -192,14 +204,13 @@ def main(datapath: str):
         nrecording_chasing_offsets.append(chasing_offsets)
         physical_contacts = timestamps[category == 2]
         nrecording_physicals.append(physical_contacts)
-    
 
     # Define time window for chirps around event analysis
     time_before_event = 30
     time_after_event = 60
     dt = 0.01
     width = 1.5   # width of kernel for all recordings, currently gaussian kernel
-    recording_width = 2 # width of kernel for each recording
+    recording_width = 2  # width of kernel for each recording
     time = np.arange(-time_before_event, time_after_event, dt)
 
     ##### Chirps around events, all fish, all recordings #####
@@ -212,7 +223,7 @@ def main(datapath: str):
     nrecording_shuffled_convolved_offset_chirps = []
     nrecording_shuffled_convolved_physical_chirps = []
 
-    nbootstrapping = 10
+    nbootstrapping = 100
 
     for i in range(len(nrecording_chirps)):
         chirps = nrecording_chirps[i]
@@ -221,14 +232,18 @@ def main(datapath: str):
         physical_contacts = nrecording_physicals[i]
 
         # Chirps around chasing onsets
-        _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(
+            chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around chasing offsets
-        _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(
+            chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around physical contacts
-        _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
+        _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(
+            physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
 
         nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
-        nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
+        nrecording_centered_offset_chirps.append(
+            centered_chasing_offset_chirps)
         nrecording_centered_physical_chirps.append(centered_physical_chirps)
 
         ## Shuffled chirps ##
@@ -236,112 +251,159 @@ def main(datapath: str):
         nshuffled_offset_chirps = []
         nshuffled_physical_chirps = []
 
-        for j in tqdm(range(nbootstrapping)):
-        # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
-            interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
-            np.random.shuffle(interchirp_intervals)
-            shuffled_chirps = np.cumsum(interchirp_intervals)
-            # Shuffled chasing onset chirps
-            _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
-            nshuffled_onset_chirps.append(cc_shuffled_onset_chirps)
-            # Shuffled chasing offset chirps
-            _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
-            nshuffled_offset_chirps.append(cc_shuffled_offset_chirps)
-            # Shuffled physical contact chirps
-            _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
-            nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
+        # for j in tqdm(range(nbootstrapping)):
+        # # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
+        #     interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
+        #     np.random.shuffle(interchirp_intervals)
+        #     shuffled_chirps = np.cumsum(interchirp_intervals)
+        #     # Shuffled chasing onset chirps
+        #     _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
+        #     nshuffled_onset_chirps.append(cc_shuffled_onset_chirps)
+        #     # Shuffled chasing offset chirps
+        #     _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
+        #     nshuffled_offset_chirps.append(cc_shuffled_offset_chirps)
+        #     # Shuffled physical contact chirps
+        #     _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
+        #     nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
 
-        rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
-        nshuffled_onset_chirps, (5, 50, 95), axis=0)
-        rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
-        nshuffled_offset_chirps, (5, 50, 95), axis=0)
-        rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
-        nshuffled_physical_chirps, (5, 50, 95), axis=0)
+        # rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
+        # nshuffled_onset_chirps, (5, 50, 95), axis=0)
+        # rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
+        # nshuffled_offset_chirps, (5, 50, 95), axis=0)
+        # rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
+        # nshuffled_physical_chirps, (5, 50, 95), axis=0)
 
-        #### Recording plots ####
-        fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
-        ax[0].set_xlabel('Time[s]')
+        # #### Recording plots ####
+        # fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
+        # ax[0].set_xlabel('Time[s]')
 
-        # Plot chasing onsets
-        ax[0].set_ylabel('Chirp rate [Hz]')
-        ax[0].plot(time, cc_chasing_onset_chirps, color=ps.yellow, zorder=2)
-        ax0 = ax[0].twinx()
-        ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
-        ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
-        ax[0].set_zorder(ax0.get_zorder()+1)
-        ax[0].patch.set_visible(False)
-        ax0.set_yticklabels([])
-        ax0.set_yticks([])
-        ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5)
-        ax[0].plot(time, rec_shuffled_median_onset, color=ps.black)
+        # # Plot chasing onsets
+        # ax[0].set_ylabel('Chirp rate [Hz]')
+        # ax[0].plot(time, cc_chasing_onset_chirps, color=ps.yellow, zorder=2)
+        # ax0 = ax[0].twinx()
+        # ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        # ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
+        # ax[0].set_zorder(ax0.get_zorder()+1)
+        # ax[0].patch.set_visible(False)
+        # ax0.set_yticklabels([])
+        # ax0.set_yticks([])
+        # ######## median - q5, median + q95
+        # ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5)
+        # ax[0].plot(time, rec_shuffled_median_onset, color=ps.black)
 
-        # Plot chasing offets
-        ax[1].set_xlabel('Time[s]')
-        ax[1].plot(time, cc_chasing_offset_chirps, color=ps.orange, zorder=2)
-        ax1 = ax[1].twinx()
-        ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
-        ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
-        ax[1].set_zorder(ax1.get_zorder()+1)
-        ax[1].patch.set_visible(False)
-        ax1.set_yticklabels([])
-        ax1.set_yticks([])
-        ax[1].fill_between(time, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5)
-        ax[1].plot(time, rec_shuffled_median_offset, color=ps.black)
+        # # Plot chasing offets
+        # ax[1].set_xlabel('Time[s]')
+        # ax[1].plot(time, cc_chasing_offset_chirps, color=ps.orange, zorder=2)
+        # ax1 = ax[1].twinx()
+        # ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        # ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
+        # ax[1].set_zorder(ax1.get_zorder()+1)
+        # ax[1].patch.set_visible(False)
+        # ax1.set_yticklabels([])
+        # ax1.set_yticks([])
+        # ax[1].fill_between(time, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5)
+        # ax[1].plot(time, rec_shuffled_median_offset, color=ps.black)
 
-        # Plot physical contacts
-        ax[2].set_xlabel('Time[s]')
-        ax[2].plot(time, cc_physical_chirps, color=ps.maroon, zorder=2)
-        ax2 = ax[2].twinx()
-        ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
-        ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
-        ax[2].set_zorder(ax2.get_zorder()+1)
-        ax[2].patch.set_visible(False)
-        ax2.set_yticklabels([])
-        ax2.set_yticks([])
-        ax[2].fill_between(time, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5)
-        ax[2].plot(time, rec_shuffled_median_physical, ps.black)
-        fig.suptitle(f'Recording: {i}')
-        plt.show()
+        # # Plot physical contacts
+        # ax[2].set_xlabel('Time[s]')
+        # ax[2].plot(time, cc_physical_chirps, color=ps.maroon, zorder=2)
+        # ax2 = ax[2].twinx()
+        # ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        # ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
+        # ax[2].set_zorder(ax2.get_zorder()+1)
+        # ax[2].patch.set_visible(False)
+        # ax2.set_yticklabels([])
+        # ax2.set_yticks([])
+        # ax[2].fill_between(time, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5)
+        # ax[2].plot(time, rec_shuffled_median_physical, ps.black)
+        # fig.suptitle(f'Recording: {i}')
+        # # plt.show()
         # plt.close()
-        
-        nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
-        nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
-        nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
+
+        # nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
+        # nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
+        # nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
+
+    #### New shuffle approach ####
+    bootstrap_onset = []
+    bootstrap_offset = []
+    bootstrap_physical = []
+
+    # New bootstrapping approach
+    for n in range(nbootstrapping):
+        diff_onset = np.diff(
+            np.sort(flatten(nrecording_centered_onset_chirps)))
+        diff_offset = np.diff(
+            np.sort(flatten(nrecording_centered_offset_chirps)))
+        diff_physical = np.diff(
+            np.sort(flatten(nrecording_centered_physical_chirps)))
+
+        np.random.shuffle(diff_onset)
+        shuffled_onset = np.cumsum(diff_onset)
+        np.random.shuffle(diff_offset)
+        shuffled_offset = np.cumsum(diff_offset)
+        np.random.shuffle(diff_physical)
+        shuffled_physical = np.cumsum(diff_physical)
+
+        kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100)
+        kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100)
+        kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100)
+
+        bootstrap_onset.append(kde_onset)
+        bootstrap_offset.append(kde_offset)
+        bootstrap_physical.append(kde_physical)
+
+    # New shuffle approach q5, q50, q95
+    onset_q5, onset_median, onset_q95 = np.percentile(
+        bootstrap_onset, [5, 50, 95], axis=0)
+    offset_q5, offset_median, offset_q95 = np.percentile(
+        bootstrap_offset, [5, 50, 95], axis=0)
+    physical_q5, physical_median, physical_q95 = np.percentile(
+        bootstrap_physical, [5, 50, 95], axis=0)
 
