export functions in modules, plot chirp

code/modules/behaviour_handling.py

@@ -0,0 +1,99 @@
+import numpy as np
+
+import os 
+
+import numpy as np
+from IPython import embed
+
+
+from pandas import read_csv
+from modules.logger import makeLogger
+
+
+logger = makeLogger(__name__)
+
+
+class Behavior:
+    """Load behavior data from csv file as class attributes
+        Attributes
+    ----------
+    behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
+    behavior_type:         
+    behavioral_category:   
+    comment_start:         
+    comment_stop:          
+    dataframe: pandas dataframe with all the data            
+    duration_s:             
+    media_file:            
+    observation_date:      
+    observation_id:        
+    start_s: start time of the event in seconds               
+    stop_s:  stop time of the event in seconds               
+    total_length:          
+    """
+
+    def __init__(self, folder_path: str) -> None:
+
+        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
+
+        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
+        logger.info(f'CSV file: {csv_filename}')
+        self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
+
+        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(folder_path, '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)    
+
+        for k, key in enumerate(self.dataframe.keys()):
+            key = key.lower() 
+            if ' ' in key:
+                key = key.replace(' ', '_')
+                if '(' in key:
+                    key = key.replace('(', '')
+                    key = key.replace(')', '')
+            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
+        
+        last_LED_t_BORIS = LED_on_time_BORIS[-1]
+        real_time_range = self.time[-1] - self.time[0]
+        factor = 1.034141
+        shift = last_LED_t_BORIS - real_time_range * factor
+        self.start_s = (self.start_s - shift) / factor
+        self.stop_s = (self.stop_s - shift) / factor
+
+
+def correct_chasing_events(
+    category: np.ndarray, 
+    timestamps: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray]:
+
+    onset_ids = np.arange(
+        len(category))[category == 0]
+    offset_ids = np.arange(
+        len(category))[category == 1]
+
+    woring_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]
+        woring_bh = np.append(woring_bh, help_index)
+
+    category = np.delete(category, woring_bh)
+    timestamps = np.delete(timestamps, woring_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)
+        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')
+
+
+    return category, timestamps

code/plot_chirp_bodylegth.py

@@ -10,100 +10,13 @@ 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__)
 
 
-class Behavior:
-    """Load behavior data from csv file as class attributes
-        Attributes
-    ----------
-    behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
-    behavior_type:         
-    behavioral_category:   
-    comment_start:         
-    comment_stop:          
-    dataframe: pandas dataframe with all the data            
-    duration_s:             
-    media_file:            
-    observation_date:      
-    observation_id:        
-    start_s: start time of the event in seconds               
-    stop_s:  stop time of the event in seconds               
-    total_length:          
-    """
-
-    def __init__(self, folder_path: str) -> None:
-        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
-
-        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
-        logger.info(f'CSV file: {csv_filename}')
-        self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
-
-        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
-        self.chirps_ids = np.load(os.path.join(folder_path, '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)    
-
-        for k, key in enumerate(self.dataframe.keys()):
-            key = key.lower() 
-            if ' ' in key:
-                key = key.replace(' ', '_')
-                if '(' in key:
-                    key = key.replace('(', '')
-                    key = key.replace(')', '')
-            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
-
-        last_LED_t_BORIS = LED_on_time_BORIS[-1]
-        real_time_range = self.time[-1] - self.time[0]
-        factor = 1.034141
-        shift = last_LED_t_BORIS - real_time_range * factor
-        self.start_s = (self.start_s - shift) / factor
-        self.stop_s = (self.stop_s - shift) / factor
-
-
-        
-
-def correct_chasing_events(
-    category: np.ndarray, 
-    timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
-
-    onset_ids = np.arange(
-        len(category))[category == 0]
-    offset_ids = np.arange(
-        len(category))[category == 1]
-
-    woring_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]
-        woring_bh = np.append(woring_bh, help_index)
-
-    category = np.delete(category, woring_bh)
-    timestamps = np.delete(timestamps, woring_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)
-        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')
-
-
-    return category, timestamps
-
-
-
 def main(datapath: str):
 
     foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]

code/plot_event_timeline.py

@@ -10,115 +10,22 @@ 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__)
 
