From c6facd6f0cfc5421c2b8a4c0e67c782342fd52f7 Mon Sep 17 00:00:00 2001
From: wendtalexander <wendtalexander@protonmail.com>
Date: Mon, 23 Jan 2023 14:51:36 +0100
Subject: [PATCH] adding mask for bodylength

---
 code/plot_chirp_bodylegth.py | 141 +++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 code/plot_chirp_bodylegth.py

diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py
new file mode 100644
index 0000000..e088da8
--- /dev/null
+++ b/code/plot_chirp_bodylegth.py
@@ -0,0 +1,141 @@
+import numpy as np
+
+import os 
+
+import numpy as np
+import matplotlib.pyplot as plt 
+from thunderfish.powerspectrum import decibel
+
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.plotstyle import PlotStyle
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+class Behavior:
+    """Load behavior data from csv file as class attributes
+        Attributes
+    ----------
+    behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
+    behavior_type:         
+    behavioral_category:   
+    comment_start:         
+    comment_stop:          
+    dataframe: pandas dataframe with all the data            
+    duration_s:             
+    media_file:            
+    observation_date:      
+    observation_id:        
+    start_s: start time of the event in seconds               
+    stop_s:  stop time of the event in seconds               
+    total_length:          
+    """
+
+    def __init__(self, folder_path: str) -> None:
+        
+
+        LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
+
+        csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
+        logger.info(f'CSV file: {csv_filename}')
+        self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
+
+        self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
+        self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
+
+        self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
+        self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
+        self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
+        self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
+        self.spec = np.load(os.path.join(folder_path, "spec.npy"), allow_pickle=True)    
+
+        for k, key in enumerate(self.dataframe.keys()):
+            key = key.lower() 
+            if ' ' in key:
+                key = key.replace(' ', '_')
+                if '(' in key:
+                    key = key.replace('(', '')
+                    key = key.replace(')', '')
+            setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
+        
+        last_LED_t_BORIS = LED_on_time_BORIS[-1]
+        real_time_range = self.time[-1] - self.time[0]
+        factor = 1.034141
+        shift = last_LED_t_BORIS - real_time_range * factor
+        self.start_s = (self.start_s - shift) / factor
+        self.stop_s = (self.stop_s - shift) / factor
+
+
+def correct_chasing_events(
+    category: np.ndarray, 
+    timestamps: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray]:
+
+    onset_ids = np.arange(
+        len(category))[category == 0]
+    offset_ids = np.arange(
+        len(category))[category == 1]
+
+    # Check whether on- or offset is longer and calculate length difference
+    if len(onset_ids) > len(offset_ids):
+        len_diff = len(onset_ids) - len(offset_ids)
+        longer_array = onset_ids
+        shorter_array = offset_ids
+        logger.info(f'Onsets are greater than offsets by {len_diff}')
+    elif len(onset_ids) < len(offset_ids):
+        len_diff = len(offset_ids) - len(onset_ids)
+        longer_array = offset_ids
+        shorter_array = onset_ids
+        logger.info(f'Offsets are greater than offsets by {len_diff}')
+    elif len(onset_ids) == len(offset_ids):
+        logger.info('Chasing events are equal')
+        return category, timestamps
+
+
+    # Correct the wrong chasing events; delete double events
+    wrong_ids = []
+    for i in range(len(longer_array)-(len_diff+1)):
+        if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
+            pass
+        else:
+            wrong_ids.append(longer_array[i])
+            longer_array = np.delete(longer_array, i)
+        
+    category = np.delete(
+        category, wrong_ids)
+    timestamps = np.delete(
+        timestamps, wrong_ids)
+    return category, timestamps
+
+
+
+def main(datapath: str):
+    # behabvior is pandas dataframe with all the data
+    bh = Behavior(datapath)
+    # chirps are not sorted in time (presumably due to prior groupings)
+    # get and sort chirps and corresponding fish_ids of the chirps
+    chirps = bh.chirps[np.argsort(bh.chirps)]
+    chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)]
+    category = bh.behavior
+    timestamps = bh.start_s
+    # Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
+    # Get rid of tracking faults (two onsets or two offsets after another)
+    category, timestamps = correct_chasing_events(category, timestamps)
+
+    
+
+    
+    pass
+
+
+
+
+if __name__ == '__main__':
+
+    # Path to the data
+    datapath = '../data/mount_data/2020-05-13-10_00/'
+    main(datapath)
\ No newline at end of file