     #  vstack um 1. Dim zu cutten
-    nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
-    nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
-    nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
-    
-    shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
-        nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
-    shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
-        nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0)
-    shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
-        nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
+    # nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
+    # nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
+    # nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
 
-    # Flatten all chirps 
+    # shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
+    #     nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
+    # shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
+    #     nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0)
+    # shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
+    #     nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
+
+    # Flatten all chirps
     all_chirps = np.concatenate(nrecording_chirps).ravel()  # not centered
 
     # Flatten event timestamps
-    all_onsets = np.concatenate(nrecording_chasing_onsets).ravel() # not centered
-    all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered
-    all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
+    all_onsets = np.concatenate(
+        nrecording_chasing_onsets).ravel()  # not centered
+    all_offsets = np.concatenate(
+        nrecording_chasing_offsets).ravel()  # not centered
+    all_physicals = np.concatenate(
+        nrecording_physicals).ravel()  # not centered
 
     # Flatten all chirps around events
-    all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel()   # centered
-    all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered
-    all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered
+    all_onset_chirps = np.concatenate(
+        nrecording_centered_onset_chirps).ravel()   # centered
+    all_offset_chirps = np.concatenate(
+        nrecording_centered_offset_chirps).ravel()  # centered
+    all_physical_chirps = np.concatenate(
+        nrecording_centered_physical_chirps).ravel()  # centered
 
     # Convolute all chirps
     # Divide by total number of each event over all recordings
-    all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets)
-    all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets)
-    all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals)
-
+    all_onset_chirps_convolved = (acausal_kde1d(
+        all_onset_chirps, time, width)) / len(all_onsets)
+    all_offset_chirps_convolved = (acausal_kde1d(
+        all_offset_chirps, time, width)) / len(all_offsets)
+    all_physical_chirps_convolved = (acausal_kde1d(
+        all_physical_chirps, time, width)) / len(all_physicals)
 
     # Plot all events with all shuffled
-    fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
+    fig, ax = plt.subplots(1, 3, figsize=(
+        28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
     # offsets = np.arange(1,28,1)
     ax[0].set_xlabel('Time[s]')
 
@@ -349,49 +411,62 @@ def main(datapath: str):
     ax[0].set_ylabel('Chirp rate [Hz]')
     ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
     ax0 = ax[0].twinx()
-    nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
-    ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_onset_chirps = np.asarray(
+        nrecording_centered_onset_chirps, dtype=object)
+    ax0.eventplot(np.array(nrecording_centered_onset_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[0].set_zorder(ax0.get_zorder()+1)
     ax[0].patch.set_visible(False)
     ax0.set_yticklabels([])
     ax0.set_yticks([])
-    ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
-    ax[0].plot(time, shuffled_median_onset, color=ps.black)
+    # ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
+    # ax[0].plot(time, shuffled_median_onset, color=ps.black)
+    ax[0].fill_between(time, onset_q5, onset_q95, color=ps.gray, alpha=0.5)
+    ax[0].plot(time, onset_median, color=ps.black)
 
     # Plot chasing offets
     ax[1].set_xlabel('Time[s]')
     ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
     ax1 = ax[1].twinx()
-    nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
-    ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_offset_chirps = np.asarray(
+        nrecording_centered_offset_chirps, dtype=object)
+    ax1.eventplot(np.array(nrecording_centered_offset_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[1].set_zorder(ax1.get_zorder()+1)
     ax[1].patch.set_visible(False)
     ax1.set_yticklabels([])
     ax1.set_yticks([])
-    ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
-    ax[1].plot(time, shuffled_median_offset, color=ps.black)
+    # ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
+    # ax[1].plot(time, shuffled_median_offset, color=ps.black)
+    ax[1].fill_between(time, offset_q5, offset_q95, color=ps.gray, alpha=0.5)
+    ax[1].plot(time, offset_median, color=ps.black)
 
     # Plot physical contacts
     ax[2].set_xlabel('Time[s]')
     ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
     ax2 = ax[2].twinx()
-    nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
-    ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
+    nrecording_centered_physical_chirps = np.asarray(
+        nrecording_centered_physical_chirps, dtype=object)
+    ax2.eventplot(np.array(nrecording_centered_physical_chirps),
+                  linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
     ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[2].set_zorder(ax2.get_zorder()+1)
     ax[2].patch.set_visible(False)
     ax2.set_yticklabels([])
     ax2.set_yticks([])
-    ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
-    ax[2].plot(time, shuffled_median_physical, ps.black)
+    # ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
+    # ax[2].plot(time, shuffled_median_physical, ps.black)
+    ax[2].fill_between(time, physical_q5, physical_q95,
+                       color=ps.gray, alpha=0.5)
+    ax[2].plot(time, physical_median, ps.black)
     fig.suptitle('All recordings')
     plt.show()
-    # plt.close()
+    plt.close()
 
     embed()
-    
+
     # chasing_durations = []
     # # Calculate chasing duration to evaluate a nice time window for kernel density estimation
     # for onset, offset in zip(chasing_onsets, chasing_offsets):
@@ -403,7 +478,6 @@ def main(datapath: str):
     # plt.show()
     # plt.close()
 
-
     # # Associate chirps to individual fish
     # fish1 = chirps[chirps_fish_ids == fish_ids[0]]
     # fish2 = chirps[chirps_fish_ids == fish_ids[1]]
@@ -412,7 +486,6 @@ def main(datapath: str):
     # Convolution over all recordings
     # Rasterplot for each recording
 
-
     # #### Chirps around events, winner VS loser, one recording ####
     # # Load file with fish ids and winner/loser info
     # meta = pd.read_csv('../data/mount_data/order_meta.csv')
@@ -421,7 +494,7 @@ def main(datapath: str):
     # fish2 = current_recording['rec_id2'].values
     # # Implement check if fish_ids from meta and chirp detection are the same???
     # winner = current_recording['winner'].values
-    
+
     # if winner == fish1:
     #     loser = fish2
     # elif winner == fish2:
@@ -505,7 +578,6 @@ def main(datapath: str):
     # ax5.set_yticks([])
     # plt.show()
     # plt.close()
-    
 
     # for i in range(len(fish_ids)):
     #     fish = fish_ids[i]
@@ -515,7 +587,6 @@ def main(datapath: str):
     #### Chirps around events, only losers, one recording ####
 
 
-
 if __name__ == '__main__':
     # Path to the data
     datapath = '../data/mount_data/'

code/extract_chirps.py

@@ -7,21 +7,12 @@ from IPython import embed
 # check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175
 
 
-def main(datapaths):
-
-    for path in datapaths:
-        chirpdetection(path, plot='show')
-
-
-if __name__ == '__main__':
-
-    dataroot = '../data/mount_data/'
+def get_valid_datasets(dataroot):
 
     datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir(
         os.path.join(dataroot, name))])
 
     valid_datasets = []
-
     for dataset in datasets:
 
         path = os.path.join(dataroot, dataset)
@@ -43,9 +34,25 @@ if __name__ == '__main__':
     datapaths = [os.path.join(dataroot, dataset) +
                  '/' for dataset in valid_datasets]
 
+    return datapaths, valid_datasets
+
+
+def main(datapaths):
+
+    for path in datapaths:
+        chirpdetection(path, plot='show')
+
+
+if __name__ == '__main__':
+
+    dataroot = '../data/mount_data/'
+
+
+    datapaths, valid_datasets= get_valid_datasets(dataroot)
+
     recs = pd.DataFrame(columns=['recording'], data=valid_datasets)
     recs.to_csv('../recs.csv', index=False)
-    datapaths = ['../data/mount_data/2020-03-25-10_00/']
+    # datapaths = ['../data/mount_data/2020-03-25-10_00/']
     main(datapaths)
 