 
-class Behavior:
-    """Load behavior data from csv file as class attributes
-        Attributes
-    ----------
-    behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
-    behavior_type:         
-    behavioral_category:   
-    comment_start:         
-    comment_stop:          
-    dataframe: pandas dataframe with all the data            
-    duration_s:             
-    media_file:            
-    observation_date:      
-    observation_id:        
-    start_s: start time of the event in seconds               
-    stop_s:  stop time of the event in seconds               
-    total_length:          
-    """
-
-    def __init__(self, folder_path: str) -> None:
-        
-
-        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
-
-        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
-        logger.info(f'CSV file: {csv_filename}')
-        self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
-
-        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
-        self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
-
-        self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
-        self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
-        self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
-        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
-        self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)    
-
-        for k, key in enumerate(self.dataframe.keys()):
-            key = key.lower() 
-            if ' ' in key:
-                key = key.replace(' ', '_')
-                if '(' in key:
-                    key = key.replace('(', '')
-                    key = key.replace(')', '')
-            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
-        
-        last_LED_t_BORIS = LED_on_time_BORIS[-1]
-        real_time_range = self.time[-1] - self.time[0]
-        factor = 1.034141
-        shift = last_LED_t_BORIS - real_time_range * factor
-        self.start_s = (self.start_s - shift) / factor
-        self.stop_s = (self.stop_s - shift) / factor
-
-def correct_chasing_events(
-    category: np.ndarray, 
-    timestamps: np.ndarray
-    ) -> tuple[np.ndarray, np.ndarray]:
-
-    onset_ids = np.arange(
-        len(category))[category == 0]
-    offset_ids = np.arange(
-        len(category))[category == 1]
-
-    # Check whether on- or offset is longer and calculate length difference
-    if len(onset_ids) > len(offset_ids):
-        len_diff = len(onset_ids) - len(offset_ids)
-        longer_array = onset_ids
-        shorter_array = offset_ids
-        logger.info(f'Onsets are greater than offsets by {len_diff}')
-    elif len(onset_ids) < len(offset_ids):
-        len_diff = len(offset_ids) - len(onset_ids)
-        longer_array = offset_ids
-        shorter_array = onset_ids
-        logger.info(f'Offsets are greater than offsets by {len_diff}')
-    elif len(onset_ids) == len(offset_ids):
-        logger.info('Chasing events are equal')
-        return category, timestamps
-
-
-    # Correct the wrong chasing events; delete double events
-    wrong_ids = []
-    for i in range(len(longer_array)-(len_diff+1)):
-        if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
-            pass
-        else:
-            wrong_ids.append(longer_array[i])
-            longer_array = np.delete(longer_array, i)
-        
-    category = np.delete(
-        category, wrong_ids)
-    timestamps = np.delete(
-        timestamps, wrong_ids)
-    return category, timestamps
-
-
-
 def main(datapath: str):
+    
+    foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    for foldername in foldernames:
+        if foldername == '../data/mount_data/2020-05-12-10_00/':
+            continue
         # behabvior is pandas dataframe with all the data
-    bh = Behavior(datapath)
+        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
-    chirps = bh.chirps[np.argsort(bh.chirps)]
-    chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
         category = bh.behavior
         timestamps = bh.start_s
         # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
@@ -130,12 +37,12 @@ def main(datapath: str):
         chasing_offset = (timestamps[category == 1]/ 60) /60
         physical_contact = (timestamps[category == 2] / 60) /60
 
-    all_fish_ids = np.unique(chirps_fish_ids)
+        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 = (chirps[chirps_fish_ids == fish1_id] / 60) /60
+        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
-    fish2 = (chirps[chirps_fish_ids == fish2_id] / 60) /60
+        fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
         fish1_color = ps.red
         fish2_color = ps.orange
 
@@ -190,6 +97,7 @@ def main(datapath: str):
         ax[3].set_ylabel('EODf')
 
         ax[3].set_xlabel('Time [h]')
+        ax[0].set_title(foldername.split('/')[-2])
 
         plt.show()
     embed()
@@ -199,5 +107,5 @@ def main(datapath: str):
 
 if __name__ == '__main__':
     # Path to the data
-    datapath = '../data/mount_data/2020-05-13-10_00/'
+    datapath = '../data/mount_data/'
     main(datapath)