 # window 1524 + 244 in dataset index 4 is nice example

code/modules/behaviour_handling.py

@@ -1,13 +1,10 @@
 import numpy as np
-
-import os 
-
-import numpy as np
+import os
 from IPython import embed
 
-
 from pandas import read_csv
 from modules.logger import makeLogger
+from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
 
 
 logger = makeLogger(__name__)
@@ -18,46 +15,60 @@ class Behavior:
         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:          
+    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)
+        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}')
+        csv_filename = os.path.split(folder_path[:-1])[-1]
+        csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv'
+        # embed()
+
+        # 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, 'chirp_ids.npy'), allow_pickle=True)
+        self.chirps = np.load(os.path.join(
+            folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(
+            folder_path, 'chirp_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)    
+        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() 
+            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]]))
-        
+            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
@@ -67,33 +78,92 @@ class Behavior:
 
 
 def correct_chasing_events(
-    category: np.ndarray, 
+    category: np.ndarray,
     timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
 
     onset_ids = np.arange(
         len(category))[category == 0]
     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 category[category != 2][-1] == 0:
+        wrong_bh = np.append(
+            wrong_bh,
+            np.arange(len(category))[category != 2][-1])
+
     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)
+
+    new_onset_ids = np.arange(
+        len(category))[category == 0]
+    new_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)
-        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)
-        logger.info(f'Offsets are greater than onsets by {len_diff}')
-    elif len(onset_ids) == len(offset_ids):
-        logger.info('Chasing events are equal')
+
+    if len(new_onset_ids) > len(new_offset_ids):
+        embed()
+        logger.warning('Onsets are greater than offsets')
+    elif len(new_onset_ids) < len(new_offset_ids):
+        logger.warning('Offsets are greater than onsets')
+    elif len(new_onset_ids) == len(new_offset_ids):
+        # logger.info('Chasing events are equal')
+        pass
+
+    return category, timestamps
 
 
-    return category, timestamps
+def center_chirps(
+    events: 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
+    # timestamps of chirps around event centered on the event timepoint
+    centered_chirps = []
+
+    for event_timestamp in events:
+
+        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)]
+
+        if len(chirps_around_event) == 0:
+            continue
+
+        centered_chirps.append(chirps_around_event - event_timestamp)
+        event_chirps.append(chirps_around_event)
+
+    centered_chirps = np.sort(flatten(centered_chirps))
+    event_chirps = np.sort(flatten(event_chirps))
+
+    if len(centered_chirps) != len(event_chirps):
+        raise ValueError(
+            'Non centered chirps and centered chirps are not equal')
+
+    # 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:
+    #     # convert list of arrays to one array for plotting
+    #     centered_chirps = np.concatenate(centered_chirps, axis=0)
+    #     centered_chirps_convolved = (acausal_kde1d(
+    #         centered_chirps, time, width)) / len(event)
+
+    return centered_chirps

code/modules/plotstyle.py

@@ -108,9 +108,10 @@ def PlotStyle() -> None:
         @classmethod
         def set_boxplot_color(cls, bp, color):
             plt.setp(bp["boxes"], color=color)
-            plt.setp(bp["whiskers"], color=color)
-            plt.setp(bp["caps"], color=color)
-            plt.setp(bp["medians"], color=color)
+            plt.setp(bp["whiskers"], color=white)
+            plt.setp(bp["caps"], color=white)
+            plt.setp(bp["medians"], color=white)
+
 
         @classmethod
         def label_subplots(cls, labels, axes, fig):

code/plot_chirp_bodylegth(old).py

@@ -0,0 +1,277 @@
+import numpy as np
+from extract_chirps import get_valid_datasets
+
+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
+from modules.behaviour_handling import Behavior, correct_chasing_events
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+    winner = winner_row['winner'].values[0].astype(int)
+    winner_fish1 = winner_row['fish1'].values[0].astype(int)
+    winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+    if winner > 0:
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+        chirp_winner = len(
+            Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+        chirp_loser = len(
+            Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+        return chirp_winner, chirp_loser
+    else:
+        return np.nan, np.nan
+
+
+def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['fish1'].values[0].astype(int)
+    fish2 = folder_row['fish2'].values[0].astype(int)
+
+    groub = folder_row['group'].values[0].astype(int)
+    size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish1)]
+    size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish2)]
+
+    size_winners = [size_fish1_row[col].values[0]
+                    for col in ['l1', 'l2', 'l3']]
+    mean_size_winner = np.nanmean(size_winners)
+
+    size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
+    mean_size_loser = np.nanmean(size_losers)
+
+    if mean_size_winner > mean_size_loser:
+        size_diff = mean_size_winner - mean_size_loser
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_id = folder_row['rec_id2'].values[0]
+
+    elif mean_size_winner < mean_size_loser:
+        size_diff = mean_size_loser - mean_size_winner
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+
+    else:
+        size_diff = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
+
+    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return size_diff, chirp_diff
+
+
+def get_chirp_freq(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['rec_id1'].values[0].astype(int)
+    fish2 = folder_row['rec_id2'].values[0].astype(int)
+    chirp_freq_fish1 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish1])
+    chirp_freq_fish2 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish2])
+
+    if chirp_freq_fish1 > chirp_freq_fish2:
+        freq_diff = chirp_freq_fish1 - chirp_freq_fish2
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_id = folder_row['rec_id2'].values[0]
+
+    elif chirp_freq_fish1 < chirp_freq_fish2:
+        freq_diff = chirp_freq_fish2 - chirp_freq_fish1
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+
+    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return freq_diff, chirp_diff
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+
+    foldernames, _ = get_valid_datasets(datapath)
+    path_order_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+    order_meta_df = read_csv(path_order_meta)
+    order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
+    path_id_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
+    id_meta_df = read_csv(path_id_meta)
+
+    chirps_winner = []
+    size_diffs = []
+    size_chirps_diffs = []
+    chirps_loser = []
+    freq_diffs = []
+    freq_chirps_diffs = []
+
+    for foldername in foldernames:
+        # behabvior is pandas dataframe with all the data
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
+            continue
+        bh = Behavior(foldername)
+        # chirps are not sorted in time (presumably due to prior groupings)
+        # get and sort chirps and corresponding fish_ids of the 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)
+
+        # winner_chirp, loser_chirp = get_chirp_winner_loser(
+        #     foldername,  bh, order_meta_df)
+        # chirps_winner.append(winner_chirp)
+        # chirps_loser.append(loser_chirp)
+        # size_diff, chirp_diff = get_chirp_size(
+        #     foldername, bh, order_meta_df, id_meta_df)
+        # size_diffs.append(size_diff)
+        # size_chirps_diffs.append(chirp_diff)
+
+        # freq_diff, freq_chirps_diff = get_chirp_freq(
+        #     foldername, bh, order_meta_df)
+        # freq_diffs.append(freq_diff)
+        # freq_chirps_diffs.append(freq_chirps_diff)
+
+        folder_name = foldername.split('/')[-2]
+        winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
+        winner = winner_row['winner'].values[0].astype(int)
+        winner_fish1 = winner_row['fish1'].values[0].astype(int)
+        winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+        groub = winner_row['group'].values[0].astype(int)
+        size_rows = id_meta_df[id_meta_df['group'] == groub]
+
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+            size_winners = []
+            for l in ['l1', 'l2', 'l3']:
+                size_winner = size_rows[size_rows['fish']
+                                        == winner_fish1][l].values[0]
+                size_winners.append(size_winner)
+            mean_size_winner = np.nanmean(size_winners)
+
+            size_losers = []
+            for l in ['l1', 'l2', 'l3']:
+                size_loser = size_rows[size_rows['fish']
+                                       == winner_fish2][l].values[0]
+                size_losers.append(size_loser)
+            mean_size_loser = np.nanmean(size_losers)
+
+            size_diffs.append(mean_size_winner - mean_size_loser)
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+            size_winners = []
+            for l in ['l1', 'l2', 'l3']:
+                size_winner = size_rows[size_rows['fish']
+                                        == winner_fish2][l].values[0]
+                size_winners.append(size_winner)
+            mean_size_winner = np.nanmean(size_winners)
+
+            size_losers = []
+            for l in ['l1', 'l2', 'l3']:
+                size_loser = size_rows[size_rows['fish']
+                                       == winner_fish1][l].values[0]
+                size_losers.append(size_loser)
+            mean_size_loser = np.nanmean(size_losers)
+
+            size_diffs.append(mean_size_winner - mean_size_loser)
+        else:
+            continue
+
+        print(foldername)
+        all_fish_ids = np.unique(bh.chirps_ids)
+        chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
+        chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
+
+        freq_winner = np.nanmedian(bh.freq[bh.ident == winner_fish_id])
+        freq_loser = np.nanmedian(bh.freq[bh.ident == loser_fish_id])
+
+        chirps_winner.append(chirp_winner)
+        chirps_loser.append(chirp_loser)
+
+        size_chirps_diffs.append(chirp_winner - chirp_loser)
+        freq_diffs.append(freq_winner - freq_loser)
+
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
+        22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1, 1])
+    plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
+    scatterwinner = 1.15
+    scatterloser = 1.85
+    chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
+    chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
+
+    bplot1 = ax1.boxplot(chirps_winner, positions=[
+        1], showfliers=False, patch_artist=True)
+    bplot2 = ax1.boxplot(chirps_loser,  positions=[
+        2], showfliers=False, patch_artist=True)
+    ax1.scatter(np.ones(len(chirps_winner)) *
+                scatterwinner, chirps_winner, color='r')
+    ax1.scatter(np.ones(len(chirps_loser)) *
+                scatterloser, chirps_loser, color='r')
+    ax1.set_xticklabels(['winner', 'loser'])
+    ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
+             transform=ax1.transAxes, color=ps.white)
+
+    for w, l in zip(chirps_winner, chirps_loser):
+        ax1.plot([scatterwinner, scatterloser], [w, l],
+                 color='r', alpha=0.5, linewidth=0.5)
+    ax1.set_ylabel('Chirps [n]', color=ps.white)
+
+    colors1 = ps.red
+    ps.set_boxplot_color(bplot1, colors1)
+    colors1 = ps.orange
+    ps.set_boxplot_color(bplot2, colors1)
+    ax2.scatter(size_diffs, size_chirps_diffs, color='r')
+    ax2.set_xlabel('Size difference [mm]')
+    ax2.set_ylabel('Chirps [n]')
+
+    ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
+    # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
+    ax3.set_xlabel('Frequency difference [Hz]')
+    ax3.set_yticklabels([])
+    ax3.set
+
+    plt.savefig('../poster/figs/chirps_winner_loser.pdf')
+    plt.show()
+
+
+if __name__ == '__main__':
+
+    # Path to the data
+    datapath = '../data/mount_data/'
+
+    main(datapath)

code/plot_chirp_bodylegth.py

@@ -1,160 +0,0 @@
-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
-from modules.behaviour_handling import Behavior, correct_chasing_events
-
-ps = PlotStyle()
-
-logger = makeLogger(__name__)
-
-
-def main(datapath: str):
-
-    foldernames = [
-        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
-    path_order_meta  = (
-        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
-    order_meta_df = read_csv(path_order_meta)
-    order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
-    path_id_meta = (
-        '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
-    id_meta_df = read_csv(path_id_meta)
-
-    chirps_winner = []
-    size_diff = []
-    chirps_diff = []
-    chirps_loser = []
-    freq_diff = []
-
-
-    for foldername in foldernames:
-        # behabvior is pandas dataframe with all the data
-        if foldername == '../data/mount_data/2020-05-12-10_00/':
-            continue
-        bh = Behavior(foldername)
-        # chirps are not sorted in time (presumably due to prior groupings)
-        # get and sort chirps and corresponding fish_ids of the 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)
-
-        folder_name = foldername.split('/')[-2]
-        winner_row = order_meta_df[order_meta_df['recording'] == folder_name]
-        winner = winner_row['winner'].values[0].astype(int)
-        winner_fish1 = winner_row['fish1'].values[0].astype(int)
-        winner_fish2 = winner_row['fish2'].values[0].astype(int)
-
-        groub = winner_row['group'].values[0].astype(int)
-        size_rows = id_meta_df[id_meta_df['group'] == groub]
-
-
-        if winner == winner_fish1:
-            winner_fish_id = winner_row['rec_id1'].values[0]
-            loser_fish_id = winner_row['rec_id2'].values[0]
-
-            size_winners = []
-            for l in ['l1', 'l2', 'l3']:
-                size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
-                size_winners.append(size_winner)
-            mean_size_winner = np.nanmean(size_winners)
-
-
-            size_losers = []
-            for l in ['l1', 'l2', 'l3']:
-                size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
-                size_losers.append(size_loser)
-            mean_size_loser = np.nanmean(size_losers)
-
-            size_diff.append(mean_size_winner - mean_size_loser)
-
-        elif winner == winner_fish2:
-            winner_fish_id = winner_row['rec_id2'].values[0]
-            loser_fish_id = winner_row['rec_id1'].values[0]
-
-            size_winners = []
-            for l in ['l1', 'l2', 'l3']:
-                size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
-                size_winners.append(size_winner)
-            mean_size_winner = np.nanmean(size_winners)
-
-            size_losers = []
-            for l in ['l1', 'l2', 'l3']:
-                size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
-                size_losers.append(size_loser)
-            mean_size_loser = np.nanmean(size_losers)
-
-            size_diff.append(mean_size_winner - mean_size_loser)
-        else:
-            continue
-
-        print(foldername)
-        all_fish_ids = np.unique(bh.chirps_ids)
-        chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
-        chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
-
-        freq_winner  = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
-        freq_loser  = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
-        
-        
-        chirps_winner.append(chirp_winner)
-        chirps_loser.append(chirp_loser)
-
-        chirps_diff.append(chirp_winner - chirp_loser)
-        freq_diff.append(freq_winner - freq_loser)
-
-        fish1_id = all_fish_ids[0]
-        fish2_id = all_fish_ids[1]
-        print(winner_fish_id)
-        print(all_fish_ids)
-
-    fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5))
-    scatterwinner = 1.15
-    scatterloser = 1.85
-    bplot1 = ax1.boxplot(chirps_winner, positions=[
-                        1], showfliers=False, patch_artist=True)
-    bplot2 = ax1.boxplot(chirps_loser,  positions=[
-                        2], showfliers=False, patch_artist=True)
-    ax1.scatter(np.ones(len(chirps_winner))*scatterwinner, chirps_winner, color='r')
-    ax1.scatter(np.ones(len(chirps_loser))*scatterloser, chirps_loser, color='r')
-    ax1.set_xticklabels(['winner', 'loser'])
-    ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}', transform=ax1.transAxes, color= ps.white)
-
-    for w, l in zip(chirps_winner, chirps_loser):
-        ax1.plot([scatterwinner, scatterloser], [w, l], color='r', alpha=0.5, linewidth=0.5)
-
-    colors1 = ps.red
-    ps.set_boxplot_color(bplot1, colors1)
-    colors1 = ps.orange
-    ps.set_boxplot_color(bplot2, colors1)
-    ax1.set_ylabel('Chirpscounts [n]')
-
-    ax2.scatter(size_diff, chirps_diff, color='r')
-    ax2.set_xlabel('Size difference [mm]')
-    ax2.set_ylabel('Chirps difference [n]')
-
-    ax3.scatter(freq_diff, chirps_diff, color='r')
-    ax3.set_xlabel('Frequency difference [Hz]')
-    ax3.set_yticklabels([])
-    ax3.set
-
-    plt.savefig('../poster/figs/chirps_winner_loser.pdf')
-    plt.show()
-
-
-if __name__ == '__main__':
-
-    # Path to the data
-    datapath = '../data/mount_data/'
-
-    main(datapath)

code/plot_chirp_size.py

@@ -0,0 +1,319 @@
+import numpy as np
+from extract_chirps import get_valid_datasets
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr, wilcoxon
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+from modules.behaviour_handling import Behavior, correct_chasing_events
+
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+    winner = winner_row['winner'].values[0].astype(int)
+    winner_fish1 = winner_row['fish1'].values[0].astype(int)
+    winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+    if winner > 0:
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+        chirp_winner = len(
+            Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+        chirp_loser = len(
+            Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+        return chirp_winner, chirp_loser
+    else:
+        return np.nan, np.nan
+
+
+def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['fish1'].values[0].astype(int)
+    fish2 = folder_row['fish2'].values[0].astype(int)
+    winner = folder_row['winner'].values[0].astype(int)
+
+    groub = folder_row['group'].values[0].astype(int)
+    size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish1)]
+    size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish2)]
+
+    size_winners = [size_fish1_row[col].values[0]
+                    for col in ['l1', 'l2', 'l3']]
+    size_fish1 = np.nanmean(size_winners)
+
+    size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
+    size_fish2 = np.nanmean(size_losers)
+    if winner == fish1:
+        if size_fish1 > size_fish2:
+            size_diff_bigger = size_fish1 - size_fish2
+            size_diff_smaller = size_fish2 - size_fish1
+
+        elif size_fish1 < size_fish2:
+            size_diff_bigger = size_fish1 - size_fish2
+            size_diff_smaller = size_fish2 - size_fish1
+        else:
+            size_diff_bigger = np.nan
+            size_diff_smaller = np.nan
+            winner_fish_id = np.nan
+            loser_fish_id = np.nan
+            return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
+
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_id = folder_row['rec_id2'].values[0]
+
+    elif winner == fish2:
+        if size_fish2 > size_fish1:
+            size_diff_bigger = size_fish2 - size_fish1
+            size_diff_smaller = size_fish1 - size_fish2
+
+        elif size_fish2 < size_fish1:
+            size_diff_bigger = size_fish2 - size_fish1
+            size_diff_smaller = size_fish1 - size_fish2
+        else:
+            size_diff_bigger = np.nan
+            size_diff_smaller = np.nan
+            winner_fish_id = np.nan
+            loser_fish_id = np.nan
+            return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
+
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+    else:
+        size_diff_bigger = np.nan
+        size_diff_smaller = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
+        return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
+
+    chirp_winner = len(
+        Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+    chirp_loser = len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return size_diff_bigger, chirp_winner,  size_diff_smaller, chirp_loser
+
+
+def get_chirp_freq(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    folder_row = order_meta_df[order_meta_df['recording'] == foldername]
+    fish1 = folder_row['fish1'].values[0].astype(int)
+    fish2 = folder_row['fish2'].values[0].astype(int)
+
+    fish1_freq = folder_row['rec_id1'].values[0].astype(int)
+    fish2_freq = folder_row['rec_id2'].values[0].astype(int)
+    winner = folder_row['winner'].values[0].astype(int)
+    chirp_freq_fish1 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish1_freq])
+    chirp_freq_fish2 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish2_freq])
+
+    if winner == fish1:
+        # if chirp_freq_fish1 > chirp_freq_fish2:
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+
+        # elif chirp_freq_fish1 < chirp_freq_fish2:
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+        # else:
+        #     freq_diff_higher = np.nan
+        #     freq_diff_lower = np.nan
+        #     winner_fish_id = np.nan
+        #     loser_fish_id = np.nan
+
+        winner_fish_id = folder_row['rec_id1'].values[0]
+        winner_fish_freq = chirp_freq_fish1
+        loser_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_freq = chirp_freq_fish2
+
+    elif winner == fish2:
+        # if chirp_freq_fish2 > chirp_freq_fish1:
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+
+        # elif chirp_freq_fish2 < chirp_freq_fish1:
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+        # else:
+        #     freq_diff_higher = np.nan
+        #     freq_diff_lower = np.nan
+        #     winner_fish_id = np.nan
+        #     loser_fish_id = np.nan
+
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        winner_fish_freq = chirp_freq_fish2
+        loser_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_freq = chirp_freq_fish1
+    else:
+        winner_fish_freq = np.nan
+        loser_fish_freq = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
+
+    chirp_winner = len(
+        Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+    chirp_loser = len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    foldernames, _ = get_valid_datasets(datapath)
+    path_order_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+    order_meta_df = read_csv(path_order_meta)
+    order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
+    path_id_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
+    id_meta_df = read_csv(path_id_meta)
+
+    chirps_winner = []
+
+    size_diffs_winner = []
+    size_diffs_loser = []
+    size_chirps_winner = []
+    size_chirps_loser = []
+
+    freq_diffs_higher = []
+    freq_diffs_lower = []
+    freq_chirps_winner = []
+    freq_chirps_loser = []
+
+    chirps_loser = []
+    freq_diffs = []
+    freq_chirps_diffs = []
+
+    for foldername in foldernames:
+        # behabvior is pandas dataframe with all the data
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
+            continue
+        bh = Behavior(foldername)
+        # chirps are not sorted in time (presumably due to prior groupings)
+        # get and sort chirps and corresponding fish_ids of the 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)
+
+        winner_chirp, loser_chirp = get_chirp_winner_loser(
+            foldername,  bh, order_meta_df)
+        chirps_winner.append(winner_chirp)
+        chirps_loser.append(loser_chirp)
+
+        size_diff_bigger, chirp_winner,  size_diff_smaller, chirp_loser = get_chirp_size(
+            foldername, bh, order_meta_df, id_meta_df)
+
+        freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq(
+            foldername, bh, order_meta_df)
+
+        freq_diffs_higher.append(freq_winner)
+        freq_diffs_lower.append(freq_loser)
+        freq_chirps_winner.append(chirp_freq_winner)
+        freq_chirps_loser.append(chirp_freq_loser)
+
+        if np.isnan(size_diff_bigger):
+            continue
+        size_diffs_winner.append(size_diff_bigger)
+        size_diffs_loser.append(size_diff_smaller)
+        size_chirps_winner.append(chirp_winner)
+        size_chirps_loser.append(chirp_loser)
+
+    size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
+    size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
+
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
+        21*ps.cm, 10*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
+    plt.subplots_adjust(left=0.11, right=0.948, top=0.905, wspace=0.343, bottom=0.145)
+    scatterwinner = 1.15
+    scatterloser = 1.85
+    chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
+    chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
+
+    stat = wilcoxon(chirps_winner, chirps_loser)
+    print(stat)
+
+    bplot1 = ax1.boxplot(chirps_winner, positions=[
+        0.9], showfliers=False, patch_artist=True)
+
+    bplot2 = ax1.boxplot(chirps_loser,  positions=[
+        2.1], showfliers=False, patch_artist=True)
+    ax1.scatter(np.ones(len(chirps_winner)) *
+                scatterwinner, chirps_winner, color=ps.red)
+    ax1.scatter(np.ones(len(chirps_loser)) *
+                scatterloser, chirps_loser, color=ps.orange)
+    ax1.set_xticklabels(['winner', 'loser'])
+
+    ax1.text(1, 2000, f'{len(chirps_winner)}',  color='gray')
+    ax1.text(1.8, 2000, f'{len(chirps_loser)}', color='gray')
+
+    for w, l in zip(chirps_winner, chirps_loser):
+        ax1.plot([scatterwinner, scatterloser], [w, l],
+                 color=ps.white, alpha=1, linewidth=0.5)
+    ax1.set_ylabel('chirpcount', color=ps.white)
+    ax1.set_xlabel('outcome',    color=ps.white)
+
+    colors1 = ps.red
+    ps.set_boxplot_color(bplot1, colors1)
+    colors1 = ps.orange
+    ps.set_boxplot_color(bplot2, colors1)
+
+    ax2.scatter(size_diffs_winner, size_chirps_winner,
+                color=ps.red, label=f'winner')
+    ax2.scatter(size_diffs_loser, size_chirps_loser,
+                color=ps.orange, label='loser')
+    ax2.text(-1, 2000, f'{len(size_chirps_winner)}', color= 'gray')
+    ax2.text(1, 2000, f'{len(size_chirps_loser)}',   color= 'gray')
+
+    ax2.set_xlabel('size difference [cm]')
+    # ax2.set_xticks(np.arange(-10, 10.1, 2))
+
+    ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=ps.red)
+    ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=ps.orange)
+
+    ax3.text(600, 2000, f'n = {len(freq_chirps_winner)}', color='gray')
+    ax3.text(650, 2000, f'{len(freq_chirps_loser)}',  color='gray')
+
+    ax3.set_xlabel('absolut frequency [Hz]')
+    handles, labels = ax2.get_legend_handles_labels()
+    fig.legend(handles, labels, loc='upper center', ncol=2)
+    # pearson r
+    plt.savefig('../poster/figs/chirps_winner_loser.pdf')
+    plt.show()
+
+
+if __name__ == '__main__':
+
+    # Path to the data
+    datapath = '../data/mount_data/'
+
+    main(datapath)

code/plot_chirps_in_chasing.py

@@ -0,0 +1,84 @@
+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+from modules.behaviour_handling import Behavior, correct_chasing_events
+from modules.datahandling import flatten
+
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    time_precents = []
+    chirps_percents = []
+    for foldername in foldernames:
+        # behabvior is pandas dataframe with all the data
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
+            continue
+        bh = Behavior(foldername)
+
+        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)
+
+        chasing_onset = timestamps[category == 0]
+        chasing_offset = timestamps[category == 1]
+        if len(chasing_onset) != len(chasing_offset):
+            embed()
+
+        chirps_in_chasings = []
+        for onset, offset in zip(chasing_onset, chasing_offset):
+            chirps_in_chasing = [c for c in bh.chirps if (c > onset) & (c < offset)]
+            chirps_in_chasings.append(chirps_in_chasing)
+
+        try:
+            time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60])
+        except:
+            time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60][:-1])
+
+
+        time_chasing_percent = (time_chasing/(3*60*60))*100
+        chirps_chasing = np.asarray(flatten(chirps_in_chasings))
+        chirps_chasing_new = chirps_chasing[chirps_chasing<3*60*60]
+        chirps_percent = (len(chirps_chasing_new)/len(bh.chirps[bh.chirps<3*60*60]))*100
+
+        time_precents.append(time_chasing_percent)
+        chirps_percents.append(chirps_percent)
+    
+    fig, ax = plt.subplots(1, 1, figsize=(14*ps.cm, 10*ps.cm))
+
+    ax.boxplot([time_precents, chirps_percents])
+    ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing'])
+    ax.set_ylabel('Percent')
+    ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white)
+    ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white)
+    for i in range(len(time_precents)):
+        ax.plot([1.25, 1.75], [time_precents[i], chirps_percents[i]], color=ps.white)
+    ax.text(0.99, 0.99, f'{len(time_precents)} fish', transform=ax.transAxes)
+    plt.savefig('../poster/figs/chirps_in_chasing.pdf')
+    plt.show()
+
+
+if __name__ == '__main__':
+    # Path to the data
+    datapath = '../data/mount_data/'
+    main(datapath)
+
+        

code/plot_event_timeline.py

@@ -1,9 +1,9 @@
 import numpy as np
 
-import os 
+import os
 
 import numpy as np
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 from thunderfish.powerspectrum import decibel
 
 from IPython import embed
@@ -12,20 +12,24 @@ from modules.logger import makeLogger
 from modules.plotstyle import PlotStyle
 from modules.behaviour_handling import Behavior, correct_chasing_events
 
+from extract_chirps import get_valid_datasets
 ps = PlotStyle()
 
 logger = makeLogger(__name__)
 
 
 def main(datapath: str):
-    
-    foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
-    for foldername in foldernames:
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    foldernames, _ = get_valid_datasets(datapath)
+    for foldername in foldernames[1:2]:
+        # foldername = foldernames[0]
         if foldername == '../data/mount_data/2020-05-12-10_00/':
             continue
         # behabvior is pandas dataframe with all the data
         bh = Behavior(foldername)
-
+        # 2020-06-11-10
         category = bh.behavior
         timestamps = bh.start_s
         # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
@@ -33,45 +37,49 @@ def main(datapath: str):
         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
+        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(bh.chirps_ids)
         fish1_id = all_fish_ids[0]
         fish2_id = all_fish_ids[1]
         # Associate chirps to inidividual fish
-        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
-        fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
-        fish1_color = ps.red
-        fish2_color = ps.orange
+        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) / 60
+        fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) / 60
+        fish1_color = ps.purple
+        fish2_color = ps.lavender
 
-        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)
-    
+        fig, ax = plt.subplots(5, 1, figsize=(
+            21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True)
+        # marker size
+        s = 80
+        ax[0].scatter(physical_contact, np.ones(
+            len(physical_contact)), color=ps.maroon, marker='|', s=s)
+        ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)),
+                      color=ps.orange, 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 == fish1_id]
+        time_temp = bh.time[bh.idx[bh.ident == fish1_id]]
+        ax[4].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)
+        freq_temp = bh.freq[bh.ident == fish2_id]
+        time_temp = bh.time[bh.idx[bh.ident == fish2_id]]
+        ax[4].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')
+        # 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
+        # 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[0].yaxis.set_label_coords(-0.1, 0.5)
 
         ax[1].grid(False)
         ax[1].set_frame_on(False)
@@ -85,22 +93,26 @@ def main(datapath: str):
         ax[2].set_xticks([])
         ps.hide_ax(ax[2])
 
+        ax[4].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
+        ax[4].set_xticks(np.arange(0, 6.1, 0.5))
+        ps.hide_ax(ax[3])
 
+        labelpad = 30
+        fsize = 12
+        ax[0].set_ylabel('contact', rotation=0,
+                         labelpad=labelpad, fontsize=fsize)
+        ax[1].set_ylabel('chasing', rotation=0,
+                         labelpad=labelpad, fontsize=fsize)
+        ax[2].set_ylabel('chirps', rotation=0,
+                         labelpad=labelpad, fontsize=fsize)
+        ax[4].set_ylabel('EODf')
 
-        ax[3].axvspan(3, 6, 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]')
-        ax[0].set_title(foldername.split('/')[-2])
+        ax[4].set_xlabel('time [h]')
+        # ax[0].set_title(foldername.split('/')[-2])
         # 2020-03-31-9_59
+        plt.subplots_adjust(left=0.158, right=0.987, top=0.918)
+        # plt.savefig('../poster/figs/timeline.pdf')
         plt.show()
-        embed()
 
     # plot chirps
 

code/plot_kdes.py

@@ -0,0 +1,471 @@
+from extract_chirps import get_valid_datasets
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
+from modules.behaviour_handling import (
+    Behavior, correct_chasing_events, center_chirps)
+from modules.plotstyle import PlotStyle
+
+logger = makeLogger(__name__)
+ps = PlotStyle()
+
+
+def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
+
+    bootstrapped_kdes = []
+    data = data[data <= 3*60*60]  # only night time
+
+    # diff_data = np.diff(np.sort(data), prepend=0)
+    # if len(data) != 0:
+    #     mean_chirprate = (len(data) - 1) / (data[-1] - data[0])
+
+    for i in tqdm(range(nresamples)):
+
+        # np.random.shuffle(diff_data)
+
+        # bootstrapped_data = np.cumsum(diff_data)
+        bootstrapped_data = data + np.random.randn(len(data)) * 10
+
+        bootstrap_data_centered = center_chirps(
+            bootstrapped_data, event_times, time_before, time_after)
+
+        bootstrapped_kde = acausal_kde1d(
+            bootstrap_data_centered, time=kde_time, width=kernel_width)
+
+        # bootstrapped_kdes = list(np.asarray(
+        #     bootstrapped_kdes) / len(event_times))
+
+        bootstrapped_kdes.append(bootstrapped_kde)
+
+    return bootstrapped_kdes
+
+
+def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times, time_before, time_after):
+
+    jackknife_kdes = []
+    data = data[data <= 3*60*60]  # only night time
+    subsetsize = int(len(data) * subsetsize)
+
+    diff_data = np.diff(np.sort(data), prepend=0)
+
+    for i in tqdm(range(nresamples)):
+
+        bootstrapped_data = np.random.sample(data, subsetsize, replace=False)
+
+        bootstrapped_data = np.cumsum(diff_data)
+
+        bootstrap_data_centered = center_chirps(
+            bootstrapped_data, event_times, time_before, time_after)
+
+        bootstrapped_kde = acausal_kde1d(
+            bootstrap_data_centered, time=kde_time, width=kernel_width)
+
+        # bootstrapped_kdes = list(np.asarray(
+        #     bootstrapped_kdes) / len(event_times))
+
+        jackknife_kdes.append(bootstrapped_kde)
+
+    return jackknife_kdes
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+    winner = winner_row['winner'].values[0].astype(int)
+    winner_fish1 = winner_row['fish1'].values[0].astype(int)
+    winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+    if winner > 0:
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+        chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
+        chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
+
+        return chirp_winner, chirp_loser
+    return None, None
+
+
+def main(dataroot):
+
+    foldernames, _ = get_valid_datasets(dataroot)
+    plot_all = True
+    time_before = 60
+    time_after = 60
+    dt = 0.001
+    kernel_width = 1
+    kde_time = np.arange(-time_before, time_after, dt)
+    nbootstraps = 2
+
+    meta_path = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+    meta = pd.read_csv(meta_path)
+    meta['recording'] = meta['recording'].str[1:-1]
+
+    winner_onsets = []
+    winner_offsets = []
+    winner_physicals = []
+
+    loser_onsets = []
+    loser_offsets = []
+    loser_physicals = []
+
+    winner_onsets_boot = []
+    winner_offsets_boot = []
+    winner_physicals_boot = []
+
+    loser_onsets_boot = []
+    loser_offsets_boot = []
+    loser_physicals_boot = []
+
+    onset_count = 0
+    offset_count = 0
+    physical_count = 0
+
+    # Iterate over all recordings and save chirp- and event-timestamps
+    for folder in tqdm(foldernames):
+
+        foldername = folder.split('/')[-2]
+        # logger.info('Loading data from folder: {}'.format(foldername))
+
+        broken_folders = ['../data/mount_data/2020-05-12-10_00/']
+        if folder in broken_folders:
+            continue
+
+        bh = Behavior(folder)
+        category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
+
+        category = category[timestamps < 3*60*60]  # only night time
+        timestamps = timestamps[timestamps < 3*60*60]  # only night time
+
+        winner, loser = get_chirp_winner_loser(folder, bh, meta)
+
+        if winner is None:
+            continue
+
+        onsets = (timestamps[category == 0])
+        offsets = (timestamps[category == 1])
+        physicals = (timestamps[category == 2])
+
+        onset_count += len(onsets)
+        offset_count += len(offsets)
+        physical_count += len(physicals)
+
+        winner_onsets.append(center_chirps(
+            winner, onsets, time_before, time_after))
+        winner_offsets.append(center_chirps(
+            winner, offsets, time_before, time_after))
+        winner_physicals.append(center_chirps(
+            winner, physicals, time_before, time_after))
+
+        loser_onsets.append(center_chirps(
+            loser, onsets, time_before, time_after))
+        loser_offsets.append(center_chirps(
+            loser, offsets, time_before, time_after))
+        loser_physicals.append(center_chirps(
+            loser, physicals, time_before, time_after))
+
+        # bootstrap
+        # chirps = [winner, winner, winner, loser, loser, loser]
+
+        winner_onsets_boot.append(bootstrap(
+            winner,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=onsets,
+            time_before=time_before,
+            time_after=time_after))
+        winner_offsets_boot.append(bootstrap(
+            winner,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=offsets,
+            time_before=time_before,
+            time_after=time_after))
+        winner_physicals_boot.append(bootstrap(
+            winner,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=physicals,
+            time_before=time_before,
+            time_after=time_after))
+
+        loser_onsets_boot.append(bootstrap(
+            loser,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=onsets,
+            time_before=time_before,
+            time_after=time_after))
+        loser_offsets_boot.append(bootstrap(
+            loser,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=offsets,
+            time_before=time_before,
+            time_after=time_after))
+        loser_physicals_boot.append(bootstrap(
+            loser,
+            nresamples=nbootstraps,
+            kde_time=kde_time,
+            kernel_width=kernel_width,
+            event_times=physicals,
+            time_before=time_before,
+            time_after=time_after))
+
+        if plot_all:
+
+            winner_onsets_conv = acausal_kde1d(
+                winner_onsets[-1], kde_time, kernel_width)
+            winner_offsets_conv = acausal_kde1d(
+                winner_offsets[-1], kde_time, kernel_width)
+            winner_physicals_conv = acausal_kde1d(
+                winner_physicals[-1], kde_time, kernel_width)
+
+            loser_onsets_conv = acausal_kde1d(
+                loser_onsets[-1], kde_time, kernel_width)
+            loser_offsets_conv = acausal_kde1d(
+                loser_offsets[-1], kde_time, kernel_width)
+            loser_physicals_conv = acausal_kde1d(
+                loser_physicals[-1], kde_time, kernel_width)
+
+            fig, ax = plt.subplots(2, 3, figsize=(
+                21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
+            ax[0, 0].set_title(
+                f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
+            ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
+            ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
+            ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
+            ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
+            ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
+            ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
+
+            # # plot bootstrap lines
+            for kde in winner_onsets_boot[-1]:
+                ax[0, 0].plot(kde_time, kde/len(onsets),
+                              color='gray')
+            for kde in winner_offsets_boot[-1]:
+                ax[0, 1].plot(kde_time, kde/len(offsets),
+                              color='gray')
+            for kde in winner_physicals_boot[-1]:
+                ax[0, 2].plot(kde_time, kde/len(physicals),
+                              color='gray')
+            for kde in loser_onsets_boot[-1]:
+                ax[1, 0].plot(kde_time, kde/len(onsets),
+                              color='gray')
+            for kde in loser_offsets_boot[-1]:
+                ax[1, 1].plot(kde_time, kde/len(offsets),
+                              color='gray')
+            for kde in loser_physicals_boot[-1]:
+                ax[1, 2].plot(kde_time, kde/len(physicals),
+                              color='gray')
+
+            # plot bootstrap percentiles
+            # ax[0, 0].fill_between(
+            #     kde_time,
+            #     np.percentile(winner_onsets_boot[-1], 5, axis=0),
+            #     np.percentile(winner_onsets_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+            # ax[0, 1].fill_between(
+            #     kde_time,
+            #     np.percentile(winner_offsets_boot[-1], 5, axis=0),
+            #     np.percentile(
+            #         winner_offsets_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+            # ax[0, 2].fill_between(
+            #     kde_time,
+            #     np.percentile(
+            #         winner_physicals_boot[-1], 5, axis=0),
+            #     np.percentile(
+            #         winner_physicals_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+            # ax[1, 0].fill_between(
+            #     kde_time,
+            #     np.percentile(loser_onsets_boot[-1], 5, axis=0),
+            #     np.percentile(loser_onsets_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+            # ax[1, 1].fill_between(
+            #     kde_time,
+            #     np.percentile(loser_offsets_boot[-1], 5, axis=0),
+            #     np.percentile(loser_offsets_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+            # ax[1, 2].fill_between(
+            #     kde_time,
+            #     np.percentile(
+            #         loser_physicals_boot[-1], 5, axis=0),
+            #     np.percentile(
+            #         loser_physicals_boot[-1], 95, axis=0),
+            #     color='gray',
+            #     alpha=0.5)
+
+            # ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+            # ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+            # ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+            # ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+            # ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+            # ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0),
+            #               color='black', linewidth=2)
+
+            ax[0, 0].set_xlim(-30, 30)
+            plt.show()
+
+    winner_onsets = np.sort(flatten(winner_onsets))
+    winner_offsets = np.sort(flatten(winner_offsets))
+    winner_physicals = np.sort(flatten(winner_physicals))
+    loser_onsets = np.sort(flatten(loser_onsets))
+    loser_offsets = np.sort(flatten(loser_offsets))
+    loser_physicals = np.sort(flatten(loser_physicals))
+
+    winner_onsets_conv = acausal_kde1d(
+        winner_onsets, kde_time, kernel_width)
+    winner_offsets_conv = acausal_kde1d(
+        winner_offsets, kde_time, kernel_width)
+    winner_physicals_conv = acausal_kde1d(
+        winner_physicals, kde_time, kernel_width)
+    loser_onsets_conv = acausal_kde1d(
+        loser_onsets, kde_time, kernel_width)
+    loser_offsets_conv = acausal_kde1d(
+        loser_offsets, kde_time, kernel_width)
+    loser_physicals_conv = acausal_kde1d(
+        loser_physicals, kde_time, kernel_width)
+
+    winner_onsets_conv = winner_onsets_conv / onset_count
+    winner_offsets_conv = winner_offsets_conv / offset_count
+    winner_physicals_conv = winner_physicals_conv / physical_count
+    loser_onsets_conv = loser_onsets_conv / onset_count
+    loser_offsets_conv = loser_offsets_conv / offset_count
+    loser_physicals_conv = loser_physicals_conv / physical_count
+
+    winner_onsets_boot = np.concatenate(
+        winner_onsets_boot)
+    winner_offsets_boot = np.concatenate(
+        winner_offsets_boot)
+    winner_physicals_boot = np.concatenate(
+        winner_physicals_boot)
+    loser_onsets_boot = np.concatenate(
+        loser_onsets_boot)
+    loser_offsets_boot = np.concatenate(
+        loser_offsets_boot)
+    loser_physicals_boot = np.concatenate(
+        loser_physicals_boot)
+
+    percs = [5, 50, 95]
+    winner_onsets_boot_quarts = np.percentile(
+        winner_onsets_boot, percs, axis=0)
+    winner_offsets_boot_quarts = np.percentile(
+        winner_offsets_boot, percs, axis=0)
+    winner_physicals_boot_quarts = np.percentile(
+        winner_physicals_boot, percs, axis=0)
+    loser_onsets_boot_quarts = np.percentile(
+        loser_onsets_boot, percs, axis=0)
+    loser_offsets_boot_quarts = np.percentile(
+        loser_offsets_boot, percs, axis=0)
+    loser_physicals_boot_quarts = np.percentile(
+        loser_physicals_boot, percs, axis=0)
+
+    fig, ax = plt.subplots(2, 3, figsize=(
+        21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
+
+    ax[0, 0].plot(kde_time, winner_onsets_conv)
+    ax[0, 1].plot(kde_time, winner_offsets_conv)
+    ax[0, 2].plot(kde_time, winner_physicals_conv)
+    ax[1, 0].plot(kde_time, loser_onsets_conv)
+    ax[1, 1].plot(kde_time, loser_offsets_conv)
+    ax[1, 2].plot(kde_time, loser_physicals_conv)
+
+    ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
+    ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
+    ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
+    ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
+    ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
+    ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
+
+    # for kde in winner_onsets_boot:
+    #     ax[0, 0].plot(kde_time, kde,
+    #                   color='gray')
+    # for kde in winner_offsets_boot:
+    #     ax[0, 1].plot(kde_time, kde,
+    #                   color='gray')
+    # for kde in winner_physicals_boot:
+    #     ax[0, 2].plot(kde_time, kde,
+    #                   color='gray')
+    # for kde in loser_onsets_boot:
+    #     ax[1, 0].plot(kde_time, kde,
+    #                   color='gray')
+    # for kde in loser_offsets_boot:
+    #     ax[1, 1].plot(kde_time, kde,
+    #                   color='gray')
+    # for kde in loser_physicals_boot:
+    #     ax[1, 2].plot(kde_time, kde,
+    #                   color='gray')
+
+    ax[0, 0].fill_between(kde_time,
+                          winner_onsets_boot_quarts[0],
+                          winner_onsets_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    ax[0, 1].fill_between(kde_time,
+                          winner_offsets_boot_quarts[0],
+                          winner_offsets_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    ax[0, 2].fill_between(kde_time,
+                          loser_physicals_boot_quarts[0],
+                          loser_physicals_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    ax[1, 0].fill_between(kde_time,
+                          loser_onsets_boot_quarts[0],
+                          loser_onsets_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    ax[1, 1].fill_between(kde_time,
+                          loser_offsets_boot_quarts[0],
+                          loser_offsets_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    ax[1, 2].fill_between(kde_time,
+                          loser_physicals_boot_quarts[0],
+                          loser_physicals_boot_quarts[2],
+                          color=ps.gray,
+                          alpha=0.5)
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main('../data/mount_data/')

poster/main.tex

@@ -7,7 +7,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
 \begin{document}
 
 \renewcommand{\baselinestretch}{1}
-\title{\parbox{1500pt}{Detection of transient communication signals in weakly electric fish}}
+\title{\parbox{1500pt}{Bypassing time-frequency uncertainty in the detection of transient communication signals in weakly electric fish}}
 \author{Sina Prause, Alexander Wendt, and Patrick Weygoldt}
 \institute{Supervised by Till Raab \& Jan Benda, Neuroethology Lab, University of Tuebingen}
 \usetitlestyle[]{sampletitle}
@@ -16,19 +16,25 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
 
 \begin{columns}
 \column{0.4}
-\myblock[TranspBlock]{Introduction}{
+\myblock[GrayBlock]{Introduction}{
     The time-frequency tradeoff makes reliable signal detecion and simultaneous
-    sender identification of freely interacting individuals impossible.
-    This profoundly limits our current understanding of chirps to experiments
+    sender identification by simple Fourier decomposition in freely interacting
+    weakly electric fish impossible. This profoundly limits our current
+    understanding of chirps to experiments
     with single - or physically separated - individuals.
     % \begin{tikzfigure}[]
     %     \label{griddrawing}
-    %     \includegraphics[width=1\linewidth]{figs/introplot}
+    %     \includegraphics[width=0.8\linewidth]{figs/introplot}
     % \end{tikzfigure}
 }
 \myblock[TranspBlock]{Chirp detection}{
     \begin{tikzfigure}[]
-        \label{fig:example_a}
+        \label{fig:alg1}
+        \includegraphics[width=0.9\linewidth]{figs/algorithm1}
+    \end{tikzfigure}
+    \vspace{2cm}
+    \begin{tikzfigure}[]
+        \label{fig:alg2}
         \includegraphics[width=1\linewidth]{figs/algorithm}
     \end{tikzfigure}
     \vspace{0cm}
@@ -38,9 +44,29 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
 \myblock[TranspBlock]{Chirps during competition}{
     \begin{tikzfigure}[]
         \label{fig:example_b}
-        \includegraphics[width=0.5\linewidth]{example-image-b}
+        \includegraphics[width=\linewidth]{figs/timeline.pdf}
     \end{tikzfigure}
     \noindent
+    \begin{itemize}
+        \setlength\itemsep{0.5em}
+        \item Two fish compete for one hidding place in one tank,
+        \item Experiment had a 3 hour long darkphase and a 3 hour long light phase.
+    \end{itemize}
+
+    \noindent
+
+    \begin{tikzfigure}[]
+        \label{fig:example_b}
+        \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
+    \end{tikzfigure}
+
+
+    \begin{itemize}
+        \setlength\itemsep{0.5em}
+        \item Fish who won the competition chirped more often than the fish who lost.
+        \item
+    \end{itemize}
+
 }
 
 \myblock[TranspBlock]{Interactions at modulations}{
@@ -50,19 +76,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
         \includegraphics[width=0.5\linewidth]{example-image-c}
     \end{tikzfigure}
 
-    \begin{multicols}{2}
-        \begin{itemize}
-            \setlength\itemsep{0.5em}
-            \item $\Delta$EOD$f$ does not appear to decrease during synchronous modulations ().
-            \item Individuals that rise their EOD$f$ first appear to rise their frequency higher compared to reactors (\textbf{B}).
-            \vfill
-            \null
-            \columnbreak
-            \item Synchronized fish keep distances below 1 m (\textbf{C}) but distances over 3 m also occur (see \textbf{movie}).
-            \item Spatial interactions increase \textbf{after} the start of a synchronous modulation (\textbf{D}).
-        \end{itemize}
-    \end{multicols}
-    \vspace{-1cm}
+
 }
 
 \myblock[GrayBlock]{Conclusion}{

poster/style.tex

@@ -34,7 +34,7 @@
     \centering
     % \vspace{1cm}
     \hspace{-10cm}
-    \includegraphics[width=\linewidth]{example-image-a}
+    \includegraphics[width=0.8\linewidth]{figs/efishlogo.pdf}
 \end{minipage}}
 % \begin{minipage}[c]{0.2\paperwidth}
 %     \vspace{1cm}\hspace{1cm}